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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

上级 7c792431 85b839f0
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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
浏览文件 @ fc117ecf
......@@ -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
......@@ -12,22 +12,57 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/increment_op.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
class IncrementOp : public framework::OperatorWithKernel {
class IncrementInferShape : 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->HasInput("X"),
"Input(X) of IncrementOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of IncrementOp should not be null.");
PADDLE_ENFORCE_EQ(1, framework::product(ctx->GetInputDim("X")));
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
struct IncrementFunctor {
IncrementFunctor(const framework::LoDTensor &x, framework::LoDTensor *out,
float value)
: x_(x), out_(out), value_(value) {}
template <typename T>
void operator()() const {
*out_->data<T>() = *x_.data<T>() + static_cast<T>(value_);
}
const framework::LoDTensor &x_;
framework::LoDTensor *out_;
float value_;
};
class IncrementOp : public framework::OperatorBase {
public:
IncrementOp(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"))->Get<framework::LoDTensor>();
auto &out =
*scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
PADDLE_ENFORCE(platform::is_cpu_place(x.place()));
out.Resize(x.dims());
out.mutable_data(x.place(), x.type());
float value = Attr<float>("step");
framework::VisitDataType(framework::ToDataType(out.type()),
IncrementFunctor(x, &out, value));
}
};
......@@ -59,10 +94,10 @@ class IncrementGradOpMaker : public framework::SingleGradOpDescMaker {
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *grad_op = new framework::OpDescBind();
grad_op->SetType("scale");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttr("scale", 1.0f);
grad_op->SetType("increment");
grad_op->SetInput("X", Output("Out"));
grad_op->SetOutput("Out", Input("X"));
grad_op->SetAttr("step", -boost::get<float>(GetAttr("step")));
return std::unique_ptr<framework::OpDescBind>(grad_op);
}
};
......@@ -71,11 +106,5 @@ class IncrementGradOpMaker : public framework::SingleGradOpDescMaker {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(increment, ops::IncrementOp, ops::IncrementOpMaker,
ops::IncrementGradOpMaker);
REGISTER_OP_CPU_KERNEL(
increment, ops::IncrementKernel<paddle::platform::CPUPlace, float>,
ops::IncrementKernel<paddle::platform::CPUPlace, double>,
ops::IncrementKernel<paddle::platform::CPUPlace, int>,
ops::IncrementKernel<paddle::platform::CPUPlace, int64_t>);
REGISTER_OPERATOR(increment, ops::IncrementOp, ops::IncrementInferShape,
ops::IncrementOpMaker, ops::IncrementGradOpMaker);
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
/* 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/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
class LoDArrayLengthOp : public framework::OperatorBase {
public:
LoDArrayLengthOp(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"))->Get<framework::LoDTensorArray>();
auto &out =
*scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
out.Resize({1});
auto cpu = platform::CPUPlace();
*out.mutable_data<int64_t>(cpu) = static_cast<int64_t>(x.size());
}
};
class LoDArrayLengthProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDArrayLengthProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensorArray) The input tensor array.");
AddOutput("Out", "(Tensor) 1x1 CPU Tensor of length, int64_t");
AddComment(R"DOC(Get the length of lod tensor array
Out = len(X)
NOTE: The output is a CPU Tensor since the control variable should be only in
CPU and the length of LoDTensorArray should be used as control variables.
)DOC");
}
};
class LoDArrayLengthInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"));
PADDLE_ENFORCE(context->HasOutput("Out"));
context->SetOutputDim("Out", {1});
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lod_array_length, ops::LoDArrayLengthOp,
ops::LoDArrayLengthInferShape, ops::LoDArrayLengthProtoMaker,
paddle::framework::EmptyGradOpMaker);
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
/* 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/lod_reset_op.h"
namespace paddle {
namespace operators {
class LoDResetOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
// input check
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of LoDResetOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of LoDResetOp should not be null.");
// If target LoD is not set form Input(), then it must be set from Attr().
if (!ctx->HasInput("TargetLoD")) {
auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
PADDLE_ENFORCE(level0.size() > 1,
"Target LoD is not found, should be set to be a valid one "
"through Input() or Attr().");
}
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LoDResetOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(LoDTensor) The input tensor of lod_reset operator.");
AddInput("TargetLoD",
"(Tensor, optional) The target level 0 LoD from Input().")
.AsDispensable();
AddOutput("Out", "(LoDTensor) The output tensor of lod_reset operator.");
AddAttr<std::vector<int>>("target_lod",
"The target level 0 LoD from Attr().")
.SetDefault(std::vector<int>{});
AddComment(R"DOC(LoDReset operator
Reset LoD of Input(X) into a new one specified by Input(TargetLoD) or
Attr(target_lod), or set LoD for Input(X) if it doesn't have one.
Currently the lod_reset operator only supports the reset of level 0 LoD.
At least one of Input(TargetLoD) and Attr(target_lod) must be set,
and if both of them are set, Input(TargetLoD) will be chosen as the
target LoD.
An example:
Given a float LoDTensor X with shape (6, 1), its transpose form represents
[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
with LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like
[1.0, 2.0], [3.0, 4.0, 5.0], [6.0].
If target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and
the sequences that the LoDTensor Output(Out) contains becomes:
[1.0, 2.0, 3.0, 4.0], [5.0, 6.0].
)DOC");
}
};
class LoDResetGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker, lod_reset_grad,
ops::LoDResetGradOp);
REGISTER_OP_CPU_KERNEL(lod_reset,
ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
ops::LoDResetKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
lod_reset_grad, ops::LoDResetGradKernel<paddle::platform::CPUPlace, float>,
ops::LoDResetGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/lod_reset_op.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 "paddle/operators/lod_reset_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(lod_reset,
ops::LoDResetKernel<paddle::platform::GPUPlace, float>,
ops::LoDResetKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
lod_reset_grad, ops::LoDResetGradKernel<paddle::platform::GPUPlace, float>,
ops::LoDResetGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/lod_reset_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"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class LoDResetKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out = ctx.Output<framework::LoDTensor>("Out");
auto* in = ctx.Input<framework::LoDTensor>("X");
auto* lod_t = ctx.Input<framework::Tensor>("TargetLoD");
std::vector<int> level0;
if (lod_t) {
auto* lod = lod_t->data<int>();
if (platform::is_gpu_place(ctx.GetPlace())) {
framework::Tensor lod_cpu;
lod_cpu.CopyFrom(*lod_t, platform::CPUPlace(), ctx.device_context());
lod = lod_cpu.data<int>();
}
level0 = std::vector<int>(lod, lod + lod_t->numel());
} else {
level0 = ctx.Attr<std::vector<int>>("target_lod");
}
PADDLE_ENFORCE(level0.size() > 1UL,
"The size of target LoD should be greater than 1.");
PADDLE_ENFORCE(level0[0] == 0,
"Target LoD should be a vector starting from 0.");
PADDLE_ENFORCE(level0.back() == in->dims()[0],
"Target LoD should be a vector end with the "
"first dimension of Input(X).");
for (size_t i = 0; i < level0.size() - 1; ++i) {
PADDLE_ENFORCE(level0[i + 1] > level0[i],
"Target LoD should be an ascending vector.");
}
out->ShareDataWith(*in);
// cast level0 to size_t
std::vector<size_t> ulevel0(level0.size(), 0);
std::transform(level0.begin(), level0.end(), ulevel0.begin(),
[](int a) { return static_cast<size_t>(a); });
framework::LoD target_lod;
target_lod.push_back(ulevel0);
out->set_lod(target_lod);
}
};
template <typename Place, typename T>
class LoDResetGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
d_x->ShareDataWith(*d_out);
}
};
} // namespace operators
} // namespace paddle
paddle/operators/lod_tensor_to_array_op.cc
浏览文件 @ fc117ecf
......@@ -133,6 +133,22 @@ class LoDTensorToArrayInferVarType : public framework::VarTypeInference {
}
};
class LoDTensorToArrayGradMaker : 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("array_to_lod_tensor");
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);
}
};
} // namespace operators
} // namespace paddle
......@@ -140,4 +156,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(lod_tensor_to_array, ops::LoDTensorToArrayOp,
ops::LoDTensorToArrayOpProtoMaker,
ops::LoDTensorToArrayInferShape,
ops::LoDTensorToArrayInferVarType);
ops::LoDTensorToArrayInferVarType,
ops::LoDTensorToArrayGradMaker);
paddle/operators/lstm_op.cc
浏览文件 @ fc117ecf
......@@ -24,6 +24,11 @@ class LSTMOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(Weight) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
......@@ -59,11 +64,13 @@ class LSTMOp : public framework::OperatorWithKernel {
"The second dimension of Input(Weight) "
"should be 4 * %d.",
frame_size);
auto b_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(b_dims.size(), 2, "The rank of Input(Bias) should be 2.");
PADDLE_ENFORCE_EQ(b_dims[0], 1,
"The first dimension of Input(Bias) should be 1.");
if (ctx->Attrs().Get<bool>("usePeepholes")) {
if (ctx->Attrs().Get<bool>("use_peepholes")) {
PADDLE_ENFORCE_EQ(b_dims[1], 7 * frame_size,
"The second dimension of Input(Bias) should be "
"7 * %d if enable peepholes connection",
......@@ -74,6 +81,7 @@ class LSTMOp : public framework::OperatorWithKernel {
"4 * %d if disable peepholes connection",
frame_size);
}
framework::DDim out_dims({in_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("Cell", out_dims);
......@@ -118,14 +126,13 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Bias",
"(Tensor) the learnable weights, which contains two parts: "
"input-hidden bias weight and peephole connections weight if "
"setting `usePeepholes` True. "
"1. `usePeepholes = False` "
"setting `use_peepholes` True. "
"1. `use_peepholes = False` "
" - The shape is (1 x 4D). "
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `usePeepholes = True` "
"2. `use_peepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.")
.AsDispensable();
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
AddOutput("Hidden",
"(LoDTensor) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
......@@ -145,29 +152,32 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor) This LoDTensor is obtained in the forward and used "
"in the backward.")
.AsIntermediate();
AddAttr<bool>("usePeepholes",
"(bool, default True) "
AddAttr<bool>("use_peepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
.SetDefault(true);
AddAttr<bool>("isReverse",
"(bool, default False) "
AddAttr<bool>("is_reverse",
"(bool, defalut: False) "
"whether to compute reversed LSTM.")
.SetDefault(false);
AddAttr<std::string>(
"gateActivation",
"(string, default sigmoid)"
"gate_activation",
"(string, default: sigmoid)"
"The activation for input gate, forget gate and output "
"gate, `sigmoid` by default.")
.SetDefault("sigmoid");
AddAttr<std::string>("cellActivation",
"(string, default tanh)"
.SetDefault("sigmoid")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("cell_activation",
"(string, default: tanh)"
"The activation for cell output, `tanh` by defalut.")
.SetDefault("tanh");
AddAttr<std::string>("candidateActivation",
"(string, default tanh)"
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddAttr<std::string>("candidate_activation",
"(string, default: tanh)"
"The activation for candidate hidden state, "
"`tanh` by default.")
.SetDefault("tanh");
.SetDefault("tanh")
.InEnum({"sigmoid", "tanh", "relu", "identity"});
AddComment(R"DOC(
Long-Short Term Memory (LSTM) Operator.
......@@ -203,7 +213,7 @@ are the cell input and cell output activation functions and `tanh` is usually
used for them. \f$\tilde{c_t}\f$ is also called candidate hidden state,
which is computed based on the current input and the previous hidden state.
Set usePeepholes False to disable peephole connection
Set `use_peepholes` False to disable peephole connection
(http://www.bioinf.jku.at/publications/older/2604.pdf). The formula
is omitted here.
......@@ -226,23 +236,27 @@ class LSTMGradOp : public framework::OperatorWithKernel {
"Input(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Cell"),
"Input(Cell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Weight"),
"Input(Weight) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
"Input(BatchGate) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
"Input(BatchGate) of LSTM should not be null.");
auto in_g_name = framework::GradVarName("Input");
if (ctx->HasOutput(in_g_name))
ctx->SetOutputDim(in_g_name, ctx->GetInputDim("Input"));
auto w_g_name = framework::GradVarName("Weight");
if (ctx->HasOutput(w_g_name))
ctx->SetOutputDim(w_g_name, ctx->GetInputDim("Weight"));
auto b_g_name = framework::GradVarName("Bias");
if (ctx->HasOutput(b_g_name))
ctx->SetOutputDim(b_g_name, ctx->GetInputDim("Bias"));
auto SetOutGradDim = [&ctx](const std::string& name) {
auto g_name = framework::GradVarName(name);
if (ctx->HasOutput(g_name))
ctx->SetOutputDim(g_name, ctx->GetInputDim(name));
};
SetOutGradDim("Input");
SetOutGradDim("Weight");
SetOutGradDim("Bias");
SetOutGradDim("H0");
SetOutGradDim("C0");
}
protected:
......
paddle/operators/lstm_op.h
浏览文件 @ fc117ecf
......@@ -28,6 +28,15 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
inline void ReorderInitState(const platform::DeviceContext& ctx,
const framework::Tensor& src, const size_t* index,
framework::Tensor* dst, bool indexed_src) {
math::CopyMatrixRowsFunctor<Place, T> row_shuffle;
dst->mutable_data<T>(src.dims(), ctx.GetPlace());
row_shuffle(ctx, src, index, *dst, indexed_src);
}
template <typename Place, typename T>
class LSTMKernel : public framework::OpKernel<T> {
public:
......@@ -36,6 +45,9 @@ class LSTMKernel : public framework::OpKernel<T> {
auto* weight = ctx.Input<Tensor>("Weight");
auto* bias = ctx.Input<Tensor>("Bias");
auto* hidden_t0 = ctx.Input<Tensor>("H0");
auto* cell_t0 = ctx.Input<Tensor>("C0");
auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
batch_gate->mutable_data<T>(ctx.GetPlace());
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
......@@ -43,12 +55,7 @@ class LSTMKernel : public framework::OpKernel<T> {
auto* cell_out = ctx.Output<LoDTensor>("Cell");
cell_out->mutable_data<T>(ctx.GetPlace());
// Now the function ShareLoD in InferShape is not implemented.
// So copy LoD here.
ctx.ShareLoD("Input", "Hidden");
ctx.ShareLoD("Input", "Cell");
bool is_reverse = ctx.Attr<bool>("isReverse");
bool is_reverse = ctx.Attr<bool>("is_reverse");
math::LoDTensor2BatchFunctor<Place, T> to_batch;
auto& device_ctx = ctx.device_context();
to_batch(device_ctx, *input, *batch_gate, true, is_reverse);
......@@ -71,7 +78,7 @@ class LSTMKernel : public framework::OpKernel<T> {
}
math::LstmMetaValue<T> lstm_value;
if (bias) {
if (bias && ctx.Attr<bool>("use_peepholes")) {
T* bias_data = const_cast<T*>(bias->data<T>());
// the code style in LstmMetaValue will be updated later.
......@@ -84,6 +91,16 @@ class LSTMKernel : public framework::OpKernel<T> {
lstm_value.checkOg = nullptr;
}
lstm_value.prevStateValue = nullptr;
Tensor ordered_c0;
const size_t* order = batch_gate->lod()[2].data();
if (cell_t0) {
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized cell state also needs
// to reorder.
ReorderInitState<Place, T>(device_ctx, *cell_t0, order, &ordered_c0,
true);
lstm_value.prevStateValue = ordered_c0.data<T>();
}
// Use the local variable as here.
LoDTensor batch_hidden, batch_cell;
......@@ -94,9 +111,9 @@ class LSTMKernel : public framework::OpKernel<T> {
auto batch_starts = batch_gate->lod()[0];
size_t num_batch = batch_starts.size() - 1;
auto gate_act = ctx.Attr<std::string>("gateActivation");
auto cell_act = ctx.Attr<std::string>("cellActivation");
auto cand_act = ctx.Attr<std::string>("candidateActivation");
auto gate_act = ctx.Attr<std::string>("gate_activation");
auto cell_act = ctx.Attr<std::string>("cell_activation");
auto cand_act = ctx.Attr<std::string>("candidate_activation");
for (size_t n = 0; n < num_batch; n++) {
int bstart = static_cast<int>(batch_starts[n]);
......@@ -109,15 +126,28 @@ class LSTMKernel : public framework::OpKernel<T> {
int cur_batch_size = bend - bstart;
if (n != 0) {
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_t = batch_hidden.Slice(pre_h_start, pre_h_end);
math::matmul<Place, T>(device_ctx, pre_hidden_t, false, *weight, false,
static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
} else if (hidden_t0) {
// If n == 0 and there is no initialized hidden state, that is to say
// the H0 is zeros, the calculation W_h * H0 will be skiped.
// If n == 0 and there is initialized hidden state, calculate W_h * H0.
// Since the batch computing for LSTM reorders the input sequence
// according to their length. The initialized hidden state also needs
// to reorder.
Tensor ordered_h0;
ReorderInitState<Place, T>(device_ctx, *hidden_t0, order, &ordered_h0,
true);
math::matmul<Place, T>(device_ctx, ordered_h0, false, *weight, false,
static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
}
// else if : FIXME support the initial hidden and cell
lstm_value.gateValue = gate_t.data<T>();
lstm_value.outputValue = out_t.data<T>();
......@@ -160,6 +190,12 @@ class LSTMGradKernel : public framework::OpKernel<T> {
auto* weight_g = ctx.Output<Tensor>(framework::GradVarName("Weight"));
auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
auto* h0 = ctx.Input<Tensor>("H0");
auto* c0 = ctx.Input<Tensor>("C0");
auto* h0_g = ctx.Output<Tensor>(framework::GradVarName("H0"));
auto* c0_g = ctx.Output<Tensor>(framework::GradVarName("C0"));
auto& device_ctx = ctx.device_context();
math::SetConstant<Place, T> zero;
if (weight_g) {
......@@ -167,13 +203,25 @@ class LSTMGradKernel : public framework::OpKernel<T> {
zero(device_ctx, weight_g, static_cast<T>(0.0));
}
// ordered_h0/c0 is the reordered hidden/cell initialization.
// ordered_h0_g/c0_g is the reordered gradient of hidden/cell
// initialization.
Tensor ordered_h0, ordered_c0, ordered_h0_g, ordered_c0_g;
const size_t* order = batch_gate->lod()[2].data();
if (c0) {
ReorderInitState<Place, T>(device_ctx, *c0, order, &ordered_c0, true);
}
if (c0 && c0_g) {
ordered_c0_g.mutable_data<T>(c0_g->dims(), ctx.GetPlace());
}
auto in_dims = input->dims();
auto out_dims = hidden_g->dims();
int frame_size = static_cast<int>(in_dims[1] / 4);
PADDLE_ENFORCE_EQ(frame_size, out_dims[1]);
math::LstmMetaValue<T> lstm_value;
if (bias) {
if (bias && ctx.Attr<bool>("use_peepholes")) {
T* bias_data = const_cast<T*>(bias->data<T>());
lstm_value.checkIg = bias_data + 4 * frame_size;
lstm_value.checkFg = lstm_value.checkIg + frame_size;
......@@ -185,9 +233,13 @@ class LSTMGradKernel : public framework::OpKernel<T> {
}
math::LstmMetaGrad<T> lstm_grad;
if (bias && bias_g) {
T* bias_g_data = const_cast<T*>(bias_g->mutable_data<T>(ctx.GetPlace()));
bias_g->mutable_data<T>(ctx.GetPlace());
zero(device_ctx, bias_g, static_cast<T>(0.0));
}
if (bias && bias_g && ctx.Attr<bool>("use_peepholes")) {
T* bias_g_data = bias_g->data<T>();
lstm_grad.checkIgGrad = bias_g_data + 4 * frame_size;
lstm_grad.checkFgGrad = lstm_grad.checkIgGrad + frame_size;
lstm_grad.checkOgGrad = lstm_grad.checkFgGrad + frame_size;
......@@ -199,36 +251,30 @@ class LSTMGradKernel : public framework::OpKernel<T> {
math::LoDTensor2BatchFunctor<Place, T> to_batch;
// use the local variable as here.
LoDTensor batch_hidden;
batch_hidden.mutable_data<T>(out_dims, ctx.GetPlace());
batch_hidden.set_lod(batch_gate->lod());
to_batch(device_ctx, *hidden_out, batch_hidden, false);
auto ToBatch = [&batch_gate, &to_batch](
const platform::DeviceContext& ctx, const framework::LoDTensor& src,
const framework::DDim& dims, framework::LoDTensor& dst) {
dst.mutable_data<T>(dims, ctx.GetPlace());
dst.set_lod(batch_gate->lod());
to_batch(ctx, src, dst, false);
};
LoDTensor batch_hidden_g;
batch_hidden_g.mutable_data<T>(out_dims, ctx.GetPlace());
batch_hidden_g.set_lod(batch_gate->lod());
to_batch(device_ctx, *hidden_g, batch_hidden_g, false);
LoDTensor batch_hidden, batch_hidden_g, batch_cell;
ToBatch(device_ctx, *hidden_out, out_dims, batch_hidden);
ToBatch(device_ctx, *hidden_g, out_dims, batch_hidden_g);
ToBatch(device_ctx, *cell_out, out_dims, batch_cell);
LoDTensor batch_cell;
batch_cell.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell.set_lod(batch_gate->lod());
to_batch(device_ctx, *cell_out, batch_cell, false);
LoDTensor batch_cell_g;
LoDTensor batch_cell_g, batch_gate_g;
batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell_g.set_lod(batch_gate->lod());
// TODO(qingqing) support the case output cell has gradient.
// to_batch(device_ctx, *cell_g, batch_cell_g, false);
zero(device_ctx, &batch_cell_g, static_cast<T>(0.0));
LoDTensor batch_gate_g;
batch_gate_g.mutable_data<T>(batch_gate->dims(), ctx.GetPlace());
batch_gate_g.set_lod(batch_gate->lod());
auto gate_act = ctx.Attr<std::string>("gateActivation");
auto cell_act = ctx.Attr<std::string>("cellActivation");
auto cand_act = ctx.Attr<std::string>("candidateActivation");
auto gate_act = ctx.Attr<std::string>("gate_activation");
auto cell_act = ctx.Attr<std::string>("cell_activation");
auto cand_act = ctx.Attr<std::string>("candidate_activation");
auto batch_starts = batch_gate->lod()[0];
size_t num_batch = batch_starts.size() - 1;
......@@ -250,15 +296,15 @@ class LSTMGradKernel : public framework::OpKernel<T> {
lstm_grad.gateGrad = gate_g.data<T>();
lstm_grad.outputGrad = out_g.data<T>();
if (n) {
if (n > 0) {
int bstart_pre = static_cast<int>(batch_starts[n - 1]);
Tensor cell_pre = batch_cell.Slice(bstart_pre, bstart);
Tensor cell_pre_g = batch_cell_g.Slice(bstart_pre, bstart);
lstm_value.prevStateValue = cell_pre.data<T>();
lstm_grad.prevStateGrad = cell_pre_g.data<T>();
} else {
lstm_value.prevStateValue = nullptr;
lstm_grad.prevStateGrad = nullptr;
lstm_value.prevStateValue = c0 ? ordered_c0.data<T>() : nullptr;
lstm_grad.prevStateGrad = c0_g ? ordered_c0_g.data<T>() : nullptr;
}
int cur_batch_size = bend - bstart;
......@@ -266,7 +312,7 @@ class LSTMGradKernel : public framework::OpKernel<T> {
device_ctx, lstm_value, lstm_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
if (n != 0) {
if (n > 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_g = batch_hidden_g.Slice(pre_h_start, pre_h_end);
......@@ -280,6 +326,19 @@ class LSTMGradKernel : public framework::OpKernel<T> {
static_cast<T>(1.0), weight_g,
static_cast<T>(1.0));
}
} else {
if (h0 && weight_g) {
ReorderInitState<Place, T>(device_ctx, *h0, order, &ordered_h0, true);
math::matmul<Place, T>(device_ctx, ordered_h0, true, gate_g, false,
static_cast<T>(1.0), weight_g,
static_cast<T>(1.0));
}
if (h0 && h0_g) {
ordered_h0_g.mutable_data<T>(h0_g->dims(), ctx.GetPlace());
math::matmul<Place, T>(device_ctx, gate_g, false, *weight, true,
static_cast<T>(1.0), &ordered_h0_g,
static_cast<T>(0.0));
}
}
}
......@@ -302,6 +361,13 @@ class LSTMGradKernel : public framework::OpKernel<T> {
math::gemv<Place, T>(device_ctx, true, m, n, 1., batch_gate_g.data<T>(),
ones.data<T>(), 0., bias_g->data<T>());
}
if (h0 && h0_g) {
ReorderInitState<Place, T>(device_ctx, ordered_h0_g, order, h0_g, false);
}
if (c0 && c0_g) {
ReorderInitState<Place, T>(device_ctx, ordered_c0_g, order, c0_g, false);
}
}
};
......
paddle/operators/lstm_unit_op.cc
浏览文件 @ fc117ecf
......@@ -34,10 +34,10 @@ class LstmUnitOp : public framework::OperatorWithKernel {
auto c_prev_dims = ctx->GetInputDim("C_prev");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE(x_dims[0] == c_prev_dims[0],
"Batch size of inputs and states must be equal");
PADDLE_ENFORCE(x_dims[1] == c_prev_dims[1] * 4,
"Dimension of FC should equal to prev state * 4");
PADDLE_ENFORCE_EQ(x_dims[0], c_prev_dims[0],
"Batch size of inputs and states must be equal");
PADDLE_ENFORCE_EQ(x_dims[1], c_prev_dims[1] * 4,
"Dimension of FC should equal to prev state * 4");
int b_size = c_prev_dims[0]; // batch size
int s_dim = c_prev_dims[1]; // state dim
......
paddle/operators/math/CMakeLists.txt
浏览文件 @ fc117ecf
......@@ -13,7 +13,7 @@ if(WITH_GPU)
nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions)
nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context operator)
cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
......
paddle/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ fc117ecf
......@@ -52,9 +52,9 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = value.checkIg[i];
rCheckF = value.checkFg[i];
rCheckO = value.checkOg[i];
rCheckI = value.checkIg ? value.checkIg[i] : 0;
rCheckF = value.checkFg ? value.checkFg[i] : 0;
rCheckO = value.checkOg ? value.checkOg[i] : 0;
if (value.prevStateValue) {
rPrevState = value.prevStateValue[i];
......@@ -114,9 +114,9 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = value.checkIg[i];
rCheckF = value.checkFg[i];
rCheckO = value.checkOg[i];
rCheckI = value.checkIg ? value.checkIg[i] : 0;
rCheckF = value.checkFg ? value.checkFg[i] : 0;
rCheckO = value.checkOg ? value.checkOg[i] : 0;
rState = value.stateValue[i];
rStateAtv = value.stateActiveValue[i];
rOutputGrad = grad.outputGrad[i];
......@@ -155,9 +155,9 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
__m256 rValueIg;
__m256 rValueFg;
__m256 rValueOg;
__m256 rCheckI;
__m256 rCheckF;
__m256 rCheckO;
__m256 rCheckI = _mm256_set1_ps(0.0f);
__m256 rCheckF = _mm256_set1_ps(0.0f);
__m256 rCheckO = _mm256_set1_ps(0.0f);
__m256 rState;
__m256 rPrevState = _mm256_set1_ps(0.0f);
__m256 rStateAtv;
......@@ -173,9 +173,11 @@ void avx_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value, int frameSize,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
if (value.checkIg) {
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
}
if (value.prevStateValue) {
rPrevState = ((__m256 *)value.prevStateValue)[i];
......@@ -216,9 +218,9 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
__m256 rState;
__m256 rStateAtv;
__m256 rOutputGrad;
__m256 rCheckI;
__m256 rCheckF;
__m256 rCheckO;
__m256 rCheckI = _mm256_set1_ps(0.0f);
__m256 rCheckF = _mm256_set1_ps(0.0f);
__m256 rCheckO = _mm256_set1_ps(0.0f);
__m256 rCheckIGrad;
__m256 rCheckFGrad;
__m256 rCheckOGrad;
......@@ -237,9 +239,11 @@ void avx_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
rValueIg = valueIg[i];
rValueFg = valueFg[i];
rValueOg = valueOg[i];
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
if (value.checkIg) {
rCheckI = ((__m256 *)value.checkIg)[i];
rCheckF = ((__m256 *)value.checkFg)[i];
rCheckO = ((__m256 *)value.checkOg)[i];
}
rState = ((__m256 *)value.stateValue)[i];
rStateAtv = ((__m256 *)value.stateActiveValue)[i];
rOutputGrad = ((__m256 *)grad.outputGrad)[i];
......
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ fc117ecf
......@@ -55,9 +55,10 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
T rValueIg;
T rValueFg;
T rValueOg;
T rCheckI = value.checkIg[frameIdx];
T rCheckF = value.checkFg[frameIdx];
T rCheckO = value.checkOg[frameIdx];
T rCheckI = value.checkIg ? value.checkIg[frameIdx] : 0;
T rCheckF = value.checkFg ? value.checkFg[frameIdx] : 0;
T rCheckO = value.checkOg ? value.checkOg[frameIdx] : 0;
rValueIn = value.gateValue[frameIdx];
rValueIg = value.gateValue[frameIdx + frameSize];
......@@ -121,9 +122,10 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
T rStateGrad;
T rStateAtv;
T rOutputGrad;
T rCheckI = value.checkIg[frameIdx];
T rCheckF = value.checkFg[frameIdx];
T rCheckO = value.checkOg[frameIdx];
T rCheckI = value.checkIg ? value.checkIg[frameIdx] : 0;
T rCheckF = value.checkFg ? value.checkFg[frameIdx] : 0;
T rCheckO = value.checkOg ? value.checkOg[frameIdx] : 0;
T rCheckIGrad;
T rCheckFGrad;
T rCheckOGrad;
......
paddle/operators/math/math_function.cc
浏览文件 @ fc117ecf
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/math/math_function.h"
#include "paddle/framework/data_type.h"
namespace paddle {
namespace operators {
......@@ -233,6 +234,52 @@ void gemv<platform::CPUPlace, double>(const platform::DeviceContext& context,
template struct SetConstant<platform::CPUPlace, float>;
struct TensorSetConstantCPU {
TensorSetConstantCPU(framework::Tensor* tensor, float value)
: tensor_(tensor), value_(value) {}
template <typename T>
void operator()() const {
auto cpu = platform::CPUPlace();
auto* begin = tensor_->mutable_data<T>(cpu);
std::fill(begin, begin + tensor_->numel(), static_cast<T>(value_));
}
framework::Tensor* tensor_;
float value_;
};
template <>
void set_constant_with_place<platform::CPUPlace>(
const platform::DeviceContext& context, framework::Tensor* tensor,
float value) {
framework::VisitDataType(framework::ToDataType(tensor->type()),
TensorSetConstantCPU(tensor, value));
}
struct TensorSetConstantWithPlace : public boost::static_visitor<void> {
TensorSetConstantWithPlace(const platform::DeviceContext& context,
framework::Tensor* tensor, float value)
: context_(context), tensor_(tensor), value_(value) {}
template <typename Place>
void operator()(Place place) const {
set_constant_with_place<Place>(context_, tensor_, value_);
}
const platform::DeviceContext& context_;
framework::Tensor* tensor_;
float value_;
};
void set_constant(const platform::DeviceContext& context,
framework::Tensor* tensor, float value) {
TensorSetConstantWithPlace func(context, tensor, value);
#ifdef PADDLE_WITH_CUDA
tensor->place().apply_visitor(func);
#else
func(platform::CPUPlace());
#endif
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/math_function.cu
浏览文件 @ fc117ecf
......@@ -12,6 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/data_type.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
......@@ -232,6 +233,30 @@ void gemv<platform::GPUPlace, double>(const platform::DeviceContext& context,
template struct SetConstant<platform::GPUPlace, float>;
struct TensorSetConstantGPU {
TensorSetConstantGPU(const platform::DeviceContext& context,
framework::Tensor* tensor, float value)
: context_(context), tensor_(tensor), value_(value) {}
template <typename T>
void operator()() const {
SetConstant<platform::GPUPlace, T> functor;
functor(context_, tensor_, static_cast<T>(value_));
}
const platform::DeviceContext& context_;
framework::Tensor* tensor_;
float value_;
};
template <>
void set_constant_with_place<platform::GPUPlace>(
const platform::DeviceContext& context, framework::Tensor* tensor,
float value) {
framework::VisitDataType(framework::ToDataType(tensor->type()),
TensorSetConstantGPU(context, tensor, value));
}
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/math_function.h
浏览文件 @ fc117ecf
......@@ -19,11 +19,6 @@ limitations under the License. */
#include <mkl_vml_functions.h>
#endif
#ifdef PADDLE_USE_MKL
#include <mkl.h>
#include <mkl_lapacke.h>
#endif
#ifdef PADDLE_USE_ATLAS
extern "C" {
#include <cblas.h>
......@@ -108,6 +103,13 @@ struct SetConstant {
}
};
template <typename Place>
void set_constant_with_place(const platform::DeviceContext& context,
framework::Tensor* tensor, float value);
void set_constant(const platform::DeviceContext& context,
framework::Tensor* tensor, float value);
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/math_function_test.cc
浏览文件 @ fc117ecf
......@@ -139,3 +139,15 @@ TEST(math_function, gemv) {
GemvTest<float>(12, 7, true);
GemvTest<double>(7, 9, true);
}
TEST(math_funciton, set_constant) {
paddle::framework::Tensor t;
t.Resize({10, 10});
t.mutable_data<int>(paddle::platform::CPUPlace());
auto* ctx = new paddle::platform::CPUDeviceContext();
paddle::operators::math::set_constant(*ctx, &t, 10);
for (int64_t i = 0; i < t.numel(); ++i) {
PADDLE_ENFORCE_EQ(10, t.data<int>()[i]);
}
delete ctx;
}
paddle/operators/math/sequence2batch.cc
浏览文件 @ fc117ecf
......@@ -22,8 +22,8 @@ template <typename T>
class CopyMatrixRowsFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& src, const size_t* index,
framework::LoDTensor& dst, bool is_src_index) {
const framework::Tensor& src, const size_t* index,
framework::Tensor& dst, bool is_src_index) {
auto src_dims = src.dims();
auto dst_dims = dst.dims();
PADDLE_ENFORCE_EQ(src_dims.size(), 2UL,
......
paddle/operators/math/sequence2batch.cu
浏览文件 @ fc117ecf
......@@ -41,8 +41,8 @@ template <typename T>
class CopyMatrixRowsFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& src, const size_t* index,
framework::LoDTensor& dst, bool is_src_index) {
const framework::Tensor& src, const size_t* index,
framework::Tensor& dst, bool is_src_index) {
auto src_dims = src.dims();
auto dst_dims = dst.dims();
PADDLE_ENFORCE_EQ(src_dims.size(), 2,
......
paddle/operators/math/sequence2batch.h
浏览文件 @ fc117ecf
......@@ -30,8 +30,8 @@ class CopyMatrixRowsFunctor {
// copy the input src to the indexed rows of output dst.
// The indexed rows are based on the input index.
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& src, const size_t* index,
framework::LoDTensor& dst, bool is_src_index);
const framework::Tensor& src, const size_t* index,
framework::Tensor& dst, bool is_src_index);
};
template <typename Place, typename T>
......@@ -57,7 +57,7 @@ class LoDTensor2BatchFunctor {
bool is_reverse = false) const {
if (!is_cal_batch_lod) {
auto lods = batch.lod();
PADDLE_ENFORCE_EQ(lods.size(), 2UL);
PADDLE_ENFORCE_GT(lods.size(), 2UL);
PADDLE_ENFORCE_EQ(lods[1].size(),
static_cast<size_t>(lod_tensor.dims()[0]));
CopyMatrixRowsFunctor<Place, T> to_batch;
......@@ -66,8 +66,8 @@ class LoDTensor2BatchFunctor {
}
auto lods = lod_tensor.lod();
PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
auto lod = lods[0];
PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence now.");
std::vector<SeqInfo> seq_info;
for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
......@@ -78,8 +78,7 @@ class LoDTensor2BatchFunctor {
std::sort(seq_info.begin(), seq_info.end(),
[](SeqInfo a, SeqInfo b) { return a.length > b.length; });
// calculate the start position of each batch
// (numBatch equal the maxLength of sequences)
// Calculate the start position of each batch.
// example: sequences = {s0, s1, s2}
// s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// num_batch = 5,
......@@ -95,19 +94,25 @@ class LoDTensor2BatchFunctor {
// 6, 2, 11,
// 7, 3,
// 8}
// The batch number represents batch size after rearranging the
// seq_order = {1, 0, 2}, the sort order.
// where 1 is the second sequence,
// 0 is the first sequence,
// 2 is the third sequence.
// The num_batch represents batch size after rearranging the
// input LodTensor. It is also the maximum length of input sequence.
paddle::framework::LoD batch_lods;
batch_lods.emplace_back(std::vector<size_t>{0});
batch_lods.emplace_back(std::vector<size_t>{0});
batch_lods.emplace_back(std::vector<size_t>{0});
// batch_lods[0] is the start positions for batch LoDTensor
int num_batch = seq_info[0].length;
batch_lods[0].resize(static_cast<size_t>(num_batch + 1));
// batch_lods[1] is the raw index in the input LoDTensor
auto dims = lod_tensor.dims();
batch_lods[1].resize(static_cast<size_t>(dims[0]));
batch_lods[1].resize(static_cast<size_t>(lod_tensor.dims()[0]));
// batch_lods[2] is the sort order for the input LoDTensor.
batch_lods[2].resize(seq_info.size());
size_t* batch_starts = batch_lods[0].data();
size_t* seq2batch_idx = batch_lods[1].data();
......@@ -127,6 +132,10 @@ class LoDTensor2BatchFunctor {
}
batch_starts[n + 1] = static_cast<size_t>(batch_id);
}
size_t* seq_order = batch_lods[2].data();
for (size_t i = 0; i < seq_info.size(); ++i) {
seq_order[i] = seq_info[i].seq_idx;
}
batch.set_lod(batch_lods);
CopyMatrixRowsFunctor<Place, T> to_batch;
......@@ -141,8 +150,7 @@ class Batch2LoDTensorFunctor {
const framework::LoDTensor& batch,
framework::LoDTensor& lod_tensor) const {
auto in_lod = batch.lod();
PADDLE_ENFORCE_EQ(in_lod.size(), 2UL,
"The LoD size of input `batch` should be 2.");
PADDLE_ENFORCE_GT(in_lod.size(), 2UL);
PADDLE_ENFORCE_EQ(in_lod[1].size(),
static_cast<size_t>(lod_tensor.dims()[0]));
CopyMatrixRowsFunctor<Place, T> to_seq;
......
paddle/operators/matmul_op.h
浏览文件 @ fc117ecf
......@@ -74,11 +74,10 @@ Tensor CombineBatchAndN(const framework::ExecutionContext& context,
Tensor output;
auto in_dims = input.dims();
if (in_dims.size() == 3) {
output.Resize(in_dims);
output.Resize({in_dims[1], in_dims[0], in_dims[2]});
output.mutable_data<T>(context.GetPlace());
EigenTranspose<Place, T, 3>(context, input, output, {1, 0, 2});
std::vector<int64_t> out_dims = {in_dims[1], in_dims[0] * in_dims[2]};
output.Resize(make_ddim(out_dims));
output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
} else {
output.ShareDataWith(input);
}
......
paddle/operators/mean_op.cc
浏览文件 @ fc117ecf
......@@ -51,6 +51,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", framework::GradVarName("X"));
}
};
......
paddle/operators/merge_lod_tensor_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/framework/op_registry.h"
#include "paddle/memory/memcpy.h"
namespace paddle {
namespace operators {
using LoD = framework::LoD;
class MergeLoDTensorOp : public framework::OperatorBase {
public:
MergeLoDTensorOp(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"))->Get<framework::LoDTensor>();
auto &mask = scope.FindVar(Input("Mask"))->Get<framework::LoDTensor>();
auto &in_true = scope.FindVar(Input("InTrue"))->Get<framework::LoDTensor>();
auto &in_false =
scope.FindVar(Input("InFalse"))->Get<framework::LoDTensor>();
auto *out =
scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
auto level = static_cast<size_t>(Attr<int>("level"));
auto &mask_dim = mask.dims();
std::unique_ptr<framework::LoDTensor> cpu_mask{new framework::LoDTensor()};
if (platform::is_cpu_place(mask.place())) {
cpu_mask->ShareDataWith(mask);
} else if (platform::is_gpu_place(mask.place())) {
#ifdef PADDLE_WITH_CUDA
cpu_mask->CopyFrom(mask, platform::CPUPlace(), dev_ctx);
#else
PADDLE_THROW("Not supported GPU, Please compile WITH_GPU option");
#endif
}
auto *mask_data = cpu_mask->data<bool>();
int rank = in_true.dims().size();
platform::Place place = in_true.place();
std::type_index data_type = in_true.type();
framework::DDim in_true_dims =
framework::slice_ddim(in_true.dims(), 1, rank);
int64_t batch_size = in_true.dims()[0] + in_false.dims()[0];
auto in_true_dim_vec = framework::vectorize(in_true_dims);
in_true_dim_vec.insert(in_true_dim_vec.begin(), batch_size);
framework::DDim out_dims = framework::make_ddim(in_true_dim_vec);
out->Resize(out_dims);
out->mutable_data(place, data_type);
auto *out_lod = out->mutable_lod();
out_lod->clear();
size_t out_offset = 0;
// Build LoDTensor `out`
size_t in_true_idx = 0;
size_t in_false_idx = 0;
for (size_t i = 0; i < static_cast<size_t>(mask_dim[0]); i++) {
const framework::LoDTensor *input = nullptr;
size_t *in_idx = nullptr;
if (static_cast<int>(mask_data[i]) == 0) {
input = &in_false;
in_idx = &in_false_idx;
} else {
input = &in_true;
in_idx = &in_true_idx;
}
auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
input->lod(), *in_idx, (*in_idx) + 1, 0);
auto &lod_length = lod_and_offset.first;
framework::AppendLoD(out_lod, lod_length);
size_t start_offset = lod_and_offset.second.first;
size_t end_offset = lod_and_offset.second.second;
PADDLE_ENFORCE_GE(end_offset, start_offset);
size_t len = end_offset - start_offset;
if (len == 0) {
continue;
}
out->Slice(out_offset, out_offset + len)
.CopyFrom(input->Slice(start_offset, end_offset), place, dev_ctx);
out_offset += len;
(*in_idx) += 1;
}
for (size_t i = 0; i < level; i++) {
out_lod->insert(out_lod->begin(), x.lod()[i]);
}
}
};
class MergeLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
MergeLoDTensorOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input LoDTensor, contains complete lod information to "
"construct the output");
AddInput("Mask", "A bool column vector which mask the input");
AddInput("InTrue", "The True branch to be merged");
AddInput("InFalse", "The False branch to be merged");
AddOutput("Out", "The merged output LoDTensor");
AddAttr<int>("level", "(int) the specific lod level to rank.")
.SetDefault(0)
.EqualGreaterThan(0);
AddComment(
R"DOC(
Merge True and False branches of LoDTensor into a single Output,
with a mask at certain lod level. X is used to obtain complete
lod information. Please refer to SplitLoDTensorOp.)DOC");
}
};
class MergeLoDTensorInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"),
"MergeLoDTensorOp must has input X.");
PADDLE_ENFORCE(context->HasInput("Mask"),
"MergeLoDTensorOp must has input Mask.");
PADDLE_ENFORCE(context->HasInput("InTrue"),
"MergeLoDTensorOp must has input InTrue.");
PADDLE_ENFORCE(context->HasInput("InFalse"),
"MergeLoDTensorOp must has input InFalse.");
PADDLE_ENFORCE(context->HasOutput("Out"),
"MergeLoDTensorOp must has output Out");
auto mask_dim = context->GetInputDim("Mask");
PADDLE_ENFORCE_EQ(mask_dim.size(), 2);
PADDLE_ENFORCE_EQ(mask_dim[1], 1);
context->SetOutputDim("Out", context->GetInputDim("InTrue"));
}
};
class MergeLoDTensorGradMaker : 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("split_lod_tensor");
grad_op->SetInput("X", OutputGrad("Out"));
grad_op->SetInput("Mask", Input("Mask"));
grad_op->SetOutput("OutTrue", InputGrad("InTrue"));
grad_op->SetOutput("OutFalse", InputGrad("InFalse"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(merge_lod_tensor, ops::MergeLoDTensorOp,
ops::MergeLoDTensorOpProtoMaker,
ops::MergeLoDTensorInferShape, ops::MergeLoDTensorGradMaker);
paddle/operators/momentum_op.cc
浏览文件 @ fc117ecf
......@@ -75,7 +75,7 @@ class MomentumOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("VelocityOut", "(Tensor) Output updated velocity");
AddAttr<float>("mu", "(float) Momentum coefficient");
AddAttr<bool>("useNesterov",
AddAttr<bool>("use_nesterov",
"(bool, default false) "
"Use Nesterov Momentum")
.SetDefault(false);
......
paddle/operators/momentum_op.h
浏览文件 @ fc117ecf
......@@ -34,7 +34,7 @@ class MomentumOpKernel : public framework::OpKernel<T> {
velocity_out->mutable_data<T>(ctx.GetPlace());
float mu = ctx.Attr<float>("mu");
bool use_nesterov = ctx.Attr<bool>("useNesterov");
bool use_nesterov = ctx.Attr<bool>("use_nesterov");
auto p_out = framework::EigenVector<T>::Flatten(*param_out);
auto v_out = framework::EigenVector<T>::Flatten(*velocity_out);
......
paddle/operators/mul_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/operators/mul_op.h"
namespace ops = paddle::operators;
......
paddle/operators/mul_op.h
浏览文件 @ fc117ecf
......@@ -16,16 +16,12 @@
#include "paddle/operators/math/math_function.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class MulKernel : public framework::OpKernel<T> {
......
paddle/operators/nccl/nccl_gpu_common.h
浏览文件 @ fc117ecf
......@@ -35,6 +35,7 @@ constexpr int kInvalidGPUId = -1;
struct Communicator {
std::vector<ncclComm_t> comms_;
std::unordered_map<int, int> comm_id_map_;
bool inited_;
Communicator() {}
......@@ -42,17 +43,21 @@ struct Communicator {
void InitAll(const std::vector<int>& gpus) {
comms_.resize(gpus.size());
inited_ = false;
for (size_t i = 0; i < gpus.size(); ++i) {
comm_id_map_[gpus[i]] = i;
}
PADDLE_ENFORCE(
dynload::ncclCommInitAll(comms_.data(), gpus.size(), gpus.data()));
inited_ = true;
}
~Communicator() {
for (size_t i = 0; i < comms_.size(); ++i) {
// FIXME(dzh) : PADDLE_ENFORCE return void
dynload::ncclCommDestroy(comms_[i]);
if (inited_) {
for (size_t i = 0; i < comms_.size(); ++i) {
// FIXME(dzh) : PADDLE_ENFORCE return void
dynload::ncclCommDestroy(comms_[i]);
}
}
}
......
paddle/operators/nccl_op_test.cu
浏览文件 @ fc117ecf
......@@ -26,7 +26,6 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/program_desc.h"
#include "paddle/framework/var_desc.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/nccl/nccl_gpu_common.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/enforce.h"
......
paddle/operators/pool_cudnn_op.cu
浏览文件 @ fc117ecf
......@@ -37,11 +37,11 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
const T *input_data = input->data<T>();
T *output_data = output->mutable_data<T>(ctx.GetPlace());
std::string pooling_type = ctx.Attr<std::string>("poolingType");
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.Attr<bool>("globalPooling")) {
if (ctx.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(input->dims()[i + 2]);
......@@ -92,12 +92,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
ctx.Input<Tensor>(framework::GradVarName("Out"));
Tensor *input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = ctx.Attr<std::string>("poolingType");
std::string pooling_type = ctx.Attr<std::string>("pooling_type");
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.Attr<bool>("globalPooling")) {
if (ctx.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(input->dims()[i + 2]);
......
paddle/operators/pool_op.cc
浏览文件 @ fc117ecf
......@@ -29,7 +29,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
auto in_x_dims = ctx->GetInputDim("X");
std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
......@@ -37,7 +37,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
"Pooling intput should be 4-D or 5-D tensor.");
if (ctx->Attrs().Get<bool>("globalPooling")) {
if (ctx->Attrs().Get<bool>("global_pooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
......@@ -83,20 +83,20 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
"H is the height of the feature, "
"and W is the width of the feature.");
AddAttr<std::string>("poolingType",
AddAttr<std::string>("pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>("ksize",
"(vector<int>) The pooling window "
"size(height, width) of the pooling operator. "
"If globalPooling = true, ksize and paddings will "
"If global_pooling = true, ksize and paddings will "
"be ignored."); // TODO(Chengduo): Add checker.
// (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("globalPooling",
AddAttr<bool>("global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If globalPooling = true, ksize and paddings will be ignored.")
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector<int>, default {1, 1}), strides(height, "
......@@ -107,7 +107,7 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
"paddings",
"(vector<int>, defalut {0,0}), paddings(height, width) of pooling "
"operator."
"If globalPooling = true, paddings and ksize will be ignored.")
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......@@ -115,7 +115,7 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
Pool2d Operator.
The pooling2d operation calculates the output based on
the input, poolingType and ksize, strides, paddings parameters.
the input, pooling_type and ksize, strides, paddings parameters.
Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
Parameters(ksize, strides, paddings) are two elements.
......@@ -152,7 +152,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
"the number of channels, and D, H and W is the depth, height and "
"width of the feature, respectively.");
AddAttr<std::string>("poolingType",
AddAttr<std::string>("pooling_type",
"(string) Pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
......@@ -160,13 +160,14 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
"ksize",
"(vector<int>) The pooling window size(depth, height, "
"width) of pooling operator. "
"If globalPooling = true, ksize and paddings will "
"If global_pooling = true, ksize and paddings will "
"be ignored."); // TODO(Chengduo): Add checker.
// (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>("globalPooling",
"(bool, default false) Whether to use the global pooling. "
"If globalPooling = true, ksize and paddings wille be ignored.")
AddAttr<bool>(
"global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings wille be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>(
"strides",
......@@ -178,7 +179,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
"paddings",
"(vector<int>, defalut {0,0,0}), paddings(depth, height, "
"width) of pooling operator. "
"If globalPooling = true, ksize and paddings will be ignored.")
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......@@ -186,7 +187,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
Pool3d Operator.
The pooling3d operation calculates the output based on
the input, poolingType, ksize, strides, and paddings parameters.
the input, pooling_type, ksize, strides, and paddings parameters.
Input(X) and output(Out) are in NCDHW format, where N is batch
size, C is the number of channels, and D, H and W are the depth, height and
width of the feature, respectively. Parameters(ksize, strides, paddings)
......
paddle/operators/pool_op.h
浏览文件 @ fc117ecf
......@@ -57,11 +57,11 @@ class PoolKernel : public framework::OpKernel<T> {
const Tensor* in_x = context.Input<Tensor>("X");
Tensor* out = context.Output<Tensor>("Out");
std::string pooling_type = context.Attr<std::string>("poolingType");
std::string pooling_type = context.Attr<std::string>("pooling_type");
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
......@@ -119,12 +119,12 @@ class PoolGradKernel : public framework::OpKernel<T> {
context.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
std::string pooling_type = context.Attr<std::string>("poolingType");
std::string pooling_type = context.Attr<std::string>("pooling_type");
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
......
paddle/operators/pool_with_index_op.cc
浏览文件 @ fc117ecf
......@@ -44,7 +44,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
"Pooling intput should be 4-D or 5-D tensor.");
if (ctx->Attrs().Get<bool>("globalPooling")) {
if (ctx->Attrs().Get<bool>("global_pooling")) {
ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
......@@ -110,14 +110,14 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>("ksize",
"(vector<int>) The pooling window size(height, "
"width) of pooling operator. "
"If globalPooling = true, ksize and paddings "
"If global_pooling = true, ksize and paddings "
"will be ignored."); // TODO(Chengduo): Add
// checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"globalPooling",
"global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If globalPooling = true, ksize and paddings will be ignored.")
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector<int>, default {1, 1}), strides(height, "
......@@ -128,7 +128,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"paddings",
"(vector<int>, defalut {0, 0}), paddings(height, width) of pooling "
"operator. "
"If globalPooling = true, paddings and will be ignored.")
"If global_pooling = true, paddings and will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......@@ -188,14 +188,14 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::vector<int>>("ksize",
"(vector<int>) The pooling window size(depth, "
"height, width) of pooling operator. "
"If globalPooling = true, ksize and paddings "
"If global_pooling = true, ksize and paddings "
"will be ignored."); // TODO(Chengduo): Add
// checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"globalPooling",
"global_pooling",
"(bool, default false) Whether to use the global pooling. "
"If globalPooling = true, ksize and paddings will be ignored.")
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector<int>, default {1,1,1}), strides(depth, "
......@@ -206,7 +206,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
"paddings",
"(vector, defalut {0,0,0}), paddings(depth, "
"height, width) of pooling operator. "
"If globalPooling = true, paddings and ksize will be ignored.")
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
......
paddle/operators/pool_with_index_op.h
浏览文件 @ fc117ecf
......@@ -35,7 +35,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
......@@ -72,7 +72,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
if (context.Attr<bool>("globalPooling")) {
if (context.Attr<bool>("global_pooling")) {
for (size_t i = 0; i < ksize.size(); ++i) {
paddings[i] = 0;
ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);
......
paddle/operators/recurrent_op.cc
浏览文件 @ fc117ecf
......@@ -387,8 +387,8 @@ class RecurrentGradOp : public RecurrentBase {
auto &p_names = Inputs(kParameters);
PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
for (size_t prog_id = 0; prog_id < pg_names.size(); ++prog_id) {
auto inside_grad_name = framework::GradVarName(p_names[prog_id]);
for (size_t param_id = 0; param_id < pg_names.size(); ++param_id) {
auto inside_grad_name = framework::GradVarName(p_names[param_id]);
// If does not compute gradient of that variable inside rnn, just
// continue
......@@ -406,27 +406,19 @@ class RecurrentGradOp : public RecurrentBase {
attrs["value"] = 0.0f;
auto zero_op = framework::OpRegistry::CreateOp(
"fill_constant", {}, {{"Out", {pg_names[prog_id]}}}, attrs);
"fill_constant", {}, {{"Out", {pg_names[param_id]}}}, attrs);
zero_op->Run(scope, dev_ctx);
}
auto new_inside_name = cur_scope.Rename(inside_grad_name);
// sum gradient
auto *outside_var = scope.FindVar(pg_names[prog_id]);
PADDLE_ENFORCE(outside_var != nullptr);
auto &outside_tensor =
*outside_var->GetMutable<framework::LoDTensor>();
std::string result_var_name;
auto *local_result_var = cur_scope.Var(&result_var_name);
auto &local_result_tensor =
*local_result_var->GetMutable<framework::LoDTensor>();
local_result_tensor.ShareDataWith(outside_tensor);
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {result_var_name, inside_grad_name}}},
{{"Out", {result_var_name}}}, {});
"sum", {{"X", {pg_names[param_id], new_inside_name}}},
{{"Out", {pg_names[param_id]}}}, {});
sum_op->Run(cur_scope, dev_ctx);
cur_scope.Rename(new_inside_name, inside_grad_name);
}
}
VLOG(5) << "Accumulate Parameter finished ";
......
paddle/operators/sequence_concat_op.cc
浏览文件 @ fc117ecf
......@@ -47,7 +47,7 @@ class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(vector<LoDTensor>) Input is a vector of LoDTensor, "
"(LodTensorArray) Input is a vector of LoDTensor, "
"each of which is a variable-length sequence or nested sequence.")
.AsDuplicable();
AddOutput("Out",
......@@ -68,38 +68,42 @@ class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
"The level should be less than the level number of inputs.")
.SetDefault(0);
AddComment(R"DOC(
Sequence Concat Operator.
The sequence_concat operator concatenates multiple LoDTensors.
It supports a sequence (LoD Tensor with level number is 1)
The sequence_concat operator concatenates multiple LoDTensors.
It only supports sequence (LoD Tensor with level number is 1)
or a nested sequence (LoD tensor with level number is 2) as its input.
The following examples explain how the operator works:
- Case1:
If the axis is other than 0(here, axis is 1 and level is 1),
each input should have the same LoD information and the LoD
each input should have the same LoD information and the LoD
information of the output keeps the same as the input.
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
- Case2:
If the axis is 0(here, leve is 0), the inputs are concatenated along
If the axis is 0(here, leve is 0), the inputs are concatenated along
time steps, the LoD information of the output need to re-compute.
The LoD information of level-1 should be same.
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,3,5}, {0,1,2,3,5}}; Dims(x1) = (5,3,4)
LoD(Out) = {{0,5,9}, {0,1,2,3,4,5,6,7,9}}; Dims(Out) = (9,3,4)
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)
- Case3:
If the axis is 0(here, level is 1).
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,3,5}, {0,1,3,4,5}}; Dims(x1) = (5,3,4)
LoD(Out) = {{0,5,9}, {0,2,5,7,9}}; Dims(Out) = (9,3,4)
LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)
NOTE: The levels of all the inputs should be the same.
- Case4:
If the LoD number is 1, axis is 0, level is 0
LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)
LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)
LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)
NOTE: The levels of all the inputs should be the same.
)DOC");
}
};
......
paddle/operators/sequence_concat_op.cu
浏览文件 @ fc117ecf
......@@ -12,8 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_concat_op.h"
namespace ops = paddle::operators;
......
paddle/operators/sequence_concat_op.h
浏览文件 @ fc117ecf
......@@ -24,28 +24,38 @@ using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;
template <typename T>
LoD concatLoD(const std::vector<const T*> ins, const size_t axis,
const size_t level) {
LoD ConcatLoD(const std::vector<const T*> ins, const size_t level) {
auto out_lod = ins[0]->lod();
auto numLevels = ins[0]->NumLevels();
const size_t n = ins.size();
if (axis == 0UL) {
for (size_t i = 1; i < n; ++i) {
for (size_t j = 0; j < ins[i]->lod()[0].size(); ++j) {
out_lod[0][j] += ins[i]->lod()[0][j];
}
const size_t level_idx = ins[0]->NumLevels() - 1 - level;
for (size_t i = 1; i < n; ++i) {
for (size_t j = 0; j < ins[i]->lod()[level_idx].size(); ++j) {
out_lod[level_idx][j] += ins[i]->lod()[level_idx][j];
}
}
if (ins[0]->NumLevels() == 2) {
for (size_t j = 1; j < ins[i]->lod()[1].size(); ++j) {
if (level == 0UL) {
out_lod[1].push_back(out_lod[1].back() + ins[i]->lod()[1][j] -
ins[i]->lod()[1][j - 1]);
} else if (level == 1UL) {
out_lod[1][j] += ins[1]->lod()[1][j];
}
for (size_t i = level_idx; i < numLevels - 1; ++i) {
size_t lod_len = 1;
for (size_t j = 0; j < n; ++j) {
lod_len += ins[j]->lod()[i + 1].size() - 1;
}
out_lod[i + 1].clear();
out_lod[i + 1].resize(lod_len);
size_t idx = 1;
for (size_t j = 0; j < ins[0]->lod()[i].size() - 1; ++j) {
for (size_t k = 0; k < n; ++k) {
for (size_t m = ins[k]->lod()[i][j]; m < ins[k]->lod()[i][j + 1]; ++m) {
out_lod[i + 1][idx] = out_lod[i + 1][idx - 1] +
ins[k]->lod()[i + 1][m + 1] -
ins[k]->lod()[i + 1][m];
idx++;
}
}
}
}
return out_lod;
}
......@@ -82,18 +92,21 @@ class SequenceConcatOpKernel : public framework::OpKernel<T> {
"should be greater than the specify level");
out->mutable_data<T>(ctx.GetPlace());
auto out_lod = concatLoD<LoDTensor>(ins, axis, level);
auto out_lod = ins[0]->lod();
if (axis == 0) {
out_lod = ConcatLoD<LoDTensor>(ins, level);
}
out->set_lod(out_lod);
auto out_lod_level = out_lod[level];
const size_t level_idx = out_lod.size() - level - 1;
auto out_lod_level = framework::ToAbsOffset(out_lod)[level_idx];
for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
Tensor out_t = out->Slice(static_cast<int>(out_lod_level[i]),
static_cast<int>(out_lod_level[i + 1]));
auto out_stride = framework::stride(out_t.dims());
size_t offset = 0;
for (size_t j = 0; j < n; ++j) {
auto in_lod_level = ins[j]->lod()[level];
auto in_lod_level = framework::ToAbsOffset(ins[j]->lod())[level_idx];
auto in_stride = framework::stride(ins[j]->dims());
Tensor in_t = ins[j]->Slice(static_cast<int>(in_lod_level[i]),
static_cast<int>(in_lod_level[i + 1]));
......@@ -124,9 +137,12 @@ class SequenceConcatGradOpKernel : public framework::OpKernel<T> {
x_grads[i]->set_lod(ins[i]->lod());
x_grads[i]->mutable_data<T>(ctx.GetPlace());
}
auto out_lod = concatLoD<LoDTensor>(ins, axis, level);
auto out_lod_level = out_lod[level];
auto out_lod = ins[0]->lod();
if (axis == 0UL) {
out_lod = ConcatLoD<LoDTensor>(ins, level);
}
const size_t level_idx = out_lod.size() - level - 1;
auto out_lod_level = framework::ToAbsOffset(out_lod)[level_idx];
for (size_t i = 0; i < out_lod_level.size() - 1; ++i) {
Tensor out_grad_t =
......@@ -136,7 +152,8 @@ class SequenceConcatGradOpKernel : public framework::OpKernel<T> {
size_t offset = 0;
for (size_t j = 0; j < n; ++j) {
auto x_grad_lod_level = x_grads[j]->lod()[level];
auto x_grad_lod_level =
framework::ToAbsOffset(x_grads[j]->lod())[level_idx];
auto x_grad_stride = framework::stride(x_grads[j]->dims());
Tensor x_grad_t =
x_grads[j]->Slice(static_cast<int>(x_grad_lod_level[i]),
......
paddle/operators/sequence_pool_op.h
浏览文件 @ fc117ecf
......@@ -126,6 +126,7 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
auto out_g_e_v = EigenVector<T>::Flatten(out_g_t);
Eigen::DSizes<int, 2> bcast(h, 1);
if (pooltype == "AVERAGE") {
......@@ -136,9 +137,9 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
} else if (pooltype == "LAST") {
in_g_e.chip(h - 1, 0).device(place) = out_g_e;
in_g_e.chip(h - 1, 0).device(place) = out_g_e_v;
} else if (pooltype == "FIRST") {
in_g_e.chip(0, 0).device(place) = out_g_e;
in_g_e.chip(0, 0).device(place) = out_g_e_v;
} else {
PADDLE_THROW("unsupported pooling pooltype");
}
......
paddle/operators/sequence_softmax_op.cu
浏览文件 @ fc117ecf
......@@ -12,8 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_softmax_op.h"
namespace ops = paddle::operators;
......
paddle/operators/sequence_softmax_op.h
浏览文件 @ fc117ecf
......@@ -14,7 +14,6 @@ limitations under the License. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/softmax.h"
......
paddle/operators/shrink_rnn_memory_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/lod_rank_table.h"
#include "paddle/operators/array_operator.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
class ShrinkRNNMemoryOp : public ArrayOp {
public:
ShrinkRNNMemoryOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto *x_var = scope.FindVar(Input("X"));
PADDLE_ENFORCE(x_var != nullptr, "Input X must be set");
auto &x_tensor = x_var->Get<framework::LoDTensor>();
size_t offset = this->GetOffset(scope, dev_ctx);
auto *rank_table_var = scope.FindVar(Input("RankTable"));
PADDLE_ENFORCE(rank_table_var != nullptr, "RankTable must be set");
auto &rank_table = rank_table_var->Get<framework::LoDRankTable>();
auto &rank_items = rank_table.items();
int dst_num_rows =
std::lower_bound(rank_items.begin(), rank_items.end(), offset,
[](const framework::LoDRankTable::TableItem &a,
size_t b) { return a.length > b; }) -
rank_items.begin();
auto *out_var = scope.FindVar(Output("Out"));
PADDLE_ENFORCE(out_var != nullptr, "Output Out must be set");
auto &out_tensor = *out_var->GetMutable<framework::LoDTensor>();
if (dst_num_rows != 0) {
out_tensor.ShareDataWith(x_tensor.Slice(0, dst_num_rows));
}
}
};
class ShrinkRNNMemoryOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
ShrinkRNNMemoryOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddInput("RankTable", "");
AddInput("I", "");
AddOutput("Out", "");
AddComment("");
}
};
class ShrinkRNNMemoryInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"));
PADDLE_ENFORCE(context->HasInput("I"));
PADDLE_ENFORCE(context->HasInput("RankTable"));
context->SetOutputDim("Out", context->GetInputDim("X"));
}
};
class ShrinkRNNMemoryGradOp : public ArrayOp {
public:
ShrinkRNNMemoryGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto *dout_var = scope.FindVar(Input(framework::GradVarName("Out")));
auto *dx_var = scope.FindVar(Output(framework::GradVarName("X")));
PADDLE_ENFORCE(dx_var != nullptr, "Input Gradient should not be nullptr");
auto *x_var = scope.FindVar(Input("X"));
PADDLE_ENFORCE(x_var != nullptr);
auto &x_tensor = x_var->Get<framework::LoDTensor>();
auto &dx_tensor = *dx_var->GetMutable<framework::LoDTensor>();
dx_tensor.Resize(x_tensor.dims());
dx_tensor.mutable_data(x_tensor.place(), x_tensor.type());
if (dout_var == nullptr) { // dx_tensor fill zero
math::set_constant(dev_ctx, &dx_tensor, 0.0f);
} else {
auto &dout_tensor = dout_var->Get<framework::LoDTensor>();
auto height = dout_tensor.dims()[0];
dx_tensor.Slice(0, static_cast<int>(height))
.CopyFrom(dout_tensor, dout_tensor.place(), dev_ctx);
if (dx_tensor.dims()[0] < height) {
auto rest_tensor = dx_tensor.Slice(
static_cast<int>(height), static_cast<int>(dout_tensor.dims()[0]));
math::set_constant(dev_ctx, &rest_tensor, 0.0f);
}
}
}
};
class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"));
PADDLE_ENFORCE(context->HasOutput(framework::GradVarName("X")));
context->SetOutputDim(framework::GradVarName("X"),
context->GetInputDim("X"));
}
};
class ShrinkRNNGradOpMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDescBind> Apply() const override {
auto *op = new framework::OpDescBind();
op->SetType("shrink_rnn_memory_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(shrink_rnn_memory, ops::ShrinkRNNMemoryOp,
ops::ShrinkRNNMemoryInferShape,
ops::ShrinkRNNMemoryOpProtoMaker, ops::ShrinkRNNGradOpMaker);
REGISTER_OPERATOR(shrink_rnn_memory_grad, ops::ShrinkRNNMemoryGradOp,
ops::ShrinkRNNMemoryGradInferShape);
paddle/operators/softmax_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/operators/softmax_op.h"
namespace ops = paddle::operators;
......
paddle/operators/softmax_op.h
浏览文件 @ fc117ecf
......@@ -13,7 +13,6 @@ 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/softmax.h"
......@@ -21,9 +20,6 @@ namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class SoftmaxKernel : public framework::OpKernel<T> {
......
paddle/operators/split_lod_tensor_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/framework/op_registry.h"
#include "paddle/memory/memcpy.h"
namespace paddle {
namespace operators {
struct CopyRange {
size_t begin;
size_t end;
};
using LoD = framework::LoD;
class SplitLoDTensorOp : public framework::OperatorBase {
public:
SplitLoDTensorOp(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"))->Get<framework::LoDTensor>();
auto &mask = scope.FindVar(Input("Mask"))->Get<framework::LoDTensor>();
auto *out_true =
scope.FindVar(Output("OutTrue"))->GetMutable<framework::LoDTensor>();
auto *out_false =
scope.FindVar(Output("OutFalse"))->GetMutable<framework::LoDTensor>();
auto level = static_cast<size_t>(Attr<int>("level"));
auto &x_lod = x.lod();
auto &mask_dim = mask.dims();
std::unique_ptr<framework::LoDTensor> cpu_mask{new framework::LoDTensor()};
if (platform::is_cpu_place(mask.place())) {
cpu_mask->ShareDataWith(mask);
} else if (platform::is_gpu_place(mask.place())) {
#ifdef PADDLE_WITH_CUDA
cpu_mask->CopyFrom(mask, platform::CPUPlace(), dev_ctx);
#else
PADDLE_THROW("Not supported GPU, Please compile WITH_GPU option");
#endif
}
auto *mask_data = cpu_mask->data<bool>();
std::vector<std::vector<CopyRange>> copy_ranges(mask_dim[0]);
// set out_true/out_false lod
for (size_t t = 0; t < 2; t++) {
LoD *lod = nullptr;
if (t == 0) {
lod = out_false->mutable_lod();
} else {
lod = out_true->mutable_lod();
}
lod->clear();
for (size_t i = 0; i < static_cast<size_t>(mask_dim[0]); i++) {
if (static_cast<size_t>(mask_data[i]) == t) {
size_t start_idx = i;
auto lod_and_offset = framework::GetSubLoDAndAbsoluteOffset(
x_lod, start_idx, start_idx + 1, level);
auto &lod_length = lod_and_offset.first;
framework::AppendLoD(lod, lod_length);
size_t start_offset = lod_and_offset.second.first;
size_t end_offset = lod_and_offset.second.second;
copy_ranges[t].emplace_back(CopyRange{start_offset, end_offset});
}
}
}
for (size_t t = 0; t < 2; ++t) {
framework::LoDTensor *out;
if (t == 0) {
out = out_false;
} else {
out = out_true;
}
auto &ranges = copy_ranges[t];
size_t height = std::accumulate(
ranges.begin(), ranges.end(), 0UL,
[](size_t a, const CopyRange &b) { return a + b.end - b.begin; });
auto x_dim = x.dims();
x_dim[0] = static_cast<int64_t>(height);
out->Resize(x_dim);
out->mutable_data(x.place(), x.type());
size_t offset = 0;
for (auto &each_range : ranges) {
size_t len = each_range.end - each_range.begin;
if (len == 0) {
continue;
}
// out[offset: offset+len] = x[each_range.begin: each_range.end]
out->Slice(static_cast<int>(offset), static_cast<int>(offset + len))
.CopyFrom(x.Slice(static_cast<int>(each_range.begin),
static_cast<int>(each_range.end)),
x.place(), dev_ctx);
offset += len;
}
}
}
};
class SplitLoDTensorOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
SplitLoDTensorOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input LoDTensor");
AddInput("Mask", "A bool column vector which mask the input");
AddOutput("OutTrue", "True branch of input LoDTensor");
AddOutput("OutFalse", "False branch of input LoDTensor");
AddAttr<int>("level", "(int) the specific lod level to split.")
.SetDefault(0)
.EqualGreaterThan(0);
AddComment(
R"DOC(
Split a LoDTensor with a Mask at certain level. The input LoDTensor
has 3 sequence at certain lod level. The Mask is a bool column vector,
such as [0, 1, 0] at the same level. The first and third sequence will
be send to False Output LoDTensor; whereas the second sequence will
be send to True Output LoDTensor. Please refer to MergeLoDTensorOp.)DOC");
}
};
class SplitLoDTensorInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("X"),
"SplitLoDTensorOp must has input X.");
PADDLE_ENFORCE(context->HasInput("Mask"),
"SplitLoDTensorOp must has input Mask.");
PADDLE_ENFORCE(context->HasOutput("OutTrue"),
"SplitLoDTensorOp must has output OutTrue.");
PADDLE_ENFORCE(context->HasOutput("OutFalse"),
"SplitLoDTensorOp must has output OutFalse.");
auto mask_dim = context->GetInputDim("Mask");
PADDLE_ENFORCE_EQ(mask_dim.size(), 2);
PADDLE_ENFORCE_EQ(mask_dim[1], 1);
context->SetOutputDim("OutTrue", context->GetInputDim("X"));
context->SetOutputDim("OutFalse", context->GetInputDim("X"));
}
};
class SplitLoDTensorArrayGradMaker : 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("merge_lod_tensor");
grad_op->SetInput("InTrue", OutputGrad("OutTrue"));
grad_op->SetInput("InFalse", OutputGrad("OutFalse"));
grad_op->SetInput("Mask", Input("Mask"));
grad_op->SetInput("X", Input("X"));
grad_op->SetOutput("Out", InputGrad("X"));
grad_op->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDescBind>(grad_op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(split_lod_tensor, ops::SplitLoDTensorOp,
ops::SplitLoDTensorOpProtoMaker,
ops::SplitLoDTensorInferShape,
ops::SplitLoDTensorArrayGradMaker);
paddle/operators/squared_l2_norm_op.h
浏览文件 @ fc117ecf
......@@ -29,7 +29,7 @@ class SquaredL2NormKernel : 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.square().sum();
......
paddle/operators/sum_op.cc
浏览文件 @ fc117ecf
......@@ -99,11 +99,12 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
bool any_input_is_lod_tensor = std::any_of(
inputs.begin(), inputs.end(), [block](const std::string& name) {
return block->Var(name)->GetType() == framework::VarDesc::LOD_TENSOR;
return block->FindRecursiveOrCreateVar(name)->GetType() ==
framework::VarDesc::LOD_TENSOR;
});
auto is_tensor_array = [block](const std::string& name) {
return block->Var(name)->GetType() ==
return block->FindRecursiveOrCreateVar(name)->GetType() ==
framework::VarDesc::LOD_TENSOR_ARRAY;
};
......@@ -120,7 +121,7 @@ class SumOpVarTypeInference : public framework::VarTypeInference {
}
auto out_var_name = op_desc.Output("Out").front();
block->Var(out_var_name)->SetType(var_type);
block->FindRecursiveOrCreateVar(out_var_name)->SetType(var_type);
}
};
......
paddle/operators/tensor_array_read_write_op.cc
浏览文件 @ fc117ecf
......@@ -11,48 +11,18 @@
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/lod_tensor_array.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/array_operator.h"
namespace paddle {
namespace operators {
class ArrayOpBase : public framework::OperatorBase {
public:
ArrayOpBase(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 {}
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;
}
};
class WriteToArrayOp : public ArrayOpBase {
class WriteToArrayOp : public ArrayOp {
public:
WriteToArrayOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ArrayOpBase(type, inputs, outputs, attrs) {}
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
......@@ -117,18 +87,19 @@ class WriteToArrayInferVarType : public framework::VarTypeInference {
framework::BlockDescBind *block) const override {
for (auto &out_var : op_desc.OutputArgumentNames()) {
VLOG(10) << "Set Variable " << out_var << " as LOD_TENSOR_ARRAY";
block->Var(out_var)->SetType(framework::VarDesc::LOD_TENSOR_ARRAY);
block->FindRecursiveOrCreateVar(out_var)->SetType(
framework::VarDesc::LOD_TENSOR_ARRAY);
}
}
};
class ReadFromArrayOp : public ArrayOpBase {
class ReadFromArrayOp : public ArrayOp {
public:
ReadFromArrayOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ArrayOpBase(type, inputs, outputs, attrs) {}
: ArrayOp(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto *x = scope.FindVar(Input("X"));
......
paddle/operators/while_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 <vector>
#include "paddle/framework/executor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
using StepScopeVar = std::vector<framework::Scope *>;
using LoDTensor = framework::LoDTensor;
constexpr char kStepBlock[] = "step_block";
constexpr char kCondition[] = "Condition";
constexpr char kStepScopes[] = "StepScopes";
constexpr char kParamGrads[] = "X@Grad";
constexpr char kParameters[] = "X";
class WhileOp : public framework::OperatorBase {
public:
WhileOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition)));
auto &cond = scope.FindVar(Input(kCondition))->Get<LoDTensor>();
PADDLE_ENFORCE_EQ(cond.dims(), paddle::framework::make_ddim({1}));
framework::Executor executor(dev_ctx);
auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
auto *program = block->Program();
auto step_scopes =
scope.FindVar(Output(kStepScopes))->GetMutable<StepScopeVar>();
while (cond.data<bool>()[0]) {
auto &current_scope = scope.NewScope();
step_scopes->push_back(&current_scope);
executor.Run(*program, &current_scope, block->ID(),
false /*create_local_scope*/);
}
}
};
class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
public:
WhileOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(kParameters,
"A set of variables, which are required by operators inside the "
"block of While Op.")
.AsDuplicable();
AddInput(
kCondition,
"(Bool) An scalar. When it's False, the While Op will be terminated.")
.AsDuplicable();
AddOutput("Out",
"A set of variables, which will be assigned with values "
"generated by perators inside the block of While Op.")
.AsDuplicable();
AddOutput(kStepScopes,
"(StepScopeVar) A vector of local scope, which size equals the "
"step number of While Op. The i'th scope storages temporary "
"variables generated in the i'th step.");
AddAttr<framework::BlockDescBind *>(kStepBlock,
"The step block inside WhileOp");
AddComment(R"DOC(
)DOC");
}
};
class WhileGradOp : public framework::OperatorBase {
public:
WhileGradOp(const std::string &type, const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
// PADDLE_ENFORCE(...)
framework::Executor executor(dev_ctx);
auto *block = Attr<framework::BlockDescBind *>(kStepBlock);
auto *program = block->Program();
auto *step_scopes =
scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();
for (auto cur_scope_iter = step_scopes->rbegin();
cur_scope_iter != step_scopes->rend(); ++cur_scope_iter) {
executor.Run(*program, *cur_scope_iter, block->ID(), false);
auto &pg_names = Outputs(kParamGrads);
auto &p_names = Inputs(kParameters);
PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
for (size_t prog_id = 0; prog_id < pg_names.size(); ++prog_id) {
auto inside_grad_name = framework::GradVarName(p_names[prog_id]);
// // TODO(tonyyang-savil: Not sure we need the following
// // If does not compute gradient of that variable inside rnn,
// just
// // continue
// if (local_var_names.find(inside_grad_name) ==
// local_var_names.end()) {
// continue;
// }
// zero gradient variable in step 0
if (cur_scope_iter == step_scopes->rbegin()) {
auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
PADDLE_ENFORCE_NOT_NULL(var);
if (var->IsType<LoDTensor>()) {
auto &inside_tensor = var->Get<framework::LoDTensor>();
framework::AttributeMap attrs;
attrs["data_type"] = framework::ToDataType(inside_tensor.type());
attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
attrs["value"] = 0.0f;
auto zero_op = framework::OpRegistry::CreateOp(
"fill_constant", {}, {{"Out", {pg_names[prog_id]}}}, attrs);
zero_op->Run(scope, dev_ctx);
}
}
// sum gradient
auto *outside_var = scope.FindVar(pg_names[prog_id]);
PADDLE_ENFORCE_NOT_NULL(outside_var);
auto &outside_tensor = *outside_var->GetMutable<framework::LoDTensor>();
std::string result_var_name;
auto *local_result_var = (*cur_scope_iter)->Var(&result_var_name);
auto &local_result_tensor =
*local_result_var->GetMutable<framework::LoDTensor>();
local_result_tensor.ShareDataWith(outside_tensor);
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {result_var_name, inside_grad_name}}},
{{"Out", {result_var_name}}}, {});
sum_op->Run(**cur_scope_iter, dev_ctx);
}
}
}
};
class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
virtual std::unique_ptr<framework::OpDescBind> Apply() const {
auto *grad = new framework::OpDescBind();
grad->SetType("while_grad");
for (auto &input_param : this->InputNames()) {
grad->SetInput(input_param, this->Input(input_param));
grad->SetOutput(framework::GradVarName(input_param),
this->InputGrad(input_param));
}
for (auto &output_param : this->OutputNames()) {
grad->SetInput(output_param, this->Output(output_param));
if (output_param != kStepScopes) {
grad->SetInput(framework::GradVarName(output_param),
this->OutputGrad(output_param));
}
}
grad->SetAttrMap(this->Attrs());
grad->SetBlockAttr(kStepBlock, *grad_block_[0]);
return std::unique_ptr<framework::OpDescBind>(grad);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(while, paddle::operators::WhileOp,
paddle::operators::WhileOpMaker,
paddle::operators::WhileGradOpDescMaker);
paddle/platform/call_once.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 <mutex>
namespace paddle {
namespace platform {
/*
The current implementation of std::call_once has a bug described in
https://stackoverflow.com/questions/41717579/stdcall-once-hangs-on-second-call-after-callable-threw-on-first-call.
This is likely caused by a deeper bug of pthread_once, which is discussed in
https://patchwork.ozlabs.org/patch/482350/
This wrap is a hack to avoid this bug.
*/
template <typename Callable, typename... Args>
inline void call_once(std::once_flag& flag, Callable&& f, Args&&... args) {
bool good = false;
std::exception ex;
std::call_once(flag,
[&](Args&&... args) {
try {
f(args...);
good = true;
} catch (const std::exception& e) {
ex = e;
} catch (...) {
ex = std::runtime_error("excption caught in call_once");
}
},
args...);
if (!good) {
throw std::exception(ex);
}
}
} // namespace platform
} // namespace paddle
paddle/platform/dynload/nccl.h
浏览文件 @ fc117ecf
......@@ -17,6 +17,7 @@
#include <dlfcn.h>
#include <nccl.h>
#include <mutex>
#include "paddle/platform/call_once.h"
#include "paddle/platform/dynload/dynamic_loader.h"
namespace paddle {
......@@ -27,18 +28,18 @@ extern std::once_flag nccl_dso_flag;
extern void* nccl_dso_handle;
#ifdef PADDLE_USE_DSO
#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using nccl_func = decltype(__name(args...)) (*)(Args...); \
std::call_once(nccl_dso_flag, \
paddle::platform::dynload::GetNCCLDsoHandle, \
&nccl_dso_handle); \
void* p_##__name = dlsym(nccl_dso_handle, #__name); \
return reinterpret_cast<nccl_func>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using nccl_func = decltype(__name(args...)) (*)(Args...); \
platform::call_once(nccl_dso_flag, \
paddle::platform::dynload::GetNCCLDsoHandle, \
&nccl_dso_handle); \
void* p_##__name = dlsym(nccl_dso_handle, #__name); \
return reinterpret_cast<nccl_func>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#else
#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \
......
paddle/platform/transform.h
浏览文件 @ fc117ecf
......@@ -49,8 +49,6 @@ struct Transform<platform::CPUPlace> {
template <typename InputIter, typename OutputIter, typename UnaryOperation>
void operator()(const DeviceContext& context, InputIter first, InputIter last,
OutputIter result, UnaryOperation op) {
auto place = context.GetPlace();
PADDLE_ENFORCE(is_cpu_place(place), "It must use CPU place.");
std::transform(first, last, result, op);
}
......@@ -59,8 +57,6 @@ struct Transform<platform::CPUPlace> {
void operator()(const DeviceContext& context, InputIter1 first1,
InputIter1 last1, InputIter2 first2, OutputIter result,
BinaryOperation op) {
auto place = context.GetPlace();
PADDLE_ENFORCE(is_cpu_place(place), "It must use CPU place.");
std::transform(first1, last1, first2, result, op);
}
};
......
paddle/pybind/pybind.cc
浏览文件 @ fc117ecf
......@@ -42,6 +42,9 @@ limitations under the License. */
#include "paddle/platform/gpu_info.h"
#endif
// disable auto conversion to list in Python
PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
namespace paddle {
namespace pybind {
static size_t UniqueIntegerGenerator(const std::string &prefix) {
......
proto/ModelConfig.proto
浏览文件 @ fc117ecf
......@@ -321,6 +321,19 @@ message ClipConfig {
required double max = 2;
}
message ROIPoolConfig {
required uint32 pooled_width = 1;
required uint32 pooled_height = 2;
required float spatial_scale = 3;
optional uint32 height = 4 [ default = 1 ];
optional uint32 width = 5 [ default = 1 ];
}
message ScaleSubRegionConfig {
required ImageConfig image_conf = 1;
required float value = 2;
}
message LayerInputConfig {
required string input_layer_name = 1;
optional string input_parameter_name = 2;
......@@ -342,6 +355,8 @@ message LayerInputConfig {
optional MultiBoxLossConfig multibox_loss_conf = 16;
optional DetectionOutputConfig detection_output_conf = 17;
optional ClipConfig clip_conf = 18;
optional ScaleSubRegionConfig scale_sub_region_conf = 19;
optional ROIPoolConfig roi_pool_conf = 20;
}
message LayerConfig {
......
python/paddle/trainer/config_parser.py
浏览文件 @ fc117ecf
......@@ -1969,6 +1969,18 @@ class DetectionOutputLayer(LayerBase):
self.config.size = size
@config_layer('roi_pool')
class ROIPoolLayer(LayerBase):
def __init__(self, name, inputs, pooled_width, pooled_height, spatial_scale,
num_channels, **xargs):
super(ROIPoolLayer, self).__init__(name, 'roi_pool', 0, inputs)
config_assert(len(inputs) == 2, 'ROIPoolLayer must have 2 inputs')
self.config.inputs[0].roi_pool_conf.pooled_width = pooled_width
self.config.inputs[0].roi_pool_conf.pooled_height = pooled_height
self.config.inputs[0].roi_pool_conf.spatial_scale = spatial_scale
self.set_cnn_layer(name, pooled_height, pooled_width, num_channels)
@config_layer('data')
class DataLayer(LayerBase):
def __init__(self,
......@@ -3801,6 +3813,25 @@ class SwitchOrderLayer(LayerBase):
self.config.reshape_conf.width_axis.extend(reshape['width'])
@config_layer('scale_sub_region')
class ScaleSubRegionLayer(LayerBase):
def __init__(self, name, inputs, value, **xargs):
super(ScaleSubRegionLayer, self).__init__(
name, 'scale_sub_region', 0, inputs=inputs, **xargs)
scale_sub_region_conf = self.config.inputs[0].scale_sub_region_conf
scale_sub_region_conf.value = value
# get channel, width and height from input_0 layer
input_layer = self.get_input_layer(0)
image_conf = scale_sub_region_conf.image_conf
image_conf.img_size = input_layer.width
image_conf.img_size_y = input_layer.height
image_conf.channels = input_layer.size / (input_layer.width *
input_layer.height)
self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
image_conf.channels)
# Deprecated, use a new layer specific class instead
@config_func
def Layer(name, type, **xargs):
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ fc117ecf
......@@ -122,6 +122,7 @@ __all__ = [
'cross_channel_norm_layer',
'multibox_loss_layer',
'detection_output_layer',
'roi_pool_layer',
'spp_layer',
'pad_layer',
'eos_layer',
......@@ -144,6 +145,7 @@ __all__ = [
'img_conv3d_layer',
'resize_layer',
'sub_seq_layer',
'scale_sub_region_layer',
]
......@@ -220,6 +222,7 @@ class LayerType(object):
PRIORBOX_LAYER = 'priorbox'
MULTIBOX_LOSS_LAYER = 'multibox_loss'
DETECTION_OUTPUT_LAYER = 'detection_output'
ROI_POOL_LAYER = 'roi_pool'
CTC_LAYER = 'ctc'
WARP_CTC_LAYER = 'warp_ctc'
......@@ -255,6 +258,8 @@ class LayerType(object):
RESIZE = 'resize'
SUB_SEQ_LAYER = 'subseq'
SCALE_SUB_REGION_LAYER = 'scale_sub_region'
@staticmethod
def is_layer_type(type_name):
"""
......@@ -786,10 +791,9 @@ class MixedLayerType(LayerOutput):
:type size: int
:param act: Activation type.
:type act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute or None
......@@ -886,10 +890,9 @@ def mixed_layer(size=0,
then this function will just return layer's name.
:param act: Activation Type. LinearActivation is the default.
:type act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The extra layer config. Default is None.
:type layer_attr: ExtraLayerAttribute
......@@ -1031,10 +1034,9 @@ def fc_layer(input,
:type act: BaseActivation
:param param_attr: The Parameter Attribute|list.
:type param_attr: ParameterAttribute
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: Extra Layer config.
:type layer_attr: ExtraLayerAttribute | None
......@@ -1305,6 +1307,50 @@ def detection_output_layer(input_loc,
name, LayerType.DETECTION_OUTPUT_LAYER, parents=parents, size=size)
@wrap_name_default("roi_pool")
def roi_pool_layer(input,
rois,
pooled_width,
pooled_height,
spatial_scale,
num_channels=None,
name=None):
"""
A layer used by Fast R-CNN to extract feature maps of ROIs from the last
feature map.
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput.
:param rois: The input ROIs' data.
:type rois: LayerOutput.
:param pooled_width: The width after pooling.
:type pooled_width: int
:param pooled_height: The height after pooling.
:type pooled_height: int
:param spatial_scale: The spatial scale between the image and feature map.
:type spatial_scale: float
:param num_channels: number of input channel.
:type num_channels: int
:return: LayerOutput
"""
if num_channels is None:
assert input.num_filters is not None
num_channels = input.num_filters
size = num_channels * pooled_width * pooled_height
Layer(
name=name,
type=LayerType.ROI_POOL_LAYER,
inputs=[input.name, rois.name],
pooled_width=pooled_width,
pooled_height=pooled_height,
spatial_scale=spatial_scale,
num_channels=num_channels)
return LayerOutput(
name, LayerType.ROI_POOL_LAYER, parents=[input, rois], size=size)
@wrap_name_default("cross_channel_norm")
def cross_channel_norm_layer(input, name=None, param_attr=None):
"""
......@@ -1387,10 +1433,9 @@ def pooling_layer(input,
:type pooling_type: BasePoolingType | None
:param stride: The step size between successive pooling regions.
:type stride: Int
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: The Extra Attributes for layer, such as dropout.
:type layer_attr: ExtraLayerAttribute | None
......@@ -1488,10 +1533,9 @@ def lstmemory(input,
:type gate_act: BaseActivation
:param state_act: state activation type, TanhActivation by default.
:type state_act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: Parameter Attribute.
:type param_attr: ParameterAttribute | None | False
......@@ -1614,10 +1658,9 @@ def grumemory(input,
This activation affects the :math:`z_t` and :math:`r_t`. It is the
:math:`\\sigma` in the above formula.
:type gate_act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: Parameter Attribute.
:type param_attr: ParameterAttribute | None | False
......@@ -1814,10 +1857,9 @@ def expand_layer(input,
:type expand_as: LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param expand_level: whether input layer is timestep(default) or sequence.
:type expand_level: ExpandLevel
......@@ -1936,10 +1978,9 @@ def seq_reshape_layer(input,
:type act: BaseActivation
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -2323,10 +2364,9 @@ def hsigmoid(input,
:type num_classes: int | None
:param name: The name of this layer. It is optional.
:type name: basestring
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: Parameter Attribute. None means default parameter.
:type param_attr: ParameterAttribute | None
......@@ -2466,10 +2506,9 @@ def img_conv_layer(input,
:type dilation: int | tuple | list
:param dilation_y: The y dimension of the dilation.
:type dilation_y: int
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: number of input channels. If None will be set
automatically from previous output.
......@@ -3216,10 +3255,9 @@ def addto_layer(input, act=None, name=None, bias_attr=None, layer_attr=None):
:type input: LayerOutput | list | tuple
:param act: Activation Type. LinearActivation is the default.
:type act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: Extra Layer attribute.
:type layer_attr: ExtraLayerAttribute
......@@ -3372,10 +3410,9 @@ def seq_concat_layer(a, b, act=None, name=None, layer_attr=None,
:type act: BaseActivation
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -3555,10 +3592,9 @@ def lstm_step_layer(input,
:type gate_act: BaseActivation
:param state_act: State Activation Type. TanhActivation is the default.
:type state_act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: layer's extra attribute.
:type layer_attr: ExtraLayerAttribute
......@@ -3614,10 +3650,9 @@ def gru_step_layer(input,
:param name: The name of this layer. It is optional.
:param gate_act: Activation type of this layer's two gates. Default is Sigmoid.
:type gate_act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: the parameter_attribute for transforming the output_mem
from previous step.
......@@ -3677,10 +3712,9 @@ def gru_step_naive_layer(input,
:type act: BaseActivation
:param gate_act: Activation type of this layer's two gates. Default is Sigmoid.
:type gate_act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr:
:param layer_attr:
......@@ -3810,10 +3844,9 @@ def recurrent_layer(input,
:type input: LayerOutput
:param act: Activation type. TanhActivation is the default.
:type act: BaseActivation
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param param_attr: parameter attribute.
:type param_attr: ParameterAttribute
......@@ -4803,10 +4836,9 @@ def tensor_layer(a,
:type act: BaseActivation
:param param_attr: The Parameter Attribute.
:type param_attr: ParameterAttribute
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: Extra Layer config.
:type layer_attr: ExtraLayerAttribute | None
......@@ -4868,10 +4900,9 @@ def selective_fc_layer(input,
:type act: BaseActivation
:param param_attr: The Parameter Attribute.
:type param_attr: ParameterAttribute
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: Extra Layer config.
:type layer_attr: ExtraLayerAttribute | None
......@@ -5494,7 +5525,11 @@ def crf_decoding_layer(input,
return LayerOutput(name, LayerType.CRF_DECODING_LAYER, parents, size=1)
@wrap_act_default(act=SigmoidActivation())
"""
Following are cost Layers.
"""
@wrap_bias_attr_default(has_bias=True)
@wrap_param_attr_default()
@wrap_name_default()
......@@ -5502,7 +5537,6 @@ def crf_decoding_layer(input,
def nce_layer(input,
label,
num_classes=None,
act=None,
param_attr=None,
weight=None,
num_neg_samples=10,
......@@ -5511,9 +5545,12 @@ def nce_layer(input,
bias_attr=None,
layer_attr=None):
"""
Noise-contrastive estimation.
Implements the method in the following paper:
A fast and simple algorithm for training neural probabilistic language models.
Noise-contrastive estimation. This layer implements the method in the
following paper:
Reference:
A fast and simple algorithm for training neural probabilistic language
models. https://www.cs.toronto.edu/~amnih/papers/ncelm.pdf
The example usage is:
......@@ -5525,32 +5562,37 @@ def nce_layer(input,
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input layers. It could be a LayerOutput of list/tuple of LayerOutput.
:param input: The input layers. It should be a LayerOutput or a list/tuple
of LayerOutput.
:type input: LayerOutput | list | tuple | collections.Sequence
:param label: label layer
:param label: The ground truth.
:type label: LayerOutput
:param weight: weight layer, can be None(default)
:param weight: The weight layer defines a weight for each sample in the
mini-batch. The default value is None.
:type weight: LayerOutput
:param num_classes: number of classes.
:param num_classes: The class number.
:type num_classes: int
:param act: Activation type. SigmoidActivation is the default.
:type act: BaseActivation
:param param_attr: The Parameter Attribute|list.
:type param_attr: ParameterAttribute
:param num_neg_samples: number of negative samples. Default is 10.
:param param_attr: The parameter attributes.
:type param_attr: ParameterAttribute|list
:param num_neg_samples: The number of sampled negative labels. The default
value is 10.
:type num_neg_samples: int
:param neg_distribution: The distribution for generating the random negative labels.
A uniform distribution will be used if not provided.
If not None, its length must be equal to num_classes.
:param neg_distribution: The discrete noisy distribution over the output
space from which num_neg_samples negative labels
are sampled. If this parameter is not set, a
uniform distribution will be used. A user defined
distribution is a list whose length must be equal
to the num_classes. Each member of the list defines
the probability of a class given input x.
:type neg_distribution: list | tuple | collections.Sequence | None
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The attribute for bias. If this parameter is set False or
any object whose type is not ParameterAttribute, no bias
is added. If this parameter is set True, the bias is
initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param layer_attr: Extra Layer Attribute.
:type layer_attr: ExtraLayerAttribute
:return: layer name.
:return: The LayerOutput object.
:rtype: LayerOutput
"""
if isinstance(input, LayerOutput):
......@@ -5573,8 +5615,6 @@ def nce_layer(input,
assert isinstance(neg_distribution, collections.Sequence)
assert len(neg_distribution) == num_classes
assert abs(sum(neg_distribution) - 1.0) < 1e-5
if not isinstance(act, BaseActivation):
raise TypeError()
ipts_for_layer = []
parents = []
......@@ -5596,7 +5636,7 @@ def nce_layer(input,
type=LayerType.NCE_LAYER,
num_classes=num_classes,
neg_sampling_dist=neg_distribution,
active_type=act.name,
active_type=SigmoidActivation().name,
num_neg_samples=num_neg_samples,
inputs=ipts_for_layer,
bias=ParamAttr.to_bias(bias_attr),
......@@ -5606,12 +5646,7 @@ def nce_layer(input,
LayerType.NCE_LAYER,
parents=parents,
size=l.config.size,
activation=act)
"""
following are cost Layers.
"""
activation=SigmoidActivation())
@wrap_name_default()
......@@ -5770,20 +5805,21 @@ def cross_entropy(input,
:param input: The first input layer.
:type input: LayerOutput.
:param label: The input label.
:type input: LayerOutput.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring.
:param coeff: The cost is multiplied with coeff.
The coefficient affects the gradient in the backward.
:type coeff: float.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default.
:type coeff: float
:param weight: The cost of each sample is multiplied with each weight.
The weight should be a layer with size=1. Note that gradient
will not be calculated for weight.
:type weight: LayerOutout
:param layer_attr: Extra Layer Attribute.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
:rtype: LayerOutput
"""
ipts, parents = __cost_input__(input, label, weight)
......@@ -5816,19 +5852,21 @@ def cross_entropy_with_selfnorm(input,
label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring.
:param coeff: The coefficient affects the gradient in the backward.
:type coeff: float.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default.
:type coeff: float
:param softmax_selfnorm_alpha: The scale factor affects the cost.
:type softmax_selfnorm_alpha: float.
:param layer_attr: Extra Layer Attribute.
:type softmax_selfnorm_alpha: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
:rtype: LayerOutput
"""
Layer(
name=name,
......@@ -5849,7 +5887,7 @@ def cross_entropy_with_selfnorm(input,
@layer_support()
def sum_cost(input, name=None, layer_attr=None):
"""
A loss layer which calculate the sum of the input as loss
A loss layer which calculates the sum of the input as loss.
The example usage is:
......@@ -5858,10 +5896,11 @@ def sum_cost(input, name=None, layer_attr=None):
cost = sum_cost(input=input_layer)
:param input: The input of this layer.
:type input: LayerOutput.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring.
:param layer_attr: Extra Layer Attribute.
:type name: basestring
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
......@@ -5901,16 +5940,18 @@ def huber_regression_cost(input,
cost = huber_regression_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring.
:type name: basestring
:param delta: The difference between the observed and predicted values.
:type delta: float.
:param coeff: The coefficient affects the gradient in the backward.
:type coeff: float.
:param layer_attr: Extra Layer Attribute.
:type delta: float
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
......@@ -5951,17 +5992,19 @@ def huber_classification_cost(input,
cost = huber_classification_cost(input=input_layer, label=label_layer)
:param input: The first input layer.
:type input: LayerOutput.
:type input: LayerOutput
:param label: The input label.
:type input: LayerOutput.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring.
:param coeff: The coefficient affects the gradient in the backward.
:type coeff: float.
:param layer_attr: Extra Layer Attribute.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default.
:type coeff: float
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
if input.size is not None:
......@@ -5998,10 +6041,12 @@ def multi_binary_label_cross_entropy(input,
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring
:param coeff: The coefficient affects the gradient in the backward.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default.
:type coeff: float
:param layer_attr: Extra Layer Attribute.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -6104,7 +6149,7 @@ def cross_entropy_over_beam(input, name=None):
:param input: Input beams for this layer.
:type input: BeamInput
:param name: The name of this layer.
:param name: The name of this layer. It is optional.
:type name: basestring
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -6139,7 +6184,7 @@ def cross_entropy_over_beam(input, name=None):
def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
"""
This is a L1 loss but more smooth. It requires that the
size of input and label are equal. The formula is as follows,
sizes of input and label are equal. The formula is as follows,
.. math::
......@@ -6151,8 +6196,9 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
smooth_{L1}(x) = \\begin{cases} 0.5x^2& \\text{if} \\ |x| < 1 \\\\ |x|-0.5& \\text{otherwise} \end{cases}
More details can be found by referring to `Fast R-CNN
<https://arxiv.org/pdf/1504.08083v2.pdf>`_
Reference:
Fast R-CNN
https://arxiv.org/pdf/1504.08083v2.pdf
The example usage is:
......@@ -6166,10 +6212,12 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
:param label: The input label.
:type input: LayerOutput
:param name: The name of this layer. It is optional.
:type name: None | basestring
:param coeff: The coefficient affects the gradient in the backward.
:type name: basestring
:param coeff: The weight of the gradient in the back propagation.
1.0 is the default.
:type coeff: float
:param layer_attr: Extra Layer Attribute.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -6191,12 +6239,12 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
@wrap_name_default()
def multiplex_layer(input, name=None, layer_attr=None):
"""
This layer multiplex multiple layers according to the index,
which is provided by the first input layer.
inputs[0]: the index of the layer to output of size batchSize.
This layer multiplex multiple layers according to the indexes,
which are provided by the first input layer.
inputs[0]: the indexes of the layers to form the output of size batchSize.
inputs[1:N]; the candidate output data.
For each index i from 0 to batchSize -1, the output is the i-th row of the
(index[i] + 1)-th layer.
For each index i from 0 to batchSize - 1, the i-th row of the output is the
the same to the i-th row of the (index[i] + 1)-th layer.
For each i-th row of output:
.. math::
......@@ -6215,7 +6263,8 @@ def multiplex_layer(input, name=None, layer_attr=None):
:type input: list of LayerOutput
:param name: The name of this layer. It is optional.
:type name: basestring
:param layer_attr: extra layer attributes.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -6319,14 +6368,14 @@ def row_conv_layer(input,
:type context_len: int
:param act: Activation Type. LinearActivation is the default.
:type act: BaseActivation
:param param_attr: The Parameter Attribute. If None, the parameter will be
initialized smartly. It's better to set it by yourself.
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param layer_attr: Extra Layer config.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput)
assert context_len > 0, "the context_len must be greatet than 0."
......@@ -6351,7 +6400,7 @@ def prelu_layer(input,
param_attr=None,
layer_attr=None):
"""
The Parameter Relu activation that actives outputs with a learnable weight.
The Parametric Relu activation that actives outputs with a learnable weight.
Reference:
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
......@@ -6371,16 +6420,17 @@ def prelu_layer(input,
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param partial_sum: this parameter makes a group of inputs share a same weight.
:param partial_sum: this parameter makes a group of inputs share the same weight.
- partial_sum = 1, indicates the element-wise activation: each element has a weight.
- partial_sum = number of elements in one channel, indicates the channel-wise activation, elements in a channel share a same weight.
- partial_sum = number of outputs, indicates all elements share a same weight.
- partial_sum = number of elements in one channel, indicates the channel-wise activation, elements in a channel share the same weight.
- partial_sum = number of outputs, indicates all elements share the same weight.
:type partial_sum: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute | None
:param layer_attr: Extra layer configurations. Default is None.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -6436,34 +6486,34 @@ def gated_unit_layer(input,
:param input: The input of this layer.
:type input: LayerOutput
:param size: output size of the gated unit.
:param size: The dimension of this layer's output.
:type size: int
:param act: Activation type of the projected input. LinearActivation is the default.
:param act: Activation type of the projection. LinearActivation is the default.
:type act: BaseActivation
:param name: The name of this layer. It is optional.
:type name: basestring
:param gate_attr: Attributes to tune the gate output, for example, error
clipping threshold, dropout and so on. See ExtraLayerAttribute for
more details.
:param gate_attr: The extra layer attribute of the gate. See ExtraLayerAttribute for
details.
:type gate_attr: ExtraLayerAttribute | None
:param gate_param_attr: Attributes to tune the learnable projected matrix
parameter of the gate.
:type gate_param_attr: ParameterAttribute | None
:param gate_bias_attr: Attributes to tune the learnable bias of the gate.
:type gate_bias_attr: ParameterAttribute | None
:param inproj_attr: Attributes to the tune the projected input, for
example, error clipping threshold, dropout and so on. See
ExtraLayerAttribute for more details.
:param gate_param_attr: The parameter attribute of the gate. See ParameterAttribute
for details.
:type gate_param_attr: ParameterAttribute
:param gate_bias_attr: The bias attribute of the gate. If the parameter is set to False or
an object whose type is not ParameterAttribute, no bias is defined.
If the parameter is set to True, the bias is initialized to zero.
:type gate_bias_attr: ParameterAttribute | bool | None | Any
:param inproj_attr: Extra layer attributes of the projection. See ExtraLayerAttribute for
details.
:type inproj_attr: ExtraLayerAttribute | None
:param inproj_param_attr: Attributes to tune the learnable parameter of
the projection of input.
:type inproj_param_attr: ParameterAttribute | None
:param inproj_bias_attr: Attributes to tune the learnable bias of
projection of the input.
:type inproj_bias_attr: ParameterAttribute | None
:param layer_attr: Attributes to tune the final output of the gated unit,
for example, error clipping threshold, dropout and so on. See
ExtraLayerAttribute for more details.
:param inproj_param_attr: The parameter attribute of the projection. See ParameterAttribute
for details.
:type inproj_param_attr: ParameterAttribute
:param inproj_bias_attr: The bias attribute of the projection. If the parameter is set to False
or an object whose type is not ParameterAttribute, no bias is defined.
If the parameter is set to True, the bias is initialized to zero.
:type inproj_bias_attr: ParameterAttribute | bool | None | Any
:param layer_attr: Extra layer attribute of the product. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute | None
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -6659,9 +6709,9 @@ def clip_layer(input, min, max, name=None):
:param input: The input of this layer.
:type input: LayerOutput.
:param min: The lower threshold for clipping.
:type min: double
:type min: float
:param max: The upper threshold for clipping.
:type max: double
:type max: float
:return: LayerOutput object.
:rtype: LayerOutput
"""
......@@ -6709,7 +6759,6 @@ def seq_slice_layer(input, starts, ends, name=None):
:type ends: LayerOutput | None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), (
......@@ -6830,20 +6879,21 @@ def img_conv3d_layer(input,
:param padding: The numbers of padding along three axises. If the parameter is set to
one integer, they will be same.
:type padding: int | tuple | list
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input .
:type num_channels: int
:param param_attr: The parameter attribute of the convolution.
:param param_attr: The parameter attribute of the convolution. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param shared_biases: Whether biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: Extra layer attributes.
:param layer_attr: The extra layer attributes. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param trans: True if it is a convTransLayer, False if it is a convLayer
:type trans: bool
......@@ -6950,12 +7000,12 @@ def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None):
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param param_attr: The parameter attribute of scaling.
:param param_attr: The parameter attribute of scaling. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -7013,10 +7063,9 @@ def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
:type sizes: LayerOutput
:param act: Activation type, LinearActivation is the default.
:type act: BaseActivation.
:param bias_attr: The Bias Attribute. If the parameter is set to
False or something not type of ParameterAttribute,
no bias is defined. If the parameter is set to
True, the bias is initialized to zero.
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -7042,3 +7091,54 @@ def sub_seq_layer(input, offsets, sizes, act=None, bias_attr=None, name=None):
LayerType.SUB_SEQ_LAYER,
parents=[input, offsets, sizes],
size=input.size)
@wrap_name_default('scale_sub_region')
def scale_sub_region_layer(input, indices, value, name=None):
"""
Given an image or feature map with CHW information, scale_sub_region_layer
can be used to multiply a real value to values of a sub continuous region.
You can provide start and end indices of CHW for each instance.
Please notice that all start indices are counting from 1.
The shape of indices should be [batch_size, 6] and the layout for each row
is [C_Start, C_End, H_Start, H_End, W_Start, W_End].
.. code-block:: python
scale_sub_region = scale_sub_region_layer(input=input,
indices=indices,
value=value)
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer which should contains CHW information.
:type input: LayerOutput
:param indices: Start index and end index for C H W, the input value should
be a 2-D matrix with shape [batch_size, 6].
:type indices: LayerOutput.
:param value: value to multiply.
:type value: float
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert isinstance(input, LayerOutput), (
'The first input of scale_sub_region_layer, '
'must be a PaddlePaddle layer.')
assert isinstance(indices, LayerOutput), (
'The start and end indices for CHW, must be a PaddlePaddle layer.')
assert isinstance(value, float), (
'The value to multiply, must be a real value.')
Layer(
name=name,
type=LayerType.SCALE_SUB_REGION_LAYER,
inputs=[input.name, indices.name],
value=value)
return LayerOutput(
name,
LayerType.SCALE_SUB_REGION_LAYER,
parents=[input, indices],
num_filters=input.num_filters,
size=input.size)
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @ fc117ecf
......@@ -9,7 +9,7 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer)
test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer)
export whole_configs=(test_split_datasource)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_roi_pool_layer.protostr 0 → 100644
浏览文件 @ fc117ecf
type: "nn"
layers {
name: "data"
type: "data"
size: 588
active_type: ""
height: 14
width: 14
}
layers {
name: "rois"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconv"
size: 3136
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 3
channels: 3
stride: 1
padding: 1
groups: 1
filter_channels: 3
output_x: 14
img_size: 14
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 1
output_y: 14
img_size_y: 14
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 16
shared_biases: true
height: 14
width: 14
}
layers {
name: "__roi_pool_0__"
type: "roi_pool"
size: 784
active_type: ""
inputs {
input_layer_name: "__conv_0__"
roi_pool_conf {
pooled_width: 7
pooled_height: 7
spatial_scale: 0.0625
}
}
inputs {
input_layer_name: "rois"
}
height: 7
width: 7
}
parameters {
name: "___conv_0__.w0"
size: 432
initial_mean: 0.0
initial_std: 0.272165526976
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
input_layer_names: "rois"
output_layer_names: "__roi_pool_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "rois"
layer_names: "__conv_0__"
layer_names: "__roi_pool_0__"
input_layer_names: "data"
input_layer_names: "rois"
output_layer_names: "__roi_pool_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_sub_region_layer.protostr 0 → 100644
浏览文件 @ fc117ecf
type: "nn"
layers {
name: "data"
type: "data"
size: 2016
active_type: ""
height: 48
width: 42
}
layers {
name: "indices"
type: "data"
size: 6
active_type: ""
}
layers {
name: "__scale_sub_region_0__"
type: "scale_sub_region"
size: 2016
active_type: ""
inputs {
input_layer_name: "data"
scale_sub_region_conf {
image_conf {
channels: 1
img_size: 42
img_size_y: 48
}
value: 0.0
}
}
inputs {
input_layer_name: "indices"
}
height: 48
width: 42
}
input_layer_names: "data"
input_layer_names: "indices"
output_layer_names: "__scale_sub_region_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "indices"
layer_names: "__scale_sub_region_0__"
input_layer_names: "data"
input_layer_names: "indices"
output_layer_names: "__scale_sub_region_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_roi_pool_layer.py 0 → 100644
浏览文件 @ fc117ecf
from paddle.trainer_config_helpers import *
data = data_layer(name='data', size=3 * 14 * 14, height=14, width=14)
rois = data_layer(name='rois', size=10)
conv = img_conv_layer(
input=data,
filter_size=3,
num_channels=3,
num_filters=16,
padding=1,
act=LinearActivation(),
bias_attr=True)
roi_pool = roi_pool_layer(
input=conv,
rois=rois,
pooled_width=7,
pooled_height=7,
spatial_scale=1. / 16)
outputs(roi_pool)
python/paddle/trainer_config_helpers/tests/configs/test_scale_sub_region_layer.py 0 → 100644
浏览文件 @ fc117ecf
from paddle.trainer_config_helpers import *
settings(batch_size=1000, learning_rate=1e-5)
data = data_layer(name='data', size=2016, height=48, width=42)
indices = data_layer(name='indices', size=6)
scale_sub_region = scale_sub_region_layer(
input=data, indices=indices, value=0.0)
outputs(scale_sub_region)
python/paddle/v2/dataset/uci_housing.py
浏览文件 @ fc117ecf
......@@ -22,6 +22,7 @@ parse training set and test set into paddle reader creators.
import numpy as np
import os
import paddle.v2.dataset.common
from paddle.v2.parameters import Parameters
__all__ = ['train', 'test']
......@@ -34,7 +35,8 @@ feature_names = [
UCI_TRAIN_DATA = None
UCI_TEST_DATA = None
URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fit_a_line.tar'
MD5_MODEL = '52fc3da8ef3937822fcdd87ee05c0c9b'
def feature_range(maximums, minimums):
import matplotlib
......@@ -111,6 +113,13 @@ def test():
return reader
def model():
tar_file = paddle.v2.dataset.common.download(URL_MODEL, 'fit_a_line.tar', MD5_MODEL)
with open(tar_file, 'r') as f:
parameters = Parameters.from_tar(f)
return parameters
def fetch():
paddle.v2.dataset.common.download(URL, 'uci_housing', MD5)
......
python/paddle/v2/framework/framework.py
浏览文件 @ fc117ecf
......@@ -285,7 +285,7 @@ class Operator(object):
self.desc.check_attrs()
no_kernel_op_set = {
'feed', 'fetch', 'save', 'load', 'recurrent',
'rnn_memory_helper_grad'
'rnn_memory_helper_grad', 'conditional_block', 'while'
}
if type not in no_kernel_op_set:
self.desc.infer_var_type(self.block.desc)
......
python/paddle/v2/framework/layer_helper.py
浏览文件 @ fc117ecf
......@@ -4,7 +4,7 @@ import itertools
from paddle.v2.framework.framework import Variable, g_main_program, \
g_startup_program, unique_name, Program
from paddle.v2.framework.initializer import ConstantInitializer, \
UniformInitializer
UniformInitializer, XavierInitializer
class LayerHelper(object):
......@@ -61,7 +61,7 @@ class LayerHelper(object):
@property
def param_attr(self):
default = {'name': None, 'initializer': UniformInitializer()}
default = {'name': None, 'initializer': XavierInitializer()}
actual = self.kwargs.get('param_attr', None)
if actual is None:
actual = default
......@@ -70,10 +70,11 @@ class LayerHelper(object):
actual[default_field] = default[default_field]
return actual
@property
def bias_attr(self):
default = {'name': None, 'initializer': ConstantInitializer()}
default = {'name': None, 'initializer': XavierInitializer()}
bias_attr = self.kwargs.get('bias_attr', None)
if bias_attr is True:
if bias_attr is None:
bias_attr = default
if isinstance(bias_attr, dict):
......@@ -166,7 +167,7 @@ class LayerHelper(object):
num_flatten_dims = 1
size = list(input_var.shape[num_flatten_dims:])
bias_attr = self.bias_attr()
bias_attr = self.bias_attr
if not bias_attr:
return input_var
......
python/paddle/v2/framework/layers.py
浏览文件 @ fc117ecf
import paddle.v2.framework.core as core
import paddle.v2.framework.proto.framework_pb2 as framework_pb2
from paddle.v2.framework.framework import OpProtoHolder, Variable, Program, \
Operator
from paddle.v2.framework.initializer import ConstantInitializer, \
NormalInitializer
from paddle.v2.framework.layer_helper import LayerHelper, unique_name
import re
import cStringIO
__all__ = [
'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat',
'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'sums', 'cos_sim',
'batch_norm', 'accuracy'
'batch_norm', 'accuracy', 'split_lod_tensor'
]
def fc(input,
size,
param_attr=None,
bias_attr=True,
bias_attr=None,
name=None,
act=None,
num_flatten_dims=1,
main_program=None,
startup_program=None):
# create helper
"""
Fully Connected Layer.
Args:
input: The input tensor to the function
size: The size of the layer
param_attr: The parameters/weights to the FC Layer
bias_attr: The bias parameter for the FC layer
name: Name/alias of the function
act: Activation to be applied to the output of FC layer
num_flatten_dims: Number of columns in input
main_program: Name of the main program that calls this
startup_program: Name of the startup program
This function can take in multiple inputs and performs the Fully Connected
function (linear transformation) on top of each of them.
So for input x, the output will be : Wx + b. Where W is the parameter,
b the bias and x is the input.
The function also applies an activation (non-linearity) on top of the
output, if activation is passed in the input.
All the input variables of this function are passed in as local variables
to the LayerHelper constructor.
"""
helper = LayerHelper('fc', **locals())
dtype = helper.input_dtype()
# mul
mul_results = []
for input_var, param_attr in helper.iter_inputs_and_params():
input_shape = input_var.shape
......@@ -68,6 +94,26 @@ def embedding(input,
param_attr=None,
main_program=None,
startup_program=None):
"""
Embedding Layer.
Args:
input: The input to the function
size: The size of the layer
data_type: The type of data : float32, float_16, int etc
is_sparse: A flag that decleares whether the input is sparse
param_attr: Parameters for this layer
main_program: Name of the main program that calls this
startup_program: Name of the startup program
This function can take in the input (which is a vector of IDs) and
performs a lookup in the lookup_table using these IDs, to result into
the embedding of each ID in the input.
All the input variables of this function are passed in as local variables
to the LayerHelper constructor.
"""
helper = LayerHelper('embedding', **locals())
w = helper.create_parameter(
attr=helper.param_attr, shape=size, dtype=data_type)
......@@ -81,13 +127,85 @@ def embedding(input,
return tmp
# TODO(qijun): expose H0 and C0
def dynamic_lstm(input,
size,
data_type='float32',
param_attr=None,
bias_attr=None,
use_peepholes=True,
is_reverse=False,
gate_activation='sigmoid',
cell_activation='tanh',
candidate_activation='tanh',
main_program=None,
startup_program=None):
helper = LayerHelper('lstm', **locals())
size = size / 4
weight = helper.create_parameter(
attr=helper.param_attr, shape=[size, 4 * size], dtype=data_type)
bias_size = [1, 7 * size]
if not use_peepholes:
bias_size[1] = 4 * size
bias = helper.create_parameter(
attr=helper.bias_attr, shape=bias_size, dtype=data_type, suffix='b')
hidden = helper.create_tmp_variable(data_type)
cell = helper.create_tmp_variable(data_type)
batch_gate = helper.create_tmp_variable(data_type)
batch_cell_pre_act = helper.create_tmp_variable(data_type)
helper.append_op(
type='lstm',
inputs={'Input': input,
'Weight': weight,
'Bias': bias},
outputs={
'Hidden': hidden,
'Cell': cell,
'BatchGate': batch_gate,
'BatchCellPreAct': batch_cell_pre_act
},
attrs={
'use_peepholes': use_peepholes,
'is_reverse': is_reverse,
'gate_activation': gate_activation,
'cell_activation': cell_activation,
'candidate_activation': candidate_activation
})
return hidden, cell
def data(name,
shape,
data_type='float32',
type=core.VarDesc.VarType.LOD_TENSOR,
append_batch_size=True,
main_program=None,
startup_program=None):
startup_program=None,
stop_gradient=True):
"""
Data Layer.
Args:
name: The name/alias of the function
shape: Tuple declaring the shape.
data_type: The type of data : float32, float_16, int etc
type: The output type. By default it is LOD_TENSOR.
append_batch_size: Whether or not to append the data as a batch.
main_program: Name of the main program that calls this
startup_program: Name of the startup program
stop_gradient: A boolean that mentions whether gradient should flow.
This function takes in input and based on whether data has
to be returned back as a minibatch, it creates the global variable using
the helper functions. The global variables can be accessed by all the
following operations and layers in the graph.
All the input variables of this function are passed in as local variables
to the LayerHelper constructor.
"""
helper = LayerHelper('data', **locals())
shape = list(shape)
for i in xrange(len(shape)):
......@@ -101,15 +219,97 @@ def data(name,
shape = [-1] + shape # append batch size as -1
return helper.create_global_variable(
name=name, shape=shape, dtype=data_type, type=type, stop_gradient=True)
name=name,
shape=shape,
dtype=data_type,
type=type,
stop_gradient=stop_gradient)
def create_tensor(dtype, name=None, main_program=None):
helper = LayerHelper("create_tensor", **locals())
return helper.create_variable(name=helper.name, dtype=dtype)
def _convert_(name):
"""
Formatting.
Args:
name: The name/alias
This function takes in a name and converts it to a standard format of
group1_group2. Where as per the regular expression, group1 can have
alphabets and numbers and group2 has capital alphabets.
"""
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', name)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
def _generate_doc_string_(op_proto):
"""
Generate docstring by OpProto
Args:
op_proto (framework_pb2.OpProto): a protobuf message typed OpProto
Returns:
str: the document string
"""
def _type_to_str_(tp):
return framework_pb2.AttrType.Name(tp)
if not isinstance(op_proto, framework_pb2.OpProto):
raise TypeError("OpProto should be `framework_pb2.OpProto`")
buf = cStringIO.StringIO()
buf.write(op_proto.comment)
buf.write('\nArgs:\n')
for each_input in op_proto.inputs:
line_begin = ' {0}: '.format(_convert_(each_input.name))
buf.write(line_begin)
buf.write(each_input.comment)
buf.write('\n')
buf.write(' ' * len(line_begin))
buf.write('Duplicable: ')
buf.write(str(each_input.duplicable))
buf.write(' Optional: ')
buf.write(str(each_input.dispensable))
buf.write('\n')
for each_attr in op_proto.attrs:
buf.write(' ')
buf.write(each_attr.name)
buf.write(' (')
buf.write(_type_to_str_(each_attr.type))
buf.write('): ')
buf.write(each_attr.comment)
buf.write('\n')
if len(op_proto.outputs) != 0:
buf.write('\nReturns:\n')
buf.write(' ')
for each_opt in op_proto.outputs:
if not each_opt.intermediate:
break
buf.write(each_opt.comment)
return buf.getvalue()
def _create_op_func_(op_type):
"""
Create an Operator for a Function.
Args:
op_type: The name of the operator to be created
This function takes in the operator type (sigmoid, mean , average etc) and
creates the operator functionality.
"""
op_proto = OpProtoHolder.instance().get_op_proto(op_type)
not_intermediate_outputs = \
filter(lambda output: not output.intermediate, op_proto.outputs)
......@@ -117,26 +317,26 @@ def _create_op_func_(op_type):
filter(lambda output: output.intermediate, op_proto.outputs)
if len(not_intermediate_outputs) != 1:
raise ValueError(
"Only one not intermediate output operator can be automatically generated"
)
raise ValueError("Only one non intermediate output operator can be",
"automatically generated")
if not_intermediate_outputs[0].duplicable:
raise ValueError(
"Only not duplicable op can be automatically generated")
"Only non duplicable op can be automatically generated")
for output in intermediate_outputs:
if output.duplicable:
raise ValueError(
"Only when all intermediate ops are not duplicable, "
"this op can be automatically generated")
raise ValueError("The op can be automatically generated only when ",
"all intermediate ops are not duplicable")
o_name = not_intermediate_outputs[0].name
intermediate_output_names = [output.name for output in intermediate_outputs]
def func(**kwargs):
helper = LayerHelper(op_type, **kwargs)
inputs = dict()
def infer_and_check_data_type(op_proto, **kwargs):
"""
This function performs the sanity check for data_type and
instance type.
"""
dtype = None
for ipt in op_proto.inputs:
name = _convert_(ipt.name)
......@@ -153,6 +353,20 @@ def _create_op_func_(op_type):
elif dtype != each.data_type:
raise ValueError(
"operator {0} must input same dtype".format(op_type))
return dtype
def func(**kwargs):
helper = LayerHelper(op_type, **kwargs)
dtype = infer_and_check_data_type(op_proto, **kwargs)
inputs = dict()
for ipt in op_proto.inputs:
name = _convert_(ipt.name)
val = kwargs.pop(name, [])
if not isinstance(val, list) and not isinstance(val, tuple):
val = [val]
inputs[ipt.name] = val
outputs = dict()
......@@ -166,6 +380,7 @@ def _create_op_func_(op_type):
func.__name__ = op_type
globals()[op_type] = func
func.__doc__ = _generate_doc_string_(op_proto)
global __all__
__all__.append(op_type)
......@@ -178,9 +393,32 @@ _create_op_func_('reshape')
_create_op_func_('elementwise_add')
_create_op_func_('sigmoid')
_create_op_func_('scale')
_create_op_func_('reshape')
_create_op_func_('transpose')
def fill_constant(data_type, shape, value=None, program=None):
"""
This function creates a tensor , with shape as mentioned in the input and
specified data_type and fills this up with a constant value that
comes in the input.
"""
helper = LayerHelper('fill_constant', **locals())
out = helper.create_tmp_variable(dtype=data_type)
helper.append_op(
type='fill_constant',
outputs={'Out': [out]},
attrs={'data_type': data_type,
'shape': shape,
'value': value})
return out
def cast(x, data_type, main_program=None):
"""
This function takes in the input with input_data_type
and casts it to the output_data_type as the output.
"""
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=data_type)
helper.append_op(
......@@ -193,6 +431,10 @@ def cast(x, data_type, main_program=None):
def concat(input, axis, main_program=None, startup_program=None):
"""
This function concats the input along the axis mentioned
and returns that as the output.
"""
helper = LayerHelper('concat', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
......@@ -204,13 +446,71 @@ def concat(input, axis, main_program=None, startup_program=None):
def sums(input, main_program=None, startup_program=None):
"""
This function takes in the input and performs the sum operation on it
and returns that as the output.
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
return out
def assign(input, output, main_program=None):
helper = LayerHelper('assign', **locals())
helper.append_op(
type='scale',
inputs={'X': [input]},
outputs={'Out': [output]},
attrs={'scale': 1.0})
return output
def split_lod_tensor(input,
mask,
level,
main_program=None,
startup_program=None):
helper = LayerHelper('split_lod_tensor', **locals())
out_true = helper.create_tmp_variable(dtype=input.data_type)
out_false = helper.create_tmp_variable(dtype=input.data_type)
helper.append_op(
type='split_lod_tensor',
inputs={
'X': input,
'Mask': mask,
},
outputs={'OutTrue': out_true,
'OutFalse': out_false},
attrs={'level': level})
return out_true, out_false
def merge_lod_tensor(in_true,
in_false,
x,
mask,
level,
main_program=None,
startup_program=None):
helper = LayerHelper('merge_lod_tensor', **locals())
out = helper.create_tmp_variable(dtype=x.data_type)
helper.append_op(
type='merge_lod_tensor',
inputs={'X': x,
'Mask': mask,
'InTrue': in_true,
'InFalse': in_false},
outputs={'Out': out},
attrs={'level': level})
return out
def cos_sim(X, Y, **kwargs):
"""
This function performs the cosine similarity between two tensors
X and Y and returns that as the output.
"""
helper = LayerHelper('cos_sim', **kwargs)
out = helper.create_tmp_variable(dtype=X.data_type)
xnorm = helper.create_tmp_variable(dtype=X.data_type)
......@@ -226,6 +526,9 @@ def cos_sim(X, Y, **kwargs):
def cross_entropy(input, label, **kwargs):
"""
This function computes cross_entropy using the input and label.
"""
helper = LayerHelper('cross_entropy', **kwargs)
out = helper.create_tmp_variable(dtype=input.data_type)
helper.append_op(
......@@ -238,6 +541,10 @@ def cross_entropy(input, label, **kwargs):
def square_error_cost(input, label, **kwargs):
"""
This functions returns the squared error cost using the input and label.
The output is appending the op to do the above.
"""
helper = LayerHelper('square_error_cost', **kwargs)
minus_out = helper.create_tmp_variable(dtype=input.data_type)
helper.append_op(
......@@ -253,6 +560,10 @@ def square_error_cost(input, label, **kwargs):
def accuracy(input, label, k=1, **kwargs):
"""
This function computes the accuracy using the input and label.
The output is the top_k inputs and their indices.
"""
helper = LayerHelper("accuracy", **kwargs)
topk_out = helper.create_tmp_variable(dtype=input.data_type)
topk_indices = helper.create_tmp_variable(dtype="int64")
......@@ -291,6 +602,11 @@ def sequence_conv(input,
param_attr=None,
main_program=None,
startup_program=None):
"""
This function creates the op for sequence_conv, using the inputs and
other convolutional configurations for the filters and stride as given
in the input parameters to the function.
"""
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes.
# such as, padding_trainable, context_start.
......@@ -331,6 +647,13 @@ def conv2d(input,
param_attr=None,
main_program=None,
startup_program=None):
"""
This function creates the op for a 2-dimensional Convolution.
This is performed using the parameters of filters(size, dimensionality etc)
, stride and other configurations for a Convolution operation.
This funciton can also append an activation on top of the
conv-2d output, if mentioned in the input parameters.
"""
helper = LayerHelper('conv2d', **locals())
dtype = helper.input_dtype()
......@@ -377,6 +700,11 @@ def conv2d(input,
def sequence_pool(input, pool_type, **kwargs):
"""
This function add the operator for sequence pooling.
This is applied on top of the input using pool_type mentioned
in the parameters.
"""
helper = LayerHelper('sequence_pool', input=input, **kwargs)
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
......@@ -400,6 +728,10 @@ def pool2d(input,
global_pooling=False,
main_program=None,
startup_program=None):
"""
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
......@@ -420,9 +752,9 @@ def pool2d(input,
inputs={"X": input},
outputs={"Out": pool_out},
attrs={
"poolingType": pool_type,
"pooling_type": pool_type,
"ksize": pool_size,
"globalPooling": global_pooling,
"global_pooling": global_pooling,
"strides": pool_stride,
"paddings": pool_padding
})
......@@ -440,6 +772,10 @@ def batch_norm(input,
data_layout='NCHW',
main_program=None,
startup_program=None):
"""
This function helps create an operator to implement
the BatchNorm layer using the configurations from the input parameters.
"""
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
......@@ -511,8 +847,10 @@ def batch_norm(input,
class BlockGuard(object):
"""
BlockGuard used to create sub-block in program by using Python `with`
keyword.
BlockGuard class.
BlockGuard class is used to create a sub-block in a program by
using the Python `with` keyword.
"""
def __init__(self, main_program):
......@@ -531,9 +869,15 @@ class BlockGuard(object):
class StaticRNNGuard(BlockGuard):
"""
StaticRNNGuard class.
StaticRNNGuard class is used to create a StaticRNN block in a program.
"""
def __init__(self, rnn):
if not isinstance(rnn, StaticRNN):
raise TypeError("StaticRNNGuard takes an StaticRNN")
raise TypeError("StaticRNNGuard takes a StaticRNN")
super(StaticRNNGuard, self).__init__(rnn.helper.main_program)
self.rnn = rnn
......@@ -551,12 +895,18 @@ class StaticRNNGuard(BlockGuard):
class StaticRNNMemoryLink(object):
"""
:param init: the initial variable for Memory
:type init: Variable
:param pre_mem: the memory variable in previous time step
:type pre_mem: Variable
:param mem: the memory variable in current time step
:type mem: Variable
StaticRNNMemoryLink class.
Args:
init: the initial variable for Memory
init: Variable
pre_mem: the memory variable in previous time step
pre_mem: Variable
mem: the memory variable in current time step
mem: Variable
StaticRNNMemoryLink class is used to create a link between two
memory cells of a StaticRNN.
"""
def __init__(self, init, pre_mem, mem=None):
......@@ -566,6 +916,12 @@ class StaticRNNMemoryLink(object):
class StaticRNN(object):
"""
StaticRNN class.
StaticRNN class is used to create a StaticRNN. The RNN will have its
own parameters like inputs, outputs, memories, status and length.
"""
BEFORE_RNN_BLOCK = 0
IN_RNN_BLOCK = 1
AFTER_RNN_BLOCK = 2
......@@ -594,15 +950,15 @@ class StaticRNN(object):
init_value=0.0,
init_batch_dim_idx=0,
ref_batch_dim_idx=1):
'''
:param init: boot memory, if not set, a shape, batch_ref must be provided
:param shape: shape of the boot memory
:param batch_ref: batch size reference variable
:param init_value: the init value of boot memory
:param init_batch_dim_idx: the index of batch size in init's dimension
:param ref_batch_dim_idx: the index of batch size in batch_ref's dimension
:return: boot memory
'''
"""
Args:
init: boot memory, if not set, a shape, batch_ref must be provided
shape: shape of the boot memory
batch_ref: batch size reference variable
init_value: the init value of boot memory
init_batch_dim_idx: the index of batch size in init's dimension
ref_batch_dim_idx: the index of batch size in batch_ref's dimension
"""
self._assert_in_rnn_block_('memory')
if init is None:
if shape is None or batch_ref is None:
......@@ -768,7 +1124,131 @@ class StaticRNN(object):
})
class WhileGuard(BlockGuard):
def __init__(self, while_op):
if not isinstance(while_op, While):
raise TypeError("WhileGuard takes a while op")
super(WhileGuard, self).__init__(while_op.helper.main_program)
self.while_op = while_op
def __enter__(self):
self.while_op.status = While.IN_WHILE_BLOCK
return super(WhileGuard, self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
self.while_op.status = While.AFTER_WHILE_BLOCK
self.while_op.complete()
return super(WhileGuard, self).__exit__(exc_type, exc_val, exc_tb)
class While(object):
BEFORE_WHILE_BLOCK = 0
IN_WHILE_BLOCK = 1
AFTER_WHILE_BLOCK = 2
def __init__(self, cond, name=None, main_program=None):
self.helper = LayerHelper("while", name=name, main_program=main_program)
self.status = While.BEFORE_WHILE_BLOCK
if not isinstance(cond, Variable):
raise TypeError("condition should be a variable")
assert isinstance(cond, Variable)
if cond.data_type != core.DataType.BOOL:
raise TypeError("condition should be a bool variable")
if reduce(lambda a, b: a * b, cond.shape, 1) != 1:
raise TypeError("condition should be a bool scalar")
self.cond_var = cond
def block(self):
return WhileGuard(self)
def complete(self):
main_program = self.helper.main_program
while_block = main_program.current_block()
parent_block = main_program.block(main_program.current_block()
.parent_idx)
inner_outputs = {self.cond_var.name}
x_name_list = set()
for op in while_block.ops:
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs:
x_name_list.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
out_vars = []
for inner_out_name in inner_outputs:
if inner_out_name in parent_block.vars:
out_vars.append(parent_block.var(inner_out_name))
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
parent_block.append_op(
type='while',
inputs={
'X': [parent_block.var(x_name) for x_name in x_name_list],
'Condition': [self.cond_var]
},
outputs={'Out': out_vars,
'StepScopes': [step_scope]},
attrs={'step_block': while_block})
def lstm(x,
c_pre_init,
hidden_dim,
forget_bias=None,
main_program=None,
startup_program=None):
"""
This function helps create an operator for the LSTM (Long Short Term
Memory) cell that can be used inside an RNN.
"""
helper = LayerHelper('lstm_unit', **locals())
rnn = StaticRNN()
with rnn.step():
c_pre = rnn.memory(init=c_pre_init)
x_t = rnn.step_input(x)
before_fc = concat(
input=[x_t, c_pre],
axis=1,
main_program=main_program,
startup_program=startup_program)
after_fc = fc(input=before_fc,
size=hidden_dim * 4,
main_program=main_program,
startup_program=startup_program)
data_type = x.data_type
c = helper.create_tmp_variable(data_type)
h = helper.create_tmp_variable(data_type)
helper.append_op(
type='lstm_unit',
inputs={"X": after_fc,
"C_prev": c_pre},
outputs={"C": c,
"H": h},
attrs={"forget_bias": forget_bias})
rnn.update_memory(c_pre, c)
rnn.output(h)
return rnn()
def lod_rank_table(x, level=0, main_program=None):
"""
This function creates an operator for creating a LOD_RANK_TABLE
using the input x.
"""
helper = LayerHelper("lod_rank_table", **locals())
table = helper.create_variable(
type=core.VarDesc.VarType.LOD_RANK_TABLE,
......@@ -782,10 +1262,15 @@ def lod_rank_table(x, level=0, main_program=None):
def lod_tensor_to_array(x, table, main_program=None):
"""
This function creates an operator to convert an LOD_Tensor to
an array.
"""
helper = LayerHelper("lod_tensor_to_array", **locals())
array = helper.create_variable(
name=unique_name("lod_tensor_to_array"),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY)
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=x.data_type)
helper.append_op(
type='lod_tensor_to_array',
inputs={'X': x,
......@@ -795,6 +1280,10 @@ def lod_tensor_to_array(x, table, main_program=None):
def array_to_lod_tensor(x, table, main_program=None):
"""
This function creates an operator to convert an array to a
LOD_Tensor.
"""
helper = LayerHelper("array_to_lod_tensor", **locals())
tmp = helper.create_tmp_variable(dtype=x.data_type)
helper.append_op(
......@@ -806,7 +1295,12 @@ def array_to_lod_tensor(x, table, main_program=None):
def fill_constant(shape, dtype, value, main_program=None):
helper = LayerHelper("ones", **locals())
"""
This function creates a tensor , with shape as mentioned in the input and
specified data_type and fills this up with a constant value that
comes in the input. It also sets the stop_gradient to be True.
"""
helper = LayerHelper("fill_constant", **locals())
out = helper.create_tmp_variable(dtype=dtype)
helper.append_op(
type='fill_constant',
......@@ -822,25 +1316,45 @@ def fill_constant(shape, dtype, value, main_program=None):
def ones(shape, dtype, main_program=None):
"""
This function performs the same function as fill_constant() declared above
with the constant value being 1.0.
"""
return fill_constant(value=1.0, **locals())
def zeros(shape, dtype, main_program=None):
"""
This function performs the same function as fill_constant() declared above
with the constant value being 0.0.
"""
return fill_constant(value=0.0, **locals())
def increment(x, value=1.0, main_program=None):
def increment(x, value=1.0, in_place=True, main_program=None):
"""
This function creates an operator to increment each value in the input
`x` by an amount: `value` as mentioned in the input parameter. This
operation is performed in-place by default.
"""
helper = LayerHelper("increment", **locals())
tmp = helper.create_tmp_variable(dtype=x.data_type)
if not in_place:
out = helper.create_tmp_variable(dtype=x.data_type)
else:
out = x
helper.append_op(
type='increment',
inputs={'X': [x]},
outputs={'Out': [tmp]},
outputs={'Out': [out]},
attrs={'step': value})
return tmp
return out
def array_write(x, i, array=None, main_program=None):
"""
This function creates an operator to write the data out as a
LOD_TENSOR_ARRAY.
"""
helper = LayerHelper('array_write', **locals())
if array is None:
array = helper.create_variable(
......@@ -855,7 +1369,31 @@ def array_write(x, i, array=None, main_program=None):
return array
def create_array(dtype, main_program=None):
helper = LayerHelper("array", **locals())
return helper.create_variable(
name="{0}.out".format(helper.name),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=dtype)
def less_than(x, y, cond=None, main_program=None):
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond.stop_gradient = True
helper.append_op(
type='less_than', inputs={'X': [x],
'Y': [y]}, outputs={'Out': [cond]})
return cond
def array_read(array, i, main_program=None):
"""
This function creates an operator to read the data in as a
LOD_TENSOR_ARRAY.
"""
helper = LayerHelper('array_read', **locals())
if not isinstance(
array,
......@@ -868,3 +1406,103 @@ def array_read(array, i, main_program=None):
'I': [i]},
outputs={'Out': [out]})
return out
def shrink_memory(x, i, table, main_program=None):
"""
This function creates an operator to shrink_rnn_memory using the RankTable
as mentioned in the input parameter.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_tmp_variable(dtype=x.data_type)
helper.append_op(
type='shrink_rnn_memory',
inputs={'X': [x],
'I': [i],
'RankTable': [table]},
outputs={'Out': [out]},
attrs={})
return out
def array_length(array, main_program=None):
"""
This function creates an operator to find the length of the
LOD_TENSOR_ARRAY.
"""
helper = LayerHelper('array_length', **locals())
tmp = helper.create_tmp_variable(dtype='int64')
tmp.stop_gradient = True
helper.append_op(
type='lod_array_length', inputs={'X': [array]}, outputs={'Out': [tmp]})
return tmp
class ConditionalBlockGuard(BlockGuard):
def __init__(self, block):
if not isinstance(block, ConditionalBlock):
raise TypeError("block should be conditional block")
super(ConditionalBlockGuard, self).__init__(block.helper.main_program)
self.block = block
def __enter__(self):
return super(ConditionalBlockGuard, self).__enter__()
def __exit__(self, exc_type, exc_val, exc_tb):
self.block.complete()
return super(ConditionalBlockGuard, self).__exit__(exc_type, exc_val,
exc_tb)
class ConditionalBlock(object):
def __init__(self, inputs, name=None, main_program=None):
for each_input in inputs:
if not isinstance(each_input, Variable):
raise TypeError("Each input should be variable")
self.inputs = inputs
self.helper = LayerHelper(
'conditional_block', name=name, main_program=main_program)
def block(self):
return ConditionalBlockGuard(self)
def complete(self):
inside_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(inside_block.parent_idx)
intermediate = set()
params = set()
for each_op in inside_block.ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
input_set = set([ipt.name for ipt in self.inputs])
param_list = [
parent_block.var(each_name) for each_name in params
if each_name not in input_set
]
out_list = [
parent_block.var(var_name) for var_name in parent_block.vars
if var_name not in intermediate
]
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
parent_block.append_op(
type='conditional_block',
inputs={
'X': self.inputs,
'Params': param_list,
},
outputs={'Out': out_list,
'Scope': [step_scope]},
attrs={'block': inside_block})
python/paddle/v2/framework/optimizer.py
浏览文件 @ fc117ecf
......@@ -35,15 +35,21 @@ class Optimizer(object):
"""
raise NotImplementedError()
def _initialize_tensors(self, block):
"""Create all necessary tensors, that will be shared for all parameter updates.
Tensors like learning rate should be initialized here.
Args:
block: the block in which the loss variable is present
"""
pass
def _create_param_lr(self, param_and_grad):
# create learning rate variable for every parameter
param = param_and_grad[0]
param_lr = param.optimize_attr['learning_rate']
param_lr_shape = [1]
param_lr_var = self.helper.create_global_variable(
name=unique_name("learning_rate"),
dtype='float32',
shape=param_lr_shape,
lod_level=1,
persistable=True)
param_lr = param_lr * self._learning_rate
self.helper.set_variable_initializer(
var=param_lr_var, initializer=ConstantInitializer(param_lr))
return param_lr_var
def _create_accumulators(self, block, parameters):
"""Create all accumulators needed by the parameters
......@@ -161,8 +167,6 @@ class Optimizer(object):
startup_program=startup_program)
self._create_accumulators(loss.block,
[p[0] for p in parameters_and_grads])
# Create any necessary tensors
self._initialize_tensors(loss.block)
optimize_ops = []
for param_and_grad in parameters_and_grads:
......@@ -214,27 +218,16 @@ class SGDOptimizer(Optimizer):
self.type = "sgd"
self._learning_rate = learning_rate
def _initialize_tensors(self, block):
lr_shape = [1]
# create a variable for learning_rate
self._lr = self.helper.create_global_variable(
name=unique_name("learning_rate"),
dtype='float32',
shape=lr_shape,
lod_level=1,
persistable=True)
self.helper.set_variable_initializer(
var=self._lr, initializer=ConstantInitializer(self._learning_rate))
def _append_optimize_op(self, block, param_and_grad):
assert isinstance(block, framework.Block)
# create the optimize op
sgd_op = block.append_op(
type=self.type,
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": self._lr
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0]})
......@@ -259,19 +252,6 @@ class MomentumOptimizer(Optimizer):
self._momentum = momentum
self._use_nesterov = bool(use_nesterov)
def _initialize_tensors(self, block):
assert isinstance(block, framework.Block)
lr_shape = [1]
# create a variable for learning_rate
self._lr = self.helper.create_global_variable(
name=unique_name("learning_rate"),
dtype='float32',
shape=lr_shape,
lod_level=1,
persistable=True)
self.helper.set_variable_initializer(
var=self._lr, initializer=ConstantInitializer(self._learning_rate))
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
......@@ -290,14 +270,14 @@ class MomentumOptimizer(Optimizer):
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Velocity": velocity_acc,
"LearningRate": self._lr
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={
"ParamOut": param_and_grad[0],
"VelocityOut": velocity_acc
},
attrs={"mu": self._momentum,
"useNesterov": self._use_nesterov})
"use_nesterov": self._use_nesterov})
return momentum_op
......@@ -315,18 +295,6 @@ class AdagradOptimizer(Optimizer):
self._learning_rate = learning_rate
self._epsilon = epsilon
def _initialize_tensors(self, block):
lr_shape = [1]
# create a variable for learning_rate
self._lr = self.helper.create_global_variable(
name=unique_name("learning_rate"),
dtype='float32',
shape=lr_shape,
lod_level=1,
persistable=True)
self.helper.set_variable_initializer(
var=self._lr, initializer=ConstantInitializer(self._learning_rate))
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
......@@ -346,7 +314,7 @@ class AdagradOptimizer(Optimizer):
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"Moment": moment_acc,
"LearningRate": self._lr
"LearningRate": self._create_param_lr(param_and_grad)
},
outputs={"ParamOut": param_and_grad[0],
"MomentOut": moment_acc},
......@@ -378,18 +346,6 @@ class AdamOptimizer(Optimizer):
self._beta2 = beta2
self._epsilon = epsilon
def _initialize_tensors(self, block):
lr_shape = [1]
# create a variable for learning_rate
self._lr = self.helper.create_global_variable(
name=unique_name("learning_rate"),
dtype='float32',
shape=lr_shape,
lod_level=1,
persistable=True)
self.helper.set_variable_initializer(
var=self._lr, initializer=ConstantInitializer(self._learning_rate))
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
......@@ -433,7 +389,7 @@ class AdamOptimizer(Optimizer):
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": self._lr,
"LearningRate": self._create_param_lr(param_and_grad),
"Moment1": moment1,
"Moment2": moment2,
"Beta1Pow": self._beta1_pow_acc,
......@@ -495,18 +451,6 @@ class AdamaxOptimizer(Optimizer):
self._beta2 = beta2
self._epsilon = epsilon
def _initialize_tensors(self, block):
lr_shape = [1]
# create a variable for learning_rate
self._lr = self.helper.create_global_variable(
name=unique_name("learning_rate"),
dtype='float32',
shape=lr_shape,
lod_level=1,
persistable=True)
self.helper.set_variable_initializer(
var=self._lr, initializer=ConstantInitializer(self._learning_rate))
def _create_accumulators(self, block, parameters):
# Create beta1 power accumulator tensor
beta_shape = [1]
......@@ -536,7 +480,7 @@ class AdamaxOptimizer(Optimizer):
inputs={
"Param": param_and_grad[0],
"Grad": param_and_grad[1],
"LearningRate": self._lr,
"LearningRate": self._create_param_lr(param_and_grad),
"Moment": moment,
"InfNorm": inf_norm,
"Beta1Pow": self._beta1_pow_acc
......
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @ fc117ecf
......@@ -3,3 +3,5 @@ string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
add_subdirectory(book)
python/paddle/v2/framework/tests/book/CMakeLists.txt 0 → 100644
浏览文件 @ fc117ecf
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/v2/framework/tests/test_fit_a_line.py → python/paddle/v2/framework/tests/book/test_fit_a_line.py
浏览文件 @ fc117ecf
......@@ -3,7 +3,7 @@ import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_main_program
from paddle.v2.framework.framework import Program
from paddle.v2.framework.io import save_persistables, load_persistables
from paddle.v2.framework.executor import Executor
......
python/paddle/v2/framework/tests/test_recognize_digits_conv.py → python/paddle/v2/framework/tests/book/test_recognize_digits_conv.py
浏览文件 @ fc117ecf
......@@ -5,7 +5,7 @@ import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
import paddle.v2.framework.evaluator as evaluator
from paddle.v2.framework.framework import Program, g_main_program
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
import numpy as np
......
python/paddle/v2/framework/tests/test_recommender_system.py → python/paddle/v2/framework/tests/book/test_recommender_system.py
浏览文件 @ fc117ecf
......@@ -4,7 +4,7 @@ import paddle.v2.framework.nets as nets
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_main_program
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
import numpy as np
......
python/paddle/v2/framework/tests/book/test_understand_sentiment_dynamic_lstm.py 0 → 100644
浏览文件 @ fc117ecf
import paddle.v2 as paddle
import paddle.v2.framework.layers as layers
import paddle.v2.framework.nets as nets
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_main_program, g_startup_program
from paddle.v2.framework.executor import Executor
import numpy as np
def stacked_lstm_net(input_dim,
class_dim=2,
emb_dim=128,
hid_dim=512,
stacked_num=3):
assert stacked_num % 2 == 1
data = layers.data(name="words", shape=[1], data_type="int64")
label = layers.data(name="label", shape=[1], data_type="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr
# TODO(qijun) linear act
fc1 = layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = layers.dynamic_lstm(input=fc1, size=hid_dim)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = layers.fc(input=inputs, size=hid_dim)
lstm, cell = layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]
fc_last = layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = layers.sequence_pool(input=inputs[1], pool_type='max')
prediction = layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
return avg_cost, acc
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
print "load word dict successfully"
dict_dim = len(word_dict)
class_dim = 2
cost, acc = stacked_lstm_net(input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
exe.run(g_startup_program)
for pass_id in xrange(PASS_NUM):
for data in train_data():
tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([BATCH_SIZE, 1])
tensor_label = core.LoDTensor()
tensor_label.set(label, place)
outs = exe.run(g_main_program,
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if cost_val < 1.0 and acc_val > 0.7:
exit(0)
exit(1)
if __name__ == '__main__':
main()
python/paddle/v2/framework/tests/book/test_understand_sentiment_lstm.py 0 → 100644
浏览文件 @ fc117ecf
import paddle.v2 as paddle
import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import g_main_program, g_startup_program
from paddle.v2.framework.executor import Executor
import numpy as np
def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
data = layers.data(
name="words",
shape=[seq_len * batch_size, 1],
append_batch_size=False,
data_type="int64")
label = layers.data(
name="label",
shape=[batch_size, 1],
append_batch_size=False,
data_type="int64")
emb = layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = layers.transpose(x=emb, axis=[1, 0, 2])
c_pre_init = layers.fill_constant(
dtype=emb.data_type, shape=[batch_size, emb_dim], value=0.0)
layer_1_out = layers.lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = layers.transpose(x=layer_1_out, axis=[1, 0, 2])
prediction = layers.fc(input=layer_1_out, size=class_dim, act="softmax")
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = optimizer.AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
return avg_cost, acc
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def chop_data(data, chop_len=80, batch_len=50):
data = [(x[0][:chop_len], x[1]) for x in data if len(x[0]) >= chop_len]
return data[:batch_len]
def prepare_feed_data(data, place):
tensor_words = to_lodtensor(map(lambda x: x[0], data), place)
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([50, 1])
tensor_label = core.LoDTensor()
tensor_label.set(label, place)
return tensor_words, tensor_label
def main():
word_dict = paddle.dataset.imdb.word_dict()
cost, acc = lstm_net(dict_dim=len(word_dict), class_dim=2)
batch_size = 100
train_data = paddle.batch(
paddle.reader.buffered(
paddle.dataset.imdb.train(word_dict), size=batch_size * 10),
batch_size=batch_size)
data = chop_data(next(train_data()))
place = core.CPUPlace()
tensor_words, tensor_label = prepare_feed_data(data, place)
exe = Executor(place)
exe.run(g_startup_program)
while True:
outs = exe.run(g_main_program,
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
print("cost=" + str(cost_val) + " acc=" + str(acc_val))
if acc_val > 0.9:
break
if __name__ == '__main__':
main()
python/paddle/v2/framework/tests/test_word2vec.py → python/paddle/v2/framework/tests/book/test_word2vec.py
浏览文件 @ fc117ecf
......@@ -3,7 +3,7 @@ import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_main_program
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
import numpy as np
......
python/paddle/v2/framework/tests/op_test.py
浏览文件 @ fc117ecf
......@@ -215,7 +215,11 @@ class OpTest(unittest.TestCase):
if isinstance(input_vars[var_name], list):
for name, np_value in self.inputs[var_name]:
tensor = core.LoDTensor()
tensor.set(np_value, place)
if isinstance(np_value, tuple):
tensor.set(np_value[0], place)
tensor.set_lod(np_value[1])
else:
tensor.set(np_value, place)
feed_map[name] = tensor
else:
tensor = core.LoDTensor()
......@@ -236,7 +240,6 @@ class OpTest(unittest.TestCase):
inputs = append_input_output(block, op_proto, self.inputs, True)
outputs = append_input_output(block, op_proto, self.outputs, False)
op = block.append_op(
type=self.op_type,
inputs=inputs,
......@@ -397,9 +400,11 @@ class OpTest(unittest.TestCase):
if not isinstance(item[0], basestring):
item = [[param_name] + list(item)]
if len(item) == 2:
# only set var name and value, set lod to None
var[i] = list(item) + [None]
if isinstance(item[1], tuple):
var[i] = [item[0], item[1][0], item[1][1]]
else:
# only set var name and value, set lod to None
var[i] = list(item) + [None]
var_descs = [(block.create_var(
name=name, shape=each.shape, dtype=each.dtype), each, lod)
for name, each, lod in var]
......
python/paddle/v2/framework/tests/test_array_read_write_op.py
浏览文件 @ fc117ecf
......@@ -20,21 +20,19 @@ class TestArrayReadWrite(unittest.TestCase):
each_x.stop_gradient = False
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = False
arr = layers.array_write(x=x[0], i=i)
i = layers.increment(x=i)
i.stop_gradient = True
arr = layers.array_write(x=x[1], i=i, array=arr)
i = layers.increment(x=i)
i.stop_gradient = True
arr = layers.array_write(x=x[2], i=i, array=arr)
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = False
a0 = layers.array_read(array=arr, i=i)
i = layers.increment(x=i)
i.stop_gradient = True # index should not calculate gradient
a1 = layers.array_read(array=arr, i=i)
i = layers.increment(x=i)
i.stop_gradient = True
a2 = layers.array_read(array=arr, i=i)
mean_a0 = layers.mean(x=a0)
......
python/paddle/v2/framework/tests/test_assign_op.py 0 → 100644
浏览文件 @ fc117ecf
import op_test
import numpy
import unittest
class TestAssignOp(op_test.OpTest):
def setUp(self):
self.op_type = "assign"
x = numpy.random.random(size=(100, 10))
self.inputs = {'X': x}
self.outputs = {'Out': x}
def test_forward(self):
self.check_output()
def test_backward(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_bilinear_tensor_product_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import numpy as np
from op_test import OpTest
class TestBilinearTensorProductOp(OpTest):
def setUp(self):
self.op_type = "bilinear_tensor_product"
batch_size = 6
size0 = 3
size1 = 4
size2 = 5
a = np.random.random((batch_size, size0)).astype("float32")
b = np.random.random((batch_size, size1)).astype("float32")
w = np.random.random((size2, size0, size1)).astype("float32")
bias = np.random.random((1, size2)).astype("float32")
output = np.zeros((batch_size, size2)).astype("float32")
for i in range(size2):
w_i = w[i, :, :]
output[:, i] = np.sum(np.matmul(a, w_i) * b, axis=1)
self.inputs = {
'X': a,
'Y': b,
'Weight': w,
'Bias': bias,
}
self.outputs = {'Out': output + bias}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y', 'Weight', 'Bias'], 'Out')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_chunk_eval_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import numpy as np
from op_test import OpTest
class Segment(object):
def __init__(self, chunk_type, start_idx, end_idx):
self.chunk_type = chunk_type
self.start_idx = start_idx
self.end_idx = end_idx
def __str__(self):
return '(Segment: %s, %s, %s)' % (self.chunk_type, self.start_idx,
self.end_idx)
__repr__ = __str__
class TestChunkEvalOp(OpTest):
num_sequences = 5
batch_size = 50
def parse_scheme(self):
if self.scheme == 'IOB':
self.num_tag_types = 2
elif self.scheme == 'IOE':
self.num_tag_types = 2
def fill_with_chunks(self, data, chunks):
for chunk in chunks:
if self.scheme == 'IOB':
data[chunk.start_idx] = chunk.chunk_type * self.num_tag_types
data[chunk.start_idx + 1:
chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
self.num_tag_types - 1)
data[chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
self.num_tag_types - 1
) if chunk.start_idx < chunk.end_idx else data[chunk.start_idx]
elif self.scheme == 'IOE':
data[chunk.start_idx:
chunk.end_idx] = chunk.chunk_type * self.num_tag_types
data[chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
self.num_tag_types - 1)
def rand_chunks(self, starts, num_chunks):
if num_chunks < 0:
num_chunks = np.random.randint(starts[-1])
chunks = []
# generate chunk beginnings
chunk_begins = sorted(
np.random.choice(
range(starts[-1]), num_chunks, replace=False))
seq_chunk_begins = []
begin_idx = 0
# divide chunks into sequences
for i in range(len(starts) - 1):
tmp_chunk_begins = []
while begin_idx < len(chunk_begins) and chunk_begins[
begin_idx] < starts[i + 1]:
tmp_chunk_begins.append(chunk_begins[begin_idx])
begin_idx += 1
seq_chunk_begins.append(tmp_chunk_begins)
# generate chunk ends
chunk_ends = []
for i in range(len(seq_chunk_begins)):
for j in range(len(seq_chunk_begins[i])):
low = seq_chunk_begins[i][j]
high = seq_chunk_begins[i][j + 1] if j < len(seq_chunk_begins[
i]) - 1 else starts[i + 1]
chunk_ends.append(np.random.randint(low, high))
# generate chunks
for chunk_pos in zip(chunk_begins, chunk_ends):
chunk_type = np.random.randint(self.num_chunk_types)
chunks.append(Segment(chunk_type, *chunk_pos))
return chunks
def gen_chunks(self, infer, label, starts):
chunks = self.rand_chunks(starts,
self.num_infer_chunks + self.num_label_chunks
- self.num_correct_chunks)
correct_chunks = np.random.choice(
range(len(chunks)), self.num_correct_chunks, replace=False)
infer_chunks = np.random.choice(
[x for x in range(len(chunks)) if x not in correct_chunks],
self.num_infer_chunks - self.num_correct_chunks,
replace=False)
infer_chunks = sorted(correct_chunks.tolist() + infer_chunks.tolist())
label_chunks = np.random.choice(
[x for x in range(len(chunks)) if x not in infer_chunks],
self.num_label_chunks - self.num_correct_chunks,
replace=False)
label_chunks = sorted(correct_chunks.tolist() + label_chunks.tolist())
self.fill_with_chunks(infer, [chunks[idx] for idx in infer_chunks])
self.fill_with_chunks(label, [chunks[idx] for idx in label_chunks])
# exclude types in excluded_chunk_types
if len(self.excluded_chunk_types) > 0:
for idx in correct_chunks:
if chunks[idx].chunk_type in self.excluded_chunk_types:
self.num_correct_chunks -= 1
for idx in infer_chunks:
if chunks[idx].chunk_type in self.excluded_chunk_types:
self.num_infer_chunks -= 1
for idx in label_chunks:
if chunks[idx].chunk_type in self.excluded_chunk_types:
self.num_label_chunks -= 1
return self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks
def set_confs(self):
# Use the IOB scheme and labels with 2 chunk types
self.scheme = 'IOB'
self.num_chunk_types = 2
self.excluded_chunk_types = []
self.other_chunk_type = self.num_chunk_types
self.attrs = {
'num_chunk_types': self.num_chunk_types,
'chunk_scheme': self.scheme,
'excluded_chunk_types': self.excluded_chunk_types
}
self.parse_scheme()
self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 4, 5, 9
def set_data(self):
infer = np.zeros((self.batch_size, )).astype('int32')
infer.fill(self.num_chunk_types * self.num_tag_types)
label = np.copy(infer)
starts = np.random.choice(
range(1, self.batch_size), self.num_sequences - 1,
replace=False).tolist()
starts.extend([0, self.batch_size])
starts = sorted(starts)
self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = self.gen_chunks(
infer, label, starts)
self.inputs = {
'Inference': (infer, [starts]),
'Label': (label, [starts])
}
precision = float(
self.num_correct_chunks
) / self.num_infer_chunks if self.num_infer_chunks else 0
recall = float(self.num_correct_chunks
) / self.num_label_chunks if self.num_label_chunks else 0
f1 = float(2 * precision * recall) / (
precision + recall) if self.num_correct_chunks else 0
self.outputs = {
'Precision': np.asarray(
[precision], dtype='float32'),
'Recall': np.asarray(
[recall], dtype='float32'),
'F1-Score': np.asarray(
[f1], dtype='float32')
}
def setUp(self):
self.op_type = 'chunk_eval'
self.set_confs()
self.set_data()
def test_check_output(self):
self.check_output()
class TestChunkEvalOpWithExclude(TestChunkEvalOp):
def set_confs(self):
# Use the IOE scheme and labels with 3 chunk types
self.scheme = 'IOE'
self.num_chunk_types = 3
self.excluded_chunk_types = [1]
self.other_chunk_type = self.num_chunk_types
self.attrs = {
'num_chunk_types': self.num_chunk_types,
'chunk_scheme': self.scheme,
'excluded_chunk_types': self.excluded_chunk_types
}
self.parse_scheme()
self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 15, 18, 20
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_clip_by_norm_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import numpy as np
from op_test import OpTest
class TestClipByNormOp(OpTest):
def setUp(self):
self.max_relative_error = 0.006
self.initTestCase()
input = np.random.random(self.shape).astype("float32")
input[np.abs(input) < self.max_relative_error] = 0.5
self.op_type = "clip_by_norm"
self.inputs = {'X': input, }
self.attrs = {}
self.attrs['max_norm'] = self.max_norm
norm = np.sqrt(np.sum(np.square(input)))
if norm > self.max_norm:
output = self.max_norm * input / norm
else:
output = input
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def initTestCase(self):
self.shape = (100, )
self.max_norm = 1.0
class TestCase1(TestClipByNormOp):
def initTestCase(self):
self.shape = (100, )
self.max_norm = 1e20
class TestCase2(TestClipByNormOp):
def initTestCase(self):
self.shape = (16, 16)
self.max_norm = 0.1
class TestCase3(TestClipByNormOp):
def initTestCase(self):
self.shape = (4, 8, 16)
self.max_norm = 1.0
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_conditional_block.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
from paddle.v2.framework.framework import g_startup_program, g_main_program
from paddle.v2.framework.executor import Executor
from paddle.v2.framework.backward import append_backward_ops
import numpy
class ConditionalBlock(unittest.TestCase):
def test_forward(self):
data = layers.data(name='X', shape=[1], data_type='float32')
data.stop_gradient = False
cond = layers.ConditionalBlock(inputs=[data])
out = layers.create_tensor(dtype='float32')
with cond.block():
hidden = layers.fc(input=data, size=10)
layers.assign(hidden, out)
cpu = core.CPUPlace()
exe = Executor(cpu)
exe.run(g_startup_program)
x = core.LoDTensor()
x.set(numpy.random.random(size=(10, 1)).astype('float32'), cpu)
outs = map(numpy.array, exe.run(feed={'X': x}, fetch_list=[out]))[0]
print outs
loss = layers.mean(x=out)
append_backward_ops(loss=loss)
outs = map(numpy.array,
exe.run(feed={'X': x},
fetch_list=[
g_main_program.block(0).var(data.name + "@GRAD")
]))[0]
print outs
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_create_op_doc_string.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import paddle.v2.framework.layers as layers
class TestDocString(unittest.TestCase):
def test_layer_doc_string(self):
print layers.dropout.__doc__
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_expand_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import numpy as np
from op_test import OpTest
class TestExpandOpRank1(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random(12).astype("float32")}
self.attrs = {'expand_times': [2]}
output = np.tile(self.inputs['X'], 2)
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank2_Corner(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
self.attrs = {'expand_times': [1, 1]}
output = np.tile(self.inputs['X'], (1, 1))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank2(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((12, 14)).astype("float32")}
self.attrs = {'expand_times': [2, 3]}
output = np.tile(self.inputs['X'], (2, 3))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank3_Corner(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
self.attrs = {'expand_times': [1, 1, 1]}
output = np.tile(self.inputs['X'], (1, 1, 1))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank3(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5)).astype("float32")}
self.attrs = {'expand_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestExpandOpRank4(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.random((2, 4, 5, 7)).astype("float32")}
self.attrs = {'expand_times': [3, 2, 1, 2]}
output = np.tile(self.inputs['X'], (3, 2, 1, 2))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_increment_op.py 已删除 100644 → 0
浏览文件 @ 7c792431
import unittest
import numpy as np
from op_test import OpTest
class TestIncrementOpPositiveStep(OpTest):
"""Test increment op with positive step
"""
def setUp(self):
self.op_type = "increment"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.attrs = {'step': 14.8}
self.outputs = {'Out': self.inputs['X'] + self.attrs['step']}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class TestIncrementOpNegativeStep(OpTest):
"""Test increment op with negative step
"""
def setUp(self):
self.op_type = "increment"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.attrs = {'step': -3.8}
self.outputs = {'Out': self.inputs['X'] + self.attrs['step']}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_inference_model_io.py
浏览文件 @ fc117ecf
......@@ -3,7 +3,7 @@ import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_main_program
from paddle.v2.framework.framework import Program
from paddle.v2.framework.io import save_inference_model, load_inference_model
import paddle.v2.framework.executor as executor
import unittest
......
python/paddle/v2/framework/tests/test_layers.py
浏览文件 @ fc117ecf
import paddle.v2.framework.layers as layers
import paddle.v2.framework.nets as nets
from paddle.v2.framework.framework import Program, g_main_program
from paddle.v2.framework.framework import Program
import paddle.v2.framework.core as core
import unittest
......
python/paddle/v2/framework/tests/test_lod_array_length_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import paddle.v2.framework.layers as layers
from paddle.v2.framework.executor import Executor
import paddle.v2.framework.core as core
import numpy
class TestLoDArrayLength(unittest.TestCase):
def test_array_length(self):
tmp = layers.zeros(shape=[10], dtype='int32')
i = layers.fill_constant(shape=[1], dtype='int64', value=10)
arr = layers.array_write(tmp, i=i)
arr_len = layers.array_length(arr)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = numpy.array(exe.run(fetch_list=[arr_len])[0])
self.assertEqual(11, result[0])
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_lod_reset_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import numpy as np
from op_test import OpTest
class TestLodResetOpByAttr(OpTest):
def setUp(self):
self.op_type = "lod_reset"
x = np.random.random((10, 20)).astype("float32")
lod = [[0, 3, 5, 10]]
target_lod_0 = [0, 7, 10]
self.inputs = {'X': (x, lod)}
self.attrs = {'target_lod': target_lod_0}
self.outputs = {'Out': (x, [target_lod_0])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestLodResetOpByInput(OpTest):
def setUp(self):
self.op_type = "lod_reset"
x = np.random.random((10, 20)).astype("float32")
lod = [[0, 3, 5, 10]]
target_lod_0 = [0, 4, 7, 10]
self.inputs = {
'X': (x, lod),
'TargetLoD': np.array([target_lod_0]).astype('int32')
}
self.outputs = {'Out': (x, [target_lod_0])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out", no_grad_set=set("TargetLoD"))
class TestLodResetOpBoth(OpTest):
def setUp(self):
self.op_type = "lod_reset"
x = np.random.random((10, 20)).astype("float32")
lod = [[0, 3, 5, 10]]
target_lod_0_attr = [0, 7, 10]
target_lod_0_in = [0, 4, 7, 10]
self.inputs = {
'X': (x, lod),
'TargetLoD': np.array(target_lod_0_in).astype('int32')
}
self.attrs = {'target_lod': target_lod_0_attr}
self.outputs = {'Out': (x, [target_lod_0_in])}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out", no_grad_set=set("TargetLoD"))
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_lod_tensor_array_ops.py
浏览文件 @ fc117ecf
......@@ -4,6 +4,7 @@ import numpy
import paddle.v2.framework.layers as layers
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
from paddle.v2.framework.backward import append_backward_ops
class TestCPULoDTensorArrayOps(unittest.TestCase):
......@@ -123,5 +124,42 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
self.assertEqual(actual.lod(), expect.lod())
class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
def test_grad(self):
place = core.CPUPlace()
program = Program()
x = layers.data(
name='x',
shape=[1],
data_type='float32',
main_program=program,
stop_gradient=False)
table = layers.lod_rank_table(x, level=0, main_program=program)
array = layers.lod_tensor_to_array(x, table, main_program=program)
result = layers.array_to_lod_tensor(array, table, main_program=program)
mean = layers.mean(x=result, main_program=program)
append_backward_ops(mean)
tensor = core.LoDTensor()
tensor.set(numpy.arange(10).reshape(10, 1).astype('float32'), place)
tensor.set_lod([[0, 3, 9, 10]])
g_vars = program.global_block().var(x.name + "@GRAD")
exe = Executor(place)
g_out = [
item.sum()
for item in map(
numpy.array,
exe.run(program, feed={'x': tensor}, fetch_list=[g_vars]))
]
g_out_sum = numpy.array(g_out).sum()
self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_lstm_op.py
浏览文件 @ fc117ecf
......@@ -117,8 +117,9 @@ class TestLstmOp(OpTest):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.has_initial_state = False
self.is_reverse = False
self.use_peepholes = True
def setUp(self):
self.set_argument()
......@@ -128,18 +129,28 @@ class TestLstmOp(OpTest):
N = len(self.lod[0]) - 1
x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
h0 = np.zeros((N, self.D)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
if self.has_initial_state:
h0 = np.random.normal(size=(N, self.D)).astype('float64')
c0 = np.random.normal(size=(N, self.D)).astype('float64')
else:
h0 = np.zeros((N, self.D)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
w = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
if self.use_peepholes:
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
else:
b = np.random.normal(size=(1, 4 * self.D)).astype('float64')
w_b = b[:, 0:4 * self.D]
w_c = b[:, 4 * self.D:]
w_c = b[:, 4 * self.D:] if self.use_peepholes else None
h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
ACTVATION[self.act_gate], ACTVATION[self.act_cell],
ACTVATION[self.act_cand])
self.inputs = {'Input': (x, self.lod), 'Weight': w, 'Bias': b}
self.inputs = {'Input': (x, self.lod), 'Weight': w}
self.inputs['Bias'] = b
if self.has_initial_state:
self.inputs['H0'] = h0
self.inputs['C0'] = c0
......@@ -149,17 +160,16 @@ class TestLstmOp(OpTest):
'Cell': (c, self.lod),
}
self.attrs = {
'usePeepholes': True,
'isReverse': self.is_reverse,
'gateActivation': self.act_gate,
'cellActivation': self.act_cell,
'candidateActivation': self.act_cand
'use_peepholes': self.use_peepholes,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
'candidate_activation': self.act_cand
}
def test_check_output(self):
self.check_output(atol=1e-8)
#TODO(qingqing) add more unit testing case
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0]) - 1
......@@ -170,7 +180,7 @@ class TestLstmOp(OpTest):
['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
class TestLstmOpHasNoInitial(TestLstmOp):
class TestLstmOpHasInitial(TestLstmOp):
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
......@@ -179,8 +189,69 @@ class TestLstmOpHasNoInitial(TestLstmOp):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = False
self.has_initial_state = True
self.is_reverse = True
self.use_peepholes = True
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias', 'H0', 'C0'], ['Hidden'],
max_relative_error=5e-4)
def test_check_grad_ingore_bias(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('Bias'))
def test_check_grad_ingore_weight(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Bias'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('Weight'))
def test_check_grad_ingore_input(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Weight', 'Bias'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('Input'))
def test_check_grad_ingore_h0(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias', 'C0'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('H0'))
def test_check_grad_ingore_c0(self):
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias', 'H0'], ['Hidden'],
max_relative_error=5e-4,
no_grad_set=set('C0'))
class TestLstmOpRerverse(TestLstmOp):
......@@ -192,8 +263,23 @@ class TestLstmOpRerverse(TestLstmOp):
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.has_initial_state = False
self.is_reverse = True
self.use_peepholes = True
class TestLstmOpNotUsePeepholes(TestLstmOp):
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = False
self.is_reverse = True
self.use_peepholes = False
if __name__ == '__main__':
......
python/paddle/v2/framework/tests/test_momentum_op.py
浏览文件 @ fc117ecf
......@@ -37,7 +37,7 @@ class TestMomentumOp1(OpTest):
class TestMomentumOp2(OpTest):
'''Test Momentum with defaukt values for attributes
'''Test Momentum with default values for attributes
'''
def setUp(self):
......@@ -57,7 +57,7 @@ class TestMomentumOp2(OpTest):
'LearningRate': learning_rate
}
self.attrs = {'mu': mu, 'useNesterov': use_nesterov}
self.attrs = {'mu': mu, 'use_nesterov': use_nesterov}
velocity_out = mu * velocity + grad
if use_nesterov:
......
python/paddle/v2/framework/tests/test_optimizer.py
浏览文件 @ fc117ecf
......@@ -98,7 +98,7 @@ class TestMomentumOptimizer(unittest.TestCase):
self.assertEqual(len(opts), 1)
sgd_op = opts[0]
self.assertEqual(sgd_op.type, "momentum")
self.assertFalse(sgd_op.attr('useNesterov'))
self.assertFalse(sgd_op.attr('use_nesterov'))
# Check accumulators
accumulators = momentum_optimizer.get_accumulators()
......@@ -143,7 +143,7 @@ class TestMomentumOptimizer(unittest.TestCase):
self.assertEqual(len(opts), 1)
sgd_op = opts[0]
self.assertEqual(sgd_op.type, "momentum")
self.assertTrue(sgd_op.attr('useNesterov'))
self.assertTrue(sgd_op.attr('use_nesterov'))
# Check accumulators
accumulators = momentum_optimizer.get_accumulators()
......
python/paddle/v2/framework/tests/test_pool2d_op.py
浏览文件 @ fc117ecf
......@@ -61,8 +61,8 @@ class TestPool2d_Op(OpTest):
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'poolingType': self.pool_type,
'globalPooling': self.global_pool,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
}
self.outputs = {'Out': output.astype('float32')}
......
python/paddle/v2/framework/tests/test_pool3d_op.py
浏览文件 @ fc117ecf
......@@ -67,8 +67,8 @@ class TestPool3d_Op(OpTest):
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'poolingType': self.pool_type,
'globalPooling': self.global_pool,
'pooling_type': self.pool_type,
'global_pooling': self.global_pool,
}
self.outputs = {'Out': output.astype('float32')}
......
python/paddle/v2/framework/tests/test_pool_max_op.py
浏览文件 @ fc117ecf
......@@ -86,7 +86,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'globalPooling': self.global_pool,
'global_pooling': self.global_pool,
}
self.inputs = {'X': input}
......
python/paddle/v2/framework/tests/test_seq_concat_op.py
浏览文件 @ fc117ecf
......@@ -2,9 +2,36 @@ import unittest
import numpy as np
import sys
from op_test import OpTest
exit(0)
class TestConcatOp(OpTest):
def to_abs_lod(lod):
if len(lod) == 0 or len(lod) == 1:
return lod
import copy
new_lod = copy.deepcopy(lod)
for idx, val in enumerate(lod[0]):
new_lod[0][idx] = lod[1][val]
return new_lod
def seq_concat(inputs, level):
lod0 = inputs['X'][0][1][1]
lod1 = inputs['X'][1][1][1]
x0 = inputs['X'][0][1][0]
x1 = inputs['X'][1][1][0]
level_idx = len(lod0) - level - 1
outs = []
for i in range(len(lod0[level_idx]) - 1):
sub_x0 = x0[to_abs_lod(lod0)[level_idx][i]:to_abs_lod(lod0)[level_idx][
i + 1], :]
sub_x1 = x1[to_abs_lod(lod1)[level_idx][i]:to_abs_lod(lod1)[level_idx][
i + 1], :]
outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
return np.concatenate(outs, axis=0)
class TestSeqConcatOp(OpTest):
def set_data(self):
# two level, batch size is 3
x0 = np.random.random((4, 6, 3)).astype('float32')
......@@ -15,13 +42,7 @@ class TestConcatOp(OpTest):
level = 1
self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
self.attrs = {'axis': axis, 'level': level}
outs = []
for i in range(4):
sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
self.outputs = {'Out': np.concatenate(outs, axis=0)}
self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
def setUp(self):
self.op_type = "sequence_concat"
......@@ -34,46 +55,50 @@ class TestConcatOp(OpTest):
self.check_grad(['x0'], 'Out')
class TestConcatOpDiffLod(TestConcatOp):
class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
def set_data(self):
# two level, batch size is 3
x0 = np.random.random((4, 6, 3)).astype('float32')
lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
x1 = np.random.random((5, 6, 3)).astype('float32')
lod1 = [[0, 3, 5], [0, 1, 2, 3, 5]]
x1 = np.random.random((7, 6, 3)).astype('float32')
lod1 = [[0, 2, 4], [0, 1, 3, 5, 7]]
axis = 0
level = 1
level = 0
self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
self.attrs = {'axis': axis, 'level': level}
outs = []
for i in range(4):
sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
out_lod = [[0, 2, 4], [0, 2, 5, 8, 11]]
self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
self.outputs = {'Out': np.concatenate(outs, axis=0)}
class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
def set_data(self):
# two level, batch size is 3
x0 = np.random.random((4, 6, 3)).astype('float32')
lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
x1 = np.random.random((7, 6, 3)).astype('float32')
lod1 = [[0, 3, 4], [0, 1, 3, 5, 7]]
axis = 0
level = 1
self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
self.attrs = {'axis': axis, 'level': level}
out_lod = [[0, 5, 8], [0, 1, 2, 3, 5, 7, 8, 9, 11]]
self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
class TestConcatOpLevelZero(TestConcatOp):
class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
def set_data(self):
# two level, batch size is 3
x0 = np.random.random((4, 3, 4)).astype('float32')
lod0 = [[0, 2, 4], [0, 1, 2, 3, 4]]
x1 = np.random.random((5, 3, 4)).astype('float32')
lod1 = [[0, 3, 5], [0, 1, 3, 4, 5]]
lod0 = [[0, 1, 2, 3, 4]]
x1 = np.random.random((7, 3, 4)).astype('float32')
lod1 = [[0, 1, 3, 5, 7]]
axis = 0
level = 0
self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
self.attrs = {'axis': axis, 'level': level}
outs = []
for i in range(2):
sub_x0 = x0[lod0[level][i]:lod0[level][i + 1], :]
sub_x1 = x1[lod1[level][i]:lod1[level][i + 1], :]
outs.append(np.concatenate((sub_x0, sub_x1), axis=axis))
self.outputs = {'Out': np.concatenate(outs, axis=0)}
out_lod = [[0, 2, 5, 8, 11]]
self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
if __name__ == '__main__':
sys.exit(0)
unittest.main()
python/paddle/v2/framework/tests/test_shrink_rnn_memory.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import paddle.v2.framework.core as core
from paddle.v2.framework.executor import Executor
import paddle.v2.framework.layers as layers
from paddle.v2.framework.backward import append_backward_ops
from paddle.v2.framework.framework import g_main_program
import numpy
class TestShrinkRNNMemory(unittest.TestCase):
def test_shrink_rnn_memory(self):
x = layers.data('x', shape=[100], data_type='float32')
x.stop_gradient = False
table = layers.lod_rank_table(x=x)
i = layers.zeros(dtype='int64', shape=[1])
mem1 = layers.shrink_memory(x=x, i=i, table=table)
i = layers.increment(x=i)
i.stop_gradient = True
mem2 = layers.shrink_memory(x=mem1, i=i, table=table)
i = layers.increment(x=i)
i.stop_gradient = True
mem3 = layers.shrink_memory(x=mem2, i=i, table=table)
cpu = core.CPUPlace()
tensor = core.LoDTensor()
tensor.set_lod([[0, 2, 5, 6]])
tensor_np = numpy.random.random(size=(3, 100)).astype('float32')
tensor.set(tensor_np, cpu)
exe = Executor(cpu)
outs = map(numpy.array,
exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3]))
self.assertTrue(numpy.allclose(tensor_np[0:3], outs[0]))
self.assertTrue(numpy.allclose(tensor_np[0:2], outs[1]))
self.assertTrue(numpy.allclose(tensor_np[0:1], outs[2]))
mem3_mean = layers.mean(x=mem3)
append_backward_ops(loss=mem3_mean)
x_grad = map(numpy.array,
exe.run(feed={'x': tensor},
fetch_list=[
g_main_program.global_block().var('x@GRAD')
]))[0]
self.assertAlmostEqual(1.0, x_grad.sum(), delta=0.1)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_split_and_merge_lod_tensor_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import paddle.v2.framework.core as core
import numpy as np
import paddle.v2.framework.layers as layers
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
from paddle.v2.framework.backward import append_backward_ops
class TestCPULoDTensorArrayOps(unittest.TestCase):
def place(self):
return core.CPUPlace()
def test_split_and_merge_lod_tensor_no_lod(self):
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('int32'), self.place())
mask_np = np.array([0, 0, 1, 1, 1, 1, 0, 0, 0, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, self.place())
expect_true_tensor = np.array([2, 3, 4, 5]).astype('int32')
expect_true_tensor = np.expand_dims(expect_true_tensor, axis=1)
expect_true = core.LoDTensor()
expect_true.set(expect_true_tensor, self.place())
expect_false_tensor = np.array([0, 1, 6, 7, 8, 9]).astype('int32')
expect_false_tensor = np.expand_dims(expect_false_tensor, axis=1)
expect_false = core.LoDTensor()
expect_false.set(expect_false_tensor, self.place())
self.main(
tensor=tensor,
mask=mask,
expect_true=expect_true,
expect_false=expect_false,
expect_out=tensor)
def test_split_and_merge_lod_tensor_level_0(self):
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('int32'), self.place())
tensor.set_lod([[0, 3, 9, 10]])
mask_np = np.array([0, 1, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, self.place())
expect_true_tensor = np.array([3, 4, 5, 6, 7, 8]).astype('int32')
expect_true_tensor = np.expand_dims(expect_true_tensor, axis=1)
expect_true = core.LoDTensor()
expect_true.set(expect_true_tensor, self.place())
expect_true.set_lod([[0, 6]])
expect_false_tensor = np.array([0, 1, 2, 9]).astype('int32')
expect_false_tensor = np.expand_dims(expect_false_tensor, axis=1)
expect_false_lod = [[0, 3, 4]]
expect_false = core.LoDTensor()
expect_false.set(expect_false_tensor, self.place())
expect_false.set_lod(expect_false_lod)
self.main(
tensor=tensor,
mask=mask,
expect_true=expect_true,
expect_false=expect_false,
expect_out=tensor)
def main(self, tensor, mask, expect_true, expect_false, expect_out,
level=0):
place = self.place()
program = Program()
x = layers.data(name='x', shape=[1], main_program=program)
x.persistable = True
y = layers.data(name='y', shape=[1], main_program=program)
y.persistable = True
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level, main_program=program)
out_true.persistable = True
out_false.persistable = True
out = layers.merge_lod_tensor(
in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level,
main_program=program)
out.persistable = True
exe = Executor(place)
scope = core.Scope()
exe.run(program, feed={'x': tensor, 'y': mask}, scope=scope)
var_true = scope.find_var(out_true.name).get_tensor()
var_false = scope.find_var(out_false.name).get_tensor()
var_out = scope.find_var(out.name).get_tensor()
self.check_tensor_same(var_true, expect_true)
self.check_tensor_same(var_false, expect_false)
self.check_tensor_same(var_out, expect_out)
def check_tensor_same(self, actual, expect):
self.assertTrue(np.allclose(np.array(actual), np.array(expect)))
self.assertEqual(actual.lod(), expect.lod())
class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
def test_grad(self):
place = core.CPUPlace()
program = Program()
x = layers.data(
name='x',
shape=[1],
data_type='float32',
main_program=program,
stop_gradient=False)
y = layers.data(
name='y',
shape=[1],
data_type='bool',
main_program=program,
stop_gradient=False)
level = 0
out_true, out_false = layers.split_lod_tensor(
input=x, mask=y, level=level, main_program=program)
out = layers.merge_lod_tensor(
in_true=out_true,
in_false=out_false,
mask=y,
x=x,
level=level,
main_program=program)
mean = layers.mean(x=out, main_program=program)
append_backward_ops(mean)
tensor = core.LoDTensor()
tensor.set(np.arange(10).reshape(10, 1).astype('float32'), place)
tensor.set_lod([[0, 3, 9, 10]])
mask_np = np.array([0, 1, 0]).astype('bool')
mask_np = np.expand_dims(mask_np, axis=1)
mask = core.LoDTensor()
mask.set(mask_np, place)
exe = Executor(place)
scope = core.Scope()
g_vars = program.global_block().var(x.name + "@GRAD")
g_out = [
item.sum()
for item in map(np.array,
exe.run(program,
feed={'x': tensor,
'y': mask},
fetch_list=[g_vars],
scope=scope))
]
g_out_sum = np.array(g_out).sum()
self.assertAlmostEqual(1.0, g_out_sum, delta=0.1)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_while_op.py 0 → 100644
浏览文件 @ fc117ecf
import unittest
import paddle.v2.framework.layers as layers
from paddle.v2.framework.executor import Executor
import paddle.v2.framework.core as core
import numpy
class TestWhileOp(unittest.TestCase):
def test_simple_forward(self):
d0 = layers.data(
"d0", shape=[10], append_batch_size=False, data_type='float32')
d1 = layers.data(
"d1", shape=[10], append_batch_size=False, data_type='float32')
d2 = layers.data(
"d2", shape=[10], append_batch_size=False, data_type='float32')
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
init = layers.zeros(shape=[10], dtype='float32')
mem_array = layers.array_write(init, i=i)
data_array = layers.array_write(x=d0, i=i)
i = layers.increment(i)
layers.array_write(d1, i, array=data_array)
i = layers.increment(i)
layers.array_write(d2, i, array=data_array)
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
array_len = layers.fill_constant(shape=[1], dtype='int64', value=3)
cond = layers.less_than(x=i, y=array_len)
while_op = layers.While(cond=cond)
with while_op.block():
d = layers.array_read(array=data_array, i=i)
prev = layers.array_read(array=mem_array, i=i)
i = layers.increment(x=i, in_place=True)
result = layers.sums(input=[d, prev])
layers.array_write(result, i=i, array=mem_array)
layers.less_than(x=i, y=array_len, cond=cond)
sum_result = layers.array_read(mem_array, i=array_len)
cpu = core.CPUPlace()
exe = Executor(cpu)
d = []
for i in xrange(3):
d.append(numpy.random.random(size=[10]).astype('float32'))
d_tensor = []
for item in d:
t = core.LoDTensor()
t.set(item, cpu)
d_tensor.append(t)
outs = map(numpy.array,
exe.run(feed={
'd0': d_tensor[0],
'd1': d_tensor[1],
'd2': d_tensor[2]
},
fetch_list=[sum_result]))
self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/image.py
浏览文件 @ fc117ecf
import numpy as np
try:
import cv2
except ImportError:
cv2 = None
import os
import tarfile
import cPickle
__all__ = [
"load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
"random_crop", "left_right_flip", "simple_transform", "load_and_transform",
"batch_images_from_tar"
]
"""
This file contains some common interfaces for image preprocess.
Many users are confused about the image layout. We introduce
the image layout as follows.
- CHW Layout
- The abbreviations: C=channel, H=Height, W=Width
- The default layout of image opened by cv2 or PIL is HWC.
PaddlePaddle only supports the CHW layout. And CHW is simply
a transpose of HWC. It must transpose the input image.
- Color format: RGB or BGR
OpenCV use BGR color format. PIL use RGB color format. Both
formats can be used for training. Noted that, the format should
be keep consistent between the training and inference peroid.
"""
import numpy as np
try:
import cv2
except ImportError:
cv2 = None
import os
import tarfile
import cPickle
__all__ = [
"load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
"random_crop", "left_right_flip", "simple_transform", "load_and_transform",
"batch_images_from_tar"
]
def batch_images_from_tar(data_file,
......@@ -36,17 +38,18 @@ def batch_images_from_tar(data_file,
num_per_batch=1024):
"""
Read images from tar file and batch them into batch file.
param data_file: path of image tar file
type data_file: string
param dataset_name: 'train','test' or 'valid'
type dataset_name: string
param img2label: a dic with image file name as key
:param data_file: path of image tar file
:type data_file: string
:param dataset_name: 'train','test' or 'valid'
:type dataset_name: string
:param img2label: a dic with image file name as key
and image's label as value
type img2label: dic
param num_per_batch: image number per batch file
type num_per_batch: int
return: path of list file containing paths of batch file
rtype: string
:type img2label: dic
:param num_per_batch: image number per batch file
:type num_per_batch: int
:return: path of list file containing paths of batch file
:rtype: string
"""
batch_dir = data_file + "_batch"
out_path = "%s/%s" % (batch_dir, dataset_name)
......@@ -99,14 +102,16 @@ def load_image_bytes(bytes, is_color=True):
Example usage:
.. code-block:: python
with open('cat.jpg') as f:
im = load_image_bytes(f.read())
:param bytes: the input image bytes array.
:type file: str
:type bytes: str
:param is_color: If set is_color True, it will load and
return a color image. Otherwise, it will
load and return a gray image.
:type is_color: bool
"""
flag = 1 if is_color else 0
file_bytes = np.asarray(bytearray(bytes), dtype=np.uint8)
......@@ -121,6 +126,7 @@ def load_image(file, is_color=True):
Example usage:
.. code-block:: python
im = load_image('cat.jpg')
:param file: the input image path.
......@@ -128,6 +134,7 @@ def load_image(file, is_color=True):
:param is_color: If set is_color True, it will load and
return a color image. Otherwise, it will
load and return a gray image.
:type is_color: bool
"""
# cv2.IMAGE_COLOR for OpenCV3
# cv2.CV_LOAD_IMAGE_COLOR for older OpenCV Version
......@@ -147,6 +154,7 @@ def resize_short(im, size):
Example usage:
.. code-block:: python
im = load_image('cat.jpg')
im = resize_short(im, 256)
......@@ -175,6 +183,7 @@ def to_chw(im, order=(2, 0, 1)):
Example usage:
.. code-block:: python
im = load_image('cat.jpg')
im = resize_short(im, 256)
im = to_chw(im)
......@@ -196,6 +205,7 @@ def center_crop(im, size, is_color=True):
Example usage:
.. code-block:: python
im = center_crop(im, 224)
:param im: the input image with HWC layout.
......@@ -223,6 +233,7 @@ def random_crop(im, size, is_color=True):
Example usage:
.. code-block:: python
im = random_crop(im, 224)
:param im: the input image with HWC layout.
......@@ -251,6 +262,7 @@ def left_right_flip(im):
Example usage:
.. code-block:: python
im = left_right_flip(im)
:paam im: input image with HWC layout
......@@ -275,6 +287,7 @@ def simple_transform(im,
Example usage:
.. code-block:: python
im = simple_transform(im, 256, 224, True)
:param im: The input image with HWC layout.
......@@ -285,6 +298,11 @@ def simple_transform(im,
:type crop_size: int
:param is_train: Whether it is training or not.
:type is_train: bool
:param is_color: whether the image is color or not.
:type is_color: bool
:param mean: the mean values, which can be element-wise mean values or
mean values per channel.
:type mean: numpy array | list
"""
im = resize_short(im, resize_size)
if is_train:
......@@ -324,6 +342,7 @@ def load_and_transform(filename,
Example usage:
.. code-block:: python
im = load_and_transform('cat.jpg', 256, 224, True)
:param filename: The file name of input image.
......@@ -334,6 +353,11 @@ def load_and_transform(filename,
:type crop_size: int
:param is_train: Whether it is training or not.
:type is_train: bool
:param is_color: whether the image is color or not.
:type is_color: bool
:param mean: the mean values, which can be element-wise mean values or
mean values per channel.
:type mean: numpy array | list
"""
im = load_image(filename)
im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean)
......
python/paddle/v2/optimizer.py
浏览文件 @ fc117ecf
......@@ -102,7 +102,7 @@ class Momentum(Optimizer):
.. math::
v_{t} &= k * v_{t-1} - \\gamma_t / (g_{t} + \\lambda w_{t-1}) \\\\
v_{t} &= k * v_{t-1} - \\gamma_t (g_{t} + \\lambda w_{t-1}) \\\\
w_{t} &= w_{t-1} + v_{t} \\\\
where, :math:`k` is momentum, :math:`\\lambda` is decay rate,
......
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