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提交 fc117ecf 编写于 作者: D Dong Zhihong
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Merge remote-tracking branch 'origin/develop' into feature/evaluator

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benchmark/paddle/image/vgg.py
浏览文件 @ fc117ecf
......@@ -13,7 +13,7 @@ define_py_data_sources2(
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_rate=0.001 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
......
cmake/cblas.cmake
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# Find the CBlas and lapack libraries
#
# It will search MKL, atlas, OpenBlas, reference-cblas in order.
# It will search MKLML, atlas, OpenBlas, reference-cblas in order.
#
# If any cblas implementation found, the following variable will be set.
# CBLAS_PROVIDER # one of MKL, ATLAS, OPENBLAS, REFERENCE
# CBLAS_PROVIDER # one of MKLML, ATLAS, OPENBLAS, REFERENCE
# CBLAS_INC_DIR # the include directory for cblas.
# CBLAS_LIBS # a list of libraries should be linked by paddle.
# # Each library should be full path to object file.
#
# User should set one of MKL_ROOT, ATLAS_ROOT, OPENBLAS_ROOT, REFERENCE_CBLAS_ROOT
# during cmake. If none of them set, it will try to find cblas implementation in
# system paths.
#
set(CBLAS_FOUND OFF)
......@@ -30,44 +25,6 @@ if(WITH_MKLML AND MKLML_INC_DIR AND MKLML_LIB)
return()
endif()
## Then find MKL.
set(INTEL_MKL_ROOT "/opt/intel/mkl" CACHE PATH "Folder contains intel mkl libs")
set(MKL_ROOT $ENV{MKL_ROOT} CACHE PATH "Folder contains env MKL")
set(MKL_INCLUDE_SEARCH_PATHS
${MKL_ROOT}/include
${INTEL_MKL_ROOT}/include)
set(MKL_LIB_SEARCH_PATHS
${MKL_ROOT}/lib
${MKL_ROOT}/lib/intel64
${INTEL_MKL_ROOT}/lib
${INTEL_MKL_ROOT}/lib/intel64)
find_path(MKL_INC_DIR mkl.h PATHS
${MKL_INCLUDE_SEARCH_PATHS})
find_path(MKL_LAPACK_INC_DIR mkl_lapacke.h PATHS
${MKL_INCLUDE_SEARCH_PATHS})
find_library(MKL_CORE_LIB NAMES mkl_core PATHS
${MKL_LIB_SEARCH_PATHS})
find_library(MKL_SEQUENTIAL_LIB NAMES mkl_sequential PATHS
${MKL_LIB_SEARCH_PATHS})
find_library(MKL_INTEL_LP64 NAMES mkl_intel_lp64 PATHS
${MKL_LIB_SEARCH_PATHS})
if(MKL_LAPACK_INC_DIR AND MKL_INC_DIR AND MKL_CORE_LIB AND MKL_SEQUENTIAL_LIB AND MKL_INTEL_LP64)
set(CBLAS_FOUND ON)
set(CBLAS_PROVIDER MKL)
set(CBLAS_INC_DIR ${MKL_INC_DIR} ${MKL_LAPACK_INC_DIR})
set(CBLAS_LIBRARIES ${MKL_INTEL_LP64} ${MKL_SEQUENTIAL_LIB} ${MKL_CORE_LIB})
add_definitions(-DPADDLE_USE_MKL)
add_definitions(-DLAPACK_FOUND)
message(STATUS "Found MKL (include: ${MKL_INC_DIR}, library: ${CBLAS_LIBRARIES})")
message(STATUS "Found lapack in MKL (include: ${MKL_LAPACK_INC_DIR})")
return()
endif()
## Then find atlas.
set(ATLAS_ROOT $ENV{ATLAS_ROOT} CACHE PATH "Folder contains Atlas")
set(ATLAS_INCLUDE_SEARCH_PATHS
......
cmake/external/mkldnn.cmake
浏览文件 @ fc117ecf
......@@ -46,16 +46,20 @@ IF(${CBLAS_PROVIDER} STREQUAL "MKLML")
MESSAGE(STATUS "Build MKLDNN with ${MKLDNN_MKLROOT}")
ENDIF()
SET(MKLDNN_CFLAG "${CMAKE_C_FLAGS} -Wno-error=strict-overflow")
SET(MKLDNN_CXXFLAG "${CMAKE_CXX_FLAGS} -Wno-error=strict-overflow")
ExternalProject_Add(
${MKLDNN_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_TAG "v0.10"
GIT_TAG "v0.11"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR}
CMAKE_ARGS -DMKLROOT=${MKLDNN_MKLROOT}
CMAKE_ARGS -DCMAKE_C_FLAGS=${MKLDNN_CFLAG}
CMAKE_ARGS -DCMAKE_CXX_FLAGS=${MKLDNN_CXXFLAG}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${MKLDNN_INSTALL_DIR}
-DMKLROOT:PATH=${MKLDNN_MKLROOT}
)
......
cmake/external/mklml.cmake
浏览文件 @ fc117ecf
......@@ -27,8 +27,8 @@ ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.20170720")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.10/${MKLML_VER}.tgz")
SET(MKLML_VER "mklml_lnx_2018.0.1.20171007")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.11/${MKLML_VER}.tgz")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
......
cmake/external/openblas.cmake
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......@@ -86,7 +86,7 @@ IF(NOT ${CBLAS_FOUND})
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
)
SET(CBLAS_PROVIDER openblas)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
......@@ -115,7 +115,7 @@ INCLUDE_DIRECTORIES(${CBLAS_INC_DIR})
# linear algebra libraries for cc_library(xxx SRCS xxx.c DEPS cblas)
SET(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/cblas_dummy.c)
FILE(WRITE ${dummyfile} "const char * dummy = \"${dummyfile}\";")
IF(${CBLAS_PROVIDER} MATCHES MKL)
IF("${CBLAS_PROVIDER}" STREQUAL "MKLML")
ADD_LIBRARY(cblas SHARED ${dummyfile})
ELSE()
ADD_LIBRARY(cblas STATIC ${dummyfile})
......
cmake/generic.cmake
浏览文件 @ fc117ecf
......@@ -93,7 +93,7 @@ include_directories(${CMAKE_CURRENT_BINARY_DIR})
if(NOT APPLE AND NOT ANDROID)
find_package(Threads REQUIRED)
link_libraries(${CMAKE_THREAD_LIBS_INIT})
set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -ldl -lrt")
set(CMAKE_CXX_LINK_EXECUTABLE "${CMAKE_CXX_LINK_EXECUTABLE} -pthread -ldl -lrt")
endif(NOT APPLE AND NOT ANDROID)
function(merge_static_libs TARGET_NAME)
......
doc/api/v2/config/layer.rst
浏览文件 @ fc117ecf
......@@ -82,6 +82,11 @@ maxout
.. autoclass:: paddle.v2.layer.maxout
:noindex:
roi_pool
--------
.. autoclass:: paddle.v2.layer.roi_pool
:noindex:
Norm Layer
==========
......
doc/api/v2/data.rst
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......@@ -2,112 +2,9 @@
Data Reader Interface and DataSets
==================================
.. toctree::
:maxdepth: 1
DataTypes
=========
.. automodule:: paddle.v2.data_type
:members:
:noindex:
DataFeeder
==========
.. automodule:: paddle.v2.data_feeder
:members:
:noindex:
Reader
======
.. automodule:: paddle.v2.reader
:members:
:noindex:
.. automodule:: paddle.v2.reader.creator
:members:
:noindex:
minibatch
=========
.. automodule:: paddle.v2.minibatch
:members:
:noindex:
Dataset
=======
.. automodule:: paddle.v2.dataset
:members:
:noindex:
mnist
+++++
.. automodule:: paddle.v2.dataset.mnist
:members:
:noindex:
cifar
+++++
.. automodule:: paddle.v2.dataset.cifar
:members:
:noindex:
conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members: get_dict,get_embedding,test
:noindex:
imdb
++++
.. automodule:: paddle.v2.dataset.imdb
:members:
:noindex:
imikolov
++++++++
.. automodule:: paddle.v2.dataset.imikolov
:members:
:noindex:
movielens
+++++++++
.. automodule:: paddle.v2.dataset.movielens
:members:
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.MovieInfo
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.UserInfo
:noindex:
sentiment
+++++++++
.. automodule:: paddle.v2.dataset.sentiment
:members:
:noindex:
uci_housing
+++++++++++
.. automodule:: paddle.v2.dataset.uci_housing
:members:
:noindex:
wmt14
+++++
.. automodule:: paddle.v2.dataset.wmt14
:members:
:noindex:
data/data_reader.rst
data/image.rst
data/dataset.rst
doc/api/v2/data/data_reader.rst 0 → 100644
浏览文件 @ fc117ecf
=====================
Data Reader Interface
=====================
DataTypes
=========
.. automodule:: paddle.v2.data_type
:members:
:noindex:
DataFeeder
==========
.. automodule:: paddle.v2.data_feeder
:members:
:noindex:
Reader
======
.. automodule:: paddle.v2.reader
:members:
:noindex:
.. automodule:: paddle.v2.reader.creator
:members:
:noindex:
minibatch
=========
.. automodule:: paddle.v2.minibatch
:members:
:noindex:
doc/api/v2/data/dataset.rst 0 → 100644
浏览文件 @ fc117ecf
Dataset
=======
.. automodule:: paddle.v2.dataset
:members:
:noindex:
mnist
+++++
.. automodule:: paddle.v2.dataset.mnist
:members:
:noindex:
cifar
+++++
.. automodule:: paddle.v2.dataset.cifar
:members:
:noindex:
conll05
+++++++
.. automodule:: paddle.v2.dataset.conll05
:members: get_dict,get_embedding,test
:noindex:
imdb
++++
.. automodule:: paddle.v2.dataset.imdb
:members:
:noindex:
imikolov
++++++++
.. automodule:: paddle.v2.dataset.imikolov
:members:
:noindex:
movielens
+++++++++
.. automodule:: paddle.v2.dataset.movielens
:members:
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.MovieInfo
:noindex:
.. autoclass:: paddle.v2.dataset.movielens.UserInfo
:noindex:
sentiment
+++++++++
.. automodule:: paddle.v2.dataset.sentiment
:members:
:noindex:
uci_housing
+++++++++++
.. automodule:: paddle.v2.dataset.uci_housing
:members:
:noindex:
wmt14
+++++
.. automodule:: paddle.v2.dataset.wmt14
:members:
:noindex:
doc/api/v2/data/image.rst 0 → 100644
浏览文件 @ fc117ecf
Image Interface
===============
.. automodule:: paddle.v2.image
:members:
doc/design/mkldnn/README.MD
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......@@ -15,6 +15,7 @@
- [CMake](#cmake)
- [Layers](#layers)
- [Activations](#activations)
- [Weights](#weights)
- [Unit Tests](#unit-tests)
- [Protobuf Messages](#protobuf-messages)
- [Python API](#python-api)
......@@ -45,17 +46,23 @@ Figure 1. PaddlePaddle on IA.
### Layers
所有MKL-DNN相关的C++ layers，都会按照PaddlePaddle的目录结构存放在
`paddle/gserver/layers`中，并且文件名都会一以*Mkldnn*开头。
`paddle/gserver/layers`中，并且文件名都会一以*MKLDNN*开头。
所有MKL-DNN的layers都会继承于一个叫做`MkldnnLayer`的父类，该父类继承于PaddlePaddle的基类`Layer`。
所有MKL-DNN的layers都会继承于一个叫做`MKLDNNLayer`的父类，该父类继承于PaddlePaddle的基类`Layer`。
在`MKLDNNLayer`中会提供一些必要的接口和函数，并且会写好`forward`和`backward`的基本逻辑。部分函数定义为纯虚函数，子类只需要实现这些函数即可。
### Activations
由于在PaddlePaddle中，激活函数是独立于layer概念的，所以会在`paddle/gserver/activations`目录下添加一个`MkldnnActivation.h`文件定义一些用于MKL-DNN的接口，实现方法还是会在`ActivationFunction.cpp`文件。
由于在PaddlePaddle中，激活函数是独立于layer概念的，所以会在`paddle/gserver/activations`目录下添加`MKLDNNActivation.h`和`MKLDNNActivation.cpp`文件用于定义和使用MKL-DNN的接口。
### Unit Tests
会在`paddle/gserver/test`目录下添加`test_Mkldnn.cpp`和`MkldnnTester.*`用于MKL-DNN的测试。
### Weights
由于有些layer是含有参数的，我们会尽量让MKL-DNN的参数与PaddlePaddle中`parameter`共享一块内存。
同时，由于MKL-DNN在训练时使用的参数layout可能与PaddlePaddle默认的`nchw`不一致，我们会在网络训练的开始和结束时分别转换这个layout，使得最终保存的参数格式与PaddlePaddle一致。
Activation的测试，计划在PaddlePaddle原有的测试文件上直接添加新的测试type。
### Unit Tests
会在`paddle/gserver/test`目录下添加`test_MKLDNN.cpp`和`MKLDNNTester.*`用于MKL-DNN的测试。
测试分为每个layer(或activation)的单元测试和简单网络的整体测试。
每个测试会对比PaddlePaddle中CPU算出的结果与MKL-DNN的结果，小于某个比较小的阈值认为通过。
### Protobuf Messages
根据具体layer的需求可能会在`proto/ModelConfig.proto`里面添加必要的选项。
......@@ -82,7 +89,7 @@ if use_mkldnn
会在`v1_api_demo`目录下添加一个`mkldnn`的文件夹，里面放入一些用于MKL-DNN测试的demo脚本。
### Benchmarking
会考虑添加部分逻辑在`benchmark/paddle/image/run.sh`，添加使用MKL-DNN的测试。
会添加`benchmark/paddle/image/run_mkldnn.sh`，用于测试使用MKL-DNN之后的性能。
### Others
1. 如果在使用MKL-DNN的情况下，会把CPU的Buffer对齐为64。
......@@ -94,14 +101,16 @@ if use_mkldnn
我们总结出一些特别需要注意的点：
1. 使用**deviceId_**。为了尽可能少的在父类Layer中添加变量或者函数，我们决定使用已有的`deviceId_`变量来区分layer的属性，定义`-2`为`MkldnnLayer`特有的设备ID。
1. 使用**deviceId_**。为了尽可能少的在父类Layer中添加变量或者函数，我们决定使用已有的`deviceId_`变量来区分layer的属性，定义`-2`为`MKLDNNLayer`特有的设备ID。
2. 重写父类Layer的**init**函数，修改`deviceId_`为`-2`，代表这个layer是用于跑在MKL-DNN的环境下。
3. 创建`MkldnnMatrix`，用于管理MKL-DNN会用到的相关memory函数、接口以及会用的到格式信息。
4. 创建`MkldnnBase`，定义一些除了layer和memory相关的类和函数。包括MKL-DNN会用到`MkldnnStream`和`CpuEngine`，和未来可能还会用到`FPGAEngine`等。
5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。
6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。
7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
8. 关于MKLDNN参数的保存。由于MKLDNN参数的格式与PaddlePaddle原有的格式存在不一样的情况，所以需要在保存参数时同时保存该格式信息。目前准备扩展[Header](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/parameter/Parameter.h#L247)里面的`int32_t version`。这个值不管是在v1还是在v2里面，一直保存的是0，所以可以充分利用这个信息，定义一个枚举处理所有MKLDNN的参数格式，从而`MKLDNNLayer`就可以从输入的参数中获取需要的格式信息。
3. 创建`MKLDNNMatrix`，同时继承`CpuMatrix`和`mkldnn::memory`。用于管理MKL-DNN会用到的相关memory函数、接口以及会用的到格式信息。
4. 创建`MKLDNNBase`，定义一些除了layer和memory相关的类和函数。包括MKL-DNN会用到`MKLDNNStream`和`CPUEngine`，和未来可能还会用到`FPGAEngine`等。
5. 每个`MKLDNNlayer`都会有`inVal_`,`inGrad_`,`outVal_`和`outGrad_`，分别代表input value， input gradient，output value和output gradient。他们会存放MKL-DNN用到的internal memory。同时还会定义以*ext*开头的`MKLDNNMatrix`(表示external的memory)，主要是在格式与PaddlePaddle默认的`nchw`格式不匹配时，用于转换内存的工作。必要的转换函数也会在`MKLDNNLayer`中提前定义好，每个子类只需要调用定义好的reset buffer函数即可。
6. 每个`MKLDNNlayer`的resetbuffer相关的函数（包括reset input、output的Value和grad），他们会根据输入参数reset internal和external的memory，当然这两者也可以相等，即表示不需要转换。只需要把握一个原则，每个`MKLDNNlayer`的子类，只需要使用internal的memory就可以了，所有external的转换工作在父类的reset函数中都提前准备好了。
7. 一般来说，external的memory会尽量与PaddlePaddle中的`value`和`grad`共享内存。同时每个`MKLDNNLayer`中的external output value和gradient(也就是`extOutVal_`和`extOutGrad_`)必须分别与`output_.value`和`output_.grad`共享内存，因为PaddlePaddle的activation会直接使用`output_.value`和`output_.grad`。如果不需要external的buffer用于转换，那么internal的buffer也会与他们共享内存。
8. 如果MKL-DNN layer的后面接有cpu device，那么就会使`output_.value`与`extOutVal_`共享内存，同时数据格式就是`nchw`，这样下一个cpu device就能拿到正确的数据。在有cpu device的时候，external的memory的格式始终是`nchw`或者`nc`。
9. 由于MKL-DNN的输出操作都是覆盖data的，不是在原来的数据上累加，所以当网络出现分支时，在`backward`时会需要merge不同layer的梯度。`MKLDNNlayer`中会实现merge的方法，此时每个小分支的input gradient会先临时保存在一个`MKLDNNMatrix`中，由分支处的layer负责求和，并把结果放到这个layer的`output_.grad`中。所以整体上，每个子类并不会需要关心分支的事情，也是在父类都实现好了。
10. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。
## References
......
doc/design/ops/sequence_decoder.md 0 → 100644
浏览文件 @ fc117ecf
# 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.
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.
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.
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** .
For example, the RNN sates, candidates IDs and probabilities of beam search can be represented 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 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 relative-offset LoD can fast retrieve any sequence 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]]
```
The first level tells that there are two sequences:
- the first's offset is `[0, 3)`
- the second's offset is `[3, 9)`
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.
So let's introduce another format of LoD,
it stores **the offsets of the lower level sequences** and is called **relative-offset** LoD.
For example, to represent the same sequences of the above data
```python
[[0, 3, 6]
[0, 2, 3, 3, 3, 9]]
```
the first level represents that there are two sequences,
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.
## 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.
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.
**Encoder**
```python
import paddle as pd
dict_size = 8000
source_dict_size = dict_size
target_dict_size = dict_size
word_vector_dim = 128
encoder_dim = 128
decoder_dim = 128
beam_size = 5
max_length = 120
# encoder
src_word_id = pd.data(
name='source_language_word',
type=pd.data.integer_value_sequence(source_dict_dim))
src_embedding = pd.embedding(size=source_dict_size, size=word_vector_dim)
src_word_vec = pd.lookup(src_embedding, src_word_id)
encoder_out_seq = pd.gru(input=src_word_vec, size=encoder_dim)
encoder_ctx = pd.last_seq(encoder_out_seq)
# encoder_ctx_proj is the learned semantic vector
encoder_ctx_proj = pd.fc(
encoder_ctx, size=decoder_dim, act=pd.activation.Tanh(), bias=None)
```
**Decoder**
```python
def generate():
decoder = pd.while_loop()
with decoder.step():
decoder_mem = decoder.memory(init=encoder_ctx) # mark the memory
generated_ids = decoder.memory() # TODO init to batch_size <s>s
generated_scores = decoder.memory() # TODO init to batch_size 1s or 0s
target_word = pd.lookup(trg_embedding, gendrated_ids)
# expand encoder_ctx's batch to fit target_word's lod
# for example
# decoder_mem.lod is
# [[0 1 3],
# [0 1 3 6]]
# its tensor content is [a1 a2 a3 a4 a5]
# which means there are 2 sentences to translate
# - the first sentence has 1 translation prefixes, the offsets are [0, 1)
# - the second sentence has 2 translation prefixes, the offsets are [1, 3) and [3, 6)
# the target_word.lod is
# [[0, 1, 6]
# [0, 2, 4, 7, 9 12]]
# which means 2 sentences to translate, each has 1 and 5 prefixes
# the first prefix has 2 candidates
# the following has 2, 3, 2, 3 candidates
# the encoder_ctx_expanded's content will be
# [a1 a1 a2 a2 a3 a3 a3 a4 a4 a5 a5 a5]
encoder_ctx_expanded = pd.lod_expand(encoder_ctx, target_word)
decoder_input = pd.fc(
act=pd.activation.Linear(),
input=[target_word, encoder_ctx],
size=3 * decoder_dim)
gru_out, cur_mem = pd.gru_step(
decoder_input, mem=decoder_mem, size=decoder_dim)
scores = pd.fc(
gru_out,
size=trg_dic_size,
bias=None,
act=pd.activation.Softmax())
# K is an config
topk_scores, topk_ids = pd.top_k(scores, K)
topk_generated_scores = pd.add_scalar(topk_scores, generated_scores)
selected_ids, selected_generation_scores = decoder.beam_search(
topk_ids, topk_generated_scores)
# update the states
decoder_mem.update(cur_mem) # tells how to update state
generated_ids.update(selected_ids)
generated_scores.update(selected_generation_scores)
decoder.output(selected_ids)
decoder.output(selected_generation_scores)
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.
In this way, users can customize anything on the inputs or outputs of beam search, for example, two ways to prune some translation prefixes
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.
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).
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.
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.
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`
* LoD is `[0, 2, 7][0 3 5 8 9 11 11]`
* the content is
- a1 a1 a1 (a1 has 3 candidates, so the state should be copied 3 times for each candidates)
- a2 a2
- b1 b1 b1
- b2
- 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 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.
Pack the `selected_scores` will get a `LoDTensor` that stores scores of each candidate of translations.
Pack 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.
## Beam search design
The beam search algorthm will be implemented as one method of the sequence decoder, it has 3 inputs
1. `topk_ids`, 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.
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).
## 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,
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`.
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`.
doc/faq/local/index_cn.rst
浏览文件 @ fc117ecf
......@@ -99,7 +99,7 @@ PaddlePaddle支持Sparse的训练，sparse训练需要训练特征是 :code:`spa
利用更多的计算资源
++++++++++++++++++
利用更多的计算资源可以分为一下几个方式来进行\:
利用更多的计算资源可以分为以下几个方式来进行\:
* 单机CPU训练
......
doc/howto/dev/new_op_cn.md
浏览文件 @ fc117ecf
......@@ -214,7 +214,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
```cpp
// if use Eigen unsupported module before include head files
#define EIGEN_USE_GPU
// #define EIGEN_USE_GPU
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
......
paddle/capi/Matrix.cpp
浏览文件 @ fc117ecf
......@@ -54,6 +54,46 @@ paddle_error paddle_matrix_set_row(paddle_matrix mat,
return kPD_NO_ERROR;
}
PD_API paddle_error paddle_matrix_set_value(paddle_matrix mat,
paddle_real* value) {
if (mat == nullptr || value == nullptr) return kPD_NULLPTR;
auto ptr = cast(mat);
if (ptr->mat == nullptr) return kPD_NULLPTR;
paddle::real* buf = ptr->mat->getRowBuf(0);
size_t width = ptr->mat->getWidth();
size_t height = ptr->mat->getHeight();
if (ptr->mat->useGpu()) {
#ifdef PADDLE_WITH_CUDA
hl_memcpy(buf, value, sizeof(paddle::real) * width * height);
#else
return kPD_NOT_SUPPORTED;
#endif
} else {
std::copy(value, value + width * height, buf);
}
return kPD_NO_ERROR;
}
PD_API paddle_error paddle_matrix_get_value(paddle_matrix mat,
paddle_real* result) {
if (mat == nullptr || result == nullptr) return kPD_NULLPTR;
auto ptr = cast(mat);
if (ptr->mat == nullptr) return kPD_NULLPTR;
paddle::real* buf = ptr->mat->getRowBuf(0);
size_t width = ptr->mat->getWidth();
size_t height = ptr->mat->getHeight();
if (ptr->mat->useGpu()) {
#ifdef PADDLE_WITH_CUDA
hl_memcpy(result, buf, width * height * sizeof(paddle::real));
#else
return kPD_NOT_SUPPORTED;
#endif
} else {
std::copy(buf, buf + width * height, result);
}
return kPD_NO_ERROR;
}
paddle_error paddle_matrix_get_row(paddle_matrix mat,
uint64_t rowID,
paddle_real** rawRowBuffer) {
......
paddle/capi/examples/model_inference/dense/main.c
浏览文件 @ fc117ecf
......@@ -27,18 +27,20 @@ int main() {
CHECK(paddle_arguments_resize(in_args, 1));
// Create input matrix.
paddle_matrix mat = paddle_matrix_create(/* sample_num */ 1,
paddle_matrix mat = paddle_matrix_create(/* sample_num */ 10,
/* size */ 784,
/* useGPU */ false);
srand(time(0));
paddle_real* array;
// Get First row.
CHECK(paddle_matrix_get_row(mat, 0, &array));
std::vector<paddle_real> input;
input.resize(784 * 10);
for (int i = 0; i < 784; ++i) {
array[i] = rand() / ((float)RAND_MAX);
for (int i = 0; i < input.size(); ++i) {
input[i] = rand() / ((float)RAND_MAX);
}
// Set value for the input matrix
CHECK(paddle_matrix_set_value(mat, input.data()));
CHECK(paddle_arguments_set_value(in_args, 0, mat));
......@@ -51,11 +53,17 @@ int main() {
CHECK(paddle_arguments_get_value(out_args, 0, prob));
CHECK(paddle_matrix_get_row(prob, 0, &array));
std::std::vector<paddle_real> result;
int height;
int width;
CHECK(paddle_matrix_get_shape(prob, &height, &width);
result.resize(height * width);
CHECK(paddle_matrix_get_value(prob, result.data()));
printf("Prob: ");
for (int i = 0; i < 10; ++i) {
printf("%.2f ", array[i]);
for (int i = 0; i < height * width; ++i) {
printf("%.2f ", result[i]);
}
printf("\n");
......
paddle/capi/matrix.h
浏览文件 @ fc117ecf
......@@ -70,6 +70,16 @@ PD_API paddle_error paddle_matrix_set_row(paddle_matrix mat,
uint64_t rowID,
paddle_real* rowArray);
/**
* @brief paddle_matrix_set_value Set value to matrix.
* @param mat Target Matrix
* @param value Row data.
* @return paddle_error
* @note value should contain enough element of data to init the mat
*/
PD_API paddle_error paddle_matrix_set_value(paddle_matrix mat,
paddle_real* value);
/**
* @brief PDMatGetRow Get raw row buffer from matrix
* @param [in] mat Target matrix
......@@ -81,6 +91,15 @@ PD_API paddle_error paddle_matrix_get_row(paddle_matrix mat,
uint64_t rowID,
paddle_real** rawRowBuffer);
/**
* @brief copy data from the matrix
* @param [in] mat Target matrix
* @param [out] result pointer to store the matrix data
* @return paddle_error
* @note the space of the result should allocated before invoke this API
*/
PD_API paddle_error paddle_matrix_get_value(paddle_matrix mat,
paddle_real* result);
/**
* @brief PDMatCreateNone Create None Matrix
* @return
......
paddle/capi/tests/test_Matrix.cpp
浏览文件 @ fc117ecf
......@@ -45,3 +45,49 @@ TEST(CAPIMatrix, createNone) {
paddle_matrix mat = paddle_matrix_create_none();
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_destroy(mat));
}
TEST(CAPIMatrix, cpu_get_set_value) {
paddle_matrix mat = paddle_matrix_create(128, 32, false);
std::vector<paddle_real> sample;
std::vector<paddle_real> result;
sample.resize(128 * 32);
result.resize(128 * 32);
for (size_t i = 0; i < sample.size(); ++i) {
sample[i] = 1.0 / (i + 1.0);
}
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_set_value(mat, sample.data()));
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_value(mat, result.data()));
for (size_t i = 0; i < sample.size(); ++i) {
ASSERT_NEAR(sample[i], result[i], 1e-5);
}
uint64_t height, width;
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_shape(mat, &height, &width));
ASSERT_EQ(128UL, height);
ASSERT_EQ(32UL, width);
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_destroy(mat));
}
#ifdef PADDLE_WITH_CUDA
TEST(CAPIMatrix, gpu_get_set_value) {
paddle_matrix mat = paddle_matrix_create(128, 32, true);
std::vector<paddle_real> sample;
std::vector<paddle_real> result;
sample.resize(128 * 32);
result.resize(128 * 32);
for (size_t i = 0; i < sample.size(); ++i) {
sample[i] = 1.0 / (i + 1.0);
}
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_set_value(mat, sample.data()));
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_value(mat, result.data()));
for (size_t i = 0; i < sample.size(); ++i) {
ASSERT_NEAR(sample[i], result[i], 1e-5);
}
uint64_t height, width;
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_get_shape(mat, &height, &width));
ASSERT_EQ(128UL, height);
ASSERT_EQ(32UL, width);
ASSERT_EQ(kPD_NO_ERROR, paddle_matrix_destroy(mat));
}
#endif
paddle/framework/backward.cc
浏览文件 @ fc117ecf
......@@ -321,8 +321,6 @@ static void CreateGradVarInBlock(
auto* param = block_desc->FindVarRecursive(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
LOG(WARNING) << "Cannot find forward variable of " << arg
<< ". Set its gradient to FP32";
grad->SetDataType(DataType::FP32);
} else {
grad->SetDataType(param->GetDataType());
......@@ -379,6 +377,12 @@ std::vector<std::unique_ptr<OpDescBind>> MakeOpGrad(
return grad_op_descs;
}
static BlockDescBind* CreateStepBlock(
ProgramDescBind& program_desc,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
int step_block_idx);
std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
......@@ -394,13 +398,13 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
if ((*it)->Type() == "recurrent") {
int step_block_idx = (*it)->GetBlockAttr("step_block");
auto backward_block_op_descs = MakeBlockBackward(
program_desc, step_block_idx, no_grad_vars, grad_to_var);
BlockDescBind* backward_block = CreateStepBlock(
program_desc, no_grad_vars, grad_to_var, step_block_idx);
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
} else if ((*it)->Type() == "conditional_block") {
BlockDescBind* backward_block =
program_desc.AppendBlock(*program_desc.MutableBlock(step_block_idx));
for (auto& ptr : backward_block_op_descs) {
backward_block->AppendAllocatedOp(std::move(ptr));
}
CreateStepBlock(program_desc, no_grad_vars, grad_to_var,
(*it)->GetBlockAttr("block"));
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var, {backward_block});
} else {
op_grads = MakeOpGrad(*it, no_grad_vars, grad_to_var);
......@@ -408,6 +412,11 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
for (const auto& desc : op_grads) {
for (const std::string& out_name : desc->OutputArgumentNames()) {
if (out_name.find("@GRAD") == std::string::npos) {
// Not all outputs of a backward operator is a gradient. Only gradient
// need to be sum. Skip variables are not gradient.
continue;
}
dup_out_ops[out_name].emplace_back(grad_desc_idx);
}
++grad_desc_idx;
......@@ -446,6 +455,21 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
return backward_descs;
}
static BlockDescBind* CreateStepBlock(
ProgramDescBind& program_desc,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var,
int step_block_idx) {
auto backward_block_op_descs = MakeBlockBackward(program_desc, step_block_idx,
no_grad_vars, grad_to_var);
BlockDescBind* backward_block =
program_desc.AppendBlock(*program_desc.MutableBlock(step_block_idx));
for (auto& ptr : backward_block_op_descs) {
backward_block->AppendAllocatedOp(move(ptr));
}
return backward_block;
}
ParamGradInfoMap AppendBackward(
ProgramDescBind& program_desc, const VarDescBind& target,
const std::unordered_set<std::string>& no_grad_vars) {
......
paddle/framework/backward_test.cc
浏览文件 @ fc117ecf
......@@ -21,7 +21,7 @@
#include "paddle/framework/var_desc.h"
#include "paddle/operators/net_op.h"
USE_OP(fill_constant);
USE_NO_KERNEL_OP(fill_constant);
namespace paddle {
namespace framework {
......
paddle/framework/block_desc.cc
浏览文件 @ fc117ecf
......@@ -50,6 +50,15 @@ VarDescBind *BlockDescBind::FindVarRecursive(const std::string &name) const {
return it->second.get();
}
VarDescBind *BlockDescBind::FindRecursiveOrCreateVar(
const std::string &name_bytes) {
VarDescBind *res = FindVarRecursive(name_bytes);
if (res == nullptr) {
res = Var(name_bytes);
}
return res;
}
bool BlockDescBind::HasVarRecursive(const std::string &name) const {
return FindVarRecursive(name) != nullptr;
}
......
paddle/framework/block_desc.h
浏览文件 @ fc117ecf
......@@ -58,6 +58,8 @@ class BlockDescBind {
VarDescBind *FindVarRecursive(const std::string &name_bytes) const;
VarDescBind *FindRecursiveOrCreateVar(const std::string &name_bytes);
bool HasVarRecursive(const std::string &var_name) const;
std::set<std::string> LocalVarNames() const {
......
paddle/framework/data_type.h
浏览文件 @ fc117ecf
......@@ -34,6 +34,21 @@ inline DataType ToDataType(std::type_index type) {
}
}
inline std::type_index ToTypeIndex(DataType type) {
switch (type) {
case DataType::FP32:
return typeid(float);
case DataType::FP64:
return typeid(double);
case DataType::INT32:
return typeid(int);
case DataType::INT64:
return typeid(int64_t);
default:
PADDLE_THROW("Not support type %d", type);
}
}
template <typename Visitor>
inline void VisitDataType(DataType type, Visitor visitor) {
switch (type) {
......
paddle/framework/ddim.cc
浏览文件 @ fc117ecf
......@@ -79,6 +79,13 @@ DDim make_ddim(const std::vector<int64_t>& dims) {
return result;
}
DDim make_ddim(const std::vector<int>& dims) {
std::vector<int64_t> res(dims.size());
std::transform(dims.begin(), dims.end(), res.begin(),
[](int d) { return static_cast<int64_t>(d); });
return make_ddim(res);
}
/// @cond HIDDEN
// XXX For some reason, putting this in an anonymous namespace causes errors
class DynamicMutableIndexer : public boost::static_visitor<int64_t&> {
......
paddle/framework/ddim.h
浏览文件 @ fc117ecf
......@@ -81,6 +81,8 @@ struct DDim {
*/
DDim make_ddim(const std::vector<int64_t>& dims);
DDim make_ddim(const std::vector<int>& dims);
/**
* \brief Make a DDim from an initializer list
*
......
paddle/framework/op_desc.cc
浏览文件 @ fc117ecf
......@@ -357,7 +357,8 @@ void OpDescBind::InferVarType(BlockDescBind *block) const {
"LOD_TENSOR";
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->Var(out_var_name)->SetType(VarDesc::LOD_TENSOR);
block->FindRecursiveOrCreateVar(out_var_name)
->SetType(VarDesc::LOD_TENSOR);
}
}
}
......
paddle/framework/scope.cc
浏览文件 @ fc117ecf
......@@ -98,5 +98,23 @@ void Scope::DeleteScope(Scope* scope) {
delete scope;
}
void Scope::Rename(const std::string& origin_name,
const std::string& new_name) const {
auto origin_it = vars_.find(origin_name);
PADDLE_ENFORCE(origin_it != vars_.end(),
"Cannot find original variable with name %s", origin_name);
auto new_it = vars_.find(new_name);
PADDLE_ENFORCE(new_it == vars_.end(),
"The variable with name %s is already in the scope", new_name);
vars_[new_name] = origin_it->second;
vars_.erase(origin_it);
}
std::string Scope::Rename(const std::string& origin_name) const {
auto var_name = string::Sprintf("%p.%d", this, vars_.size());
Rename(origin_name, var_name);
return var_name;
}
} // namespace framework
} // namespace paddle
paddle/framework/scope.h
浏览文件 @ fc117ecf
......@@ -68,11 +68,18 @@ class Scope {
// enumerate all the variables current contains.
std::vector<std::string> GetAllNames(bool recursive = false) const;
// Rename variable to a new name
void Rename(const std::string& origin_name,
const std::string& new_name) const;
// Rename variable to a new name and return the new name
std::string Rename(const std::string& origin_name) const;
private:
// Call Scope::NewScope for a sub-scope.
explicit Scope(Scope const* parent) : parent_(parent) {}
std::unordered_map<std::string, Variable*> vars_;
mutable std::unordered_map<std::string, Variable*> vars_;
mutable std::list<Scope*> kids_;
Scope const* parent_{nullptr};
......
paddle/framework/var_type.h
浏览文件 @ fc117ecf
......@@ -27,10 +27,32 @@ inline VarDesc::VarType ToVarType(std::type_index type) {
return VarDesc_VarType_LOD_RANK_TABLE;
} else if (type.hash_code() == typeid(LoDTensorArray).hash_code()) {
return VarDesc_VarType_LOD_TENSOR_ARRAY;
} else if (type.hash_code() == typeid(SelectedRows).hash_code()) {
return VarDesc_VarType_SELECTED_ROWS;
} else {
PADDLE_THROW("ToVarType:Unsupported type %s", type.name());
}
}
template <typename Visitor>
inline void VisitVarType(const Variable& var, Visitor visitor) {
switch (ToVarType(var.Type())) {
case VarDesc_VarType_LOD_TENSOR:
visitor(var.Get<framework::LoDTensor>());
return;
case VarDesc_VarType_LOD_RANK_TABLE:
visitor(var.Get<LoDRankTable>());
return;
case VarDesc_VarType_LOD_TENSOR_ARRAY:
visitor(var.Get<LoDTensorArray>());
return;
case VarDesc_VarType_SELECTED_ROWS:
visitor(var.Get<SelectedRows>());
return;
default:
PADDLE_THROW("Not supported visit type, %d", ToVarType(var.Type()));
}
}
} // namespace framework
} // namespace paddle
paddle/function/CMakeLists.txt
浏览文件 @ fc117ecf
......@@ -45,6 +45,7 @@ if(WITH_GPU)
add_simple_unittest(BlockExpandOpTest)
add_simple_unittest(CropOpTest)
add_simple_unittest(SwitchOpTest)
add_simple_unittest(ScaleSubRegionOpTest)
endif()
add_simple_unittest(Im2ColTest)
......
paddle/function/FunctionTest.h
浏览文件 @ fc117ecf
......@@ -110,6 +110,7 @@ public:
function2_(FunctionBase::funcRegistrar_.createByType(name2)) {
function1_->init(config);
function2_->init(config);
initArgsCallback_ = nullptr;
}
~Compare2Function() {}
......@@ -170,6 +171,10 @@ public:
*seq2_));
}
void registerInitCallback(std::function<void(BufferArg&, size_t)> callback) {
initArgsCallback_ = callback;
}
// output need only contains shape, do not contains data.
void addOutputs(const BufferArg& output, ArgType argType = ASSIGN_TO) {
size_t size =
......@@ -340,6 +345,10 @@ protected:
initArg(*func1Inputs_[i]);
}
if (initArgsCallback_ != nullptr) {
initArgsCallback_(*func1Inputs_[i], i);
}
copyArg_(*func1Inputs_[i], *func2Inputs_[i]);
}
}
......@@ -386,6 +395,7 @@ protected:
std::shared_ptr<SequenceIdArg> seq1_;
std::shared_ptr<SequenceIdArg> seq2_;
test::CopyArgument<DType1, DType2> copyArg_;
std::function<void(BufferArg&, size_t)> initArgsCallback_;
};
class CpuGpuFuncCompare
......
paddle/function/ScaleSubRegionOp.cpp 0 → 100644
浏览文件 @ fc117ecf
/* 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 "ScaleSubRegionOp.h"
#include "paddle/function/TensorShape.h"
namespace paddle {
template <>
void ScaleSubRegion<DEVICE_TYPE_CPU>(real* outputs,
const real* inputs,
const real* indices,
const TensorShape shape,
const FuncConfig& conf) {
real value = conf.get<real>("value");
int number = shape[0];
int channel = shape[1];
int height = shape[2];
int width = shape[3];
memcpy(outputs, inputs, number * channel * height * width * sizeof(real));
for (int n = 0; n < number; ++n) {
// indices start from 1
int offset = n * 6;
for (int c = indices[offset] - 1; c < indices[offset + 1]; ++c) {
for (int h = indices[offset + 2] - 1; h < indices[offset + 3]; ++h) {
for (int w = indices[offset + 4] - 1; w < indices[offset + 5]; ++w) {
int idx = ((n * channel + c) * height + h) * width + w;
outputs[idx] *= value;
}
}
}
}
}
template <>
void ScaleSubRegionGrad<DEVICE_TYPE_CPU>(const real* inGrad,
real* outGrad,
const real* indices,
const TensorShape shape,
const FuncConfig& conf) {
real value = conf.get<real>("value");
int number = shape[0];
int channel = shape[1];
int height = shape[2];
int width = shape[3];
for (int n = 0; n < number; ++n) {
for (int c = 0; c < channel; ++c) {
for (int h = 0; h < height; ++h) {
for (int w = 0; w < width; ++w) {
int idx = ((n * channel + c) * height + h) * width + w;
int offset = n * 6;
if (c >= (indices[offset] - 1) && c <= (indices[offset + 1] - 1) &&
h >= (indices[offset + 2] - 1) &&
h <= (indices[offset + 3] - 1) &&
w >= (indices[offset + 4] - 1) &&
w <= (indices[offset + 5] - 1)) {
outGrad[idx] += inGrad[idx] * value;
} else {
outGrad[idx] += inGrad[idx];
}
}
}
}
}
}
/**
* \brief For each instance, ScaleSubRegion can be used to multiply a value to
* a specified sub continuous region. By providing start index and end
* index for C/H/W, you can specify the location and shape of the region.
*
* Argument in this Function:
* \param inputs A 4-D tensor with shape [N, C, H, W], only one input.
* \param indices A 2-D tensor with shape [N, 6], indicates the sub region.
* \param outputs A 4-D tensor with same shape as inputs, output value.
*/
template <DeviceType Device>
class ScaleSubRegionFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override { conf_ = config; }
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(2UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
TensorShape shape = inputs[0].shape();
ScaleSubRegion<Device>(outputs[0].data<real>(),
inputs[0].data<real>(),
inputs[1].data<real>(),
shape,
conf_);
}
private:
FuncConfig conf_;
};
/**
* \brief The backward propagation of ScaleSubRegion Function.
*
* Argument in this Function:
* \param inputs A 4-D tensor with shape [N, C, H, W], output gradient.
* \param indices A 2-D tensor with shape [N, 6], indicates the sub region.
* \param outputs A 4-D tensor with shape [N, C, H, W], gradient of input value.
*/
template <DeviceType Device>
class ScaleSubRegionGradFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override { conf_ = config; }
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(2UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
CHECK_EQ(outputs[0].getArgType(), ADD_TO);
TensorShape shape = inputs[0].shape();
ScaleSubRegionGrad<Device>(inputs[0].data<real>(),
outputs[0].data<real>(),
inputs[1].data<real>(),
shape,
conf_);
}
private:
FuncConfig conf_;
};
REGISTER_TYPED_FUNC(ScaleSubRegion, CPU, ScaleSubRegionFunc);
REGISTER_TYPED_FUNC(ScaleSubRegionGrad, CPU, ScaleSubRegionGradFunc);
#ifdef PADDLE_WITH_CUDA
REGISTER_TYPED_FUNC(ScaleSubRegion, GPU, ScaleSubRegionFunc);
REGISTER_TYPED_FUNC(ScaleSubRegionGrad, GPU, ScaleSubRegionGradFunc);
#endif
} // namespace paddle
paddle/function/ScaleSubRegionOp.h 0 → 100644
浏览文件 @ fc117ecf
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Function.h"
namespace paddle {
/**
* \brief Function to multiply a value to values in specified sub continuous
* region. Indices must be provided to indcate the location and shape of
* the region and the multiplied value is passed by configure variable.
*
*
* \param[out] outputs Output value.
* \param[in] inputs Input data which contains NCHW information.
* \param[in] indices Indices data to indcate the sub region.
* \param[in] shape Tensor shape of input value.
* \param[in] conf Configure variable which contains the multiplied value.
*/
template <DeviceType Device>
void ScaleSubRegion(real* outputs,
const real* inputs,
const real* indices,
const TensorShape shape,
const FuncConfig& conf);
/**
* \brief Backward propagation function of ScaleSubRegion.
*
* \param[out] inGrad Gradients of previous layer.
* \param[in] outGrad Output gradient.
* \param[in] indices Indices data.
* \param[in] shape The Shape of input tensor.
* \param[in] conf Configure variable.
*/
template <DeviceType Device>
void ScaleSubRegionGrad(const real* inGrad,
real* outGrad,
const real* indices,
const TensorShape shape,
const FuncConfig& conf);
} // namespace paddle
paddle/function/ScaleSubRegionOpGpu.cu 0 → 100644
浏览文件 @ fc117ecf
/* 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 "ScaleSubRegionOp.h"
#include "hl_base.h"
namespace paddle {
__global__ void KeScaleSubRegion(real* outputs,
const real* inputs,
const real* indices,
real value,
int channel,
int height,
int width,
int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % width;
const int h = (idx / width) % height;
const int c = (idx / width / height) % channel;
const int n = idx / width / height / channel;
const int offset = n * 6;
if (c >= (indices[offset] - 1) && c <= (indices[offset + 1] - 1) &&
h >= (indices[offset + 2] - 1) && h <= (indices[offset + 3] - 1) &&
w >= (indices[offset + 4] - 1) && w <= (indices[offset + 5] - 1)) {
outputs[idx] = inputs[idx] * value;
} else {
outputs[idx] = inputs[idx];
}
}
}
template <>
void ScaleSubRegion<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const real* indices,
const TensorShape shape,
const FuncConfig& conf) {
real value = conf.get<real>("value");
int number = shape[0];
int channel = shape[1];
int height = shape[2];
int width = shape[3];
size_t nth = number * channel * height * width;
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeScaleSubRegion<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
outputs, inputs, indices, value, channel, height, width, nth);
CHECK_SYNC("ScaleSubRegion");
}
__global__ void KeScaleSubRegionDiff(const real* inGrad,
real* outGrad,
const real* indices,
real value,
int channel,
int height,
int width,
int nthreads) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % width;
const int h = (idx / width) % height;
const int c = (idx / width / height) % channel;
const int n = idx / width / height / channel;
const int offset = n * 6;
if (c >= (indices[offset] - 1) && c <= (indices[offset + 1] - 1) &&
h >= (indices[offset + 2] - 1) && h <= (indices[offset + 3] - 1) &&
w >= (indices[offset + 4] - 1) && w <= (indices[offset + 5] - 1)) {
outGrad[idx] += inGrad[idx] * value;
} else {
outGrad[idx] += inGrad[idx];
}
}
}
template <>
void ScaleSubRegionGrad<DEVICE_TYPE_GPU>(const real* inGrad,
real* outGrad,
const real* indices,
const TensorShape shape,
const FuncConfig& conf) {
real value = conf.get<real>("value");
int number = shape[0];
int channel = shape[1];
int height = shape[2];
int width = shape[3];
size_t nth = number * channel * height * width;
int blockSize = 1024;
int gridSize = (nth + blockSize - 1) / blockSize;
KeScaleSubRegionDiff<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
inGrad, outGrad, indices, value, channel, height, width, nth);
CHECK_SYNC("ScaleSubRegionGrad");
}
} // namespace paddle
paddle/function/ScaleSubRegionOpTest.cpp 0 → 100644
浏览文件 @ fc117ecf
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "FunctionTest.h"
namespace paddle {
TEST(ScaleSubRegion, real) {
for (size_t numSamples : {5, 32}) {
for (size_t channels : {5, 32}) {
for (size_t imgSizeH : {5, 33}) {
for (size_t imgSizeW : {5, 32}) {
for (real value : {-0.5, 0.0, 0.5}) {
for (bool firstHalf : {false, true}) {
VLOG(3) << " numSamples=" << numSamples
<< " channels=" << channels << " imgSizeH=" << imgSizeH
<< " imgSizeW=" << imgSizeW;
for (bool testGrad : {false, true}) {
CpuGpuFuncCompare compare(
testGrad ? "ScaleSubRegionGrad" : "ScaleSubRegion",
FuncConfig().set<real>("value", value));
TensorShape shape{numSamples, channels, imgSizeH, imgSizeW};
TensorShape indicesShape{numSamples, 6};
compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape));
compare.addInputs(BufferArg(VALUE_TYPE_FLOAT, indicesShape));
compare.registerInitCallback([=](BufferArg& arg, size_t index) {
if (index == 1) {
real* data = (real*)arg.data();
for (size_t i = 0; i < numSamples; ++i) {
size_t offset = i * 6;
data[offset] = firstHalf ? 1 : channels / 2;
data[offset + 1] = firstHalf ? channels / 2 : channels;
data[offset + 2] = firstHalf ? 1 : imgSizeH / 2;
data[offset + 3] = firstHalf ? imgSizeH / 2 : imgSizeH;
data[offset + 4] = firstHalf ? 1 : imgSizeW / 2;
data[offset + 5] = firstHalf ? imgSizeW / 2 : imgSizeW;
}
}
});
compare.addOutputs(
BufferArg(
VALUE_TYPE_FLOAT, shape, testGrad ? ADD_TO : ASSIGN_TO),
testGrad ? ADD_TO : ASSIGN_TO);
compare.run();
}
}
}
}
}
}
}
}
} // namespace paddle
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
浏览文件 @ fc117ecf
......@@ -54,7 +54,6 @@ void MKLDNNAddtoLayer::reshape(
ow = iw;
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
printSizeInfo();
}
void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
......@@ -62,16 +61,14 @@ void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
if (biases_) {
LOG(FATAL) << "not implemented yet";
}
resetFwdBuffers(inVals_, out);
resetFwdBuffers(inVals_, bias, out);
in = inVals_[0];
std::shared_ptr<sum::primitive_desc> fwdPD;
resetFwdPD(fwdPD, inVals_, out);
std::shared_ptr<sum::primitive_desc> biasPD;
resetFwdPD(fwdPD, biasPD, inVals_, bias, out);
resetFwdPipeline(pipeline, fwdPD, inVals_, out);
resetFwdPipeline(pipeline, fwdPD, biasPD, inVals_, bias, out);
}
void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
......@@ -79,7 +76,7 @@ void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, out);
resetBwdBuffers(inGrads_, bias, out);
in = inGrads_[0];
// backward only need share output grad to input grad
......@@ -89,6 +86,20 @@ void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData());
}
}
// backward bias
bwdBias_ = nullptr;
if (bias) {
std::vector<float> scales(bs_, 1.0);
std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc());
auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs);
std::vector<primitive::at> srcs;
for (size_t i = 0; i < grads_.size(); ++i) {
srcs.push_back(*(grads_[i]));
}
bwdBias_.reset(new sum(biasPD, srcs, *bias));
pipeline.push_back(*bwdBias_);
}
}
void MKLDNNAddtoLayer::updateWeights(const UpdateCallback& callback) {
......@@ -97,7 +108,25 @@ void MKLDNNAddtoLayer::updateWeights(const UpdateCallback& callback) {
}
}
void MKLDNNAddtoLayer::prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out,
std::vector<MKLDNNMatrixPtr>& outs) {
auto pd = MKLDNNMatrix::createPrimitiveDesc(
{(int)layerSize_}, memory::format::x, engine_);
bias = MKLDNNMatrix::create(pd, biasMat);
outs.clear();
real* data = out->getData();
CHECK_EQ(bs_ * layerSize_, out->getElementCnt());
for (int i = 0; i < bs_; ++i) {
MatrixPtr tmp =
Matrix::create(data + i * layerSize_, 1, layerSize_, false, false);
outs.push_back(MKLDNNMatrix::create(bias->getPrimitiveDesc(), tmp));
}
}
void MKLDNNAddtoLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
......@@ -110,12 +139,20 @@ void MKLDNNAddtoLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
}
resetOutValue(out, inputs[0]->getPrimitiveDesc());
if (biases_ && biases_->getW()) {
prepareBias(bias, biases_->getW(), out, vals_);
} else {
bias = nullptr;
}
}
void MKLDNNAddtoLayer::resetFwdPD(std::shared_ptr<sum::primitive_desc>& pd,
std::shared_ptr<sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr bias,
MKLDNNMatrixPtr out) {
std::vector<double> scales(inputs.size(), 1.0);
std::vector<float> scales(inputs.size(), 1.0);
std::vector<memory::primitive_desc> srcPDs;
for (size_t i = 0; i < inputs.size(); i++) {
srcPDs.push_back(inputs[i]->getPrimitiveDesc());
......@@ -123,12 +160,23 @@ void MKLDNNAddtoLayer::resetFwdPD(std::shared_ptr<sum::primitive_desc>& pd,
CHECK(out);
pd.reset(new sum::primitive_desc(out->getMemoryDesc(), scales, srcPDs));
CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
biasPD = nullptr;
if (bias) {
std::vector<float> scales(2, 1.0);
std::vector<memory::primitive_desc> srcPDs(2, bias->getPrimitiveDesc());
biasPD.reset(
new sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs));
CHECK_PRIMITIVE_DESC_EQ(bias, biasPD->dst_primitive_desc());
}
}
void MKLDNNAddtoLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<sum::primitive_desc>& pd,
std::shared_ptr<sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
std::vector<primitive::at> srcs;
for (size_t i = 0; i < inputs.size(); i++) {
......@@ -136,9 +184,23 @@ void MKLDNNAddtoLayer::resetFwdPipeline(
}
fwd_.reset(new sum(*pd, srcs, *out));
pipeline.push_back(*fwd_);
fwdBias_.clear();
if (biasPD == nullptr || bias == nullptr) {
return;
}
fwdBias_.resize(vals_.size());
for (size_t i = 0; i < vals_.size(); ++i) {
std::vector<primitive::at> srcs;
srcs.push_back(*(vals_[i]));
srcs.push_back(*bias);
fwdBias_[i].reset(new sum(*biasPD, srcs, *vals_[i]));
pipeline.push_back(*fwdBias_[i]);
}
}
void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
......@@ -149,6 +211,12 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
resetInGrad(inputs[i], inVal_->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
}
if (biases_ && biases_->getWGrad()) {
prepareBias(bias, biases_->getWGrad(), out, grads_);
} else {
bias = nullptr;
}
}
} // namespace paddle
paddle/gserver/layers/MKLDNNAddtoLayer.h
浏览文件 @ fc117ecf
......@@ -32,9 +32,15 @@ protected:
// layer size == ic * ih * iw == oc * oh *ow, and can not be changed
size_t layerSize_;
// TODO(TJ): this part has not been optimized by MKL-DNN
std::unique_ptr<Weight> biases_;
// buffers for adding bias
std::vector<MKLDNNMatrixPtr> vals_;
std::vector<MKLDNNMatrixPtr> grads_;
// primitives for adding bias
std::vector<std::shared_ptr<mkldnn::primitive>> fwdBias_;
std::shared_ptr<mkldnn::primitive> bwdBias_;
public:
explicit MKLDNNAddtoLayer(const LayerConfig& config) : MKLDNNLayer(config) {}
......@@ -91,20 +97,34 @@ protected:
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<mkldnn::sum::primitive_desc>& pd,
std::shared_ptr<mkldnn::sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr bias,
MKLDNNMatrixPtr out);
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<mkldnn::sum::primitive_desc>& pd,
std::shared_ptr<mkldnn::sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* prepare for bias
*/
void prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out,
std::vector<MKLDNNMatrixPtr>& outs);
};
} // namespace paddle
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
浏览文件 @ fc117ecf
......@@ -119,13 +119,12 @@ void MKLDNNBatchNormLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
oh = ih;
ow = ow;
ow = iw;
// ic_ and oc can not be changed
CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
<< "Input channel can not be changed";
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
printSizeInfo();
}
void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
......
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ fc117ecf
......@@ -102,8 +102,6 @@ void MKLDNNConvLayer::reshape(
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
printSizeInfo();
}
void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
......
paddle/gserver/layers/MKLDNNConvLayer.h
浏览文件 @ fc117ecf
......@@ -92,7 +92,7 @@ public:
void printSizeInfo() override {
MKLDNNLayer::printSizeInfo();
VLOG(MKLDNN_SIZES) << getName() << ": fh: " << fh_ << ", fw: " << fw_
<< ": ph: " << ph_ << ", pw: " << pw_ << ", sh: " << sh_
<< ", ph: " << ph_ << ", pw: " << pw_ << ", sh: " << sh_
<< ", sw: " << sw_ << ", dh: " << dh_ << ", dw: " << dw_;
}
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ fc117ecf
......@@ -84,8 +84,6 @@ void MKLDNNFcLayer::reshape(
reshapeOutput(oh, ow);
resizeOutput(bs, oc);
printSizeInfo();
}
void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ fc117ecf
......@@ -287,7 +287,7 @@ void MKLDNNLayer::resetMergeGrad(MKLDNNMatrixPtr& out) {
return;
}
CHECK(out) << "should have reset internal ouput grad";
std::vector<double> scales(outputMap_.size(), 1.0);
std::vector<float> scales(outputMap_.size(), 1.0);
std::vector<memory::primitive_desc> srcPDs;
std::vector<primitive::at> srcs;
for (auto it = outputMap_.begin(); it != outputMap_.end(); ++it) {
......
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ fc117ecf
......@@ -71,8 +71,6 @@ void MKLDNNPoolLayer::reshape(
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
printSizeInfo();
}
void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
......
paddle/gserver/layers/ROIPoolLayer.cpp 0 → 100644
浏览文件 @ fc117ecf
/* 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 "ROIPoolLayer.h"
namespace paddle {
REGISTER_LAYER(roi_pool, ROIPoolLayer);
bool ROIPoolLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
const ROIPoolConfig& layerConf = config_.inputs(0).roi_pool_conf();
pooledWidth_ = layerConf.pooled_width();
pooledHeight_ = layerConf.pooled_height();
spatialScale_ = layerConf.spatial_scale();
return true;
}
void ROIPoolLayer::forward(PassType passType) {
Layer::forward(passType);
const ROIPoolConfig& layerConf = config_.inputs(0).roi_pool_conf();
height_ = getInput(0).getFrameHeight();
if (!height_) height_ = layerConf.height();
width_ = getInput(0).getFrameWidth();
if (!width_) width_ = layerConf.width();
channels_ = getInputValue(0)->getWidth() / width_ / height_;
size_t batchSize = getInput(0).getBatchSize();
size_t numROIs = getInput(1).getBatchSize();
MatrixPtr dataValue = getInputValue(0);
MatrixPtr roiValue = getInputValue(1);
resetOutput(numROIs, channels_ * pooledHeight_ * pooledWidth_);
MatrixPtr outputValue = getOutputValue();
if (useGpu_) { // TODO(guosheng): implement on GPU later
MatrixPtr dataCpuBuffer;
Matrix::resizeOrCreate(dataCpuBuffer,
dataValue->getHeight(),
dataValue->getWidth(),
false,
false);
MatrixPtr roiCpuBuffer;
Matrix::resizeOrCreate(roiCpuBuffer,
roiValue->getHeight(),
roiValue->getWidth(),
false,
false);
dataCpuBuffer->copyFrom(*dataValue);
roiCpuBuffer->copyFrom(*roiValue);
dataValue = dataCpuBuffer;
roiValue = roiCpuBuffer;
MatrixPtr outputCpuBuffer;
Matrix::resizeOrCreate(outputCpuBuffer,
outputValue->getHeight(),
outputValue->getWidth(),
false,
false);
outputCpuBuffer->copyFrom(*outputValue);
outputValue = outputCpuBuffer;
}
real* bottomData = dataValue->getData();
size_t batchOffset = dataValue->getWidth();
size_t channelOffset = height_ * width_;
real* bottomROIs = roiValue->getData();
size_t roiOffset = roiValue->getWidth();
size_t poolChannelOffset = pooledHeight_ * pooledWidth_;
real* outputData = outputValue->getData();
Matrix::resizeOrCreate(maxIdxs_,
numROIs,
channels_ * pooledHeight_ * pooledWidth_,
false,
false);
real* argmaxData = maxIdxs_->getData();
for (size_t n = 0; n < numROIs; ++n) {
// the first five elememts of each RoI should be:
// batch_idx, roi_x_start, roi_y_start, roi_x_end, roi_y_end
size_t roiBatchIdx = bottomROIs[0];
size_t roiStartW = round(bottomROIs[1] * spatialScale_);
size_t roiStartH = round(bottomROIs[2] * spatialScale_);
size_t roiEndW = round(bottomROIs[3] * spatialScale_);
size_t roiEndH = round(bottomROIs[4] * spatialScale_);
CHECK_GE(roiBatchIdx, 0UL);
CHECK_LT(roiBatchIdx, batchSize);
size_t roiHeight = std::max(roiEndH - roiStartH + 1, 1UL);
size_t roiWidth = std::max(roiEndW - roiStartW + 1, 1UL);
real binSizeH =
static_cast<real>(roiHeight) / static_cast<real>(pooledHeight_);
real binSizeW =
static_cast<real>(roiWidth) / static_cast<real>(pooledWidth_);
real* batchData = bottomData + batchOffset * roiBatchIdx;
for (size_t c = 0; c < channels_; ++c) {
for (size_t ph = 0; ph < pooledHeight_; ++ph) {
for (size_t pw = 0; pw < pooledWidth_; ++pw) {
size_t hstart = static_cast<size_t>(std::floor(ph * binSizeH));
size_t wstart = static_cast<size_t>(std::floor(pw * binSizeW));
size_t hend = static_cast<size_t>(std::ceil((ph + 1) * binSizeH));
size_t wend = static_cast<size_t>(std::ceil((pw + 1) * binSizeW));
hstart = std::min(std::max(hstart + roiStartH, 0UL), height_);
wstart = std::min(std::max(wstart + roiStartW, 0UL), width_);
hend = std::min(std::max(hend + roiStartH, 0UL), height_);
wend = std::min(std::max(wend + roiStartW, 0UL), width_);
bool isEmpty = (hend <= hstart) || (wend <= wstart);
size_t poolIndex = ph * pooledWidth_ + pw;
if (isEmpty) {
outputData[poolIndex] = 0;
argmaxData[poolIndex] = -1;
}
for (size_t h = hstart; h < hend; ++h) {
for (size_t w = wstart; w < wend; ++w) {
size_t index = h * width_ + w;
if (batchData[index] > outputData[poolIndex]) {
outputData[poolIndex] = batchData[index];
argmaxData[poolIndex] = index;
}
}
}
}
}
batchData += channelOffset;
outputData += poolChannelOffset;
argmaxData += poolChannelOffset;
}
bottomROIs += roiOffset;
}
if (useGpu_) {
getOutputValue()->copyFrom(*outputValue);
}
}
void ROIPoolLayer::backward(const UpdateCallback& callback) {
MatrixPtr inGradValue = getInputGrad(0);
MatrixPtr outGradValue = getOutputGrad();
MatrixPtr roiValue = getInputValue(1);
if (useGpu_) {
MatrixPtr inGradCpuBuffer;
Matrix::resizeOrCreate(inGradCpuBuffer,
inGradValue->getHeight(),
inGradValue->getWidth(),
false,
false);
MatrixPtr outGradCpuBuffer;
Matrix::resizeOrCreate(outGradCpuBuffer,
outGradValue->getHeight(),
outGradValue->getWidth(),
false,
false);
MatrixPtr roiCpuBuffer;
Matrix::resizeOrCreate(roiCpuBuffer,
roiValue->getHeight(),
roiValue->getWidth(),
false,
false);
inGradCpuBuffer->copyFrom(*inGradValue);
outGradCpuBuffer->copyFrom(*outGradValue);
roiCpuBuffer->copyFrom(*roiValue);
inGradValue = inGradCpuBuffer;
outGradValue = outGradCpuBuffer;
roiValue = roiCpuBuffer;
}
real* bottomROIs = roiValue->getData();
size_t numROIs = getInput(1).getBatchSize();
size_t roiOffset = getInputValue(1)->getWidth();
real* inDiffData = inGradValue->getData();
size_t batchOffset = getInputValue(0)->getWidth();
size_t channelOffset = height_ * width_;
real* outDiffData = outGradValue->getData();
size_t poolChannelOffset = pooledHeight_ * pooledWidth_;
real* argmaxData = maxIdxs_->getData();
for (size_t n = 0; n < numROIs; ++n) {
size_t roiBatchIdx = bottomROIs[0];
real* batchDiffData = inDiffData + batchOffset * roiBatchIdx;
for (size_t c = 0; c < channels_; ++c) {
for (size_t ph = 0; ph < pooledHeight_; ++ph) {
for (size_t pw = 0; pw < pooledWidth_; ++pw) {
size_t poolIndex = ph * pooledWidth_ + pw;
if (argmaxData[poolIndex] > 0) {
size_t index = static_cast<size_t>(argmaxData[poolIndex]);
batchDiffData[index] += outDiffData[poolIndex];
}
}
}
batchDiffData += channelOffset;
outDiffData += poolChannelOffset;
argmaxData += poolChannelOffset;
}
bottomROIs += roiOffset;
}
if (useGpu_) {
getInputGrad(0)->copyFrom(*inGradValue);
}
}
} // namespace paddle
paddle/gserver/layers/ROIPoolLayer.h 0 → 100644
浏览文件 @ fc117ecf
/* 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 "Layer.h"
namespace paddle {
/**
* A layer used by Fast R-CNN to extract feature maps of ROIs from the last
* feature map.
* - Input: This layer needs two input layers: The first input layer is a
* convolution layer; The second input layer contains the ROI data
* which is the output of ProposalLayer in Faster R-CNN. layers for
* generating bbox location offset and the classification confidence.
* - Output: The ROIs' feature map.
* Reference:
* Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.
* Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
* Networks
*/
class ROIPoolLayer : public Layer {
protected:
size_t channels_;
size_t width_;
size_t height_;
size_t pooledWidth_;
size_t pooledHeight_;
real spatialScale_;
// Since there is no int matrix, use real maxtrix instead.
MatrixPtr maxIdxs_;
public:
explicit ROIPoolLayer(const LayerConfig& config) : Layer(config) {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
};
} // namespace paddle
paddle/gserver/layers/ScaleSubRegionLayer.cpp 0 → 100644
浏览文件 @ fc117ecf
/* 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 "ScaleSubRegionLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(scale_sub_region, ScaleSubRegionLayer);
bool ScaleSubRegionLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
Layer::init(layerMap, parameterMap);
CHECK_EQ(static_cast<int>(inputLayers_.size()), 2);
auto& conf = config_.inputs(0).scale_sub_region_conf();
value_ = conf.value();
createFunction(forward_, "ScaleSubRegion", FuncConfig().set("value", value_));
createFunction(
backward_, "ScaleSubRegionGrad", FuncConfig().set("value", value_));
return true;
}
void ScaleSubRegionLayer::forward(PassType passType) {
Layer::forward(passType);
auto in0 = getInput(0);
imgH_ = in0.getFrameHeight();
imgW_ = in0.getFrameWidth();
if (imgH_ == 0 || imgW_ == 0) {
auto& conf = config_.inputs(0).scale_sub_region_conf();
imgH_ = conf.image_conf().img_size_y();
imgW_ = conf.image_conf().img_size();
}
MatrixPtr imgV = in0.value;
size_t batchSize = imgV->getHeight();
size_t spatialSize = imgH_ * imgW_;
channelsNum_ = imgV->getWidth() / spatialSize;
shape_ = TensorShape({batchSize, channelsNum_, imgH_, imgW_});
resetOutput(batchSize, imgV->getWidth());
auto& out = getOutput();
out.setFrameHeight(imgH_);
out.setFrameWidth(imgW_);
MatrixPtr indicesV = getInputValue(1);
indicesShape_ = TensorShape({batchSize, 6});
REGISTER_TIMER_INFO("ScaleSubRegionForward", getName().c_str());
BufferArgs inArgs;
BufferArgs outArgs;
inArgs.addArg(*imgV, shape_);
inArgs.addArg(*indicesV, indicesShape_);
outArgs.addArg(*out.value, shape_, ASSIGN_TO);
forward_[0]->calc(inArgs, outArgs);
}
void ScaleSubRegionLayer::backward(const UpdateCallback& callback) {
REGISTER_TIMER_INFO("ScaleSubRegionBackward", getName().c_str());
BufferArgs inArgs;
BufferArgs outArgs;
inArgs.addArg(*getOutputGrad(), shape_);
inArgs.addArg(*getInputValue(1), indicesShape_);
outArgs.addArg(*getInputGrad(0), shape_, ADD_TO);
backward_[0]->calc(inArgs, outArgs);
}
} // namespace paddle
paddle/gserver/layers/ScaleSubRegionLayer.h 0 → 100644
浏览文件 @ fc117ecf
/* 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 "Layer.h"
namespace paddle {
/**
* \brief For each instance, this layer can be used to multiply a value to a
* specified sub continuous region. By providing start index and end
* index for C/H/W, you can specify the location and shape of the
* region.
*
* input_0: Input value.
* input_1: Indices value to specify the location an shape of the
* region.
*/
class ScaleSubRegionLayer : public Layer {
public:
explicit ScaleSubRegionLayer(const LayerConfig& config) : Layer(config) {}
~ScaleSubRegionLayer() {}
bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
void forward(PassType passType);
void backward(const UpdateCallback& callback = nullptr);
protected:
TensorShape shape_;
TensorShape indicesShape_;
size_t imgH_;
size_t imgW_;
size_t channelsNum_;
real value_;
};
} // namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ fc117ecf
......@@ -53,7 +53,7 @@ TEST(Operator, dot_mul) {
TEST(Projection, context) {
for (auto contextStart : {-5, -3, -1, 0, 3}) {
for (auto contextLength : {1, 2, 5, 7}) {
for (auto batchSize : {1, 2, 5, 20, 50}) {
for (auto batchSize : {1, 2, 5, 20}) {
for (auto trainablePadding : {false, true}) {
LOG(INFO) << " contextStart=" << contextStart
<< " contextLength=" << contextLength
......@@ -585,14 +585,14 @@ TEST(Layer, maxoutLayer) {
}
void testFcLayer(string format, size_t nnz) {
TestConfig config;
config.biasSize = 4096;
config.biasSize = 1024;
config.layerConfig.set_type("fc");
config.layerConfig.set_size(4096);
config.layerConfig.set_size(1024);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_drop_rate(0.1);
config.inputDefs.push_back(
{INPUT_DATA, "layer_0", 8192, nnz, ParaSparse(format)});
{INPUT_DATA, "layer_0", 2048, nnz, ParaSparse(format)});
config.layerConfig.add_inputs();
LOG(INFO) << config.inputDefs[0].sparse.sparse << " "
......@@ -609,9 +609,9 @@ void testFcLayer(string format, size_t nnz) {
}
TEST(Layer, fcLayer) {
testFcLayer("", 4096 * 4096 * 2);
testFcLayer("csc", 4096 * 40);
testFcLayer("csr", 4096 * 40);
testFcLayer("", 1024 * 1024 * 2);
testFcLayer("csc", 1024 * 10);
testFcLayer("csr", 1024 * 10);
}
TEST(Layer, SelectiveFullyConnectedLayer) {
......@@ -1995,7 +1995,7 @@ TEST(Layer, multibox_loss) {
TEST(Layer, TransLayer) {
TestConfig config;
const int height = 128;
const int width = 1028;
const int width = 256;
config.layerConfig.set_type("trans");
config.layerConfig.set_size(width);
......@@ -2056,6 +2056,43 @@ TEST(Layer, CropLayer) {
}
}
TEST(Layer, roi_pool) {
TestConfig config;
config.layerConfig.set_type("roi_pool");
config.biasSize = 0;
LayerInputConfig* input = config.layerConfig.add_inputs();
ROIPoolConfig* roiPoolConf = input->mutable_roi_pool_conf();
roiPoolConf->set_pooled_width(7);
roiPoolConf->set_pooled_height(7);
roiPoolConf->set_spatial_scale(1. / 16);
roiPoolConf->set_width(14);
roiPoolConf->set_height(14);
const size_t roiNum = 10;
const size_t roiDim = 10;
const size_t batchSize = 5;
MatrixPtr roiValue = Matrix::create(roiNum, roiDim, false, false);
roiValue->zeroMem();
real* roiData = roiValue->getData();
for (size_t i = 0; i < roiNum; ++i) {
roiData[i * roiDim + 0] = std::rand() % batchSize;
roiData[i * roiDim + 1] = std::rand() % 224; // xMin
roiData[i * roiDim + 2] = std::rand() % 224; // yMin
size_t xMin = static_cast<size_t>(roiData[i * roiDim + 1]);
size_t yMin = static_cast<size_t>(roiData[i * roiDim + 2]);
roiData[i * roiDim + 3] = xMin + std::rand() % (224 - xMin); // xMax
roiData[i * roiDim + 4] = yMin + std::rand() % (224 - yMin); // yMax
}
config.inputDefs.push_back({INPUT_DATA, "input", 3 * 14 * 14, {}});
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "rois", roiValue, {}});
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "roi_pool", batchSize, false, useGpu, false);
}
}
TEST(Layer, SwitchOrderLayer) {
TestConfig config;
// config input_0
......@@ -2358,6 +2395,38 @@ TEST(Layer, ScaleShiftLayer) {
}
}
TEST(Layer, ScaleSubRegionLayer) {
const size_t batchSize = 64;
const size_t size = 4096;
TestConfig config;
config.layerConfig.set_type("scale_sub_region");
config.inputDefs.push_back({INPUT_DATA, "input", size, 0});
MatrixPtr indicesV = Matrix::create(batchSize, 6, false, false);
auto* data = indicesV->getData();
for (size_t i = 0; i < batchSize; ++i) {
data[i * 2] = 2;
data[i * 2 + 1] = 4;
data[i * 2 + 2] = 16;
data[i * 2 + 3] = 32;
data[i * 2 + 4] = 16;
data[i * 2 + 5] = 32;
}
config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "indices", indicesV, {}});
LayerInputConfig* input = config.layerConfig.add_inputs();
ScaleSubRegionConfig* scaleSubRegionConf =
input->mutable_scale_sub_region_conf();
ImageConfig* imgConf = scaleSubRegionConf->mutable_image_conf();
imgConf->set_img_size(32);
imgConf->set_img_size_y(32);
imgConf->set_channels(4);
scaleSubRegionConf->set_value(2.0);
config.layerConfig.add_inputs();
for (auto useGpu : {false, true}) {
testLayerGrad(config, "scale_sub_region", batchSize, false, useGpu, false);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
......
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ fc117ecf
......@@ -269,6 +269,7 @@ void testBatchNormLayer(const testBatchNormDesc& pm) {
TEST(MKLDNNLayer, BatchNormLayer) {
testBatchNormLayer({4, 10, 6, 6});
testBatchNormLayer({16, 32, 16, 16});
testBatchNormLayer({4, 16, 8, 10});
}
struct testImageDesc {
......@@ -296,17 +297,12 @@ static void getAddtoConfig(TestConfig& cfg,
}
void testAddtoLayer(const testImageDesc& pm, const size_t nInputs) {
CHECK_GE(nInputs, 1);
CHECK_GE(nInputs, 1UL);
TestConfig dnnConfig;
getAddtoConfig(dnnConfig, pm, nInputs);
dnnConfig.layerConfig.set_type("mkldnn_addto");
// TODO(TJ): test with bias
for (auto withBias : {false}) {
if (withBias) {
dnnConfig.biasSize = pm.ic * pm.ih * pm.iw;
} else {
dnnConfig.biasSize = 0;
}
for (auto withBias : {false, true}) {
dnnConfig.biasSize = withBias ? pm.ic * pm.ih * pm.iw : 0;
RUN_MKLDNN_TEST_LAYER(dnnConfig, "addto", pm)
}
}
......
paddle/math/MKLDNNMatrix.cpp
浏览文件 @ fc117ecf
......@@ -152,12 +152,7 @@ void MKLDNNMatrix::downSpatial() {
}
memory::desc md = memory::desc(dstDims, getDtype(), dstFmt);
memory::primitive_desc pd = memory::primitive_desc(md, getEngine());
mkldnn_primitive_t result;
mkldnn::error::wrap_c_api(
mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr),
"could not create a memory primitive");
reset(result);
set_data_handle(data_);
resetMKLDNNMemory(pd, data_);
}
} // namespace paddle
paddle/math/MKLDNNMatrix.h
浏览文件 @ fc117ecf
......@@ -145,6 +145,27 @@ public:
m_.reset();
}
/**
* override the CpuMatrix::resize
*/
void resize(size_t newHeight, size_t newWidth) override {
m_->resize(newHeight, newWidth);
if (data_ == m_->getData() && elementCnt_ == newHeight * newWidth) {
return;
}
CpuMatrix::setData(data_);
height_ = newHeight;
width_ = newWidth;
elementCnt_ = newHeight * newWidth;
stride_ = width_;
auto pd = mkldnn::memory::primitive_desc(
mkldnn::memory::desc({(int)newHeight, (int)newWidth},
getDtype(),
mkldnn::memory::format::nc),
getEngine());
resetMKLDNNMemory(pd, data_);
}
/**
* override Matrix::getData
* check data before return
......@@ -215,6 +236,17 @@ protected:
memory::format srcFmt,
memory::format dstFmt,
memory::dims dm);
/**
* reset this MKLDNN Memory from primitve desc
*/
void resetMKLDNNMemory(memory::primitive_desc pd, real* data) {
mkldnn_primitive_t result;
mkldnn::error::wrap_c_api(
mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr),
"could not create a memory primitive");
reset(result);
set_data_handle(data);
}
private:
// save the CpuMatrixPtr in case the buffer released outside
......
paddle/math/MathFunctions.cpp
浏览文件 @ fc117ecf
......@@ -206,7 +206,7 @@ double dotProduct<double>(const int n, const double* x, const double* y) {
}
#endif
#if defined(PADDLE_USE_MKL) || defined(PADDLE_USE_MKLML)
#if defined(PADDLE_USE_MKLML)
template <>
void vExp<float>(const int n, const float* a, float* r) {
......@@ -295,38 +295,6 @@ template void vAdd(const int n, const double* a, const double* b, double* r);
#endif
#ifdef PADDLE_USE_MKL
template <>
void vInvSqrt<float>(const int n, const float* a, float* r) {
vsInvSqrt(n, a, r);
}
template <>
void vInvSqrt<double>(const int n, const double* a, double* r) {
vdInvSqrt(n, a, r);
}
template <>
void vLog1p<float>(const int n, const float* a, float* r) {
vsLog1p(n, a, r);
}
template <>
void vLog1p<double>(const int n, const double* a, double* r) {
vdLog1p(n, a, r);
}
template <>
void vTanh<float>(const int n, const float* a, float* r) {
vsTanh(n, a, r);
}
template <>
void vTanh<double>(const int n, const double* a, double* r) {
vdTanh(n, a, r);
}
#else
DEFINE_MATRIX_BINARY_OP(vInvSqrt, b = 1.0f / std::sqrt(a));
template <class T>
void vInvSqrt(const int n, const T* a, T* r) {
......@@ -357,6 +325,4 @@ template void vLog1p(const int n, const double* a, double* r);
template void vTanh(const int n, const float* a, float* r);
template void vTanh(const int n, const double* a, double* r);
#endif
} // namespace paddle
paddle/math/MathFunctions.h
浏览文件 @ fc117ecf
......@@ -21,11 +21,6 @@ limitations under the License. */
#include <mkl_vml_functions.h>
#endif
#ifdef PADDLE_USE_MKL
#include <mkl.h>
#include <mkl_lapacke.h>
#endif
#if defined(PADDLE_USE_ATLAS) || defined(PADDLE_USE_VECLIB)
extern "C" {
#include <cblas.h>
......
paddle/math/tests/TensorCheck.h
浏览文件 @ fc117ecf
......@@ -169,7 +169,7 @@ void TensorCheck(AssertEq compare,
count++;
}
}
EXPECT_EQ(count, 0) << "There are " << count << " different element.";
EXPECT_EQ(count, 0) << "There are " << count << " different elements.";
}
template <typename AssertEq, typename Tensor1, typename Tensor2>
......
paddle/operators/CMakeLists.txt
浏览文件 @ fc117ecf
......@@ -195,8 +195,13 @@ op_library(sequence_pool_op DEPS sequence_pooling)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(conv_transpose_op DEPS vol2col)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array)
if(WITH_TESTING)
op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array gtest)
else()
op_library(dynamic_recurrent_op SRCS dynamic_recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS net_op tensor_array)
endif()
op_library(recurrent_op SRCS recurrent_op.cc DEPS executor)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
......@@ -209,6 +214,7 @@ set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library")
cc_test(gather_test SRCS gather_test.cc DEPS tensor)
cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor)
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory)
cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc
rnn/recurrent_op_utils.cc
......
paddle/operators/accuracy_op.cu
浏览文件 @ fc117ecf
......@@ -65,7 +65,7 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
size_t num_samples = inference->dims()[0];
size_t infer_width = inference->dims()[1];
cudaMemset((void**)&accuracy_data, 0, sizeof(float));
PADDLE_ENFORCE(cudaMemset(accuracy_data, 0, sizeof(float)));
if (num_samples == 0) {
return;
......
paddle/operators/accuracy_op.h
浏览文件 @ fc117ecf
......@@ -14,7 +14,6 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
......@@ -22,18 +21,6 @@ namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename Place, typename T>
class AccuracyKernel : public framework::OpKernel<T> {
public:
......
paddle/operators/array_operator.h 0 → 100644
浏览文件 @ fc117ecf
/* 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. */
#pragma once
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
class ArrayOp : public framework::OperatorBase {
public:
ArrayOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
protected:
size_t GetOffset(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const {
auto *i = scope.FindVar(Input("I"));
PADDLE_ENFORCE(i != nullptr, "I must be set");
auto &i_tensor = i->Get<framework::LoDTensor>();
PADDLE_ENFORCE_EQ(i_tensor.numel(), 1);
size_t offset;
if (platform::is_gpu_place(i_tensor.place())) {
// FIXME: Avoid copy from GPU to CPU
framework::Tensor t;
t.CopyFrom(i_tensor, platform::CPUPlace(), dev_ctx);
dev_ctx.Wait();
offset = static_cast<size_t>(*t.data<int64_t>());
} else {
offset = static_cast<size_t>(*i_tensor.data<int64_t>());
}
return offset;
}
};
} // namespace operators
} // namespace paddle
paddle/operators/array_to_lod_tensor_op.cc
浏览文件 @ fc117ecf
......@@ -140,6 +140,23 @@ class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
"ArrayToLoDTensorOp must has input X.");
PADDLE_ENFORCE(context->HasInput("RankTable"),
"ArrayToLoDTensorOp must has input RankTable.");
context->SetOutputDim("Out", context->GetInputDim("X"));
}
};
class ArrayToLoDTensorGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
grad_op->SetType("lod_tensor_to_array");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetInput("RankTable", Input("RankTable"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
}
};
......@@ -149,4 +166,5 @@ class ArrayToLoDTensorInferShape : public framework::InferShapeBase {
namespace ops = paddle::operators;
REGISTER_OPERATOR(array_to_lod_tensor, ops::ArrayToLoDTensorOp,
ops::ArrayToLoDTensorOpProtoMaker,
ops::ArrayToLoDTensorInferShape);
ops::ArrayToLoDTensorInferShape,
ops::ArrayToLoDTensorGradMaker);
paddle/operators/assign_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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. */
#include "paddle/framework/data_type.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/var_type.h"
namespace paddle {
namespace operators {
class AssignFunctor {
public:
AssignFunctor(framework::Variable *out,
const platform::DeviceContext &dev_ctx)
: out_(out), dev_ctx_(dev_ctx) {}
void operator()(const framework::LoDTensor &lod_tensor) const {
auto &out_tensor = *out_->GetMutable<framework::LoDTensor>();
copy_tensor(lod_tensor, &out_tensor);
}
void operator()(const framework::LoDTensorArray &array) const {
auto &out_array = *out_->GetMutable<framework::LoDTensorArray>();
out_array.resize(array.size());
for (size_t i = 0; i < array.size(); ++i) {
copy_tensor(array[i], &out_array[i]);
}
}
void operator()(const framework::SelectedRows &rows) const {
framework::SelectedRows &out_rows =
*out_->GetMutable<framework::SelectedRows>();
out_rows.set_rows(rows.rows());
out_rows.set_height(rows.height());
auto &t = rows.value();
out_rows.mutable_value()->CopyFrom(t, t.place(), dev_ctx_);
}
template <typename T>
void operator()(const T &v) const {
PADDLE_THROW("Not support type for assign op %s", typeid(T).name());
}
private:
void copy_tensor(const framework::LoDTensor &lod_tensor,
framework::LoDTensor *out) const {
auto &out_tensor = *out;
out_tensor.CopyFrom(lod_tensor, lod_tensor.place(), dev_ctx_);
out_tensor.set_lod(lod_tensor.lod());
}
framework::Variable *out_;
const platform::DeviceContext &dev_ctx_;
};
class AssignOp : public framework::OperatorBase {
public:
AssignOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto *x = scope.FindVar(Input("X"));
if (x == nullptr) {
return;
}
auto *out = scope.FindVar(Output("Out"));
PADDLE_ENFORCE(
out != nullptr,
"The Output(Out) should not be null if the Input(X) is set.");
framework::VisitVarType(*x, AssignFunctor(out, dev_ctx));
}
};
class AssignOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
AssignOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor, SelectedRows or LoDTensorArray) The input variable "
"could be LoDTensor, SelectedRows or LoDTensorArray.")
.AsDispensable();
AddOutput("Out",
"(LoDTensor, SelectedRows or LoDTensorArray) The type of output "
"is the same as input X.");
AddComment(R"DOC(Assign Operator
Out = X, when type in [LoDTensor/SelectedRows/LoDTensorArray]
raise error if the type is not listed above.
)DOC");
}
};
class AssignInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
if (context->HasInput("X")) {
auto type = context->GetInputsVarType("X")[0];
if (type == framework::VarDesc_VarType_SELECTED_ROWS ||
type == framework::VarDesc_VarType_LOD_TENSOR) {
context->SetOutputDim("Out", context->GetInputDim("X"));
}
}
}
};
class AssignGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *op = new framework::OpDescBind();
op->SetType("assign");
op->SetInput("X", OutputGrad("Out"));
op->SetOutput("Out", InputGrad("X"));
return std::unique_ptr<framework::OpDescBind>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(assign, ops::AssignOp, ops::AssignGradMaker,
ops::AssignInferShape, ops::AssignOpProtoMaker);
paddle/operators/batch_norm_op.cc
浏览文件 @ fc117ecf
......@@ -19,9 +19,6 @@ namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
using EigenArrayMap =
......
paddle/operators/beam_search_decode_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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_decode_op.h"
namespace paddle {
namespace operators {
class BeamSearchDecodeOp : public framework::OperatorBase {
public:
BeamSearchDecodeOp(const std::string& type,
const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
framework::ExecutionContext ctx(*this, scope, dev_ctx);
const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");
const size_t step_num = ids->size();
PADDLE_ENFORCE_GT(step_num, 0UL,
"beam search steps should be larger than 0");
const size_t source_num = ids->at(0).lod().at(0).size() - 1;
PADDLE_ENFORCE_GT(source_num, 0UL, "source num should be larger than 0");
for (size_t i = 0; i < step_num; ++i) {
PADDLE_ENFORCE_EQ(ids->at(i).lod().size(), 2UL,
"Level of LodTensor should be 2");
}
// prepare output
LoDTensor* sentenceIds = ctx.Output<LoDTensor>("SentenceIds");
LoDTensor* sentenceScores = ctx.Output<LoDTensor>("SentenceScores");
BeamSearchDecoder<float> beam_search_decoder;
beam_search_decoder.PackAllSteps(*ids, *scores, sentenceIds,
sentenceScores);
}
};
class BeamSearchDecodeOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
BeamSearchDecodeOpProtoMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ids",
"(LodTensorArray)"
"score of the candidate words in each step");
AddInput("Scores",
"(LodTensorArray)"
"score of the candidate words in each step");
AddOutput("SentenceIds",
"(LodTensor)"
"All possible result sentences of word ids");
AddOutput("SentenceScores",
"(LodTensor)"
"All possible result sentences of word scores");
AddComment(R"DOC(
Pack the result of Beam search op into SentenceIds and SentenceScores.
)DOC");
}
};
class BeamSearchDecodeInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext* context) const override {
PADDLE_ENFORCE(context->HasInput("Ids"),
"BeamSearchDecodeOp must has input Ids");
PADDLE_ENFORCE(context->HasInput("Scores"),
"BeamSearchDecodeOp must has input Scores");
PADDLE_ENFORCE(context->HasOutput("SentenceIds"),
"BeamSearchDecodeOp must has output SentenceIds");
PADDLE_ENFORCE(context->HasOutput("SentenceScores"),
"BeamSearchDecodeOp must has output SentenceScores");
}
};
class BeamSearchDecodeInferVarType : public framework::VarTypeInference {
public:
void operator()(const framework::OpDescBind& op_desc,
framework::BlockDescBind* block) const override {
for (auto& o : op_desc.Output("SentenceIds")) {
block->Var(o)->SetType(framework::VarDesc::LOD_TENSOR);
}
for (auto& o : op_desc.Output("SentenceScores")) {
block->Var(o)->SetType(framework::VarDesc::LOD_TENSOR);
}
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(beam_search_decode, paddle::operators::BeamSearchDecodeOp,
paddle::operators::BeamSearchDecodeOpProtoMaker,
paddle::operators::BeamSearchDecodeInferShape,
paddle::operators::BeamSearchDecodeInferVarType,
paddle::framework::EmptyGradOpMaker);
paddle/operators/beam_search_decode_op.h 0 → 100644
浏览文件 @ fc117ecf
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
using LoDTensorArray = framework::LoDTensorArray;
// all the lod have 2 levels.
// The First is source level, the second is sentence level.
// source level describe how many candidate words for this source.
// sentence level describe these candidates belong to which prefix
const size_t kSourceLevel = 0;
const size_t kSentenceLevel = 1;
template <typename T>
struct BeamNode {
BeamNode(int64_t word_id, T score) : word_id_(word_id), score_(score) {}
~BeamNode() {
if (parent_) {
parent_->DropKid(this);
if (parent_->kids_.size() == 0UL) {
delete parent_;
}
}
VLOG(3) << "Delete BeamNode root with word_id:" << this->word_id_;
}
void AppendTo(BeamNode* parent) {
parent_ = parent;
parent->kids_.insert(this);
}
void DropKid(BeamNode* kid) { kids_.erase(kid); }
BeamNode* parent_ = nullptr;
std::unordered_set<BeamNode*> kids_;
int64_t word_id_;
T score_;
};
template <typename T>
using BeamNodeVector = std::vector<std::unique_ptr<BeamNode<T>>>;
template <typename T>
struct Sentence {
std::vector<int64_t> word_ids;
std::vector<T> scores;
};
template <typename T>
using SentenceVector = std::vector<Sentence<T>>;
template <typename T>
struct BeamSearchDecoder {
/**
* make a BeamNode and all it's related prefix BeanNode into a Sentence.
*/
Sentence<T> MakeSentence(const BeamNode<T>* node) const;
/**
* Param:
* cur_ids: LoDTensor of One step for word ID
* cur_scores: LoDTensor of One Step for word score
* prefixes_list: prefixes for each source sentence.
* sentence_vector_list: result sentence_vector for each source sentence.
* Return:
* a new prefixes list for each source of current step
*/
std::vector<BeamNodeVector<T>> PackTwoSteps(
const LoDTensor& cur_ids, const LoDTensor& cur_scores,
std::vector<BeamNodeVector<T>>& prefixes_list,
std::vector<SentenceVector<T>>* sentence_vector_list) const;
/**
* convert the result sentence_vector for each source sentence into two
* LodTensor.
* One is all candidate sentences with word id, one is all candidate sentences
* with word score.
* Param:
* sentence_vector_list: sentence_vector for each source sentence.
* id_tensor: result LoDTensor for sentences of id.
* score_tensor: result LoDTensor for sentences of score.
*/
void ConvertSentenceVectorToLodTensor(
std::vector<SentenceVector<T>> sentence_vector_list, LoDTensor* id_tensor,
LoDTensor* score_tensor) const;
/**
* Pack all steps of id/score LodTensor into sentence LoDTensor
* it's main logic is:
* ```python
* prefix
* result_sentence
* result_lod_tensor
*
* for (step in steps):
* prefix = PackTwoSteps(prefix, step, &result_sentence)
* ConvertSentenceVector<T>ToLodTensor(result_sentence, &result_lod_tensor)
* ```
*/
void PackAllSteps(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores, LoDTensor* id_tensor,
LoDTensor* score_tensor) const;
};
template <typename T>
Sentence<T> BeamSearchDecoder<T>::MakeSentence(const BeamNode<T>* node) const {
Sentence<T> sentence;
while (node != nullptr) {
sentence.word_ids.emplace_back(node->word_id_);
sentence.scores.emplace_back(node->score_);
node = node->parent_;
}
std::reverse(std::begin(sentence.word_ids), std::end(sentence.word_ids));
std::reverse(std::begin(sentence.scores), std::end(sentence.scores));
return sentence;
}
template <typename T>
std::vector<BeamNodeVector<T>> BeamSearchDecoder<T>::PackTwoSteps(
const LoDTensor& cur_ids, const LoDTensor& cur_scores,
std::vector<BeamNodeVector<T>>& prefixes_list,
std::vector<SentenceVector<T>>* sentence_vector_list) const {
std::vector<BeamNodeVector<T>> result;
for (size_t src_idx = 0; src_idx < cur_ids.lod()[kSourceLevel].size() - 1;
++src_idx) {
size_t src_start = cur_ids.lod().at(kSourceLevel)[src_idx];
size_t src_end = cur_ids.lod().at(kSourceLevel)[src_idx + 1];
BeamNodeVector<T> beam_nodes;
// if prefixes size is 0, it means this is the first step. In this step,
// all candidate id is the start of candidate sentences.
if (prefixes_list.empty()) {
PADDLE_ENFORCE_EQ(cur_ids.lod().at(kSourceLevel).back(),
cur_ids.lod().at(kSentenceLevel).back(),
"in the first step");
for (size_t id_idx = src_start; id_idx < src_end; ++id_idx) {
beam_nodes.push_back(std::unique_ptr<BeamNode<T>>(new BeamNode<T>(
cur_ids.data<int64_t>()[id_idx], cur_scores.data<T>()[id_idx])));
}
} else {
BeamNodeVector<T>& prefixes = prefixes_list[src_idx];
SentenceVector<T>& sentence_vector = (*sentence_vector_list)[src_idx];
PADDLE_ENFORCE_EQ(src_end - src_start, prefixes.size(),
"prefix and candidate set number should be the same");
auto candidate_offset = cur_ids.lod()[kSentenceLevel];
for (size_t prefix_idx = 0; prefix_idx < prefixes.size(); ++prefix_idx) {
std::unique_ptr<BeamNode<T>>& prefix = prefixes[prefix_idx];
size_t candidate_start = candidate_offset[src_start + prefix_idx];
size_t candidate_end = candidate_offset[src_start + prefix_idx + 1];
if (candidate_start == candidate_end) {
VLOG(3) << "this sentence has no more candidate, "
"add to result sentence and rm it from beam tree";
sentence_vector.push_back(MakeSentence(prefix.get()));
prefix.reset();
} else {
for (size_t candidate_idx = candidate_start;
candidate_idx < candidate_end; ++candidate_idx) {
auto* candidate =
new BeamNode<T>(cur_ids.data<int64_t>()[candidate_idx],
cur_scores.data<T>()[candidate_idx]);
candidate->AppendTo(prefix.get());
beam_nodes.push_back(std::unique_ptr<BeamNode<T>>(candidate));
}
prefix.release();
}
}
}
result.push_back(std::move(beam_nodes));
}
return result;
}
template <typename T>
void BeamSearchDecoder<T>::ConvertSentenceVectorToLodTensor(
std::vector<SentenceVector<T>> sentence_vector_list, LoDTensor* id_tensor,
LoDTensor* score_tensor) const {
size_t src_num = sentence_vector_list.size();
PADDLE_ENFORCE_NE(src_num, 0, "src_num should not be 0");
std::vector<size_t> source_level_lod = {0};
std::vector<size_t> sentence_level_lod = {0};
std::vector<int64_t> id_data;
std::vector<T> score_data;
for (size_t src_idx = 0; src_idx < src_num; ++src_idx) {
for (Sentence<T>& sentence : sentence_vector_list[src_idx]) {
id_data.insert(id_data.end(), sentence.word_ids.begin(),
sentence.word_ids.end());
score_data.insert(score_data.end(), sentence.scores.begin(),
sentence.scores.end());
sentence_level_lod.push_back(sentence_level_lod.back() +
sentence.word_ids.size());
}
source_level_lod.push_back(source_level_lod.back() +
sentence_vector_list[src_idx].size());
}
auto cpu_place = new paddle::platform::CPUPlace();
paddle::platform::CPUDeviceContext cpu_ctx(*cpu_place);
framework::LoD lod;
lod.push_back(source_level_lod);
lod.push_back(sentence_level_lod);
id_tensor->set_lod(lod);
id_tensor->Resize({static_cast<int64_t>(id_data.size())});
id_tensor->mutable_data<int64_t>(paddle::platform::CPUPlace());
id_tensor->CopyFromVector<int64_t>(id_data, cpu_ctx);
score_tensor->set_lod(lod);
score_tensor->Resize({static_cast<int64_t>(score_data.size())});
score_tensor->mutable_data<T>(paddle::platform::CPUPlace());
score_tensor->CopyFromVector<T>(score_data, cpu_ctx);
}
template <typename T>
void BeamSearchDecoder<T>::PackAllSteps(const LoDTensorArray& step_ids,
const LoDTensorArray& step_scores,
LoDTensor* id_tensor,
LoDTensor* score_tensor) const {
PADDLE_ENFORCE(!step_ids.empty(), "step num should be larger than 0");
PADDLE_ENFORCE_EQ(step_ids.size(), step_scores.size(),
"step_ids and step_scores should be the same");
const size_t step_num = step_ids.size();
const size_t src_num = step_ids.at(0).lod().at(kSourceLevel).size() - 1;
PADDLE_ENFORCE_GT(src_num, 0UL, "source num should be larger than 0");
// previous prefixes for each step,
// the init length is 0, means this is the first step.
std::vector<BeamNodeVector<T>> beamnode_vector_list(0);
std::vector<SentenceVector<T>> sentence_vector_list(src_num);
// pack all steps for one batch first, then another batch
for (size_t step_id = 0; step_id < step_num; ++step_id) {
beamnode_vector_list =
PackTwoSteps(step_ids.at(step_id), step_scores.at(step_id),
beamnode_vector_list, &sentence_vector_list);
}
// append last beam_node to result
for (size_t src_idx = 0; src_idx < src_num; ++src_idx) {
for (auto& beam_node : beamnode_vector_list.at(src_idx)) {
sentence_vector_list[src_idx].push_back(MakeSentence(beam_node.get()));
beam_node.reset();
}
}
ConvertSentenceVectorToLodTensor(sentence_vector_list, id_tensor,
score_tensor);
}
} // namespace operators
} // namespace paddle
paddle/operators/beam_search_decode_op_test.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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_decode_op.h"
#include "gtest/gtest.h"
using CPUPlace = paddle::platform::CPUPlace;
using LoD = paddle::framework::LoD;
using LoDTensor = paddle::framework::LoDTensor;
using LoDTensorArray = paddle::framework::LoDTensorArray;
template <typename T>
using BeamNode = paddle::operators::BeamNode<T>;
template <typename T>
using BeamSearchDecoder = paddle::operators::BeamSearchDecoder<T>;
template <typename T>
using Sentence = paddle::operators::Sentence<T>;
template <typename T>
using BeamNodeVector = paddle::operators::BeamNodeVector<T>;
template <typename T>
using SentenceVector = paddle::operators::SentenceVector<T>;
namespace paddle {
namespace test {
void GenerateExample(const std::vector<size_t>& level_0,
const std::vector<size_t>& level_1,
const std::vector<int>& data, LoDTensorArray* ids,
LoDTensorArray* scores) {
PADDLE_ENFORCE_EQ(level_0.back(), level_1.size() - 1,
"source level is used to describe candidate set");
PADDLE_ENFORCE_EQ(level_1.back(), data.size(),
"the lowest level is used to describe data"
", so it's last element should be data length");
CPUPlace place;
LoD lod;
lod.push_back(level_0);
lod.push_back(level_1);
// Ids
LoDTensor tensor_id;
tensor_id.set_lod(lod);
tensor_id.Resize({static_cast<int64_t>(data.size())});
// malloc memory
int64_t* id_ptr = tensor_id.mutable_data<int64_t>(place);
for (size_t i = 0; i < data.size(); ++i) {
id_ptr[i] = static_cast<int64_t>(data.at(i));
}
// Scores
LoDTensor tensor_score;
tensor_score.set_lod(lod);
tensor_score.Resize({static_cast<int64_t>(data.size())});
// malloc memory
float* score_ptr = tensor_score.mutable_data<float>(place);
for (size_t i = 0; i < data.size(); ++i) {
score_ptr[i] = static_cast<float>(data.at(i));
}
ids->push_back(tensor_id);
scores->push_back(tensor_score);
}
} // namespace test
} // namespace paddle
TEST(BeamSearchDecodeOp, DeleteBeamNode) {
auto* root = new BeamNode<float>(0, 0);
auto* b1 = new BeamNode<float>(1, 1);
auto* b2 = new BeamNode<float>(2, 2);
auto* b3 = new BeamNode<float>(3, 3);
b1->AppendTo(root);
b2->AppendTo(root);
b3->AppendTo(b1);
delete b3;
delete b2;
}
TEST(BeamSearchDecodeOp, MakeSentence) {
auto* root = new BeamNode<float>(0, 0);
auto* b1 = new BeamNode<float>(1, 1);
auto* end = new BeamNode<float>(2, 2);
b1->AppendTo(root);
end->AppendTo(b1);
BeamSearchDecoder<float> helper;
Sentence<float> sentence = helper.MakeSentence(end);
delete end;
std::vector<int64_t> expect_ids = {0, 1, 2};
ASSERT_EQ(sentence.word_ids, expect_ids);
std::vector<float> expect_scores = {0, 1, 2};
ASSERT_EQ(sentence.scores, expect_scores);
}
TEST(BeamSearchDecodeOp, PackTwoStepsFistStep) {
CPUPlace place;
LoDTensorArray ids;
LoDTensorArray scores;
paddle::test::GenerateExample(
std::vector<size_t>{0, 2, 6}, std::vector<size_t>{0, 1, 2, 3, 4, 5, 6},
std::vector<int>{1, 2, 3, 4, 5, 6}, &ids, &scores);
std::vector<BeamNodeVector<float>> beamnode_vector_list;
std::vector<SentenceVector<float>> sentence_vector_list(
2, SentenceVector<float>());
BeamSearchDecoder<float> helper;
beamnode_vector_list = helper.PackTwoSteps(
ids[0], scores[0], beamnode_vector_list, &sentence_vector_list);
ASSERT_EQ(beamnode_vector_list.size(), 2UL);
ASSERT_EQ(beamnode_vector_list[0].size(), 2UL);
ASSERT_EQ(beamnode_vector_list[1].size(), 4UL);
}
TEST(BeamSearchDecodeOp, PackTwoSteps) {
CPUPlace place;
// first source has three prefix
BeamNodeVector<float> source0_prefixes;
source0_prefixes.push_back(
std::unique_ptr<BeamNode<float>>(new BeamNode<float>(1, 1)));
source0_prefixes.push_back(
std::unique_ptr<BeamNode<float>>(new BeamNode<float>(0, 0)));
source0_prefixes.push_back(
std::unique_ptr<BeamNode<float>>(new BeamNode<float>(3, 3)));
// second source has two prefix
BeamNodeVector<float> source1_prefixes;
source1_prefixes.push_back(
std::unique_ptr<BeamNode<float>>(new BeamNode<float>(4, 4)));
source1_prefixes.push_back(
std::unique_ptr<BeamNode<float>>(new BeamNode<float>(5, 5)));
std::vector<BeamNodeVector<float>> beamnode_vector_list;
std::vector<SentenceVector<float>> sentence_vector_list(
2, SentenceVector<float>());
beamnode_vector_list.push_back(std::move(source0_prefixes));
beamnode_vector_list.push_back(std::move(source1_prefixes));
// generate data for one step
LoDTensorArray ids;
LoDTensorArray scores;
paddle::test::GenerateExample(std::vector<size_t>{0, 3, 5},
std::vector<size_t>{0, 1, 1, 3, 4, 5},
std::vector<int>{0, 1, 2, 3, 4}, &ids, &scores);
BeamSearchDecoder<float> helper1;
beamnode_vector_list = helper1.PackTwoSteps(
ids[0], scores[0], beamnode_vector_list, &sentence_vector_list);
ASSERT_EQ(sentence_vector_list[0].size(), 1UL);
ASSERT_EQ(sentence_vector_list[1].size(), 0UL);
ASSERT_EQ(beamnode_vector_list[0].size(), 3UL);
ASSERT_EQ(beamnode_vector_list[1].size(), 2UL);
}
TEST(BeamSearchDecodeOp, PackAllSteps) {
CPUPlace place;
// we will constuct a sample data with 3 steps and 2 source sentences
LoDTensorArray ids;
LoDTensorArray scores;
paddle::test::GenerateExample(
std::vector<size_t>{0, 3, 6}, std::vector<size_t>{0, 1, 2, 3, 4, 5, 6},
std::vector<int>{1, 2, 3, 4, 5, 6}, &ids, &scores);
paddle::test::GenerateExample(
std::vector<size_t>{0, 3, 6}, std::vector<size_t>{0, 1, 1, 3, 5, 5, 6},
std::vector<int>{0, 1, 2, 3, 4, 5}, &ids, &scores);
paddle::test::GenerateExample(std::vector<size_t>{0, 3, 6},
std::vector<size_t>{0, 0, 1, 2, 3, 4, 5},
std::vector<int>{0, 1, 2, 3, 4}, &ids, &scores);
ASSERT_EQ(ids.size(), 3UL);
ASSERT_EQ(scores.size(), 3UL);
BeamSearchDecoder<float> helper;
LoDTensor id_tensor;
LoDTensor score_tensor;
helper.PackAllSteps(ids, scores, &id_tensor, &score_tensor);
LoD lod = id_tensor.lod();
std::vector<size_t> expect_source_lod = {0, 4, 8};
EXPECT_EQ(lod[0], expect_source_lod);
std::vector<size_t> expect_sentence_lod = {0, 1, 3, 6, 9, 10, 13, 16, 19};
EXPECT_EQ(lod[1], expect_sentence_lod);
// 2| 1, 0| 3, 1, 0| 3, 2, 1| 5| 4, 3, 2| 4, 4, 3| 6, 5, 4
std::vector<int> expect_data = {2, 1, 0, 3, 1, 0, 3, 2, 1, 5,
4, 3, 2, 4, 4, 3, 6, 5, 4};
ASSERT_EQ(id_tensor.dims()[0], static_cast<int64_t>(expect_data.size()));
for (size_t i = 0; i < expect_data.size(); ++i) {
ASSERT_EQ(id_tensor.data<int64_t>()[i],
static_cast<int64_t>(expect_data[i]));
}
for (int64_t i = 0; i < id_tensor.dims()[0]; ++i) {
ASSERT_EQ(score_tensor.data<float>()[i],
static_cast<float>(id_tensor.data<int64_t>()[i]));
}
}
paddle/operators/bilinear_tensor_product_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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/bilinear_tensor_product_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class BilinearTensorProductOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(Weight) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto weight_dims = ctx->GetInputDim("Weight");
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "The input(X) must be a 2D Tensor.");
PADDLE_ENFORCE_EQ(y_dims.size(), 2UL, "The input(Y) must be a 2D Tensor.");
PADDLE_ENFORCE_EQ(weight_dims.size(), 3UL,
"The input(Weight) must be a 3D tensor.");
PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0],
"The first dimension(batch_size) of input(X) must be "
"equal to the first dimension of the input(Y).");
PADDLE_ENFORCE_EQ(x_dims[1], weight_dims[1],
"The second dimension of input(X) must be equal to "
"the second dimension of the input(Weight).");
PADDLE_ENFORCE_EQ(y_dims[1], weight_dims[2],
"The second dimension of input(Y) must be equal to "
"the third dimension of the input(Weight).");
if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE(bias_dims.size() == 2UL && bias_dims[0] == 1UL,
"The Input(Bias) must be a 2-D tensor with "
"the 2nd dimension fixed to 1 (a row vector).");
PADDLE_ENFORCE_EQ(bias_dims[1], weight_dims[0],
"The second dimension of input(Bias) must be equal "
"to the first dimension of the input(Weight).");
}
ctx->SetOutputDim("Out", {x_dims[0], weight_dims[0]});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class BilinearTensorProductOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BilinearTensorProductOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of bilinear_tensor_product operator.");
AddInput("Y", "The second input of bilinear_tensor_product operator.");
AddInput("Weight",
"The learnable parameters of bilinear_tensor_product operator.");
AddInput("Bias", "The learnable bias of bilinear_tensor_product operator.")
.AsDispensable();
AddOutput("Out", "The output of bilinear_tensor_product operator.");
AddComment(R"DOC(
Bilinear Tensor Product operator.
Given input X and Y, a 3D tensor weight, and bias. Each column of the
output is computed by one slice i = 1, . . . , k of the tensor:
M = (X W_i) \cdot Y
Out_i = \sum_i {M_i} + Bias_i
)DOC");
}
};
class BilinearTensorProductOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(Weight) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto weight_dims = ctx->GetInputDim("Weight");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(out_dims.size(), 2UL,
"The input(Out@GRAD) must be a 2D Tensor.");
PADDLE_ENFORCE_EQ(
x_dims[0], out_dims[0],
"The first dimension(batch_size) of input(Out@GRAD) must be "
"equal to the first dimension of the Input(X).");
PADDLE_ENFORCE_EQ(
weight_dims[0], out_dims[1],
"The second dimension of input(Out@GRAD) must be equal to "
"the third dimension of the Input(Weight).");
if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(
bias_dims[1], out_dims[1],
"The second dimension of input(Out@GRAD) must be equal to "
"the second dimension of the Input(Bias).");
auto bias_grad_name = framework::GradVarName("Bias");
if (ctx->HasOutput(bias_grad_name))
ctx->SetOutputDim(bias_grad_name, bias_dims);
}
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
auto weight_grad_name = framework::GradVarName("Weight");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
if (ctx->HasOutput(weight_grad_name)) {
ctx->SetOutputDim(weight_grad_name, weight_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(bilinear_tensor_product, ops::BilinearTensorProductOp,
ops::BilinearTensorProductOpMaker, bilinear_tensor_product_grad,
ops::BilinearTensorProductOpGrad);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product,
ops::BilinearTensorProductKernel<paddle::platform::CPUPlace, float>,
ops::BilinearTensorProductKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, float>,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/fill_constant_op.h → paddle/operators/bilinear_tensor_product_op.cu
浏览文件 @ fc117ecf
......@@ -12,26 +12,15 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class FillConstantOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* out = ctx.Output<framework::Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto value = ctx.Attr<float>("value");
auto out_eigen = framework::EigenVector<T>::Flatten(*out);
auto place = ctx.GetEigenDevice<Place>();
out_eigen.device(place) = out_eigen.constant(static_cast<T>(value));
}
};
} // namespace operators
} // namespace paddle
#define EIGEN_USE_GPU
#include "paddle/operators/bilinear_tensor_product_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
bilinear_tensor_product,
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, float>,
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, float>,
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/bilinear_tensor_product_op.h 0 → 100644
浏览文件 @ fc117ecf
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
using framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class BilinearTensorProductKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Input<Tensor>("Y");
auto* weight = ctx.Input<Tensor>("Weight");
auto* bias = ctx.Input<Tensor>("Bias");
auto* out = ctx.Output<Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
auto y_mat = EigenMatrix<T>::From(*y);
auto output_mat = EigenMatrix<T>::From(*out);
auto batch_size = x->dims()[0];
auto weight_dims = weight->dims();
int out_dim = weight_dims[0];
auto x_dim = weight_dims[1];
auto y_dim = weight_dims[2];
auto place = ctx.GetEigenDevice<Place>();
// Create the intermediate variable to caculate the result of
// Input(X) multiplied by Input(Weight_i), the formula is:
// left_mul = X Weight_i.
Tensor left_mul;
left_mul.mutable_data<T>(framework::make_ddim({batch_size, y_dim}),
ctx.GetPlace());
auto left_mul_mat = EigenMatrix<T>::From(left_mul);
for (int i = 0; i < out_dim; ++i) {
auto output_col_vec = output_mat.chip(i, 1);
Tensor weight_mat =
weight->Slice(i, i + 1).Resize(framework::make_ddim({x_dim, y_dim}));
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
batch_size, y_dim, x_dim, 1, x->data<T>(),
weight_mat.data<T>(), 0, left_mul.data<T>());
output_col_vec.device(place) =
(left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1));
}
if (bias) {
auto bias_vec = EigenMatrix<T>::From(*bias);
Eigen::DSizes<int, 2> bcast(batch_size, 1);
output_mat.device(place) = bias_vec.broadcast(bcast) + output_mat;
}
}
};
template <typename Place, typename T>
class BilinearTensorProductGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const Tensor* x = ctx.Input<Tensor>("X");
const Tensor* y = ctx.Input<Tensor>("Y");
const Tensor* weight = ctx.Input<Tensor>("Weight");
Tensor* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
Tensor* d_y = ctx.Output<Tensor>(framework::GradVarName("Y"));
Tensor* d_weight = ctx.Output<Tensor>(framework::GradVarName("Weight"));
Tensor* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const Tensor* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto batch_size = x->dims()[0];
auto weight_dims = weight->dims();
int out_dim = weight_dims[0];
auto x_dim = weight_dims[1];
auto y_dim = weight_dims[2];
auto x_mat = EigenMatrix<T>::From(*x);
auto y_mat = EigenMatrix<T>::From(*y);
auto d_out_mat = EigenMatrix<T>::From(*d_out);
auto place = ctx.GetEigenDevice<Place>();
// Create the intermediate variable to caculate the Output(Y@Grad).
Tensor x_scale;
x_scale.mutable_data<T>(framework::make_ddim({batch_size, x_dim}),
ctx.GetPlace());
auto x_scale_mat = EigenMatrix<T>::From(x_scale);
// Create the intermediate variable to caculate the Output(X@Grad).
Tensor y_scale;
y_scale.mutable_data<T>(framework::make_ddim({batch_size, y_dim}),
ctx.GetPlace());
auto y_scale_mat = EigenMatrix<T>::From(y_scale);
math::SetConstant<Place, T> set_zero;
// Set Output(X@Grad) be zero.
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
set_zero(ctx.device_context(), d_x, static_cast<T>(0));
}
// Set Output(Y@Grad) be zero.
if (d_y) {
d_y->mutable_data<T>(ctx.GetPlace());
set_zero(ctx.device_context(), d_y, static_cast<T>(0));
}
// Caculate the Output(X@Grad) and Output(Y@Grad).
if (d_x || d_y) {
Eigen::DSizes<int, 2> bcast_for_x(1, y_dim);
Eigen::DSizes<int, 2> bcast_for_y(1, x_dim);
for (int i = 0; i < out_dim; ++i) {
Tensor weight_i = weight->Slice(i, i + 1).Resize(
framework::make_ddim({x_dim, y_dim}));
auto output_vec = d_out_mat.chip(i, 1);
if (d_x) {
y_scale_mat.device(place) =
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_x) *
y_mat;
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans,
batch_size, x_dim, y_dim, 1, y_scale.data<T>(),
weight_i.data<T>(), 1, d_x->data<T>());
}
if (d_y) {
x_scale_mat.device(place) =
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_y) *
x_mat;
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
batch_size, y_dim, x_dim, 1, x_scale.data<T>(),
weight_i.data<T>(), 1, d_y->data<T>());
}
}
}
// Caculate the gradient of Input(Weight).
if (d_weight) {
d_weight->mutable_data<T>(ctx.GetPlace());
Eigen::DSizes<int, 2> bcast_for_weight(1, x_dim);
for (int i = 0; i < out_dim; ++i) {
Tensor d_weight_i = d_weight->Slice(i, i + 1).Resize(
framework::make_ddim({x_dim, y_dim}));
auto output_vec = d_out_mat.chip(i, 1);
x_scale_mat.device(place) =
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1))
.broadcast(bcast_for_weight) *
x_mat;
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans,
x_dim, y_dim, batch_size, 1, x_scale.data<T>(),
y->data<T>(), 0, d_weight_i.data<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);
d_bias_mat.device(place) = d_out_mat.sum(Eigen::DSizes<int, 1>(0));
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/chunk_eval_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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/chunk_eval_op.h"
namespace paddle {
namespace operators {
class ChunkEvalOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Inference"),
"Input(Inference) of ChunkEvalOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"),
"Input(Label) of ChunkEvalOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Precision"),
"Output(Precision) of ChunkEvalOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Recall"),
"Output(Recall) of ChunkEvalOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("F1-Score"),
"Output(F1-Score) of ChunkEvalOp should not be null.");
auto inference_dim = ctx->GetInputDim("Inference");
auto label_dim = ctx->GetInputDim("Label");
PADDLE_ENFORCE(inference_dim == label_dim,
"Inference's shape must be the same as Label's shape.");
ctx->SetOutputDim("Precision", {1});
ctx->SetOutputDim("Recall", {1});
ctx->SetOutputDim("F1-Score", {1});
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(framework::DataType::FP32,
ctx.device_context());
}
};
class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ChunkEvalOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Inference",
"(Tensor, default: Tensor<int>). Predictions from the network.");
AddInput("Label",
"(Tensor, default: Tensor<int>). The true tag sequences.");
AddOutput("Precision",
"(float). The evaluated precision (called positive predictive "
"value) of chunks on the given mini-batch.");
AddOutput("Recall",
"(float). The evaluated recall (true positive rate or "
"sensitivity) of chunks on the given mini-batch.");
AddOutput("F1-Score",
"(float). The evaluated F1-Score on the given mini-batch.");
AddAttr<int>("num_chunk_types",
"(int). The number of chunk type. See below for details.");
AddAttr<std::string>(
"chunk_scheme",
"(string, default IOB). The labeling scheme indicating "
"how to encode the chunks. Must be IOB, IOE, IOBES or plain. See below "
"for details.")
.SetDefault("IOB");
AddAttr<std::vector<int>>("excluded_chunk_types",
"(list<int>) A list including chunk type ids "
"indicating chunk types that are not counted. "
"See below for details.")
.SetDefault(std::vector<int>{});
AddComment(R"DOC(
For some basics of chunking, please refer to
‘Chunking with Support Vector Mechines <https://aclanthology.info/pdf/N/N01/N01-1025.pdf>’.
CheckEvalOp computes the precision, recall, and F1-score of chunk detection,
and supports IOB, IOE, IOBES and IO (also known as plain) tagging schemes.
Here is a NER example of labeling for these tagging schemes:
Li Ming works at Agricultural Bank of China in Beijing.
IO: I-PER I-PER O O I-ORG I-ORG I-ORG I-ORG O I-LOC
IOB: B-PER I-PER O O B-ORG I-ORG I-ORG I-ORG O B-LOC
IOE: I-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O E-LOC
IOBES: B-PER E-PER O O I-ORG I-ORG I-ORG E-ORG O S-LOC
There are three chunk types(named entity types) including PER(person), ORG(orgnazation)
and LOC(LOCATION), and we can see that the labels have the form <tag type>-<chunk type>.
Since the calculations actually use label ids rather than labels, extra attention
should be paid when mapping labels to ids to make CheckEvalOp work. The key point
is that the listed equations are satisfied by ids.
tag_type = label % num_tag_type
chunk_type = label / num_tag_type
where `num_tag_type` is the num of tag types in the tagging scheme, `num_chunk_type`
is the num of chunk types, and `tag_type` get its value from the following table.
Scheme Begin Inside End Single
plain 0 - - -
IOB 0 1 - -
IOE - 0 1 -
IOBES 0 1 2 3
Still use NER as example, assuming the tagging scheme is IOB while chunk types are ORG,
PER and LOC. To satisfy the above equations, the label map can be like this:
B-ORG 0
I-ORG 1
B-PER 2
I-PER 3
B-LOC 4
I-LOC 5
O 6
It’s not hard to verify the equations noting that the num of chunk types
is 3 and the num of tag types in IOB scheme is 2. For example, the label
id of I-LOC is 5, the tag type id of I-LOC is 1, and the chunk type id of
I-LOC is 2, which consistent with the results from the equations.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(chunk_eval, ops::ChunkEvalOp,
ops::ChunkEvalOpMaker);
REGISTER_OP_CPU_KERNEL(chunk_eval,
ops::ChunkEvalKernel<paddle::platform::CPUPlace, float>);
paddle/operators/chunk_eval_op.h 0 → 100644
浏览文件 @ fc117ecf
/* 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 <set>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename Place, typename T>
class ChunkEvalKernel : public framework::OpKernel<T> {
public:
struct Segment {
int begin;
int end;
int type;
bool operator==(const Segment& y) const {
return begin == y.begin && end == y.end && type == y.type;
}
};
void GetSegments(const int* label, int length, std::vector<Segment>& segments,
int num_chunk_types, int num_tag_types, int other_chunk_type,
int tag_begin, int tag_inside, int tag_end,
int tag_single) const {
segments.clear();
segments.reserve(length);
int chunk_start = 0;
bool in_chunk = false;
int tag = -1;
int type = other_chunk_type;
for (int i = 0; i < length; ++i) {
int prev_tag = tag;
int prev_type = type;
PADDLE_ENFORCE_LE(label[i], num_chunk_types * num_tag_types);
tag = label[i] % num_tag_types;
type = label[i] / num_tag_types;
if (in_chunk && ChunkEnd(prev_tag, prev_type, tag, type, other_chunk_type,
tag_begin, tag_inside, tag_end, tag_single)) {
Segment segment{
chunk_start, // begin
i - 1, // end
prev_type,
};
segments.push_back(segment);
in_chunk = false;
}
if (ChunkBegin(prev_tag, prev_type, tag, type, other_chunk_type,
tag_begin, tag_inside, tag_end, tag_single)) {
chunk_start = i;
in_chunk = true;
}
}
if (in_chunk) {
Segment segment{
chunk_start, // begin
length - 1, // end
type,
};
segments.push_back(segment);
}
}
bool ChunkEnd(int prev_tag, int prev_type, int tag, int type,
int other_chunk_type, int tag_begin, int tag_inside,
int tag_end, int tag_single) const {
if (prev_type == other_chunk_type) return false;
if (type == other_chunk_type) return true;
if (type != prev_type) return true;
if (prev_tag == tag_begin) return tag == tag_begin || tag == tag_single;
if (prev_tag == tag_inside) return tag == tag_begin || tag == tag_single;
if (prev_tag == tag_end) return true;
if (prev_tag == tag_single) return true;
return false;
}
bool ChunkBegin(int prev_tag, int prev_type, int tag, int type,
int other_chunk_type, int tag_begin, int tag_inside,
int tag_end, int tag_single) const {
if (prev_type == other_chunk_type) return type != other_chunk_type;
if (type == other_chunk_type) return false;
if (type != prev_type) return true;
if (tag == tag_begin) return true;
if (tag == tag_inside) return prev_tag == tag_end || prev_tag == tag_single;
if (tag == tag_end) return prev_tag == tag_end || prev_tag == tag_single;
if (tag == tag_single) return true;
return false;
}
void Compute(const framework::ExecutionContext& context) const override {
// initialize to parse configurations
int num_chunk_types, num_tag_types;
int other_chunk_type;
int tag_begin, tag_inside, tag_end, tag_single;
std::vector<Segment> label_segments;
std::vector<Segment> output_segments;
std::set<int> excluded_chunk_types;
int64_t num_output_segments = 0;
int64_t num_label_segments = 0;
int64_t num_correct = 0;
if (context.Attr<std::string>("chunk_scheme") == "IOB") {
num_tag_types = 2;
tag_begin = 0;
tag_inside = 1;
tag_end = -1;
tag_single = -1;
} else if (context.Attr<std::string>("chunk_scheme") == "IOE") {
num_tag_types = 2;
tag_begin = -1;
tag_inside = 0;
tag_end = 1;
tag_single = -1;
} else if (context.Attr<std::string>("chunk_scheme") == "IOBES") {
num_tag_types = 4;
tag_begin = 0;
tag_inside = 1;
tag_end = 2;
tag_single = 3;
} else if (context.Attr<std::string>("chunk_scheme") == "plain") {
num_tag_types = 1;
tag_begin = -1;
tag_inside = -1;
tag_end = -1;
tag_single = -1;
} else {
PADDLE_THROW("Unknown chunk scheme.");
}
other_chunk_type = num_chunk_types = context.Attr<int>("num_chunk_types");
excluded_chunk_types.insert(
context.Attr<std::vector<int>>("excluded_chunk_types").begin(),
context.Attr<std::vector<int>>("excluded_chunk_types").end());
auto* inference = context.Input<LoDTensor>("Inference");
auto* label = context.Input<LoDTensor>("Label");
auto* precision = context.Output<Tensor>("Precision");
auto* recall = context.Output<Tensor>("Recall");
auto* f1 = context.Output<Tensor>("F1-Score");
const int* inference_data = inference->data<int>();
const int* label_data = label->data<int>();
T* precision_data = precision->mutable_data<T>(context.GetPlace());
T* racall_data = recall->mutable_data<T>(context.GetPlace());
T* f1_data = f1->mutable_data<T>(context.GetPlace());
auto lod = label->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE(lod == inference->lod(),
"LoD must be same between Inference and Label.");
int num_sequences = lod[0].size() - 1;
for (int i = 0; i < num_sequences; ++i) {
int seq_length = lod[0][i + 1] - lod[0][i];
EvalOneSeq(inference_data + lod[0][i], label_data + lod[0][i], seq_length,
output_segments, label_segments, num_output_segments,
num_label_segments, num_correct, num_chunk_types,
num_tag_types, other_chunk_type, tag_begin, tag_inside,
tag_end, tag_single, excluded_chunk_types);
}
*precision_data = !num_output_segments ? 0 : static_cast<T>(num_correct) /
num_output_segments;
*racall_data = !num_label_segments ? 0 : static_cast<T>(num_correct) /
num_label_segments;
*f1_data = !num_correct ? 0 : 2 * (*precision_data) * (*racall_data) /
((*precision_data) + (*racall_data));
}
void EvalOneSeq(const int* output, const int* label, int length,
std::vector<Segment>& output_segments,
std::vector<Segment>& label_segments,
int64_t& num_output_segments, int64_t& num_label_segments,
int64_t& num_correct, int num_chunk_types, int num_tag_types,
int other_chunk_type, int tag_begin, int tag_inside,
int tag_end, int tag_single,
const std::set<int>& excluded_chunk_types) const {
GetSegments(output, length, output_segments, num_chunk_types, num_tag_types,
other_chunk_type, tag_begin, tag_inside, tag_end, tag_single);
GetSegments(label, length, label_segments, num_chunk_types, num_tag_types,
other_chunk_type, tag_begin, tag_inside, tag_end, tag_single);
size_t i = 0, j = 0;
while (i < output_segments.size() && j < label_segments.size()) {
if (output_segments[i] == label_segments[j] &&
excluded_chunk_types.count(output_segments[i].type) != 1) {
++num_correct;
}
if (output_segments[i].end < label_segments[j].end) {
++i;
} else if (output_segments[i].end > label_segments[j].end) {
++j;
} else {
++i;
++j;
}
}
for (auto& segment : label_segments) {
if (excluded_chunk_types.count(segment.type) != 1) ++num_label_segments;
}
for (auto& segment : output_segments) {
if (excluded_chunk_types.count(segment.type) != 1) ++num_output_segments;
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/clip_by_norm_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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/clip_by_norm_op.h"
namespace paddle {
namespace operators {
class ClipByNormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ClipByNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ClipByNormOp should not be null.");
auto max_norm = ctx->Attrs().Get<float>("max_norm");
PADDLE_ENFORCE_GT(max_norm, 0, "max_norm should be greater than 0.");
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class ClipByNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ClipByNormOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input of clip_by_norm op."
"The number of dimensions must be between [1, 9].");
AddOutput("Out",
"(Tensor) The output of clip_by_norm op with shape as input(X)");
AddAttr<float>("max_norm", "(float) The maximum norm value.");
AddComment(R"DOC(
ClipByNorm operator limits the L2 norm of the input 'X' within 'max_norm'.
If the L2 norm of 'X' is less than or equal to 'max_norm', 'Out' will be
the same as 'X'. If the L2 norm of 'X' is greater than 'max_norm', 'X' will
be linearly scaled to make the L2 norm of 'Out' equal to 'max_norm', as
shown in the following formula：
'Out' = 'max_norm' * 'X' / norm('X'),
where norm('X') represents the L2 norm of 'X'.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(clip_by_norm, ops::ClipByNormOp,
ops::ClipByNormOpMaker);
REGISTER_OP_CPU_KERNEL(
clip_by_norm, ops::ClipByNormKernel<paddle::platform::CPUPlace, float>);
paddle/operators/increment_op.cu → paddle/operators/clip_by_norm_op.cu
浏览文件 @ fc117ecf
......@@ -12,11 +12,8 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/increment_op.h"
#include "paddle/operators/clip_by_norm_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
increment,
paddle::operators::IncrementKernel<paddle::platform::GPUPlace, float>,
paddle::operators::IncrementKernel<paddle::platform::GPUPlace, double>,
paddle::operators::IncrementKernel<paddle::platform::GPUPlace, int>,
paddle::operators::IncrementKernel<paddle::platform::GPUPlace, int64_t>);
clip_by_norm, ops::ClipByNormKernel<paddle::platform::GPUPlace, float>);
paddle/operators/increment_op.h → paddle/operators/clip_by_norm_op.h
浏览文件 @ fc117ecf
......@@ -16,23 +16,35 @@
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/platform/transform.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class IncrementKernel : public framework::OpKernel<T> {
public:
virtual void Compute(const framework::ExecutionContext& context) const {
auto* tensor = context.Output<framework::Tensor>("Out");
auto* in = context.Input<framework::Tensor>("X");
tensor->mutable_data<T>(in->place());
auto step = static_cast<T>(context.Attr<float>("step"));
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
auto eigen_in = framework::EigenVector<T>::Flatten(*in);
auto& place = context.GetEigenDevice<Place>();
eigen_out.device(place) = eigen_in + step;
template <typename Place, typename T>
class ClipByNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto max_norm = context.Attr<T>("max_norm");
auto* input = context.Input<Tensor>("X");
auto* output = context.Output<Tensor>("Out");
output->mutable_data<T>(context.GetPlace());
auto x = EigenVector<T>::Flatten(*input);
auto out = EigenVector<T>::Flatten(*output);
auto x_norm = x.square().sum().sqrt();
auto place = context.GetEigenDevice<Place>();
auto temp = (x_norm <= max_norm).template cast<T>().eval();
auto scaling = temp + (static_cast<T>(1) - temp) * max_norm / x_norm;
Eigen::array<int, 1> one_dim{{1}};
Eigen::DSizes<int, 1> m_dsize(input->numel());
out.device(place) = x * scaling.reshape(one_dim).broadcast(m_dsize);
}
};
......
paddle/operators/compare_op.cc
浏览文件 @ fc117ecf
......@@ -14,6 +14,7 @@
#include "paddle/operators/compare_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
template <typename OpComment>
......@@ -61,19 +62,34 @@ class CompareOpInferShape : public framework::InferShapeBase {
}
};
class CompareOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
framework::OpKernelType kt = OperatorWithKernel::GetKernelType(ctx);
// CompareOp kernel's device type is decided by input tensor place
kt.place_ = ctx.Input<framework::LoDTensor>("X")->place();
return kt;
}
};
} // namespace operators
} // namespace paddle
#define REGISTER_LOGICAL_OP(op_type, _equation) \
struct _##op_type##Comment { \
static char type[]; \
static char equation[]; \
}; \
char _##op_type##Comment::type[]{#op_type}; \
char _##op_type##Comment::equation[]{_equation}; \
REGISTER_OP_WITH_KERNEL( \
op_type, ::paddle::operators::CompareOpProtoMaker<_##op_type##Comment>, \
::paddle::operators::CompareOpInferShape<_##op_type##Comment>, \
#define REGISTER_LOGICAL_OP(op_type, _equation) \
struct _##op_type##Comment { \
static char type[]; \
static char equation[]; \
}; \
char _##op_type##Comment::type[]{#op_type}; \
char _##op_type##Comment::equation[]{_equation}; \
REGISTER_OPERATOR( \
op_type, ::paddle::operators::CompareOp, \
::paddle::operators::CompareOpProtoMaker<_##op_type##Comment>, \
::paddle::operators::CompareOpInferShape<_##op_type##Comment>, \
::paddle::framework::EmptyGradOpMaker);
REGISTER_LOGICAL_OP(less_than, "Out = X < Y");
......
paddle/operators/conditional_block_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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. */
#include <algorithm>
#include "paddle/framework/executor.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
class ConditionalOp : public framework::OperatorBase {
public:
ConditionalOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
protected:
std::vector<const framework::LoDTensor *> InputTensors(
const framework::Scope &scope) const {
std::vector<const framework::LoDTensor *> retv;
auto xs = Inputs("X");
retv.resize(xs.size(), nullptr);
std::transform(
xs.begin(), xs.end(), retv.begin(),
[&scope](const std::string &var_name) -> const framework::LoDTensor * {
auto *var = scope.FindVar(var_name);
PADDLE_ENFORCE(var != nullptr, "Cannot find variable %s", var_name);
return &var->Get<framework::LoDTensor>();
});
return retv;
}
};
class ConditionalBlockOp : public ConditionalOp {
public:
ConditionalBlockOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ConditionalOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto xs = InputTensors(scope);
bool need_run = std::all_of(
xs.begin(), xs.end(),
[](const framework::LoDTensor *t) { return t->numel() != 0; });
if (need_run) {
auto *scope_var = scope.FindVar(Output("Scope"));
PADDLE_ENFORCE(scope_var != nullptr, "Must set scope");
auto *scopes = scope_var->GetMutable<std::vector<framework::Scope *>>();
scopes->resize(1);
scopes->front() = &scope.NewScope();
auto &cur_scope = *scopes->front();
auto *block = Attr<framework::BlockDescBind *>("block");
framework::Executor exec(dev_ctx);
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
}
}
};
class ConditionalBlockOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
ConditionalBlockOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The conditional variable of this operator. If X is empty, the "
"whole sub-block will not be executed.")
.AsDuplicable();
AddInput("Params", "The input variables of the sub-block.").AsDuplicable();
AddOutput("Out", "The output variables of the sub-block.").AsDuplicable();
AddOutput("Scope",
"(std::vector<Scope*>) The step scope of conditional block. To "
"unify the conditional block, rnn and while op, the type of "
"scope is std::vector<Scope*>");
AddAttr<framework::BlockDescBind *>(
"block", "The step block of conditional block operator");
AddComment(R"DOC(Conditional block operator
Run the sub-block if X is not empty. Params is the other inputs and Out is the
outputs of the sub-block.
)DOC");
}
};
class ConditionalBlockGradOp : public ConditionalOp {
public:
ConditionalBlockGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ConditionalOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto xs = this->InputTensors(scope);
bool need_run = std::all_of(
xs.begin(), xs.end(),
[](const framework::LoDTensor *t) { return t->numel() != 0; });
if (need_run) {
auto *scope_var = scope.FindVar(Input("Scope"));
PADDLE_ENFORCE(scope_var != nullptr, "Must set scope");
auto &scopes = scope_var->Get<std::vector<framework::Scope *>>();
framework::Scope &cur_scope = *scopes[0];
auto *block = Attr<framework::BlockDescBind *>("block");
framework::Executor exec(dev_ctx);
exec.Run(*block->Program(), &cur_scope, block->ID(), false);
AssignLocalGradientToGlobal(dev_ctx, cur_scope, Inputs("Params"),
Outputs(framework::GradVarName("Params")));
AssignLocalGradientToGlobal(dev_ctx, cur_scope, Inputs("X"),
Outputs(framework::GradVarName("X")));
}
}
private:
void AssignLocalGradientToGlobal(
const platform::DeviceContext &dev_ctx, const framework::Scope &cur_scope,
const std::vector<std::string> &p_names,
const std::vector<std::string> &pg_names) const {
for (size_t i = 0; i < p_names.size(); ++i) {
auto out_grad_name = pg_names[i];
auto in_grad_name = framework::GradVarName(p_names[i]);
auto *in_var = cur_scope.FindVar(in_grad_name);
if (in_var == nullptr) {
continue;
}
auto new_in_grad_name = cur_scope.Rename(in_grad_name);
auto assign =
framework::OpRegistry::CreateOp("assign", {{"X", {new_in_grad_name}}},
{{"Out", {out_grad_name}}}, {});
assign->Run(cur_scope, dev_ctx);
cur_scope.Rename(new_in_grad_name, in_grad_name);
}
}
};
class ConditionalBlockGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInputs("X"));
if (context->HasInputs("Params")) {
PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("Params")));
context->SetOutputsDim(framework::GradVarName("Params"),
context->GetInputsDim("Params"));
}
PADDLE_ENFORCE(context->HasOutputs(framework::GradVarName("X")));
context->SetOutputsDim(framework::GradVarName("X"),
context->GetInputsDim("X"));
}
};
class ConditionalBlockGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto grad_op = new framework::OpDescBind();
grad_op->SetType("conditional_block_grad");
grad_op->SetInput("X", Input("X"));
grad_op->SetInput("Params", Input("Params"));
grad_op->SetInput("Out", Output("Out"));
grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
grad_op->SetInput("Scope", Output("Scope"));
grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
grad_op->SetOutput(framework::GradVarName("Params"), InputGrad("Params"));
grad_op->SetBlockAttr("block", *this->grad_block_[0]);
return std::unique_ptr<framework::OpDescBind>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(conditional_block, ops::ConditionalBlockOp,
ops::ConditionalBlockOpProtoMaker,
ops::ConditionalBlockGradMaker);
REGISTER_OPERATOR(conditional_block_grad, ops::ConditionalBlockGradOp,
ops::ConditionalBlockGradInferShape);
paddle/operators/expand_op.cc 0 → 100644
浏览文件 @ fc117ecf
/* 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/expand_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class ExpandOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
std::vector<int> expand_times =
ctx->Attrs().Get<std::vector<int>>("expand_times");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(static_cast<size_t>(x_dims.size()), expand_times.size(),
"The number of Attr(expand_times)'s value must be equal "
"to the rank of Input(X).");
PADDLE_ENFORCE_LE(x_dims.size(), 6,
"The rank of Input(X) must not be greater than 6.");
std::vector<int64_t> out_shape(x_dims.size());
for (size_t i = 0; i < expand_times.size(); ++i) {
PADDLE_ENFORCE_GE(expand_times[i], 1,
"Each value of Attr(expand_times) should not be "
"less than 1.");
out_shape[i] = x_dims[i] * expand_times[i];
}
ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
if (out_shape[0] == x_dims[0]) {
ctx->ShareLoD("X", "Out");
}
}
};
class ExpandOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ExpandOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>) A tensor with rank in [1, 6]."
"X is the input tensor to be expanded.");
AddOutput("Out",
"(Tensor, default Tensor<float>) A tensor with rank in [1, 6]."
"The rank of Output(Out) is same as Input(X) except that each "
"dimension size of Output(Out) is equal to corresponding "
"dimension size of Input(X) multiplying corresponding value of "
"Attr(expand_times).");
AddAttr<std::vector<int>>("expand_times",
"Expand times number for each dimension.");
AddComment(R"DOC(
Expand operator tiles the input by given times number. You should set times
number for each dimension by providing attribute 'expand_times'. The rank of X
should be in [1, 6]. Please notice that size of 'expand_times' must be same with
X's rank. Following is a using case:
Input(X) is a 3-D tensor with shape [2, 3, 1]:
[
[[1], [2], [3]],
[[4], [5], [6]]
]
Attr(expand_times): [1, 2, 2]
Output(Out) is a 3-D tensor with shape [2, 6, 2]:
[
[[1, 1], [2, 2], [3, 3], [1, 1], [2, 2], [3, 3]],
[[4, 4], [5, 5], [6, 6], [4, 4], [5, 5], [6, 6]]
]
)DOC");
}
};
class ExpandGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
auto x_dims = ctx->GetInputDim("X");
std::vector<int> expand_times =
ctx->Attrs().Get<std::vector<int>>("expand_times");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
for (size_t i = 0; i < expand_times.size(); ++i) {
PADDLE_ENFORCE_EQ(x_dims[i] * expand_times[i], out_dims[i],
"Each dimension size of Input(Out@GRAD) should be "
"equal to multiplication of crroresponding dimension "
"size of Input(X) and Attr(expand_times) value.");
}
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(expand, ops::ExpandOp, ops::ExpandOpMaker, expand_grad,
ops::ExpandGradOp);
REGISTER_OP_CPU_KERNEL(expand,
ops::ExpandKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
expand_grad, ops::ExpandGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/fill_constant_op.cu → paddle/operators/expand_op.cu
浏览文件 @ fc117ecf
......@@ -13,12 +13,11 @@
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/fill_constant_op.h"
#include "paddle/operators/expand_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(expand,
ops::ExpandKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
fill_constant, ops::FillConstantOpKernel<paddle::platform::GPUPlace, float>,
ops::FillConstantOpKernel<paddle::platform::GPUPlace, double>,
ops::FillConstantOpKernel<paddle::platform::GPUPlace, int>,
ops::FillConstantOpKernel<paddle::platform::GPUPlace, int64_t>);
expand_grad, ops::ExpandGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/expand_op.h 0 → 100644
浏览文件 @ fc117ecf
/* 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 <boost/preprocessor/arithmetic/div.hpp>
#include <boost/preprocessor/arithmetic/mod.hpp>
#include <boost/preprocessor/comparison/greater.hpp>
#include <boost/preprocessor/comparison/greater_equal.hpp>
#include <boost/preprocessor/control/if.hpp>
#include <boost/preprocessor/repetition/repeat.hpp>
#include <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#define MAX_RANK_SUPPORTED 6
#define EXPAND_TEMPLATE(z, n, data) \
case n + 1: { \
Expand<n + 1>(context); \
break; \
}
#define REP_EXPAND_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_TEMPLATE, ~)
#define COND(n) \
BOOST_PP_GREATER_EQUAL(BOOST_PP_DIV(n, MAX_RANK_SUPPORTED), \
BOOST_PP_MOD(n, MAX_RANK_SUPPORTED))
#define EXPAND_GRAD_CASE(n) \
case n: { \
ExpandBackward<n>(context, reshape_dims_vec, reduce_dims_vec); \
break; \
}
#define EXPAND_GRAD_TEMPLATE(z, n, data) \
BOOST_PP_IF(COND(n), EXPAND_GRAD_CASE(n), )
#define REP_EXPAND_GRAD_TEMPLATE(n) BOOST_PP_REPEAT(n, EXPAND_GRAD_TEMPLATE, ~)
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename Place, typename T>
class ExpandKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
REP_EXPAND_TEMPLATE(MAX_RANK_SUPPORTED)
default:
PADDLE_ENFORCE(false,
"Only support tensor with rank being between 1 and 6.");
}
}
protected:
template <int Rank>
void Expand(const framework::ExecutionContext& context) const {
auto* in0 = context.Input<Tensor>("X");
auto& expand_times = context.Attr<std::vector<int>>("expand_times");
auto* out0 = context.Output<Tensor>("Out");
Eigen::DSizes<int, Rank> bcast_dims;
auto x_dims = in0->dims();
for (size_t i = 0; i < expand_times.size(); ++i) {
bcast_dims[i] = expand_times[i];
}
auto x = EigenTensor<T, Rank>::From(*in0);
out0->mutable_data<T>(context.GetPlace());
auto y = EigenTensor<T, Rank>::From(*out0);
auto place = context.GetEigenDevice<Place>();
y.device(place) = x.broadcast(bcast_dims);
}
};
template <typename Place, typename T>
class ExpandGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in0 = context.Input<Tensor>("X");
auto& expand_times = context.Attr<std::vector<int>>("expand_times");
auto x_dims = in0->dims();
// 1. reshape_dims_vec is the broadcast parameter. For each dimension i,
// if expand_times[i] > 1 and x_dims[i] > 1, i will be splitted to two
// dimensions [expand_times[i], x_dims[i]].
// 2. reduce_dims_vec is the dimension parameter to compute gradients. For
// each dimension expanded, the gradients should be summed to original
// size.
std::vector<int> reshape_dims_vec;
std::vector<int> reduce_dims_vec;
for (size_t i = 0; i < expand_times.size(); ++i) {
if (expand_times[i] == 1) {
reshape_dims_vec.push_back(x_dims[i]);
} else {
if (x_dims[i] == 1) {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(expand_times[i]);
} else {
reduce_dims_vec.push_back(reshape_dims_vec.size());
reshape_dims_vec.push_back(expand_times[i]);
reshape_dims_vec.push_back(x_dims[i]);
}
}
}
int dims = reshape_dims_vec.size() * MAX_RANK_SUPPORTED +
reduce_dims_vec.size() - MAX_RANK_SUPPORTED - 1;
// no need reduce, just copy
if (reduce_dims_vec.size() == 0) {
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
out0->mutable_data<T>(context.GetPlace());
out0->CopyFrom(*in0, context.GetPlace(), context.device_context());
} else {
switch (dims) {
REP_EXPAND_GRAD_TEMPLATE(72)
default:
PADDLE_ENFORCE(
false, "Only support tensor with rank being between 1 and 6.");
}
}
}
protected:
template <int Dims>
void ExpandBackward(const framework::ExecutionContext& context,
const std::vector<int>& reshape_dims_vec,
const std::vector<int>& reduce_dims_vec) const {
size_t reshape_size = Dims / MAX_RANK_SUPPORTED + 1;
size_t reduce_size = Dims % MAX_RANK_SUPPORTED + 1;
PADDLE_ENFORCE_EQ(reshape_size, reshape_dims_vec.size(),
"Inconsistent size between template Dims and "
"reshape dimensions.");
PADDLE_ENFORCE_EQ(reduce_size, reduce_dims_vec.size(),
"Inconsistent size between template Dims and "
"reduce dimensions.");
auto* in0 = context.Input<Tensor>(framework::GradVarName("Out"));
auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
auto x = EigenVector<T>::Flatten(*(context.Input<Tensor>("X")));
out0->mutable_data<T>(context.GetPlace());
auto x_grad = EigenVector<T>::Flatten(*out0);
Eigen::DSizes<int, Dims / MAX_RANK_SUPPORTED + 1> reshape_dims;
for (size_t i = 0; i < reshape_size; ++i) {
reshape_dims[i] = reshape_dims_vec[i];
}
Eigen::DSizes<int, Dims % MAX_RANK_SUPPORTED + 1> reduce_dims;
for (size_t i = 0; i < reduce_size; ++i) {
reduce_dims[i] = reduce_dims_vec[i];
}
auto out_grad = EigenVector<T>::Flatten(*in0);
x_grad.device(context.GetEigenDevice<Place>()) =
out_grad.reshape(reshape_dims).sum(reduce_dims).reshape(x.dimensions());
}
};
} // namespace operators
} // namespace paddle
paddle/operators/fill_constant_batch_size_like_op.cc
浏览文件 @ fc117ecf
......@@ -75,10 +75,10 @@ class FillConstantBatchSizeLikeOpMaker
"with the specified value");
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<int>("input_dim_idx",
"(int, default 0) the index of input's batch size dimension")
"(int, default 0) The index of input's batch size dimension")
.SetDefault(0);
AddAttr<int>("output_dim_idx",
"(int, default 0) the index of output's batch size dimension")
"(int, default 0) The index of output's batch size dimension")
.SetDefault(0);
AddAttr<float>("value", "(float, default 0) The value to be filled")
.SetDefault(0.0f);
......
paddle/operators/fill_constant_batch_size_like_op.cu
浏览文件 @ fc117ecf
......@@ -12,7 +12,6 @@
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/fill_constant_batch_size_like_op.h"
......
paddle/operators/fill_constant_batch_size_like_op.h
浏览文件 @ fc117ecf
......@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
......@@ -27,9 +27,8 @@ class FillConstantBatchSizeLikeOpKernel : public framework::OpKernel<T> {
out->mutable_data<T>(ctx.GetPlace());
auto value = ctx.Attr<float>("value");
auto out_eigen = framework::EigenVector<T>::Flatten(*out);
auto place = ctx.GetEigenDevice<Place>();
out_eigen.device(place) = out_eigen.constant(static_cast<T>(value));
math::SetConstant<Place, T> setter;
setter(ctx.device_context(), out, static_cast<T>(value));
}
};
......
paddle/operators/fill_constant_op.cc
浏览文件 @ fc117ecf
......@@ -12,33 +12,41 @@ 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/fill_constant_op.h"
#include "paddle/framework/data_type.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
class FillConstantOp : public framework::OperatorWithKernel {
class FillConstantInferShape : public framework::InferShapeBase {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of FillConstantOp should not be null.");
auto &shape = ctx->Attrs().Get<std::vector<int>>("shape");
std::vector<int64_t> shape_int64(shape.size(), 0);
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto dims = framework::make_ddim(shape_int64);
ctx->SetOutputDim("Out", dims);
ctx->SetOutputDim("Out", framework::make_ddim(shape));
}
};
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
int data_type = ctx.Attr<int>("data_type");
VLOG(10) << " FillConstant data_type = " << data_type;
return framework::OpKernelType(static_cast<framework::DataType>(data_type),
ctx.device_context());
class FillConstantOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto data_type = static_cast<framework::DataType>(Attr<int>("data_type"));
auto value = Attr<float>("value");
auto force_cpu = Attr<bool>("force_cpu");
auto &out =
*scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
out.Resize(framework::make_ddim(Attr<std::vector<int>>("shape")));
if (force_cpu) {
auto cpu = platform::CPUPlace();
out.mutable_data(cpu, framework::ToTypeIndex(data_type));
} else {
out.mutable_data(dev_ctx.GetPlace(), framework::ToTypeIndex(data_type));
}
math::set_constant(dev_ctx, &out, value);
}
};
......@@ -54,6 +62,11 @@ class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>("shape", "(vector<int>) The shape of the output");
AddAttr<float>("value", "(float, default 0) The value to be filled")
.SetDefault(0.0f);
AddAttr<bool>("force_cpu",
"(bool, default false) Force fill output variable to cpu "
"memory. Otherwise, fill output variable to the running "
"device")
.SetDefault(false);
AddOutput("Out",
"(Tensor) Tensor of specified shape will be filled "
"with the specified value");
......@@ -69,10 +82,6 @@ Fill up a variable with specified constant value.
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(fill_constant, ops::FillConstantOp,
ops::FillConstantOpMaker);
REGISTER_OP_CPU_KERNEL(
fill_constant, ops::FillConstantOpKernel<paddle::platform::CPUPlace, float>,
ops::FillConstantOpKernel<paddle::platform::CPUPlace, double>,
ops::FillConstantOpKernel<paddle::platform::CPUPlace, int>,
ops::FillConstantOpKernel<paddle::platform::CPUPlace, int64_t>);
REGISTER_OPERATOR(fill_constant, ops::FillConstantOp,
ops::FillConstantInferShape, ops::FillConstantOpMaker,
paddle::framework::EmptyGradOpMaker);
paddle/operators/fill_zeros_like_op.cu
浏览文件 @ fc117ecf
......@@ -12,7 +12,6 @@
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/fill_zeros_like_op.h"
......
paddle/operators/fill_zeros_like_op.h
浏览文件 @ fc117ecf
......@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
......@@ -23,10 +23,11 @@ template <typename Place, typename T>
class FillZerosLikeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* output = context.Output<framework::Tensor>("Y");
output->mutable_data<T>(context.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*output);
t.device(context.GetEigenDevice<Place>()) = t.constant(static_cast<T>(0));
auto* out = context.Output<framework::Tensor>("Y");
out->mutable_data<T>(context.GetPlace());
math::SetConstant<Place, T> setter;
setter(context.device_context(), out, static_cast<T>(0));
}
};
......
paddle/operators/increment_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/l1_norm_op.h
浏览文件 @ fc117ecf
......@@ -29,7 +29,7 @@ class L1NormKernel : public framework::OpKernel<T> {
Out->mutable_data<T>(context.GetPlace());
auto x = framework::EigenVector<T>::Flatten(*X);
auto out = framework::EigenVector<T>::Flatten(*Out);
auto out = framework::EigenScalar<T>::From(*Out);
auto place = context.GetEigenDevice<Place>();
out.device(place) = x.abs().sum();
......
paddle/operators/lod_array_length_op.cc 0 → 100644
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/lod_rank_table_op.cc
浏览文件 @ fc117ecf
......@@ -66,7 +66,8 @@ class LoDRankTableInferVarType : public framework::VarTypeInference {
void operator()(const framework::OpDescBind &op_desc,
framework::BlockDescBind *block) const override {
for (auto &o : op_desc.Output("Out")) {
block->Var(o)->SetType(framework::VarDesc::LOD_RANK_TABLE);
block->FindRecursiveOrCreateVar(o)->SetType(
framework::VarDesc::LOD_RANK_TABLE);
}
}
};
......
paddle/operators/lod_reset_op.cc 0 → 100644
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/lod_reset_op.cu 0 → 100644
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/lod_reset_op.h 0 → 100644
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/lstm_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/lstm_op.h
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/lstm_unit_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/matmul_op.h
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/mean_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/merge_lod_tensor_op.cc 0 → 100644
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/momentum_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/momentum_op.h
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/mul_op.cu
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/mul_op.h
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/nccl_op_test.cu
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/pool_cudnn_op.cu
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/pool_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/pool_op.h
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/recurrent_op.cc
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/sequence_pool_op.h
浏览文件 @ fc117ecf
此差异已折叠。
paddle/operators/shrink_rnn_memory_op.cc 0 → 100644
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paddle/operators/softmax_op.cu
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paddle/operators/softmax_op.h
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paddle/operators/sum_op.cc
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paddle/platform/call_once.h 0 → 100644
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paddle/platform/dynload/nccl.h
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paddle/platform/transform.h
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paddle/pybind/pybind.cc
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proto/ModelConfig.proto
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python/paddle/v2/image.py
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