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提交 1971f3ce 编写于 作者: Y Yancey1989
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Merge branch 'develop' of github.com:PaddlePaddle/Paddle into hsigmoid_op

上级 a25c3aeb 56f28a0f
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CMakeLists.txt
浏览文件 @ 1971f3ce
......@@ -36,8 +36,7 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......@@ -82,10 +81,8 @@ if(ANDROID OR IOS)
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLDNN OFF CACHE STRING
"Disable MKLDNN when cross-compiling for Android and iOS" FORCE)
set(WITH_MKLML OFF CACHE STRING
"Disable MKLML package when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
# Compile PaddlePaddle mobile inference library
if (NOT WITH_C_API)
......@@ -111,6 +108,17 @@ else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
if(WITH_MKL)
set(WITH_MKLML ON)
set(WITH_MKLDNN ${AVX2_FOUND})
if(NOT WITH_MKLDNN)
message(WARNING "Do not have AVX2 intrinsics and disabled MKL-DNN")
endif()
else()
set(WITH_MKLML OFF)
set(WITH_MKLDNN OFF)
endif()
########################################################################################
include(external/mklml) # download mklml package
......@@ -164,8 +172,12 @@ if(WITH_GPU)
endif(NOT WITH_DSO)
endif(WITH_GPU)
if(WITH_MKLML)
list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
endif()
if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
endif()
if(USE_NNPACK)
......
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @ 1971f3ce
set -e
function train() {
unset OMP_NUM_THREADS MKL_NUM_THREADS
export OMP_DYNAMIC="FALSE"
export KMP_AFFINITY="granularity=fine,compact,0,0"
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
......@@ -14,8 +12,6 @@ function train() {
elif [ $4 == "False" ]; then
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
log="logs/${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $3, use True or False."
......
cmake/configure.cmake
浏览文件 @ 1971f3ce
......@@ -76,27 +76,14 @@ else()
include_directories(${CUDA_TOOLKIT_INCLUDE})
endif(NOT WITH_GPU)
if(WITH_MKLDNN)
add_definitions(-DPADDLE_USE_MKLDNN)
if (WITH_MKLML AND MKLDNN_IOMP_DIR)
message(STATUS "Enable Intel OpenMP at ${MKLDNN_IOMP_DIR}")
if (WITH_MKLML AND MKLML_IOMP_LIB)
message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
set(OPENMP_FLAGS "-fopenmp")
set(CMAKE_C_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_CXX_CREATE_SHARED_LIBRARY_FORBIDDEN_FLAGS ${OPENMP_FLAGS})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OPENMP_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OPENMP_FLAGS}")
else()
find_package(OpenMP)
if(OPENMP_FOUND)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
else()
message(WARNING "Can not find OpenMP."
"Some performance features in MKLDNN may not be available")
endif()
endif()
endif(WITH_MKLDNN)
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${SIMD_FLAG}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${SIMD_FLAG}")
......
cmake/cross_compiling/ios.cmake
浏览文件 @ 1971f3ce
......@@ -76,11 +76,9 @@ set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform")
# Set the architecture for iOS
if(NOT DEFINED IOS_ARCH)
if(IOS_PLATFORM STREQUAL "OS")
# FIXME(liuyiqun): support "armv7;armv7s;arm64" future
set(IOS_ARCH "arm64")
set(IOS_ARCH "armv7;armv7s;arm64")
elseif(IOS_PLATFORM STREQUAL "SIMULATOR")
# FIXME(liuyiqun): support "i386;x86_64" future
set(IOS_ARCH "x86_64")
set(IOS_ARCH "i386;x86_64")
endif()
endif()
set(CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string "Build architecture for iOS")
......@@ -248,7 +246,7 @@ set(IOS_COMPILER_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} ${XCODE_IOS_BITCODE_
# Hidden visibilty is required for cxx on iOS
set(CMAKE_C_FLAGS "${IOS_COMPILER_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags")
set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility=hidden -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags")
set(IOS_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first")
......
cmake/external/mkldnn.cmake
浏览文件 @ 1971f3ce
......@@ -40,10 +40,9 @@ INCLUDE_DIRECTORIES(${MKLDNN_INC_DIR})
IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
SET(MKLDNN_DEPENDS ${MKLML_PROJECT})
SET(MKLDNN_MKLROOT ${MKLML_ROOT})
SET(MKLDNN_IOMP_LIB ${MKLML_IOMP_LIB})
SET(MKLDNN_IOMP_DIR ${MKLML_LIB_DIR})
MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
MESSAGE(STATUS "Build MKLDNN with MKLML ${MKLML_ROOT}")
ELSE()
MESSAGE(FATAL_ERROR "Should enable MKLML when build MKLDNN")
ENDIF()
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
......@@ -57,15 +56,16 @@ ExternalProject_Add(
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DMKLROOT=${MKLDNN_MKLROOT}
CMAKE_ARGS -DMKLROOT=${MKLML_ROOT}
CMAKE_ARGS -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-DMKLROOT:PATH=${MKLDNN_MKLROOT}
-DMKLROOT:PATH=${MKLML_ROOT}
)
ADD_LIBRARY(mkldnn SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET mkldnn PROPERTY IMPORTED_LOCATION ${MKLDNN_LIB})
ADD_DEPENDENCIES(mkldnn ${MKLDNN_PROJECT})
MESSAGE(STATUS "Mkldnn library: ${MKLDNN_LIB}")
MESSAGE(STATUS "MKLDNN library: ${MKLDNN_LIB}")
add_definitions(-DPADDLE_USE_MKLDNN)
LIST(APPEND external_project_dependencies mkldnn)
cmake/external/openblas.cmake
浏览文件 @ 1971f3ce
......@@ -29,7 +29,7 @@ IF(NOT ${CBLAS_FOUND})
"${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}"
CACHE FILEPATH "openblas library." FORCE)
SET(OPENBLAS_CC "${CMAKE_C_COMPILER}")
SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -Wno-unused-but-set-variable -Wno-unused-variable")
IF(CMAKE_CROSSCOMPILING)
SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER})
......@@ -45,15 +45,14 @@ IF(NOT ${CBLAS_FOUND})
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0)
ENDIF()
ELSEIF(IOS)
# FIXME(liuyiqun): support multiple architectures
IF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5")
SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}")
IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch armv7")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0)
ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64")
SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64")
SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX})
ELSE()
MESSAGE(FATAL_ERROR "OpenBLAS only support arm64 architectures on iOS. "
"You can set IOS_USE_VECLIB_FOR_BLAS=ON or USE_EIGEN_FOR_BLAS=ON to use other blas library instead.")
ENDIF()
ELSEIF(RPI)
# use hardfp
......
cmake/external/warpctc.cmake
浏览文件 @ 1971f3ce
......@@ -12,6 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF(MOBILE_INFERENCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc)
......
cmake/util.cmake
浏览文件 @ 1971f3ce
......@@ -115,8 +115,8 @@ function(link_paddle_exe TARGET_NAME)
target_link_libraries(${TARGET_NAME} log)
endif(ANDROID)
if(WITH_MKLDNN AND WITH_MKLML AND MKLDNN_IOMP_DIR)
target_link_libraries(${TARGET_NAME} "-L${MKLDNN_IOMP_DIR} -liomp5 -Wl,--as-needed")
if(WITH_MKLML AND MKLML_LIB_DIR AND MKLML_IOMP_LIB)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
endif()
add_dependencies(${TARGET_NAME} ${external_project_dependencies})
......
doc/design/mkldnn/README.MD
浏览文件 @ 1971f3ce
......@@ -36,13 +36,13 @@ Figure 1. PaddlePaddle on IA.
我们把集成方案大致分为了如下几个方面。
### CMake
我们会在`CMakeLists.txt`中会添加`WITH_MKLDNN`的选项，当设置这个值为`ON`的时候会启用编译MKL-DNN功能。同时会自动开启OpenMP用于提高MKL-DNN的性能。
我们会在`CMakeLists.txt`中会给用户添加一个`WITH_MKL`的开关，他是负责`WITH_MKLML`和`WITH_MKLDNN`的总开关。
同时，我们会引入`WITH_MKLML`选项，用于选择是否使用MKL-DNN自带的MKLML安装包。这个安装包可以独立于MKL-DNN使用，但是建议在开启MKL-DNN的同时也打开MKLML的开关，这样才能发挥最好的性能。
当打开`WITH_MKL`时，会开启MKLML的功能，作为PaddlePaddle的CBLAS和LAPACK库，同时会开启Intel OpenMP用于提高MKLML的性能。 如果系统支持AVX2指令集及以上，同时会开启MKL-DNN功能。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
当关闭`WITH_MKL`时，MKLML和MKL-DNN功能会同时关闭。
**备注**：当`WITH_MKLML=ON`的时候，会优先使用这个包作为PaddlePaddle的CBLAS和LAPACK库，所以会稍微改动`cmake/cblas.cmake`中的逻辑。
所以，我们会在`cmake/external`目录新建`mkldnn.cmake`和`mklml.cmake`文件，它们会在编译PaddlePaddle的时候下载对应的软件包，并放到PaddlePaddle的third party目录中。
### Layers
所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
......
doc/design/ops/images/2_level_rnn.dot
浏览文件 @ 1971f3ce
digraph G {
rnn [label="1-th level RNN" shape=box]
rnn [label="1st level RNN" shape=box]
subgraph cluster0 {
label = "time step 0"
......@@ -8,7 +8,7 @@ digraph G {
sent0 [label="sentence"]
sent1 [label="sentence"]
rnn1 [label="2-th level RNN" shape=box]
rnn1 [label="2nd level RNN" shape=box]
sent0 -> rnn1
sent1 -> rnn1
......@@ -20,7 +20,7 @@ digraph G {
sent2 [label="sentence"]
sent3 [label="sentence"]
rnn2 [label="2-th level RNN" shape=box]
rnn2 [label="2nd level RNN" shape=box]
sent2 -> rnn2
sent3 -> rnn2
......@@ -32,7 +32,7 @@ digraph G {
sent4 [label="sentence"]
sent5 [label="sentence"]
rnn3 [label="2-th level RNN" shape=box]
rnn3 [label="2nd level RNN" shape=box]
sent4 -> rnn3
sent5 -> rnn3
......
doc/design/ops/rnn.md
浏览文件 @ 1971f3ce
# 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.
This document describes the RNN (Recurrent Neural Network) operator and how it is implemented in PaddlePaddle. The RNN op requires that all instances in a mini-batch have the same length. We will have a more flexible dynamic RNN operator in the future.
## RNN Algorithm Implementation
<p aligh="center">
<p align="center">
<img src="./images/rnn.jpg"/>
</p>
The above diagram shows an RNN unrolled into a full network.
There are several important concepts:
There are several important concepts here:
- *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-net*: the sub-graph that runs 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 memory of the first (initial) 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.
There could be local variables defined in each step-net. PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.
<p aligh="center">
<p align="center">
<img src="./images/rnn.png"/><br/>
Figure 2 the RNN's data flow
Figure 2 illustrates the RNN's data flow
</p>
Please be aware that all steps run the same step-net. Each step
Please be aware that every step runs the same step-net. Each step does the following:
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.
1. Creates the step-scope.
2. Initializes the local variables including step-outputs, in the step-scope.
3. Runs the step-net, which uses the above mentioned variables.
The RNN operator will compose its output from step outputs in step scopes.
The RNN operator will compose its output from step outputs in each of the step scopes.
### Memory and Ex-memory
Let's give more details about memory and ex-memory via a simply example:
Let's give more details about memory and ex-memory using a simple 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.
where $h_t$ and $h_{t-1}$ are the memory and ex-memory (previous memory) of step $t$ 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.
In the implementation, we can make an ex-memory variable either "refer to" the memory variable of the previous step,
or copy the memory value of the previous step 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:
We can define an RNN's step-net using a Block:
```python
import paddle as pd
X = some_op() # x is some operator's output, and is a LoDTensor
X = some_op() # x is some operator's output and is a LoDTensor
a = some_op()
# declare parameters
......@@ -68,7 +68,7 @@ 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
# h.pre_state(), the 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)
......@@ -80,19 +80,19 @@ 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.
- `rnn.add_input`: indicates that 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`: marks 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.
For example, we could have a 2-level RNN, where the top level corresponds to paragraphs, and the lower level corresponds to sentences. Each step of the higher level RNN also receives an input from the corresponding step of the lower level, and additionally the output from the previous time step at the same level.
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.
The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.
<p aligh="center">
<p align="center">
<img src="./images/2_level_rnn.png"/>
</p>
......@@ -110,7 +110,7 @@ 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
# the second level of LoD is a chapter
chapter_data = pd.Variable(shape=[None, 128], type=pd.lod_tensor, level=2)
def lower_level_rnn(paragraph):
......@@ -138,14 +138,14 @@ with top_level_rnn.stepnet():
pd.matmul(W0, paragraph_data) + pd.matmul(U0, h.pre_state()))
top_level_rnn.add_outputs(h)
# just output the last step
# 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.
In the above example, the construction of the `top_level_rnn` calls `lower_level_rnn`. The input is an 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.
By default, the `RNNOp` will concatenate the outputs from all the time steps.
If the `output_all_steps` is set to False, it will only output the final time step.
<p align="center">
......
doc/design/ops/sequence_decoder.md
浏览文件 @ 1971f3ce
# Design: Sequence Decoder Generating LoDTensors
In tasks such as machine translation and image to text,
a [sequence decoder](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md) is necessary to generate sequences.
In tasks such as machine translation and visual captioning,
a [sequence decoder](https://github.com/PaddlePaddle/book/blob/develop/08.machine_translation/README.md) is necessary to generate sequences, one word at a time.
This documentation describes how to implement the sequence decoder as an operator.
## Beam Search based Decoder
The [beam search algorithm](https://en.wikipedia.org/wiki/Beam_search) is necessary when generating sequences,
it is a heuristic search algorithm that explores the paths by expanding the most promising node in a limited set.
The [beam search algorithm](https://en.wikipedia.org/wiki/Beam_search) is necessary when generating sequences. It is a heuristic search algorithm that explores the paths by expanding the most promising node in a limited set.
In the old version of PaddlePaddle, a C++ class `RecurrentGradientMachine` implements the general sequence decoder based on beam search,
due to the complexity, the implementation relays on a lot of special data structures,
quite trivial and hard to be customized by users.
In the old version of PaddlePaddle, the C++ class `RecurrentGradientMachine` implements the general sequence decoder based on beam search, due to the complexity involved, the implementation relies on a lot of special data structures that are quite trivial and hard to be customized by users.
There are a lot of heuristic tricks in the sequence generation tasks,
so the flexibility of sequence decoder is very important to users.
There are a lot of heuristic tricks in the sequence generation tasks, so the flexibility of sequence decoder is very important to users.
During PaddlePaddle's refactoring work,
some new concept is proposed such as [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) and [TensorArray](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/tensor_array.md) that can better support sequence usage,
and they can help to make the implementation of beam search based sequence decoder **more transparent and modular** .
During the refactoring of PaddlePaddle, some new concepts are proposed such as: [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) and [TensorArray](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/tensor_array.md) that can better support the sequence usage, and they can also help make the implementation of beam search based sequence decoder **more transparent and modular** .
For example, the RNN sates, candidates IDs and probabilities of beam search can be represented as `LoDTensors`;
For example, the RNN states, candidates IDs and probabilities of beam search can be represented all as `LoDTensors`;
the selected candidate's IDs in each time step can be stored in a `TensorArray`, and `Packed` to the sentences translated.
## Changing LoD's absolute offset to relative offsets
The current `LoDTensor` is designed to store levels of variable-length sequences,
it stores several arrays of integers each represents a level.
The current `LoDTensor` is designed to store levels of variable-length sequences. It stores several arrays of integers where each represents a level.
The integers in each level represents the begin and end (not inclusive) offset of a sequence **in the underlying tensor**,
let's call this format the **absolute-offset LoD** for clear.
The integers in each level represent the begin and end (not inclusive) offset of a sequence **in the underlying tensor**,
let's call this format the **absolute-offset LoD** for clarity.
The relative-offset LoD can fast retrieve any sequence but fails to represent empty sequences, for example, a two-level LoD is as follows
The relative-offset LoD can retrieve any sequence very quickly but fails to represent empty sequences, for example, a two-level LoD is as follows
```python
[[0, 3, 9]
[0, 2, 3, 3, 3, 9]]
......@@ -41,8 +34,7 @@ The first level tells that there are two sequences:
while on the second level, there are several empty sequences that both begin and end at `3`.
It is impossible to tell how many empty second-level sequences exist in the first-level sequences.
There are many scenarios that relay on empty sequence representation,
such as machine translation or image to text, one instance has no translations or the empty candidate set for a prefix.
There are many scenarios that rely on empty sequence representation, for example in machine translation or visual captioning, one instance has no translation or the empty candidate set for a prefix.
So let's introduce another format of LoD,
it stores **the offsets of the lower level sequences** and is called **relative-offset** LoD.
......@@ -60,13 +52,12 @@ their offsets in the second-level LoD is `[0, 3)` and `[3, 5)`.
The second level is the same with the relative offset example because the lower level is a tensor.
It is easy to find out the second sequence in the first-level LoD has two empty sequences.
The following demos are based on relative-offset LoD.
The following examples are based on relative-offset LoD.
## Usage in a simple machine translation model
Let's start from a simple machine translation model that is simplified from [machine translation chapter](https://github.com/PaddlePaddle/book/tree/develop/08.machine_translation) to draw a simple blueprint of what a sequence decoder can do and how to use it.
Let's start from a simple machine translation model that is simplified from the [machine translation chapter](https://github.com/PaddlePaddle/book/tree/develop/08.machine_translation) to draw a blueprint of what a sequence decoder can do and how to use it.
The model has an encoder that learns the semantic vector from a sequence,
and a decoder which uses the sequence decoder to generate new sentences.
The model has an encoder that learns the semantic vector from a sequence, and a decoder which uses the sequence encoder to generate new sentences.
**Encoder**
```python
......@@ -154,34 +145,33 @@ def generate():
translation_ids, translation_scores = decoder()
```
The `decoder.beam_search` is a operator that given the candidates and the scores of translations including the candidates,
return the result of the beam search algorithm.
The `decoder.beam_search` is an operator that, given the candidates and the scores of translations including the candidates,
returns the result of the beam search algorithm.
In this way, users can customize anything on the inputs or outputs of beam search, for example, two ways to prune some translation prefixes
In this way, users can customize anything on the input or output of beam search, for example:
1. meke the correspondind elements in `topk_generated_scores` zero or some small values, beam_search will discard this candidate.
2. remove some specific candidate in `selected_ids`
3. get the final `translation_ids`, remove the translation sequence in it.
1. Make the corresponding elements in `topk_generated_scores` zero or some small values, beam_search will discard this candidate.
2. Remove some specific candidate in `selected_ids`.
3. Get the final `translation_ids`, remove the translation sequence in it.
The implementation of sequence decoder can reuse the C++ class [RNNAlgorithm](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/paddle/operators/dynamic_recurrent_op.h#L30),
so the python syntax is quite similar to a [RNN](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/doc/design/block.md#blocks-with-for-and-rnnop).
The implementation of sequence decoder can reuse the C++ class: [RNNAlgorithm](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/paddle/operators/dynamic_recurrent_op.h#L30),
so the python syntax is quite similar to that of an [RNN](https://github.com/Superjom/Paddle/blob/68cac3c0f8451fe62a4cdf156747d6dc0ee000b3/doc/design/block.md#blocks-with-for-and-rnnop).
Both of them are two-level `LoDTensors`
Both of them are two-level `LoDTensors`:
- the first level represents `batch_size` of (source) sentences;
- the second level represents the candidate ID sets for translation prefix.
- The first level represents `batch_size` of (source) sentences.
- The second level represents the candidate ID sets for translation prefix.
for example, 3 source sentences to translate, and has 2, 3, 1 candidates.
For example, 3 source sentences to translate, and has 2, 3, 1 candidates.
Unlike an RNN, in sequence decoder, the previous state and the current state have different LoD and shape,
a `lod_expand` operator is used to expand the LoD of the previous state to fit the current state.
Unlike an RNN, in sequence decoder, the previous state and the current state have different LoD and shape, and an `lod_expand` operator is used to expand the LoD of the previous state to fit the current state.
For example, the previous state
For example, the previous state:
* LoD is `[0, 1, 3][0, 2, 5, 6]`
* content of tensor is `a1 a2 b1 b2 b3 c1`
the current state stored in `encoder_ctx_expanded`
the current state is stored in `encoder_ctx_expanded`:
* LoD is `[0, 2, 7][0 3 5 8 9 11 11]`
* the content is
......@@ -192,54 +182,48 @@ the current state stored in `encoder_ctx_expanded`
- b3 b3
- None (c1 has 0 candidates, so c1 is dropped)
Benefit from the relative offset LoD, empty candidate set can be represented naturally.
The benefit from the relative offset LoD is that the empty candidate set can be represented naturally.
the status in each time step can be stored in `TensorArray`, and `Pack`ed to a final LoDTensor, the corresponding syntax is
The status in each time step can be stored in `TensorArray`, and `Pack`ed to a final LoDTensor. The corresponding syntax is:
```python
decoder.output(selected_ids)
decoder.output(selected_generation_scores)
```
the `selected_ids` is the candidate ids for the prefixes,
it will be `Packed` by `TensorArray` to a two-level `LoDTensor`,
the first level represents the source sequences,
the second level represents generated sequences.
The `selected_ids` are the candidate ids for the prefixes, and will be `Packed` by `TensorArray` to a two-level `LoDTensor`, where the first level represents the source sequences and the second level represents generated sequences.
Pack the `selected_scores` will get a `LoDTensor` that stores scores of each candidate of translations.
Packing the `selected_scores` will get a `LoDTensor` that stores scores of each translation candidate.
Pack the `selected_generation_scores` will get a `LoDTensor`, and each tail is the probability of the translation.
Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail is the probability of the translation.
## LoD and shape changes during decoding
<p align="center">
<img src="./images/LOD-and-shape-changes-during-decoding.jpg"/>
</p>
According the image above, the only phrase to change LoD is beam search.
According to the image above, the only phase that changes the LoD is beam search.
## Beam search design
The beam search algorthm will be implemented as one method of the sequence decoder, it has 3 inputs
The beam search algorithm will be implemented as one method of the sequence decoder and has 3 inputs:
1. `topk_ids`, top K candidate ids for each prefix.
1. `topk_ids`, the top K candidate ids for each prefix.
2. `topk_scores`, the corresponding scores for `topk_ids`
3. `generated_scores`, the score of the prefixes.
All of the are LoDTensors, so that the sequence affilication is clear.
Beam search will keep a beam for each prefix and select a smaller candidate set for each prefix.
All of these are LoDTensors, so that the sequence affiliation is clear. Beam search will keep a beam for each prefix and select a smaller candidate set for each prefix.
It will return three variables
It will return three variables:
1. `selected_ids`, the final candidate beam search function selected for the next step.
2. `selected_scores`, the scores for the candidates.
3. `generated_scores`, the updated scores for each prefixes (with the new candidates appended).
3. `generated_scores`, the updated scores for each prefix (with the new candidates appended).
## Introducing the LoD-based `Pack` and `Unpack` methods in `TensorArray`
The `selected_ids`, `selected_scores` and `generated_scores` are LoDTensors,
and they exist in each time step,
The `selected_ids`, `selected_scores` and `generated_scores` are LoDTensors that exist at each time step,
so it is natural to store them in arrays.
Currently, PaddlePaddle has a module called `TensorArray` which can store an array of tensors,
the results of beam search are better to store in a `TensorArray`.
Currently, PaddlePaddle has a module called `TensorArray` which can store an array of tensors. It is better to store the results of beam search in a `TensorArray`.
The `Pack` and `UnPack` in `TensorArray` are used to package tensors in the array to a `LoDTensor` or split the `LoDTensor` to an array of tensors.
It needs some extensions to support pack or unpack an array of `LoDTensors`.
The `Pack` and `UnPack` in `TensorArray` are used to pack tensors in the array to an `LoDTensor` or split the `LoDTensor` to an array of tensors.
It needs some extensions to support the packing or unpacking an array of `LoDTensors`.
doc/howto/dev/write_docs_cn.rst
浏览文件 @ 1971f3ce
......@@ -34,7 +34,7 @@ PaddlePaddle的文档构建有两种方式。
cd TO_YOUR_PADDLE_CLONE_PATH
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
make gen_proto_py
make paddle_docs paddle_docs_cn
......
doc/mobile/cross_compiling_for_android_cn.md
浏览文件 @ 1971f3ce
# 构建Android平台上的PaddlePaddle库
# Android平台编译指南
用户可通过如下两种方式，交叉编译Android平台上适用的PaddlePaddle库：
- 基于Docker容器的编译方式
......
doc/mobile/cross_compiling_for_ios_cn.md
浏览文件 @ 1971f3ce
# 构建iOS平台上的PaddlePaddle库
# iOS平台编译指南
交叉编译iOS平台上适用的PaddlePaddle库，需要在MacOS系统上进行。本文的将介绍在MacOS上，从源码交叉编译iOS平台上适用的PaddlePaddle库。
## 准备交叉编译环境
......@@ -25,7 +25,7 @@ iOS平台可选配置参数：
- `IOS_PLATFORM`，可设置为`OS/SIMULATOR`，默认值为`OS`。
- `OS`，构建目标为`arm`架构的iPhone或者iPad等物理设备。
- `SIMULATOR`，构建目标为`x86`架构的模拟器平台。
- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示：
- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示，默认编译所有架构：
<table class="docutils">
<colgroup>
......@@ -41,11 +41,11 @@ iOS平台可选配置参数：
<tbody valign="top">
<tr class="row-even">
<td>OS</td>
<td>armv7, armv7s, arm64 (默认)</td>
<td>armv7, armv7s, arm64 </td>
</tr>
<tr class="row-odd">
<td>SIMULATOR</td>
<td>i386, x86_64 (默认)</td>
<td>i386, x86_64 </td>
</tr>
</tbody>
</table>
......@@ -66,7 +66,7 @@ iOS平台可选配置参数：
```bash
cmake -DCMAKE_SYSTEM_NAME=iOS \
-DIOS_PLATFORM=OS \
-DIOS_ARCH="arm64" \
-DIOS_ARCH="armv7;arm64" \
-DIOS_ENABLE_BITCODE=ON \
-DIOS_USE_VECLIB_FOR_BLAS=ON \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
......@@ -112,6 +112,6 @@ $ make install
- `lib`目录，其中包含PaddlePaddle的C-API静态库
- `third_party`目录，其中包含所依赖的所有第三方库
注意，不同架构的PaddlePaddle库建议安装到不同的目录下，然后使用`lipo`工具将多个静态库合并成一个支持多个架构的fat库。
注意，如果PaddlePaddle库需要同时支持真机和模拟器，则需要分别编译真机和模拟器版本，然后使用`lipo`工具合并fat库。
自此，PaddlePaddle库已经安装完成，用户可将合成的fat库用于深度学习相关的iOS App中，调用方法见C-API文档。
doc/mobile/cross_compiling_for_raspberry_cn.md
浏览文件 @ 1971f3ce
# 构建Raspberry Pi平台上的PaddlePaddle库
# Raspberry Pi平台编译指南
通常有两个方法来构建基于 Rasspberry Pi 的版本：
......
paddle/capi/Main.cpp
浏览文件 @ 1971f3ce
......@@ -29,6 +29,9 @@ static void initPaddle(int argc, char** argv) {
extern "C" {
paddle_error paddle_init(int argc, char** argv) {
static bool isInit = false;
if (isInit) return kPD_NO_ERROR;
std::vector<char*> realArgv;
realArgv.reserve(argc + 1);
realArgv.push_back(strdup(""));
......@@ -37,6 +40,7 @@ paddle_error paddle_init(int argc, char** argv) {
}
initPaddle(argc + 1, realArgv.data());
free(realArgv[0]);
isInit = true;
return kPD_NO_ERROR;
}
}
paddle/cuda/include/hl_gpu.h
浏览文件 @ 1971f3ce
......@@ -25,7 +25,9 @@ limitations under the License. */
#include "hl_matrix.h"
#include "hl_sequence.h"
#include "hl_sparse.h"
#ifndef PADDLE_MOBILE_INFERENCE
#include "hl_warpctc_wrap.h"
#endif
#ifdef HPPL_STUB_FUNC
#include "stub/hl_aggregate_stub.h"
......
paddle/framework/backward.cc
浏览文件 @ 1971f3ce
......@@ -270,6 +270,19 @@ static bool AllGradInSet(const std::vector<std::string>& names,
return false;
}
}
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
sout << "All input {";
for (auto& name : names) {
sout << name << ",";
}
sout << "} is in {";
for (auto& name : set) {
sout << name << ",";
}
sout << "}";
VLOG(10) << sout.str();
}
return true;
}
......@@ -290,14 +303,12 @@ static void CreateGradVarInBlock(
auto ops = block_desc->AllOps();
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
bool need_infer_shape = false;
std::unordered_set<std::string> new_vars;
ForEachVarName(ops[op_index]->Outputs(),
[&](const std::string& grad_var_name) {
if (block_desc->HasVar(grad_var_name)) {
return false;
}
need_infer_shape = true;
auto var = block_desc->Var(grad_var_name);
new_vars.insert(var->Name());
auto it = param_name_map.find(grad_var_name);
......@@ -311,7 +322,6 @@ static void CreateGradVarInBlock(
grad_record.op_idx_ = static_cast<int>(op_index);
return false; /* not break */
});
if (need_infer_shape) {
ops[op_index]->InferVarType(block_desc);
for (auto& arg : ops[op_index]->OutputArgumentNames()) {
if (new_vars.find(arg) == new_vars.end()) {
......@@ -328,7 +338,6 @@ static void CreateGradVarInBlock(
}
ops[op_index]->InferShape(*block_desc);
}
}
}
std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
......@@ -387,6 +396,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
VLOG(5) << "MakeBlockBackward";
BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDescBind*> op_descs = cur_block->AllOps();
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
......@@ -394,9 +404,10 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
std::vector<std::unique_ptr<OpDescBind>> backward_descs;
for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) {
VLOG(5) << "Making backward " << (*it)->Type() << " op";
std::vector<std::unique_ptr<OpDescBind>> op_grads;
if ((*it)->Type() == "recurrent") {
if ((*it)->Type() == "recurrent" || (*it)->Type() == "while") {
int step_block_idx = (*it)->GetBlockAttr("step_block");
BlockDescBind* backward_block = CreateStepBlock(
program_desc, no_grad_vars, grad_to_var, step_block_idx);
......@@ -410,6 +421,15 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
}
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
sout << "Made ";
for (auto& op_grad : op_grads) {
sout << op_grad->Type() << " ";
}
VLOG(10) << sout.str();
}
for (const auto& desc : op_grads) {
for (const std::string& out_name : desc->OutputArgumentNames()) {
if (out_name.find("@GRAD") == std::string::npos) {
......@@ -425,6 +445,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs),
[](std::unique_ptr<OpDescBind>& ptr) { return std::move(ptr); });
}
VLOG(5) << "Appending Sums";
// Check whether some variables are written more than once
std::list<std::pair<size_t, std::unique_ptr<OpDescBind>>> pending_sum_ops;
for (const auto& dup : dup_out_ops) {
......@@ -432,16 +454,22 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
const std::vector<size_t> dup_op = dup.second;
if (out_name != kEmptyVarName && dup_op.size() > 1) {
std::vector<std::string> sum_op_inputs;
std::string next_g_name = out_name;
for (size_t i = 0; i < dup_op.size(); ++i) {
VLOG(10) << backward_descs[dup_op[i]]->Type() << " has " << out_name
<< " duplicated";
std::string new_name = out_name + "@RENAME@" + std::to_string(i);
backward_descs[dup_op[i]]->Rename(out_name, new_name);
backward_descs[dup_op[i]]->RenameOutput(out_name, new_name);
backward_descs[dup_op[i]]->RenameInput(out_name, next_g_name);
sum_op_inputs.emplace_back(new_name);
next_g_name = sum_op_inputs.back();
}
std::unique_ptr<OpDescBind> sum_op(new OpDescBind(
"sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {}));
pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)});
}
}
pending_sum_ops.sort(
[](const std::pair<size_t, std::unique_ptr<OpDescBind>>& a,
const std::pair<size_t, std::unique_ptr<OpDescBind>>& b) {
......@@ -452,6 +480,8 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
std::move(p.second));
}
VLOG(5) << "MakeBlockBackward Finished";
return backward_descs;
}
......
paddle/framework/data_type.h
浏览文件 @ 1971f3ce
......@@ -29,6 +29,8 @@ inline DataType ToDataType(std::type_index type) {
return DataType::INT32;
} else if (typeid(int64_t).hash_code() == type.hash_code()) {
return DataType::INT64;
} else if (typeid(bool).hash_code() == type.hash_code()) {
return DataType::BOOL;
} else {
PADDLE_THROW("Not supported");
}
......
paddle/framework/ddim.cc
浏览文件 @ 1971f3ce
......@@ -60,8 +60,7 @@ void make_ddim(DDim& ddim, const int64_t* dims, int n) {
ddim = make_dim<9>(dims);
break;
default:
throw std::invalid_argument(
"Dynamic dimensions must have between [1, 9] dimensions.");
PADDLE_THROW("Dynamic dimensions must have between [1, 9] dimensions.");
}
}
......
paddle/framework/executor.cc
浏览文件 @ 1971f3ce
......@@ -120,6 +120,7 @@ void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id,
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
VLOG(10) << op->DebugString();
op->Run(*local_scope, *device);
}
if (create_local_scope) {
......
paddle/framework/op_desc.cc
浏览文件 @ 1971f3ce
......@@ -235,6 +235,23 @@ void OpDescBind::Rename(const std::string &old_name,
need_update_ = true;
}
void OpDescBind::RenameOutput(const std::string &old_name,
const std::string &new_name) {
for (auto &output : outputs_) {
std::replace(output.second.begin(), output.second.end(), old_name,
new_name);
}
need_update_ = true;
}
void OpDescBind::RenameInput(const std::string &old_name,
const std::string &new_name) {
for (auto &input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
need_update_ = true;
}
struct SetAttrDescVisitor : public boost::static_visitor<void> {
explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {}
mutable OpDesc::Attr *attr_;
......@@ -448,7 +465,12 @@ const std::vector<std::string> &CompileTimeInferShapeContext::Outputs(
DDim CompileTimeInferShapeContext::GetDim(const std::string &name) const {
auto var = block_.FindVarRecursive(name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", name);
try {
return framework::make_ddim(var->Shape());
} catch (...) {
VLOG(5) << "GetDim of variable " << name << " error";
std::rethrow_exception(std::current_exception());
}
}
void CompileTimeInferShapeContext::SetDim(const std::string &name,
......
paddle/framework/op_desc.h
浏览文件 @ 1971f3ce
......@@ -73,6 +73,10 @@ class OpDescBind {
void Rename(const std::string &old_name, const std::string &new_name);
void RenameOutput(const std::string &old_name, const std::string &new_name);
void RenameInput(const std::string &old_name, const std::string &new_name);
// Only be used in C++
const AttributeMap &GetAttrMap() const;
......
paddle/framework/operator.cc
浏览文件 @ 1971f3ce
......@@ -403,19 +403,6 @@ class RuntimeInferShapeContext : public InferShapeContext {
void OperatorWithKernel::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
if (VLOG_IS_ON(1)) {
auto inputs = this->InputVars();
auto outputs = this->OutputVars(true);
std::ostringstream sout;
sout << "Run operator " << this->Type() << " From [";
std::ostream_iterator<std::string> out_it(sout, ",");
std::copy(inputs.begin(), inputs.end(), out_it);
sout << "] to [";
std::copy(outputs.begin(), outputs.end(), out_it);
sout << "]";
VLOG(1) << sout.str();
}
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
......
paddle/framework/scope.cc
浏览文件 @ 1971f3ce
......@@ -38,11 +38,12 @@ Scope& Scope::NewScope() const {
Variable* Scope::Var(const std::string& name) {
auto iter = vars_.find(name);
if (iter != vars_.end()) {
VLOG(3) << "Get existing variable " << name;
return iter->second;
}
Variable* v = new Variable();
vars_[name] = v;
VLOG(3) << "Create variable " << name << " on scope";
VLOG(3) << "Create variable " << name;
v->name_ = &(vars_.find(name)->first);
return v;
}
......
paddle/framework/shape_inference.h
浏览文件 @ 1971f3ce
......@@ -53,6 +53,10 @@ class InferShapeContext {
virtual bool IsRuntime() const = 0;
// Note: In while op, we need this to be public
void SetDims(const std::vector<std::string> &names,
const std::vector<framework::DDim> &dims);
protected:
virtual framework::DDim GetDim(const std::string &name) const = 0;
virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
......@@ -60,9 +64,6 @@ class InferShapeContext {
std::vector<framework::DDim> GetDims(
const std::vector<std::string> &names) const;
void SetDims(const std::vector<std::string> &names,
const std::vector<framework::DDim> &dims);
std::vector<VarDesc::VarType> GetVarTypes(
const std::vector<std::string> &names) const;
......
paddle/gserver/CMakeLists.txt
浏览文件 @ 1971f3ce
......@@ -73,7 +73,6 @@ if(MOBILE_INFERENCE)
list(REMOVE_ITEM GSERVER_SOURCES
dataproviders/DataProvider.cpp
dataproviders/MultiDataProvider.cpp
dataproviders/ProtoDataProvider.cpp
dataproviders/PyDataProvider2.cpp
dataproviders/PyDataProvider.cpp)
......
paddle/gserver/dataproviders/DataProvider.cpp
浏览文件 @ 1971f3ce
......@@ -16,8 +16,8 @@ limitations under the License. */
#include <unistd.h>
#include <algorithm>
#include "ProtoDataProvider.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
......@@ -164,8 +164,6 @@ DataProvider* DataProvider::create(const DataConfig& config,
REGISTER_DATA_PROVIDER(simple, SimpleDataProvider);
REGISTER_DATA_PROVIDER(dummy, DummyDataProvider);
REGISTER_DATA_PROVIDER(proto, ProtoDataProvider);
REGISTER_DATA_PROVIDER(proto_sequence, ProtoSequenceDataProvider);
int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
int64_t batchSize = doubleBuffer_ ? getNextBatchFromBuffer(size, batch)
......
paddle/gserver/dataproviders/ProtoDataProvider.h 已删除 100644 → 0
浏览文件 @ a25c3aeb
/* 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 "DataFormat.pb.h"
#include "paddle/utils/Stat.h"
#include "DataProvider.h"
#include "ProtoReader.h"
namespace paddle {
/**
* @brief Provider data from protobuf data file with each sample
* specified by proto message
*
* DataSample defined in DataFormat.proto.
*
* The file format is
*
* header
*
* sample1
*
* sample2
*
* ...
*
* sampleN
*
* @note: In the data file, each message is prefixed with its length.
* The read/write of the protbuf are implemented in ProtoReader.h
*/
class ProtoDataProvider : public DataProvider {
public:
ProtoDataProvider(const DataConfig& config,
bool useGpu,
bool loadDataAll = true);
virtual void reset();
/**
* @note this size includes the sequences which are skipped because they
* are longer than the batch size.
*/
virtual int64_t getSize() {
int64_t size = sampleNums_;
if (usageRatio_ < 1.0f) {
size = static_cast<int64_t>(size * usageRatio_);
}
return size;
}
virtual void shuffle();
void loadData(const std::vector<std::string>& fileList);
virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
protected:
/**
* @brief load protobuf data from a list of file
* @param[in] fileName file name of a file which contains
* a list of file names
*/
void loadData(const std::string& fileName);
/**
* @brief load protobuf data from file
* @param[in] fileName data file name
*/
void loadDataFile(const std::string& fileName);
/** @brief check data header of each data sample
* @param[in] header data header read from protobuf data
*/
void checkDataHeader(const DataHeader& header);
/**
* @brief fill protobuf data into slot_,
* slot_ is a vector of ProtoSlot in memory.
* @param[in] sample data sample read from protobuf data
*/
void fillSlots(const DataSample& sample);
/**
* @brief return true if each sample is one sequence, i.e., independent
* of other samples.
*/
inline bool iidData() const { return sequenceStartPositions_.empty(); }
/**
* @brief check that sample is consistent with header_
*/
void checkSample(const DataSample& sample);
template <class Op>
int64_t sequenceLoop(Op op, int64_t size);
template <class Op>
int64_t sampleLoop(Op op, int64_t size);
template <class Op>
int64_t subSampleLoop(Op op, int64_t size, int slot);
void showDataStats();
protected:
struct ProtoVarSlot {
std::vector<real> data;
std::vector<int> dims;
};
struct ProtoSlot {
SlotDef::SlotType type;
int dim;
std::vector<int> indexData;
std::vector<real> denseData;
std::vector<sparse_non_value_t> sparseNonValueData;
std::vector<sparse_float_value_t> sparseFloatValueData;
std::vector<int64_t> indices;
std::vector<int64_t> subIndices;
std::vector<ProtoVarSlot> varDenseData;
std::vector<std::vector<int>> varIndices;
std::vector<std::string> strData;
};
DataHeader header_;
int numVecSlots_;
std::vector<ProtoSlot> slots_;
size_t sampleNums_;
/**
* The starting position of each sequence in samples.
* The last element should be num of samples.
* If empty, each sample is one sequence.
*/
std::vector<size_t> sequenceStartPositions_;
int64_t currentSequenceIndex_;
// The size should be the number of sequences.
std::vector<size_t> shuffledSequenceIds_;
ThreadLocalD<DataBatch> cpuBatch_;
ThreadLocalD<DataBatch> gpuBatch_;
RWLock lock_;
std::vector<StatPtr> nnzStats_; // stats for number of none-zeros entries
};
/**
* @brief Special use for Proto data: instances should contain sparse-non-value
* slots
* and label.
*
* @note ProtoSequenceDataProvider treats each SPARSE SLOT as a SEQUENCE
*/
class ProtoSequenceDataProvider : public ProtoDataProvider {
public:
ProtoSequenceDataProvider(const DataConfig& config,
bool useGpu,
bool loadDataAll = true);
~ProtoSequenceDataProvider() {}
virtual int64_t getNextBatchInternal(int64_t size, DataBatch* batch);
};
} // namespace paddle
paddle/gserver/layers/MKLDNNConcatLayer.cpp 0 → 100644
浏览文件 @ 1971f3ce
/* 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 "MKLDNNConcatLayer.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
namespace paddle {
REGISTER_LAYER(mkldnn_concat, MKLDNNConcatLayer);
bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
if (!MKLDNNLayer::init(layerMap, parameterMap)) {
return false;
}
CHECK_GT(inputLayers_.size(), 1UL);
CHECK(!biasParameter_);
return true;
}
void MKLDNNConcatLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
CHECK_GT(inputLayers_.size(), 1UL);
channels_.resize(inputLayers_.size());
channels_[0] = ic;
// need change the output channel, so use oc_ instead
// TODO(TJ): change API, use &oc
oc_ = ic;
for (size_t i = 1; i < inputLayers_.size(); i++) {
int batchsize, height, witdh;
reshapeInput(batchsize, height, witdh, i);
CHECK_EQ(bs, batchsize);
CHECK_EQ(ih, height);
CHECK_EQ(iw, witdh);
channels_[i] = inputLayers_[i]->getSize() / height / witdh;
CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
oc_ += channels_[i];
}
oh = ih;
ow = iw;
reshapeOutput(oh, ow);
resizeOutput(bs, oc_ * oh * ow);
}
void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(inVals_, out);
in = inVals_[0];
std::shared_ptr<concat::primitive_desc> fwdPD;
resetFwdPD(fwdPD, inVals_, out);
resetFwdPipeline(pipeline, fwdPD, inVals_, out);
}
void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, out);
in = inGrads_[0];
resetBwdPipeline(pipeline, bwds_, inGrads_, out);
}
void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
inputs.resize(inputLayers_.size());
bool has8c = false, has16c = false, hasnc = false;
for (size_t i = 0; i < inputs.size(); i++) {
// resetInValue will use ic_ so temporary change as current input's channel
// TODO(TJ): change ic_ as vector then can remove channels_
ic_ = channels_[i];
resetInValue(inputs[i], nullptr, i);
CHECK(inputs[i]);
auto dm = inputs[i]->getDims();
// inputs format can be different, but ndims must equal
CHECK(i == 0 || dm.size() == inputs[0]->getDims().size());
CHECK_EQ(bs_, dm[0]);
CHECK_EQ(channels_[i], dm[1]);
if (dm.size() > 2) {
CHECK_EQ(ih_, dm[2]);
CHECK_EQ(iw_, dm[3]);
}
if (inputs[i]->getFormat() == format::nc) {
hasnc = true;
}
if (inputs[i]->getFormat() == format::nChw8c) {
has8c = true;
}
if (inputs[i]->getFormat() == format::nChw16c) {
has16c = true;
}
}
// change back, ic_ always save the input 0 size
ic_ = channels_[0];
format outFmt;
if (has16c && oc_ % 16 == 0) {
outFmt = format::nChw16c;
} else if (has8c && oc_ % 8 == 0) {
outFmt = format::nChw8c;
} else if (hasnc) {
CHECK(oh_ == 1 && ow_ == 1);
outFmt = format::nc;
} else {
outFmt = format::nchw;
}
memory::dims outDims =
hasnc ? memory::dims{bs_, oc_} : memory::dims{bs_, oc_, oh_, ow_};
auto outPD = MKLDNNMatrix::createPrimitiveDesc(outDims, outFmt, engine_);
resetOutValue(out, outPD);
}
void MKLDNNConcatLayer::resetFwdPD(std::shared_ptr<concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr out) {
std::vector<memory::primitive_desc> srcPDs;
for (size_t i = 0; i < inputs.size(); i++) {
srcPDs.push_back(inputs[i]->getPrimitiveDesc());
}
CHECK(out);
pd.reset(new concat::primitive_desc(out->getMemoryDesc(), axis_, srcPDs));
CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
}
void MKLDNNConcatLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::vector<primitive::at> srcs;
for (size_t i = 0; i < inputs.size(); i++) {
srcs.push_back(*(inputs[i]));
}
fwd_.reset(new concat(*pd, srcs, *out));
pipeline.push_back(*fwd_);
}
void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
CHECK(outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
CHECK(out);
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
CHECK(inVals_[i]);
// resetInGrad will use inVal_
// TODO(TJ): change move inVals_ to MKLDNNLayer ans remove inVal_
inVal_ = inVals_[i];
resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
}
// change back, inVal_ always save the input 0
inVal_ = inVals_[0];
}
void MKLDNNConcatLayer::resetBwdPipeline(
std::vector<mkldnn::primitive>& pipeline,
std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
// reset the backward primitives
memory::dims offsets = {0, 0, 0, 0};
prims.resize(inputs.size());
CHECK_EQ(inputs.size(), channels_.size());
for (size_t i = 0; i < inputs.size(); i++) {
auto viewPD = view::primitive_desc(
out->getPrimitiveDesc(), inputs[i]->getDims(), offsets);
auto bwdPD = reorder::primitive_desc(viewPD.dst_primitive_desc(),
inputs[i]->getPrimitiveDesc());
prims[i].reset(new reorder(bwdPD, *out, *(inputs[i])));
offsets[axis_] += channels_[i];
// push to pipeline
pipeline.push_back(*prims[i]);
}
}
} // namespace paddle
paddle/gserver/layers/MKLDNNConcatLayer.h 0 → 100644
浏览文件 @ 1971f3ce
/* 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 {
/**
* @brief A subclass of MKLDNNLayer Concatenate layer.
*
* The config file api is mkldnn_concat
*/
class MKLDNNConcatLayer : public MKLDNNLayer {
protected:
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
// input channel numbers
std::vector<int> channels_;
// concat_dimension in MKLDNN
// if axis_ == 0, concat batchsize
// if axis_ == 1, concat channel (default)
int axis_;
public:
explicit MKLDNNConcatLayer(const LayerConfig& config)
: MKLDNNLayer(config), axis_(1) {}
~MKLDNNConcatLayer() {}
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 printSizeInfo() override {
CHECK_EQ(channels_.size(), inputLayers_.size());
for (size_t i = 0; i < channels_.size(); ++i) {
VLOG(MKLDNN_SIZES) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << bs_ << ", " << channels_[i] << ", " << ih_
<< ", " << iw_;
}
VLOG(MKLDNN_SIZES) << "Output: " << bs_ << ", " << oc_ << ", " << oh_
<< ", " << ow_;
}
void printValueFormat() override {
for (size_t i = 0; i < inVals_.size(); ++i) {
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << inVals_[i]->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
}
}
void printGradFormat() override {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
}
for (size_t i = 0; i < inGrads_.size(); ++i) {
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << inGrads_[i]->getFormat() << "<<<";
}
}
protected:
/**
* Forward functions: reset buffers(inputs, output, bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr out);
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
* reset primitives and pipeline
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::vector<std::shared_ptr<mkldnn::primitive>>& prims,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
};
} // namespace paddle
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ 1971f3ce
......@@ -21,8 +21,8 @@ namespace paddle {
bool MKLDNNLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn."
<< "Please set WITH_MKLDNN=ON "
CHECK(FLAGS_use_mkldnn) << "MKLDNNLayers only support use_mkldnn."
<< "Please set WITH_MKL=ON "
<< "and set use_mkldnn=True";
CHECK(!useGpu_) << "Do not support GPU yet";
......@@ -138,8 +138,11 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
}
}
void MKLDNNLayer::reshapeInput(int& batchsize, int& height, int& width) {
const Argument& input = inputLayers_[0]->getOutput();
void MKLDNNLayer::reshapeInput(int& batchsize,
int& height,
int& width,
size_t inputIdx) {
const Argument& input = inputLayers_[inputIdx]->getOutput();
batchsize = input.getBatchSize();
int h = input.getFrameHeight();
int w = input.getFrameWidth();
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ 1971f3ce
......@@ -178,7 +178,10 @@ protected:
/**
* reshape the input image sizes and input batchsize
*/
void reshapeInput(int& batchsize, int& height, int& width);
void reshapeInput(int& batchsize,
int& height,
int& width,
size_t inputIdx = 0);
/**
* reshape output image sizes
......
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ 1971f3ce
......@@ -29,7 +29,7 @@ gserver_test(test_KmaxSeqScore)
gserver_test(test_Expand)
gserver_test(test_MaxPoolingWithMaskOutput)
########## test_Mkldnn layers and activations ##########
########## test_MKLDNN layers and activations ##########
if(WITH_MKLDNN)
add_unittest_without_exec(test_MKLDNN
test_MKLDNN.cpp
......@@ -62,17 +62,6 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
endif()
if(NOT MOBILE_INFERENCE)
################### test_ProtoDataProvider ############
add_unittest_without_exec(test_ProtoDataProvider
test_ProtoDataProvider.cpp)
# test_ProtoDataProvider will mkdir as same name,
# so if WORKING_DIRECTORY is default directory, then
# mkdir will get error.
add_test(NAME test_ProtoDataProvider
COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
################## test_Evaluator #######################
add_unittest(test_Evaluator
test_Evaluator.cpp)
......@@ -110,3 +99,24 @@ add_test(NAME test_PyDataProvider2
COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
)
################# test_CompareSparse ##################
add_unittest_without_exec(test_CompareSparse
test_CompareSparse.cpp)
if(NOT ON_TRAVIS)
add_test(NAME test_CompareSparse
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
./.set_port.sh -p port -n 6
${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
endif()
################ test_CompareTwoNets ######################
add_unittest_without_exec(test_CompareTwoNets
test_CompareTwoNets.cpp)
add_test(NAME test_CompareTwoNets
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 1971f3ce
......@@ -23,7 +23,7 @@ limitations under the License. */
namespace paddle {
/**
* @brief test the functionality of Mkldnnlayers
* @brief test the functionality of MKLDNNlayers and MKLDNNActivations
* refer to paddle original function
*/
class MKLDNNTester {
......
paddle/gserver/tests/proto_files.txt 已删除 100644 → 0
浏览文件 @ a25c3aeb
./test_ProtoDataProvider/data1.bin
./test_ProtoDataProvider/data2.bin
paddle/gserver/tests/proto_files_compressed.txt 已删除 100644 → 0
浏览文件 @ a25c3aeb
./test_ProtoDataProvider/data1.bin.gz
./test_ProtoDataProvider/data2.bin.gz
paddle/trainer/tests/sample_trainer_config_opt_a.conf → paddle/gserver/tests/sequence_lstm.conf
浏览文件 @ 1971f3ce
#!/usr/bin/env python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -14,27 +15,50 @@
from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 1000,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
fc1 = fc_layer(input=data, size=800,
bias_attr=True,
act=SigmoidActivation())
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
fc2 = fc_layer(input=fc1, size=800,
bias_attr=True,
act=SigmoidActivation())
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 256
label_dim = 3
sparse_update = get_config_arg("sparse_update", bool, False)
output = fc_layer(input=[fc1, fc2], size=10,
bias_attr=True,
act=SoftmaxActivation())
data = data_layer(name="word", size=dict_dim)
lbl = data_layer(name ="label", size=1)
emb = embedding_layer(
input=data,
size=word_dim,
param_attr=ParamAttr(sparse_update=sparse_update))
cost = classification_cost(input=output, label=lbl)
outputs(cost)
with mixed_layer(size=hidden_dim * 4) as lstm_input:
lstm_input += full_matrix_projection(input=emb)
lstm = lstmemory(
input=lstm_input,
act=TanhActivation(),
gate_act=SigmoidActivation(),
state_act=TanhActivation())
lstm_last = last_seq(input=lstm)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=lstm_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
paddle/trainer/tests/sample_trainer_config_opt_b.conf → paddle/gserver/tests/sequence_recurrent.py
浏览文件 @ 1971f3ce
#!/usr/bin/env python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -14,27 +15,42 @@
from paddle.trainer_config_helpers import *
################################### Data Configuration ###################################
TrainData(ProtoData(files = "trainer/tests/mnist.list"))
################################### Algorithm Configuration ###################################
settings(batch_size = 1000,
learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
################################### Network Configuration ###################################
data = data_layer(name ="input", size=784)
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
fc1 = fc_layer(input=data, size=800,
bias_attr=True,
act=SigmoidActivation())
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
fc2 = fc_layer(input=fc1, size=800,
bias_attr=True,
act=SigmoidActivation())
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3
output = fc_layer(input=[fc1, fc2], size=10,
bias_attr=True,
act=SoftmaxActivation())
# This config is designed to be equivalent with sequence_recurrent_group.py
lbl = data_layer(name ="label", size=1)
data = data_layer(name="word", size=dict_dim)
cost = classification_cost(input=output, label=lbl)
outputs(cost)
emb = embedding_layer(
input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
recurrent = recurrent_layer(input=emb, bias_attr=False, act=SoftmaxActivation())
recurrent_last = last_seq(input=recurrent)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=recurrent_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
paddle/gserver/tests/sequence_recurrent_group.py 0 → 100644
浏览文件 @ 1971f3ce
#!/usr/bin/env python
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer_config_helpers import *
######################## data source ################################
dict_path = 'gserver/tests/Sequence/tour_dict_phrase.dict'
dict_file = dict()
for line_count, line in enumerate(open(dict_path, "r")):
dict_file[line.strip()] = line_count
define_py_data_sources2(
train_list='gserver/tests/Sequence/train.list',
test_list=None,
module='sequenceGen',
obj='process',
args={"dict_file": dict_file})
settings(batch_size=5)
######################## network configure ################################
dict_dim = len(open(dict_path, 'r').readlines())
word_dim = 128
hidden_dim = 128
label_dim = 3
# This config is designed to be equivalent with sequence_recurrent.py
data = data_layer(name="word", size=dict_dim)
emb = embedding_layer(
input=data, size=word_dim, param_attr=ParamAttr(name="emb"))
def step(y):
mem = memory(name="rnn_state", size=hidden_dim)
with mixed_layer(
name="rnn_state",
size=hidden_dim,
bias_attr=False,
act=SoftmaxActivation()) as out:
out += identity_projection(input=y)
out += full_matrix_projection(
input=mem, param_attr=ParamAttr(name="___recurrent_layer_0__"))
return out
recurrent = recurrent_group(name="rnn", step=step, input=emb)
recurrent_last = last_seq(input=recurrent)
with mixed_layer(
size=label_dim, act=SoftmaxActivation(), bias_attr=True) as output:
output += full_matrix_projection(input=recurrent_last)
outputs(
classification_cost(
input=output, label=data_layer(
name="label", size=1)))
paddle/trainer/tests/test_CompareSparse.cpp → paddle/gserver/tests/test_CompareSparse.cpp
浏览文件 @ 1971f3ce
......@@ -22,8 +22,7 @@ limitations under the License. */
using namespace paddle; // NOLINT
using namespace std; // NOLINT
static const string& configFile1 =
"trainer/tests/sample_trainer_config_compare_sparse.conf";
static const string& configFile1 = "gserver/tests/sequence_lstm.conf";
DECLARE_bool(use_gpu);
DECLARE_string(config);
......
paddle/trainer/tests/test_CompareTwoNets.cpp → paddle/gserver/tests/test_CompareTwoNets.cpp
浏览文件 @ 1971f3ce
......@@ -30,8 +30,6 @@ DECLARE_bool(use_gpu);
DECLARE_string(config);
DECLARE_string(nics);
DEFINE_string(config_file_a, "", "config of one network to compare");
DEFINE_string(config_file_b, "", "config of another network to compare");
DEFINE_bool(need_high_accuracy,
false,
"whether need to run in double accuracy");
......@@ -42,6 +40,10 @@ DEFINE_double(
DECLARE_bool(thread_local_rand_use_global_seed);
DECLARE_int32(seed);
static const string& config_file_a = "gserver/tests/sequence_recurrent.py";
static const string& config_file_b =
"gserver/tests/sequence_recurrent_group.py";
struct ComData {
vector<Argument> outArgs;
vector<ParameterPtr> parameters;
......@@ -66,6 +68,7 @@ void calcGradient(ComData& data, const string configFile) {
DataBatch dataBatch;
int32_t batchSize = trainer.getConfig().opt_config().batch_size();
trainer.getDataProvider()->reset();
trainer.getDataProvider()->setSkipShuffle();
trainer.getDataProvider()->getNextBatch(batchSize, &dataBatch);
......@@ -167,11 +170,11 @@ void compareGradient(ComData& comDataA, ComData& comDataB) {
TEST(Trainer, create) {
ComData dataA;
calcGradient(dataA, FLAGS_config_file_a);
calcGradient(dataA, config_file_a);
LOG(INFO) << "\n\nforwardBackward of Network A is finished\n\n";
ComData dataB;
calcGradient(dataB, FLAGS_config_file_b);
calcGradient(dataB, config_file_b);
LOG(INFO) << "\n\nforwardBackward of the Network B is finished\n\n";
compareGradient(dataA, dataB);
......
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 1971f3ce
......@@ -313,6 +313,47 @@ TEST(MKLDNNLayer, AddtoLayer) {
testAddtoLayer({4, 12, 1, 1}, 3);
}
static void getMKLDNNConcatConfig(TestConfig& cfg,
const std::vector<testImageDesc>& inputs) {
CHECK_GE(inputs.size(), 2) << "at least two inputs";
int oc = inputs[0].ic;
for (size_t i = 1; i < inputs.size(); ++i) {
CHECK_EQ(inputs[i].bs, inputs[0].bs);
CHECK_EQ(inputs[i].ih, inputs[0].ih);
CHECK_EQ(inputs[i].iw, inputs[0].iw);
oc += inputs[i].ic;
}
cfg.biasSize = 0;
cfg.layerConfig.set_type("mkldnn_concat");
cfg.layerConfig.set_size(oc * inputs[0].ih * inputs[0].iw);
cfg.layerConfig.set_active_type("relu");
for (size_t i = 0; i < inputs.size(); ++i) {
std::stringstream ss;
ss << "layer_" << i;
cfg.inputDefs.push_back(
{INPUT_DATA,
ss.str(),
(size_t)(inputs[i].ic) * inputs[i].ih * inputs[i].iw,
0});
LayerInputConfig* input = cfg.layerConfig.add_inputs();
ImageConfig* img_conf = input->mutable_image_conf();
img_conf->set_channels(inputs[i].ic);
img_conf->set_img_size_y(inputs[i].ih);
img_conf->set_img_size(inputs[i].iw);
}
}
void testConcatLayer(const std::vector<testImageDesc>& inputs) {
TestConfig dnnConfig;
getMKLDNNConcatConfig(dnnConfig, inputs);
RUN_MKLDNN_TEST_LAYER(dnnConfig, "concat", inputs[0])
}
TEST(MKLDNNLayer, ConcatLayer) {
testConcatLayer({{64, 128, 1, 1}, {64, 32, 1, 1}, {64, 64, 1, 1}});
testConcatLayer({{32, 100, 8, 8}, {32, 10, 8, 8}});
}
void testActivation(std::string actType, const testImageDesc& pm) {
// TODO(TJ): remove me when paddle support elu activation
if (actType == "mkldnn_elu") {
......
paddle/math/Storage.cpp
浏览文件 @ 1971f3ce
......@@ -17,9 +17,13 @@ limitations under the License. */
#include "paddle/utils/StringUtil.h"
#include "paddle/utils/Util.h"
#ifndef PADDLE_MOBILE_INFERENCE
DEFINE_int32(pool_limit_size,
536870912,
"maximum memory size managed by a memory pool, default is 512M");
#else
DEFINE_int32(pool_limit_size, 0, "default is 0");
#endif
namespace paddle {
......
paddle/memory/README.md
浏览文件 @ 1971f3ce
# Region-based Heterogeneous Memory Management
## Design
Please check out the [design documentation](http://gangliao.me) to find out more details about
buddy memory allocator for both CPU and GPU.
### Usage
To allocate 4KB CPU memory:
```cpp
p = memory::Alloc(platform::CPUPlace(), 4*1024);
```
To allocate 4KB memory on the 3rd GPU:
```cpp
p = memory::Alloc(platform::GPUPlace(2), 4*1024);
```
To free memory and check the so-far used amount of memory on a place:
```cpp
auto pl = platform::GPUPlace(0);
p = memory::Alloc(pl, 4*1024);
cout << memory::Used(pl);
memory::Free(pl, p);
```
### API
In `paddle/memory/memory.h` we have:
```cpp
namespace memory {
template <typename Place> void* Alloc(Place, size_t);
template <typename Place> void Free(Place, void*);
template <typename Place> size_t Used(Place);
} // namespace memory
```
These function templates have specializations on either `platform::CPUPlace` or `platform::GPUPlace`:
```cpp
template<>
void* Alloc<CPUPlace>(CPUPlace p, size_t size) {
return GetCPUBuddyAllocator()->Alloc(size);
}
```
and
```cpp
template<>
void Alloc<GPUPlace>(GPUPlace p, size_t size) {
return GetGPUBuddyAllocator(p.id)->Alloc(size);
}
```
Similar specializations exist for `Free` and `Used`.
### Implementation
`GetCPUBuddyAllocator` and `GetGPUBuddyAllocator` are singletions.
```cpp
BuddyAllocator* GetCPUBuddyAllocator() {
static BuddyAllocator* a = NULL;
if (a == NULL) {
a = new BuddyAllocator(new CPUAllocator /*backup allocator*/, ...);
}
return a;
}
BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
static BuddyAllocator* as = NULL;
if (as == NULL) {
as = new BuddyAllocator*[platform::NumGPUs()];
for (int gpu = 0; gpu < platform::NumGPUs(); gpu++) {
as[gpu] = new BuddyAllocator(new GPUAllocator(gpu) /* backup allocator */, ...);
}
}
return as[gpu_id);
```
#### `BuddyAllocator`
`BuddyAllocator` implements the buddy allocation algorithm. Its constructor takes parameters only related with the algorithm:
```cpp
BuddyAllocator::BuddyAllocator(initial_pool_size, max_pool_size) {
...
}
```
Please be aware that **`BuddyAllocator` always allocate aligned memory**, aligned on 32-bytes, which can hold a `BuddyAllocator::Block` object:
```cpp
class BuddyAllocator {
private:
struct Block {
size_t size;
Block* left, right;
size_t index; // allocator id
};
...
};
```
Because BuddyAllocator has the meta-data of each block, it can trace the used memory -- record the amount returned by `Alloc` freed in `Free`. Instead, `CPUAllocator` and `GPUAllocator` doesn't know the size of freed memory block and cannot do the trace.
#### System Allocators
The `GPUAllocator` and `CPUAllocator` are calls *system allocators*. They work as the fallback allocators of `BuddyAllocator`.
## Justification
I got inspiration from Majel and Caffe2, though above design look different from both.
### Caffe2
In Caffe2, `Tensor<Context>::mutable_data()` allocates the memroy. In particular, [`Tensor<Context>::mutable_data`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L523) calls [`Tensor<Context>::raw_mutable_data`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L459), which in turn calls [`Context::New`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/tensor.h#L479).
There are two implementations of `Context`:
1. [`CPUContext`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.h#L105), whose [`New` method](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.h#L131) calls [`g_cpu_allocator.get()->New(size_t)`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context.cc#L15) to allocate the memory.
1. [`CUDAContext`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.h#L99), which has a data member [`int gpu_id_`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.h#L202). This looks very similar to class `majel::GPUPlace`, who also has an `int id_` data member. `CUDAContext::New(size_t)` calls [`g_cub_allocator->DeviceAllocate(&ptr, nbytes)`](https://github.com/caffe2/caffe2/blob/v0.7.0/caffe2/core/context_gpu.cu#L355) to allocate the memory.
### Majel
In Majel, there are basically two allocator types:
1. `cpu::SystemAllocator`, which has similar functionality to `caffe2::CPUContext::New/Delete`.
1. `gpu::SystemAllocator`, which has similar functionality to `caffe2::CUDAContext::New/Delete`.
However, memory allocation is not via these two allocators. Instead, these two allocators are defined in hidden namespaces.
In Majel there are hidden global variables like:
1. `cpu::SystemAllocator g_cpu_allocator`, and
1. `vector<gpu::SystemAllocator*> g_gpu_allocators(NUM_GPUS)`.
Programs allocate memory via a BuddyAllocator, which can take the `g_cpu_allocator` or a `g_gpu_allocators[gpu_id]` as its *fallback allocator*, so that if BuddyAllocator cannot find a block in its memory pool, it extends its memory pool by calling the fallback allocator's `New(size_t)`.
paddle/operators/CMakeLists.txt
浏览文件 @ 1971f3ce
......@@ -9,6 +9,7 @@ function(op_library TARGET)
set(OP_LIBRARY ${TARGET} ${OP_LIBRARY} PARENT_SCOPE)
set(cc_srcs)
set(cu_srcs)
set(cu_cc_srcs)
set(op_common_deps operator op_registry math_function)
set(options "")
set(oneValueArgs "")
......@@ -22,6 +23,9 @@ function(op_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc)
list(APPEND cc_srcs ${TARGET}.cc)
endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu.cc)
list(APPEND cu_cc_srcs ${TARGET}.cu.cc)
endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
list(APPEND cu_srcs ${TARGET}.cu)
endif()
......@@ -29,6 +33,8 @@ function(op_library TARGET)
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*\\.cu.cc$")
list(APPEND cu_cc_srcs ${src})
elseif(${src} MATCHES ".*\\.cc$")
list(APPEND cc_srcs ${src})
else()
......@@ -43,7 +49,7 @@ function(op_library TARGET)
endif()
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
else()
cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${op_library_DEPS}
......@@ -140,7 +146,9 @@ function(op_library TARGET)
# pybind USE_CPU_ONLY_OP
list(LENGTH cu_srcs cu_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${cu_srcs_len} EQUAL 0)
list(LENGTH cu_cc_srcs cu_cc_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0)
file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n")
set(pybind_flag 1)
endif()
......@@ -160,11 +168,12 @@ set(DEPS_OPS
recurrent_op
dynamic_recurrent_op
softmax_with_cross_entropy_op
softmax_op
sequence_softmax_op
sum_op
pool_op
pool_with_index_op
conv_op
lstm_op
conv_transpose_op
nccl_op
sequence_conv_op
......@@ -174,13 +183,20 @@ set(DEPS_OPS
array_to_lod_tensor_op
lstm_op
tensor_array_read_write_op
gru_op)
gru_op
adagrad_op
sgd_op)
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
op_library(cross_entropy_op DEPS cross_entropy)
op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax)
op_library(softmax_op DEPS softmax)
op_library(sequence_softmax_op DEPS softmax)
op_library(sum_op DEPS selected_rows_functor)
op_library(sgd_op DEPS selected_rows_functor)
op_library(adagrad_op DEPS selected_rows_functor)
op_library(conv_op DEPS vol2col)
op_library(sum_op DEPS net_op selected_rows_functor)
op_library(pool_op DEPS pooling)
op_library(pool_with_index_op DEPS pooling)
op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
......@@ -220,6 +236,6 @@ cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc
rnn/recurrent_op_utils.cc
DEPS dynamic_recurrent_op)
if(WITH_GPU)
nv_test(nccl_op_test SRCS nccl_op_test.cu DEPS nccl_op gpu_info device_context)
cc_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
endif()
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
paddle/operators/accuracy_op.cu
浏览文件 @ 1971f3ce
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <thrust/reduce.h>
#include "paddle/operators/accuracy_op.h"
#include "paddle/platform/cuda_helper.h"
#include "paddle/platform/gpu_info.h"
namespace paddle {
namespace operators {
......@@ -73,26 +74,28 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
int num_samples = static_cast<int>(inference->dims()[0]);
size_t infer_width = inference->dims()[1];
PADDLE_ENFORCE(cudaMemset(accuracy_data, 0, sizeof(float)));
// cudaMemset((void**)&correct_data, 0, sizeof(float));
auto stream = ctx.cuda_device_context().stream();
platform::GpuMemsetAsync(accuracy_data, 0, sizeof(float), stream);
if (num_samples == 0) {
return;
}
cudaMemcpy(total_data, &num_samples, sizeof(int), cudaMemcpyHostToDevice);
platform::GpuMemcpyAsync(total_data, &num_samples, sizeof(int),
cudaMemcpyHostToDevice, stream);
AccuracyCudaKernel<PADDLE_CUDA_NUM_THREADS><<<
1, PADDLE_CUDA_NUM_THREADS, 0, ctx.cuda_device_context().stream()>>>(
AccuracyCudaKernel<
PADDLE_CUDA_NUM_THREADS><<<1, PADDLE_CUDA_NUM_THREADS, 0, stream>>>(
num_samples, infer_width, indices_data, label_data, correct_data,
accuracy_data);
int d_num_samples, d_num_correct;
float d_accuracy;
cudaMemcpy(&d_num_correct, correct_data, sizeof(int),
cudaMemcpyDeviceToHost);
cudaMemcpy(&d_num_samples, total_data, sizeof(int), cudaMemcpyDeviceToHost);
cudaMemcpy(&d_accuracy, accuracy_data, sizeof(float),
cudaMemcpyDeviceToHost);
platform::GpuMemcpyAsync(&d_num_correct, correct_data, sizeof(int),
cudaMemcpyDeviceToHost, stream);
platform::GpuMemcpyAsync(&d_num_samples, total_data, sizeof(int),
cudaMemcpyDeviceToHost, stream);
platform::GpuMemcpyAsync(&d_accuracy, accuracy_data, sizeof(float),
cudaMemcpyDeviceToHost, stream);
}
};
......
paddle/operators/adagrad_op.cc
浏览文件 @ 1971f3ce
......@@ -14,6 +14,11 @@ limitations under the License. */
#include "paddle/operators/adagrad_op.h"
#include <cmath>
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
namespace paddle {
namespace operators {
......@@ -21,7 +26,7 @@ class AdagradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Param"),
"Input(Param) of AdagradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Grad"),
......@@ -54,8 +59,8 @@ class AdagradOp : public framework::OperatorWithKernel {
class AdagradOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AdagradOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
AdagradOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Grad", "(Tensor) Input gradient");
......@@ -87,10 +92,85 @@ for numerical stability to avoid the division by zero error.
)DOC");
}
};
namespace {
size_t FindPos(const std::vector<int64_t>& rows, int64_t value) {
return std::find(rows.begin(), rows.end(), value) - rows.begin();
}
} // namespace
template <typename T>
struct SparseAdagradFunctor<platform::CPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param) {
// 1. g_m.rows = set(g.rows)
auto grad_rows = grad.rows();
std::set<int64_t> row_set(grad_rows.begin(), grad_rows.end());
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
auto grad_width = grad.value().dims()[1];
std::unique_ptr<framework::SelectedRows> grad_merge{
new framework::SelectedRows()};
grad_merge->set_rows(merge_rows);
grad_merge->set_height(grad.height());
grad_merge->mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), grad_width}),
context.GetPlace());
math::SetConstant<platform::CPUPlace, T> constant_functor;
constant_functor(context, grad_merge->mutable_value(), 0.0);
auto* grad_merge_data = grad_merge->mutable_value()->data<T>();
auto* grad_data = grad.value().data<T>();
for (size_t i = 0; i < grad_rows.size(); i++) {
size_t grad_merge_i = FindPos(merge_rows, grad_rows[i]);
for (int64_t j = 0; j < grad_width; j++) {
grad_merge_data[grad_merge_i * grad_width + j] +=
grad_data[i * grad_width + j];
}
}
// 2. m += g_m * g_m
std::unique_ptr<framework::SelectedRows> grad_square{
new framework::SelectedRows()};
grad_square->set_rows(grad_merge->rows());
grad_square->set_height(grad_merge->height());
grad_square->mutable_value()->mutable_data<T>(grad_merge->value().dims(),
context.GetPlace());
auto gs =
framework::EigenVector<T>::Flatten(*(grad_square->mutable_value()));
auto gm = framework::EigenVector<T>::Flatten(grad_merge->value());
gs.device(*context.GetEigenDevice<platform::CPUPlace>()) = gm * gm;
math::SelectedRowsAddToTensor<platform::CPUPlace, T> functor;
functor(context, *grad_square, moment);
// 3. update parameter
auto* lr = learning_rate.data<T>();
auto* param_data = param->data<T>();
auto* moment_data = moment->data<T>();
for (size_t i = 0; i < merge_rows.size(); i++) {
for (int64_t j = 0; j < grad_width; j++) {
param_data[merge_rows[i] * grad_width + j] -=
lr[0] * grad_merge_data[i * grad_width + j] /
(std::sqrt(moment_data[merge_rows[i] * grad_width + j]) + epsilon);
}
}
}
};
template struct SparseAdagradFunctor<platform::CPUPlace, float>;
template struct SparseAdagradFunctor<platform::CPUPlace, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adagrad, ops::AdagradOp, ops::AdagradOpMaker);
REGISTER_OP_CPU_KERNEL(adagrad,
ops::AdagradOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
adagrad, ops::AdagradOpKernel<paddle::platform::CPUPlace, float>,
ops::AdagradOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adagrad_op.cu
浏览文件 @ 1971f3ce
......@@ -14,7 +14,138 @@
#define EIGEN_USE_GPU
#include "paddle/operators/adagrad_op.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
namespace {
template <typename T, int block_size>
__global__ void MergeGradKernel(const T* grad, const int64_t* grad_rows,
T* grad_merge, const int64_t* grad_merge_rows,
size_t grad_merge_rows_size,
int64_t row_numel) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
__shared__ size_t grad_merge_idx;
if (tid == 0) {
for (size_t i = 0; i < grad_merge_rows_size; i++) {
if (grad_rows[ty] == grad_merge_rows[i]) {
grad_merge_idx = i;
}
}
}
__syncthreads();
grad += ty * row_numel;
grad_merge += grad_merge_idx * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
paddle::platform::CudaAtomicAdd(grad_merge + index, grad[index]);
}
}
template <typename T, int block_size>
__global__ void SparseAdagradFunctorKernel(const T* grad, const int64_t* rows,
const T* learning_rate, T* param,
T* moment, int64_t row_numel,
T epsilon) {
const int ty = blockIdx.y;
int tid = threadIdx.x;
grad += ty * row_numel;
param += rows[ty] * row_numel;
moment += rows[ty] * row_numel;
for (int index = tid; index < row_numel; index += block_size) {
// Since index in rows of SelectedRows can be duplicate, we have to use
// Atomic Operation to avoid concurrent write error.
paddle::platform::CudaAtomicAdd(param + index,
-1.0 * learning_rate[0] * grad[index] /
(sqrt(moment[index]) + epsilon));
}
}
} // namespace
template <typename T>
struct SparseAdagradFunctor<platform::GPUPlace, T> {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param) {
// 1. g_m.rows = set(g.rows)
auto grad_rows = grad.rows();
std::set<int64_t> row_set(grad_rows.begin(), grad_rows.end());
std::vector<int64_t> merge_rows(row_set.begin(), row_set.end());
auto grad_width = grad.value().dims()[1];
std::unique_ptr<framework::SelectedRows> grad_merge{
new framework::SelectedRows()};
grad_merge->set_rows(merge_rows);
grad_merge->set_height(grad.height());
grad_merge->mutable_value()->mutable_data<T>(
framework::make_ddim(
{static_cast<int64_t>(merge_rows.size()), grad_width}),
context.GetPlace());
math::SetConstant<platform::GPUPlace, T> constant_functor;
constant_functor(context, grad_merge->mutable_value(), 0.0);
auto* grad_merge_data = grad_merge->mutable_value()->data<T>();
auto* grad_data = grad.value().data<T>();
const int block_size = 256;
dim3 threads(block_size, 1);
dim3 grid1(1, grad_rows.size());
MergeGradKernel<
T, 256><<<grid1, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(grad_data, grad.rows().data(),
grad_merge_data, grad_merge->rows().data(),
grad_merge->rows().size(), grad_width);
// 2. m += g_m * g_m
std::unique_ptr<framework::SelectedRows> grad_square{
new framework::SelectedRows()};
grad_square->set_rows(grad_merge->rows());
grad_square->set_height(grad_merge->height());
grad_square->mutable_value()->mutable_data<T>(grad_merge->value().dims(),
context.GetPlace());
auto gs =
framework::EigenVector<T>::Flatten(*(grad_square->mutable_value()));
auto gm = framework::EigenVector<T>::Flatten(grad_merge->value());
gs.device(*context.GetEigenDevice<platform::GPUPlace>()) = gm * gm;
math::SelectedRowsAddToTensor<platform::GPUPlace, T> functor;
functor(context, *grad_square, moment);
// 3. update parameter
auto* lr = learning_rate.data<T>();
auto* param_data = param->data<T>();
auto* moment_data = moment->data<T>();
dim3 grid2(1, merge_rows.size());
SparseAdagradFunctorKernel<
T, 256><<<grid2, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(grad_merge_data, grad_merge->rows().data(),
lr, param_data,
moment_data, grad_width, epsilon);
}
};
template struct SparseAdagradFunctor<platform::GPUPlace, float>;
template struct SparseAdagradFunctor<platform::GPUPlace, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adagrad,
ops::AdagradOpKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
adagrad, ops::AdagradOpKernel<paddle::platform::GPUPlace, float>,
ops::AdagradOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/adagrad_op.h
浏览文件 @ 1971f3ce
......@@ -19,18 +19,28 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename Place, typename T>
struct SparseAdagradFunctor {
void operator()(const platform::DeviceContext& context,
const framework::SelectedRows& grad,
const framework::Tensor& learning_rate, T epsilon,
framework::Tensor* moment, framework::Tensor* param);
};
template <typename Place, typename T>
class AdagradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
auto* param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
auto* moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
param_out_tensor->mutable_data<T>(ctx.GetPlace());
moment_out_tensor->mutable_data<T>(ctx.GetPlace());
float epsilon = ctx.Attr<float>("epsilon");
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto* grad_var = ctx.InputVar("Grad");
if (grad_var->IsType<framework::LoDTensor>()) {
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
auto grad = framework::EigenVector<T>::Flatten(
......@@ -48,6 +58,20 @@ class AdagradOpKernel : public framework::OpKernel<T> {
Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
param_out.device(place) =
param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
} else if (grad_var->IsType<framework::SelectedRows>()) {
auto* param_tensor = ctx.Input<framework::Tensor>("Param");
PADDLE_ENFORCE_EQ(param_tensor, param_out_tensor);
auto* moment_tensor = ctx.Input<framework::Tensor>("Moment");
PADDLE_ENFORCE_EQ(moment_tensor, moment_out_tensor);
SparseAdagradFunctor<Place, T> functor;
functor(ctx.device_context(), *ctx.Input<framework::SelectedRows>("Grad"),
*ctx.Input<framework::Tensor>("LearningRate"), epsilon,
moment_out_tensor, param_out_tensor);
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
}
}
};
......
paddle/operators/array_operator.h
浏览文件 @ 1971f3ce
......@@ -42,6 +42,7 @@ class ArrayOp : public framework::OperatorBase {
} else {
offset = static_cast<size_t>(*i_tensor.data<int64_t>());
}
VLOG(10) << " Offset = " << offset;
return offset;
}
};
......
paddle/operators/beam_search_op.cc 0 → 100644
浏览文件 @ 1971f3ce
/* 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/beam_search_op.h"
#include <map>
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
void BeamSearch::operator()(const framework::LoDTensor &pre_ids,
framework::LoDTensor *selected_ids,
framework::LoDTensor *selected_scores) {
auto items = SelectTopBeamSizeItems();
auto selected_items = ToMap(items);
PruneEndidCandidates(pre_ids, &selected_items);
// calculate the output tensor's height
size_t num_instances = std::accumulate(
std::begin(items), std::end(items), 0,
[](size_t a, std::vector<Item> &b) { return a + b.size(); });
// the output tensor shape should be [num_instances, 1]
auto dims = framework::make_ddim(
std::vector<int64_t>({static_cast<int>(num_instances), 1}));
selected_ids->Resize(dims);
selected_scores->Resize(dims);
std::map<size_t /*offset*/, std::vector<Item>> hash;
framework::LoD new_lod;
auto *ids_data = selected_ids->mutable_data<int>(platform::CPUPlace());
auto *scores_data =
selected_scores->mutable_data<float>(platform::CPUPlace());
// fill in data
std::vector<size_t> low_level;
size_t low_offset = 0;
for (auto &items : selected_items) {
low_level.push_back(low_offset);
for (auto &item : items) {
ids_data[low_offset] = item.id;
scores_data[low_offset] = item.score;
low_offset++;
}
}
// fill lod
auto abs_lod = framework::ToAbsOffset(ids_->lod());
auto &high_level = abs_lod[lod_level_];
framework::LoD lod(2);
lod[0].assign(high_level.begin(), high_level.end());
lod[1].assign(low_level.begin(), low_level.end());
selected_ids->set_lod(lod);
selected_scores->set_lod(lod);
}
void BeamSearch::PruneEndidCandidates(const framework::LoDTensor &pre_ids,
std::vector<std::vector<Item>> *items) {
auto *pre_ids_data = pre_ids.data<int>();
for (size_t offset = 0; offset < items->size(); offset++) {
auto prefix_id = pre_ids_data[offset];
if (prefix_id == end_id_) {
items->at(offset).clear();
}
}
}
std::vector<std::vector<BeamSearch::Item>> BeamSearch::ToMap(
const std::vector<std::vector<Item>> &items) {
std::vector<std::vector<Item>> result;
for (auto &entries : items) {
for (const auto &item : entries) {
if (item.offset >= result.size()) {
result.resize(item.offset + 1);
}
result[item.offset].push_back(item);
}
}
return result;
}
std::vector<std::vector<BeamSearch::Item>>
BeamSearch::SelectTopBeamSizeItems() {
std::vector<std::vector<Item>> result;
std::vector<Item> items;
// for each source sentence, select the top beam_size items across all
// candidate sets.
while (NextItemSet(&items)) {
std::nth_element(std::begin(items), std::begin(items) + beam_size_,
std::end(items), [](const Item &a, const Item &b) {
// TODO(superjom) make score's comparation customizable.
// partial sort in descending order
return a.score > b.score;
});
// prune the top beam_size items.
if (items.size() > beam_size_) {
items.resize(beam_size_);
}
result.emplace_back(items);
}
return result;
}
// the candidates of a source
bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
if (sent_offset_ >= ids_->NumElements(lod_level_)) {
return false;
}
// find the current candidates
auto ids = *ids_;
auto scores = *scores_;
auto source_abs_two_level_lod = framework::SliceInLevel(
ids.lod(), lod_level_, sent_offset_, sent_offset_ + 1);
source_abs_two_level_lod = framework::ToAbsOffset(source_abs_two_level_lod);
auto abs_lod = framework::ToAbsOffset(ids.lod());
PADDLE_ENFORCE_GE(source_abs_two_level_lod.size(), 2UL);
auto *ids_data = ids.data<int>();
auto *scores_data = scores.data<float>();
size_t instance_dim = 1;
for (int i = 1; i < ids.dims().size(); i++) {
instance_dim *= ids.dims()[i];
}
items->clear();
items->reserve(framework::product(ids.dims()));
for (size_t offset = abs_lod[lod_level_][sent_offset_];
offset < abs_lod[lod_level_][sent_offset_ + 1]; offset++) {
for (int d = 0; d < instance_dim; d++) {
const size_t dim_offset = offset * instance_dim + d;
items->emplace_back(offset, ids_data[dim_offset],
scores_data[dim_offset]);
}
}
sent_offset_++;
return true;
}
class BeamSearchProtoAndCheckerMaker
: public framework::OpProtoAndCheckerMaker {
public:
BeamSearchProtoAndCheckerMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
// inputs and outputs stored in proto
AddInput("pre_ids", "ids in previous step");
AddInput("ids", "a LoDTensor of shape of [None,k]");
AddInput("scores",
"a LoDTensor that has the same shape and LoD with `ids`");
AddOutput("selected_ids",
"a LoDTensor that stores the IDs selected by beam search");
AddOutput(
"selected_scores",
"a LoDTensor that has the same shape and LoD with `selected_ids`");
// Attributes stored in AttributeMap
AddAttr<int>("level", "the level of LoDTensor");
AddAttr<int>("beam_size", "beam size for beam search");
AddAttr<int>("end_id",
"the token id which indicates the end of a sequence");
AddComment(
"This is a beam search operator that help to generate sequences.");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(beam_search, paddle::operators::BeamSearchOp,
paddle::operators::BeamSearchProtoAndCheckerMaker);
paddle/operators/beam_search_op.h 0 → 100644
浏览文件 @ 1971f3ce
/* 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
#ifdef PADDLE_WITH_TESTING
#include "gtest/gtest.h"
#endif
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
/*
* This is an implementation of beam search.
*
* To explain the details, lets take machine translation task for example, in
* this task, one source sentence is translated to multiple target sentences,
* during this period, one sentence will be translated to multiple translation
* prefixes(target sentence that have not ended), in each time step a prefix
* will have some candidates, input the candidate ids and their corresponding
* scores (probabilities), it will sort and select the top beam_size candidates
* for each source sentence, and store the selected candidates's score and their
* corresponding ids to LoDTensors.
*
* A detailed example:
*
* Input
*
* ids:
* LoD (should have 2 levels)
* first level: [0, 1, 4]
* second level: [0, 1, 2, 3, 4]
*
* tensor's data
* [
* [4, 2, 5]
* [2, 1, 3]
* [3, 5, 2]
* [8, 2, 1]
* ]
*
* scores:
* LoD same as `ids`
* tensor's data
* [
* [0.5, 0.3, 0.2]
* [0.6, 0.3, 0.1]
* [0.9, 0.5, 0.1]
* [0.7, 0.5, 0.1]
* ]
*
* the inputs means that there are 2 source sentences to translate, and the
* first source has 1 prefix, the second source has 2 prefix.
*
* lets assume beam size is 2, and the beam search's output should be
* LoD
* first level:
* [0, 1, 2]
* second level:
* [0, 2, 4]
*
* tensor's data
* [[
* 0.5,
* 0.3,
* 0.9,
* 0.7
* ]]
*
* TODO all the prune operations should be in the beam search, so it is better
* to split the beam search algorithm into a sequence of smaller operators, and
* the prune operators can be inserted in this sequence.
*/
class BeamSearch {
public:
// TODO(superjom) make type customizable
using id_t = size_t;
using score_t = float;
/*
* Input the arguments that needed by this class.
*/
BeamSearch(const framework::LoDTensor& ids,
const framework::LoDTensor& scores, size_t level, size_t beam_size,
int end_id)
: beam_size_(beam_size),
ids_(&ids),
scores_(&scores),
lod_level_(level),
end_id_(end_id) {}
/*
* The main function of beam search.
*
* @selected_ids: a [None, 1]-shaped tensor with LoD.
* In a machine translation model, it might be the candidate term id sets,
* each set stored as a varience-length sequence.
* The format might be described with a two-level LoD
* - [[0 1]
* - [0 1 2]]
* - [[]
* - [0 1]]
* the first level of LoD tells that there are two source sentences. The
* second level describes the details of the candidate id set's offsets in
* the
* source sentences.
*
* @selected_scores: a LoD tensor with the same shape and LoD with
* selected_ids.
* It stores the corresponding scores of candidate ids in selected_ids.
*
* Return false if all the input tensor is empty, in machine translation task
* that means no candidates is provided, and the task will stop running.
*/
void operator()(const framework::LoDTensor& pre_ids,
framework::LoDTensor* selected_ids,
framework::LoDTensor* selected_scores);
protected:
/*
* The basic items help to sort.
*/
struct Item {
Item() {}
Item(size_t offset, size_t id, float score)
: offset(offset), id(id), score(score) {}
// offset in the lod_level_+1
size_t offset;
// the candidate id
id_t id;
// the corresponding score
score_t score;
};
void PruneEndidCandidates(const framework::LoDTensor& pre_ids,
std::vector<std::vector<Item>>* items);
/*
* Transform the items into a map whose key is offset, value is the items.
* NOTE low performance
*/
std::vector<std::vector<Item>> ToMap(
const std::vector<std::vector<Item>>& inputs);
/*
* For each source, select top beam_size records.
*/
std::vector<std::vector<Item>> SelectTopBeamSizeItems();
/*
* Get the items of next source sequence, return false if no remaining items.
*/
bool NextItemSet(std::vector<Item>* items);
private:
size_t beam_size_;
const framework::LoDTensor* ids_;
const framework::LoDTensor* scores_;
size_t lod_level_{0};
size_t sent_offset_{0};
int end_id_{0};
};
class BeamSearchOp : public framework::OperatorBase {
public:
BeamSearchOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
BeamSearchOp(const BeamSearchOp& o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase&>(o)) {
PADDLE_THROW("Not Implemented");
}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
LOG(INFO) << "run beam search op";
auto ids_var = scope.FindVar(Input("ids"));
auto scores_var = scope.FindVar(Input("scores"));
auto pre_ids_var = scope.FindVar(Input("pre_ids"));
PADDLE_ENFORCE_NOT_NULL(ids_var);
PADDLE_ENFORCE_NOT_NULL(scores_var);
PADDLE_ENFORCE_NOT_NULL(pre_ids_var);
auto& ids = ids_var->Get<framework::LoDTensor>();
auto& scores = scores_var->Get<framework::LoDTensor>();
auto& pre_ids = pre_ids_var->Get<framework::LoDTensor>();
size_t level = Attr<int>("level");
size_t beam_size = Attr<int>("beam_size");
int end_id = Attr<int>("end_id");
LOG(INFO) << "init beam search";
BeamSearch alg(ids, scores, level, beam_size, end_id);
LOG(INFO) << "after beam search";
auto selected_ids_var = scope.FindVar(Output("selected_ids"));
auto selected_scores_var = scope.FindVar(Output("selected_scores"));
PADDLE_ENFORCE_NOT_NULL(selected_ids_var);
PADDLE_ENFORCE_NOT_NULL(selected_scores_var);
auto& selected_ids_tensor =
*selected_ids_var->GetMutable<framework::LoDTensor>();
auto& selected_scores_tensor =
*selected_scores_var->GetMutable<framework::LoDTensor>();
LOG(INFO) << "run beam search";
alg(pre_ids, &selected_ids_tensor, &selected_scores_tensor);
LOG(INFO) << "finish beam search";
}
};
} // namespace operators
} // namespace paddle
paddle/operators/bilinear_tensor_product_op.h
浏览文件 @ 1971f3ce
......@@ -174,7 +174,7 @@ class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
// Caculate the gradient of Input(Bias).
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
auto d_bias_mat = EigenMatrix<T>::From(*d_bias);
auto d_bias_mat = framework::EigenVector<T>::Flatten(*d_bias);
d_bias_mat.device(place) = d_out_mat.sum(Eigen::DSizes<int, 1>(0));
}
}
......
paddle/operators/conv2d_transpose_cudnn_op.cu → paddle/operators/conv2d_transpose_cudnn_op.cu.cc
浏览文件 @ 1971f3ce
......@@ -200,9 +200,7 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
T alpha = 1.0f, beta = 0.0f;
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*input_grad);
t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
math::set_constant(ctx.device_context(), input_grad, 0);
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc, output_grad_data,
......@@ -214,9 +212,8 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*filter_grad);
t.device(ctx.GetEigenDevice<platform::GPUPlace>()) =
t.constant(static_cast<T>(0));
math::set_constant(ctx.device_context(), filter_grad, 0);
// Gradient with respect to the filter
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc, output_grad_data, cudnn_input_desc,
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ 1971f3ce
......@@ -22,8 +22,6 @@ class CudnnConvOpMaker : public Conv2DOpMaker {
CudnnConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv2DOpMaker(proto, op_checker) {
AddAttr<std::vector<int>>("dilations", "dilations of convolution operator.")
.SetDefault(std::vector<int>{1, 1});
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
......
paddle/operators/conv_op.cc
浏览文件 @ 1971f3ce
......@@ -30,6 +30,7 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
......@@ -52,9 +53,15 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
"The number of output channels should be divided by groups.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < paddings.size(); ++i) {
for (size_t i = 0; i < strides.size(); ++i) {
PADDLE_ENFORCE(in_dims[i + 2] + 2 * paddings[i] -
(dilations[i] * (filter_dims[i + 2] - 1) + 1) >
0,
"Due to the settings of paddings, filter_dims and "
"dilations, the output size is less than 0, please check "
"again.");
output_shape.push_back(OutputSize(in_dims[i + 2], filter_dims[i + 2],
paddings[i], strides[i]));
dilations[i], paddings[i], strides[i]));
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}
......@@ -78,9 +85,15 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.");
AddAttr<std::vector<int>>("strides", "strides of convolution operator.")
AddAttr<std::vector<int>>("strides",
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
"convolution operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.")
AddAttr<std::vector<int>>("paddings",
"(vector<int> default:{0, 0}), the "
"paddings(h_pad, w_pad) of "
"convolution operator.")
.SetDefault({0, 0});
AddAttr<int>(
"groups",
......@@ -90,15 +103,20 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
"first half of the input channels, while the second half of the filters "
"is only connected to the second half of the input channels.")
.SetDefault(1);
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of "
"convolution operator.")
.SetDefault({1, 1});
AddComment(R"DOC(
Convolution Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and strides, paddings, groups, dilations parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature. Parameters(ksize, strides, paddings) are two elements.
the width of the feature. Parameters(ksize, strides, paddings, dilations) are two elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
......@@ -109,8 +127,8 @@ Example:
Output:
Output shape: (N, C_out, H_out, W_out)
where
H_out = (H_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
W_out = (W_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
H_out = (H_in + 2 * paddings[0] - (dilations[0]*(filter_size[0] - 1) + 1)) / strides[0] + 1;
W_out = (W_in + 2 * paddings[1] - (dilations[1]*(filter_size[1] - 1) + 1)) / strides[1] + 1;
)DOC");
}
......@@ -135,13 +153,15 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>(
"strides",
"(vector, default:{0, 0, 0}), the strides of convolution operator.")
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
"convolution operator.")
.SetDefault({1, 1, 1});
AddAttr<std::vector<int>>(
"paddings",
"(vector, default:{0, 0, 0}), the paddings of convolution operator.")
AddAttr<std::vector<int>>("paddings",
"(vector<int>, default:{0, 0, 0}), the "
"paddings(d_pad, h_pad, w_pad) of convolution "
"operator.")
.SetDefault({0, 0, 0});
AddAttr<int>(
"groups",
......@@ -151,6 +171,12 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
"first half of the input channels, while the second half of the filters "
"is only connected to the second half of the input channels.")
.SetDefault(1);
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation, h_dilation, w_dilation) of "
"convolution operator. Currently, conv3d doesn't "
"support dilation.")
.SetDefault({1, 1, 1});
AddComment(R"DOC(
Convolution3D Operator.
......
paddle/operators/conv_op.h
浏览文件 @ 1971f3ce
......@@ -27,11 +27,24 @@ using Tensor = framework::Tensor;
// Base convolution operator definations for other conv
// like operators to reuse the implementation.
inline int OutputSize(int input_size, int filter_size, int padding,
int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
inline int OutputSize(int input_size, int filter_size, int dilation,
int padding, int stride) {
const int dkernel = dilation * (filter_size - 1) + 1;
const int output_size = (input_size + 2 * padding - dkernel) / stride + 1;
return output_size;
}
inline bool IsExpand(std::vector<int64_t>& filter_dim,
std::vector<int>& strides, std::vector<int>& paddings,
std::vector<int>& dilations) {
bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
for (size_t j = 0; j < strides.size(); ++j) {
filter_1 = filter_1 && (static_cast<int>(filter_dim[j]) == 1);
strides_1 = strides_1 && (strides[j] == 1);
padding_0 = padding_0 && (paddings[j] == 0);
dilation_1 = dilation_1 && (dilations[j] == 1);
}
return !(filter_1 && strides_1 && padding_0 && dilation_1);
}
// Define Op classes in .h file so that other conv
// operator implementations can reuse the code.
......@@ -50,14 +63,12 @@ class Conv3DOpMaker : public framework::OpProtoAndCheckerMaker {
class ConvOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
};
class ConvOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
};
......@@ -73,9 +84,10 @@ class GemmConvKernel : public framework::OpKernel<T> {
Tensor* output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
int groups = context.Attr<int>("groups");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -106,14 +118,17 @@ class GemmConvKernel : public framework::OpKernel<T> {
framework::DDim col_matrix_shape =
framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<T>(col_shape, context.GetPlace());
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
framework::DDim input_shape = framework::slice_ddim(
input->dims(), 1, static_cast<int>(input->dims().size()));
......@@ -130,24 +145,30 @@ class GemmConvKernel : public framework::OpKernel<T> {
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output->dims()[1]) / groups;
math::Vol2ColFunctor<Place, T> vol2col;
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
for (int i = 0; i < batch_size; i++) {
Tensor in_batch = input->Slice(i, i + 1).Resize(input_shape);
Tensor out_batch = output->Slice(i, i + 1).Resize(output_matrix_shape);
for (int g = 0; g < groups; g++) {
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (filter_shape_vec.size() == 2) {
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (filter_shape_vec.size() == 2) {
// im2col
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
im2col(context.device_context(), in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (filter_shape_vec.size() == 3) {
// vol2col
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
vol2col(context.device_context(), in_slice, dilations, strides,
paddings, &col);
}
// gemm
......@@ -178,9 +199,10 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
if (!input_grad && !filter_grad) return;
int groups = context.Attr<int>("groups");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int groups = context.Attr<int>("groups");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
const int batch_size = static_cast<int>(input->dims()[0]);
......@@ -230,14 +252,17 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
int in_step = static_cast<int>(input->dims()[1]) / groups;
int out_step = static_cast<int>(output_grad->dims()[1]) / groups;
bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
Tensor col;
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
if (is_expand) {
col.mutable_data<T>(col_shape, context.GetPlace());
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
math::SetConstant<Place, T> set_zero;
......@@ -245,6 +270,9 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(context.device_context(), input_grad, static_cast<T>(0));
math::Col2VolFunctor<Place, T> col2vol;
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
......@@ -254,24 +282,26 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
Tensor out_grad_slice =
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor filter_slice = filter.Slice(g * out_step, (g + 1) * out_step);
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
// col2im
Tensor in_grad_slice =
in_grad_batch.Slice(g * in_step, (g + 1) * in_step);
if (filter_shape_vec.size() == 2) {
math::Col2ImFunctor<math::ColFormat::kCFO, Place, T> col2im;
col2im(context.device_context(), in_grad_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
if (!is_expand) {
col_matrix.ShareDataWith(in_grad_slice);
col_matrix.Resize(col_matrix_shape);
}
math::matmul<Place, T>(context.device_context(), filter_slice, true,
out_grad_slice, false, T(1.0), &col_matrix,
T(0.0));
} else if (filter_shape_vec.size() == 3) {
math::Col2VolFunctor<Place, T> col2vol;
col2vol(context.device_context(), in_grad_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
if (is_expand && filter_shape_vec.size() == 2) {
col2im(context.device_context(), col, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&in_grad_slice);
} else if (is_expand && filter_shape_vec.size() == 3) {
col2vol(context.device_context(), col, dilations, strides, paddings,
&in_grad_slice);
}
}
}
......@@ -282,7 +312,8 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
Tensor filter_grad_ = *filter_grad;
filter_grad_.Resize(filter_matrix_shape);
set_zero(context.device_context(), filter_grad, static_cast<T>(0));
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
math::Vol2ColFunctor<Place, T> vol2col;
for (int i = 0; i < batch_size; i++) {
Tensor out_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_matrix_shape);
......@@ -293,16 +324,18 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
if (filter_shape_vec.size() == 2) {
math::Im2ColFunctor<math::ColFormat::kCFO, Place, T> im2col;
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
if (!is_expand) {
col.ShareDataWith(in_slice);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
} else if (filter_shape_vec.size() == 2) {
im2col(context.device_context(), in_slice, dilations, strides,
std::vector<int>{paddings[0], paddings[1], paddings[0],
paddings[1]},
&col);
} else if (filter_shape_vec.size() == 3) {
math::Vol2ColFunctor<Place, T> vol2col;
vol2col(context.device_context(), in_slice, col, strides[0],
strides[1], strides[2], paddings[0], paddings[1],
paddings[2]);
vol2col(context.device_context(), in_slice, dilations, strides,
paddings, &col);
}
// gemm
......
paddle/operators/conv_shift_op.cu
浏览文件 @ 1971f3ce
......@@ -13,6 +13,7 @@
limitations under the License. */
#include "paddle/operators/conv_shift_op.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
......@@ -22,7 +23,7 @@ using framework::Tensor;
namespace {
inline int div_up(int x, int y) { return (x + y - 1) / y; }
inline int DivUp(int x, int y) { return (x + y - 1) / y; }
// Some notes on the design:
//
......@@ -33,9 +34,9 @@ inline int div_up(int x, int y) { return (x + y - 1) / y; }
// y is fairly small. For large y, it would probably be more efficient
// to also tile across y.
template <typename T>
__global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
int y_width, int y_half_width,
int batch_size) {
__global__ void ConvShiftForward(const T *x, const T *y, int x_width,
int y_width, int y_half_width, int batch_size,
T *out) {
extern __shared__ T mem[];
int tx = threadIdx.x;
......@@ -62,11 +63,10 @@ __global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
if (tx < num_x) {
int load_i = (i - y_half_width + x_width) % x_width;
sx[tx] = x[k * x_width + load_i];
} else {
return;
}
__syncthreads();
if (tx < num_x) {
// Compute dot product of sx[tx:tx + y_width] and sy.
T sum = 0;
for (int j = 0; j < y_width; ++j) {
......@@ -75,12 +75,14 @@ __global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width,
// Save to out[k, i].
out[k * x_width + i] = sum;
}
}
// Compute x gradient - initial naive implementation with atomic add.
template <typename T>
__global__ void conv_shift_dx(const T *dout, const T *y, T *dx, int x_width,
int y_width, int y_half_width, int batch_size) {
__global__ void ConvShiftGradX(const T *dout, const T *y, int x_width,
int y_width, int y_half_width, int batch_size,
T *dx) {
int i = blockIdx.x * blockDim.x + threadIdx.x; // x index
int j = blockIdx.y; // y index
int k = blockIdx.z; // batch index
......@@ -94,8 +96,8 @@ __global__ void conv_shift_dx(const T *dout, const T *y, T *dx, int x_width,
// Compute y gradient - initial naive implementation with atomic add.
template <typename T>
__global__ void conv_shift_dy(const T *x, const T *dout, T *dy, int x_width,
int y_width, int y_half_width, int batch_size) {
__global__ void ConvShiftDy(const T *x, const T *dout, int x_width, int y_width,
int y_half_width, int batch_size, T *dy) {
int i = blockIdx.x * blockDim.x + threadIdx.x; // x index
int j = blockIdx.y; // y index
int k = blockIdx.z; // batch index
......@@ -125,15 +127,15 @@ class ConvShiftKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
int y_half_width = (y_width - 1) / 2;
const int x_per_block = 256;
int num_x_blocks = div_up(x_width, x_per_block);
int num_x_blocks = DivUp(x_width, x_per_block);
int mem_per_block = (x_per_block + 2 * y_width) * sizeof(T);
dim3 grid_dim(num_x_blocks, batch_size);
auto stream = context.cuda_device_context().stream();
conv_shift_forward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
x_data, y_data, out_data, x_width, y_width, y_half_width, batch_size);
ConvShiftForward<T><<<grid_dim, x_per_block, mem_per_block, stream>>>(
x_data, y_data, x_width, y_width, y_half_width, batch_size, out_data);
}
};
......@@ -157,25 +159,26 @@ class ConvShiftGradKernel<platform::GPUPlace, T>
int y_width = Y->dims()[1];
int y_half_width = (y_width - 1) / 2;
auto stream = context.cuda_device_context().stream();
auto &device_ctx = context.cuda_device_context();
math::SetConstant<platform::GPUPlace, T> zero;
const int x_per_block = 256;
int num_x_blocks = div_up(x_width, x_per_block);
int num_x_blocks = DivUp(x_width, x_per_block);
dim3 grid_dim(num_x_blocks, y_width, batch_size);
if (dX) {
T *dx_data = dX->mutable_data<T>(context.GetPlace());
cudaMemsetAsync(dx_data, 0, dX->numel() * sizeof(T), stream);
conv_shift_dx<T><<<grid_dim, x_per_block, 0, stream>>>(
dout_data, y_data, dx_data, x_width, y_width, y_half_width,
batch_size);
zero(device_ctx, dX, static_cast<T>(0.0));
ConvShiftGradX<T><<<grid_dim, x_per_block, 0, device_ctx.stream()>>>(
dout_data, y_data, x_width, y_width, y_half_width, batch_size,
dx_data);
}
if (dY) {
T *dy_data = dY->mutable_data<T>(context.GetPlace());
cudaMemsetAsync(dy_data, 0, dY->numel() * sizeof(T), stream);
conv_shift_dy<T><<<grid_dim, x_per_block, 0, stream>>>(
x_data, dout_data, dy_data, x_width, y_width, y_half_width,
batch_size);
zero(device_ctx, dY, static_cast<T>(0.0));
ConvShiftDy<T><<<grid_dim, x_per_block, 0, device_ctx.stream()>>>(
x_data, dout_data, x_width, y_width, y_half_width, batch_size,
dy_data);
}
}
};
......
paddle/operators/cos_sim_op.h
浏览文件 @ 1971f3ce
......@@ -132,7 +132,7 @@ class CosSimGradKernel : public framework::OpKernel<T> {
// compute dy
if (out_grad_y) {
out_grad_y->mutable_data<T>(context.GetPlace());
auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
auto dy = EigenVector<T>::Flatten(*out_grad_y);
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}}));
}
......
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python/paddle/v2/fluid/layers.py
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python/paddle/v2/fluid/tests/test_beam_search_op.py 0 → 100644
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python/paddle/v2/fluid/tests/test_is_empty_op.py 0 → 100644
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python/paddle/v2/model.py 已删除 100644 → 0
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