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提交 d9642cb3 编写于 作者: K Kexin Zhao
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into float16

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.gitignore
浏览文件 @ d9642cb3
......@@ -21,11 +21,10 @@ third_party/
cmake-build-*
# generated while compiling
python/paddle/v2/framework/core.so
python/paddle/v2/fluid/core.so
paddle/pybind/pybind.h
CMakeFiles
cmake_install.cmake
paddle/.timestamp
python/paddlepaddle.egg-info/
paddle/pybind/pybind.h
python/paddle/v2/framework/tests/tmp/*
benchmark/paddle/image/resnet.py 0 → 100644
浏览文件 @ d9642cb3
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg("layer_num", int, 50)
is_test = get_config_arg("is_test", bool, False)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
#######################Network Configuration #############
def conv_bn_layer(name,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
active_type=ReluActivation()):
"""
A wrapper for conv layer with batch normalization layers.
Note:
conv layer has no activation.
"""
tmp = img_conv_layer(
name=name + "_conv",
input=input,
filter_size=filter_size,
num_channels=channels,
num_filters=num_filters,
stride=stride,
padding=padding,
act=LinearActivation(),
bias_attr=False)
return batch_norm_layer(
name=name + "_bn", input=tmp, act=active_type, use_global_stats=is_test)
def bottleneck_block(name, input, num_filters1, num_filters2):
"""
A wrapper for bottlenect building block in ResNet.
Last conv_bn_layer has no activation.
Addto layer has activation of relu.
"""
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=1,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[input, last_name], act=ReluActivation())
def mid_projection(name, input, num_filters1, num_filters2, stride=2):
"""
A wrapper for middile projection in ResNet.
projection shortcuts are used for increasing dimensions,
and other shortcuts are identity
branch1: projection shortcuts are used for increasing
dimensions, has no activation.
branch2x: bottleneck building block, shortcuts are identity.
"""
# stride = 2
branch1 = conv_bn_layer(
name=name + '_branch1',
input=input,
filter_size=1,
num_filters=num_filters2,
stride=stride,
padding=0,
active_type=LinearActivation())
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=stride,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
img = data_layer(name='image', size=height * width * 3)
def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
"""
A wrapper for 50,101,152 layers of ResNet.
res2_num: number of blocks stacked in conv2_x
res3_num: number of blocks stacked in conv3_x
res4_num: number of blocks stacked in conv4_x
res5_num: number of blocks stacked in conv5_x
"""
# For ImageNet
# conv1: 112x112
tmp = conv_bn_layer(
"conv1",
input=img,
filter_size=7,
channels=3,
num_filters=64,
stride=2,
padding=3)
tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
# conv2_x: 56x56
tmp = mid_projection(
name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
for i in xrange(2, res2_num + 1, 1):
tmp = bottleneck_block(
name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
# conv3_x: 28x28
tmp = mid_projection(
name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
for i in xrange(2, res3_num + 1, 1):
tmp = bottleneck_block(
name="res3_" + str(i),
input=tmp,
num_filters1=128,
num_filters2=512)
# conv4_x: 14x14
tmp = mid_projection(
name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
for i in xrange(2, res4_num + 1, 1):
tmp = bottleneck_block(
name="res4_" + str(i),
input=tmp,
num_filters1=256,
num_filters2=1024)
# conv5_x: 7x7
tmp = mid_projection(
name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
for i in xrange(2, res5_num + 1, 1):
tmp = bottleneck_block(
name="res5_" + str(i),
input=tmp,
num_filters1=512,
num_filters2=2048)
tmp = img_pool_layer(
name='avgpool',
input=tmp,
pool_size=7,
stride=1,
pool_type=AvgPooling())
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 50:
resnet = deep_res_net(3, 4, 6, 3)
elif layer_num == 101:
resnet = deep_res_net(3, 4, 23, 3)
elif layer_num == 152:
resnet = deep_res_net(3, 8, 36, 3)
else:
print("Wrong layer number.")
lbl = data_layer(name="label", size=num_class)
loss = cross_entropy(name='loss', input=resnet, label=lbl)
inputs(img, lbl)
outputs(loss)
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @ d9642cb3
......@@ -5,22 +5,23 @@ function train() {
export OMP_DYNAMIC="FALSE"
export KMP_AFFINITY="granularity=fine,compact,0,0"
topology=$1
bs=$2
use_mkldnn=$3
if [ $3 == "True" ]; then
layer_num=$2
bs=$3
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/${topology}-mkldnn-${bs}.log"
elif [ $3 == "False" ]; then
log="logs/${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
log="logs/${topology}-${thread}mklml-${bs}.log"
log="logs/${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $3, use True or False."
exit 0
fi
args="batch_size=${bs}"
args="batch_size=${bs},layer_num=${layer_num}"
config="${topology}.py"
paddle train --job=time \
--config=$config \
......@@ -40,12 +41,9 @@ if [ ! -d "logs" ]; then
mkdir logs
fi
#========== mkldnn ==========#
train vgg 64 True
train vgg 128 True
train vgg 256 True
#========== mklml ===========#
train vgg 64 False
train vgg 128 False
train vgg 256 False
for use_mkldnn in True False; do
for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
done
done
benchmark/paddle/image/vgg.py
浏览文件 @ d9642cb3
......@@ -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
浏览文件 @ d9642cb3
# 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 $ENV{ATLAS_ROOT} CACHE PATH "Folder contains Atlas")
set(ATLAS_INCLUDE_SEARCH_PATHS
......
cmake/external/mkldnn.cmake
浏览文件 @ d9642cb3
......@@ -46,16 +46,20 @@ IF(${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=${MKLDNN_MKLROOT}
)
......
cmake/external/mklml.cmake
浏览文件 @ d9642cb3
......@@ -27,8 +27,8 @@ ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.20170720")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.10/${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 @ d9642cb3
......@@ -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
......@@ -98,7 +98,7 @@ IF(NOT ${CBLAS_FOUND})
ENDIF()
INSTALL(CODE "execute_process(
COMMAND ${CMAKE_COMMAND} -E copy_directory ${CBLAS_INSTALL_DIR}/lib
destination ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
)"
)
INSTALL(CODE "MESSAGE(STATUS \"Installing: \"
......@@ -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 ${dummyfile})
......
cmake/generic.cmake
浏览文件 @ d9642cb3
......@@ -93,7 +93,7 @@ include_directories(${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 @ d9642cb3
......@@ -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
浏览文件 @ d9642cb3
......@@ -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 @ d9642cb3
=====================
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 @ d9642cb3
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 @ d9642cb3
Image Interface
===============
.. automodule:: paddle.v2.image
:members:
doc/design/evaluator.md 0 → 100644
浏览文件 @ d9642cb3
## Evaluator Design
### The Problem
During training or serving, we provide the evaluation function to measure the model performance, e.g., accuracy, precision. In the operator based framework design, the data go through the network pipeline batch by batch. As a result, inside the operator, we only can calculate one minibatch metrics. We need to provide a mechanism to calculate the metrics for each N pass/batch the user wanted.
### Evaluator Design
Currently, every operation is expressed in the graph. we divide the evaluator process into three steps.
1. Initialize the metric state and add it into the block.
2. Calculate the statistic of the metric state in every mini-batch. The single operator is only responsible for calculating necessary statistics for one mini-batch. For example, accuracy operator only calculate a minibatch data if run once.
3. Merge the mini-batch statistics to form the evaluation result for multiple mini-batches. When it comes to distributed training/Multi-GPU training, aggregate the value from different devices.
### Implementation
This design is shown in python API.
Each metric operator need to caculate the metric statistic and return the batch aware states, Python side responsible for accumulate the states for each pass.
```python
class Evaluator(object):
"""
Evaluator Base class.
"""
def __init__(self, name, **kwargs):
"""
Different evaluator may has different metric states. E.g, Accuracy need two variables, total and right sample counts.
Auc need four variables, `true_positives`,
`true_negatives`, `false_positives` and `false_negatives`. So every evaluator should create its needed variables and append to main_program
The initialization of Evaluator should be responsible for:
create metric states and append to the main_program
"""
pass
def _update_ops(self, input, label, **kwargs)
"""
Add mini-batch evaluator caculate operators to the main_program.
Add increment operator to accumulate the metric states.
"""
def reset(self, executor, reset_program=None):
"""
Reset metric states at the begin of each pass/user specified batch number.
Execute the reset_program to reset the states.
"""
def eval(self, executor, eval_program=None):
"""
Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
Execute the eval_program and return the result.
"""
return eval_result
```
doc/design/float16.md
浏览文件 @ d9642cb3
......@@ -55,6 +55,6 @@ After float16 class is available, some of the future items are below:
- Update pybind/tensor_py.h to bind c++ float16 with numpy float16.
- Modify `IndicateDataType()` method in `framework/operator.h` to make it compatible with float16.
- Modify `GetKernelType()` method in `framework/operator.h` to make it compatible with float16.
- Create a type-casting operator that can convert the data type in tensor between float16 and other types.
doc/design/mkldnn/README.MD
浏览文件 @ d9642cb3
......@@ -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
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# 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
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......@@ -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
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......@@ -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
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......@@ -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) {
......@@ -81,6 +121,7 @@ paddle_error paddle_matrix_get_shape(paddle_matrix mat,
paddle_matrix paddle_matrix_create_sparse(
uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu) {
#ifndef PADDLE_MOBILE_INFERENCE
auto ptr = new paddle::capi::CMatrix();
ptr->mat = paddle::Matrix::createSparseMatrix(
height,
......@@ -91,6 +132,9 @@ paddle_matrix paddle_matrix_create_sparse(
false,
useGpu);
return ptr;
#else
return nullptr;
#endif
}
paddle_error paddle_matrix_sparse_copy_from(paddle_matrix mat,
......@@ -100,6 +144,7 @@ paddle_error paddle_matrix_sparse_copy_from(paddle_matrix mat,
uint64_t colSize,
float* valueArray,
uint64_t valueSize) {
#ifndef PADDLE_MOBILE_INFERENCE
if (mat == nullptr) return kPD_NULLPTR;
auto ptr = cast(mat);
if (rowArray == nullptr || colArray == nullptr ||
......@@ -120,4 +165,7 @@ paddle_error paddle_matrix_sparse_copy_from(paddle_matrix mat,
} else {
return kPD_NOT_SUPPORTED;
}
#else
return kPD_NOT_SUPPORTED;
#endif
}
paddle/capi/examples/model_inference/dense/main.c
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......@@ -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
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......@@ -48,6 +48,7 @@ PD_API paddle_matrix paddle_matrix_create(uint64_t height,
* @param isBinary is binary (either 1 or 0 in matrix) or not.
* @param useGpu is using GPU or not.
* @return paddle_matrix.
* @note Mobile inference does not support this interface.
*/
PD_API paddle_matrix paddle_matrix_create_sparse(
uint64_t height, uint64_t width, uint64_t nnz, bool isBinary, bool useGpu);
......@@ -70,6 +71,16 @@ 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 +92,15 @@ 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
......@@ -110,6 +130,7 @@ PD_API paddle_error paddle_matrix_get_shape(paddle_matrix mat,
* NULL if the matrix is binary.
* @param [in] valueSize length of value array. Zero if the matrix is binary.
* @return paddle_error
* @note Mobile inference does not support this interface.
*/
PD_API paddle_error paddle_matrix_sparse_copy_from(paddle_matrix mat,
int* rowArray,
......
paddle/capi/tests/test_Matrix.cpp
浏览文件 @ d9642cb3
......@@ -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/cuda/CMakeLists.txt
浏览文件 @ d9642cb3
......@@ -27,7 +27,9 @@ if(WITH_GPU)
set_source_files_properties(${CUDA_CXX_SOURCES}
PROPERTIES COMPILE_FLAGS "-D__NVCC__")
else()
if (NOT MOBILE_INFERENCE)
set(CUDA_CXX_SOURCES src/hl_warpctc_wrap.cc)
endif()
endif()
set(CUDA_CU_SOURCES
......
paddle/cuda/include/hl_cnn.h
浏览文件 @ d9642cb3
......@@ -18,7 +18,7 @@ limitations under the License. */
#include "hl_base.h"
/**
* @brief Maximum pool forward.
* @brief Maximum pool forward with Mask output.
*
* @param[in] frameCnt batch size of input image.
* @param[in] inputData input data.
......@@ -35,7 +35,7 @@ limitations under the License. */
* @param[in] paddingW padding width.
* @param[out] tgtData output data.
* @param[in] tgtStride stride between output data samples.
*
* @param[out] maskData the location indices of select max data.
*/
extern void hl_maxpool_forward(const int frameCnt,
const real* inputData,
......@@ -51,7 +51,8 @@ extern void hl_maxpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride);
const int tgtStride,
real* maskData = NULL);
/**
* @brief Maximum pool backward.
......
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @ d9642cb3
......@@ -31,7 +31,8 @@ inline void hl_maxpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {}
const int tgtStride,
real* MaskData) {}
inline void hl_maxpool_backward(const int frameCnt,
const real* inputData,
......
paddle/cuda/src/hl_cuda_cnn.cu
浏览文件 @ d9642cb3
......@@ -31,7 +31,8 @@ __global__ void KeMaxPoolForward(const int nthreads,
const int offsetH,
const int offsetW,
real* tgtData,
const int tgtStride) {
const int tgtStride,
real* maskData) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
if (index < nthreads) {
int pw = index % pooledW;
......@@ -45,16 +46,22 @@ __global__ void KeMaxPoolForward(const int nthreads,
hstart = max(hstart, 0);
wstart = max(wstart, 0);
real maxval = -FLT_MAX;
int max_index = -1;
inputData += (frameNum * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
if (maxval < inputData[h * width + w])
maxval = inputData[h * width + w];
if (maxval < inputData[h * width + w]) {
max_index = h * width + w;
maxval = inputData[max_index];
}
}
}
int tgtIndex =
index % (pooledW * pooledH * channels) + frameNum * tgtStride;
tgtData[tgtIndex] = maxval;
if (maskData != NULL) {
maskData[tgtIndex] = max_index;
}
}
}
......@@ -72,7 +79,8 @@ void hl_maxpool_forward(const int frameCnt,
const int paddingH,
const int paddingW,
real* tgtData,
const int tgtStride) {
const int tgtStride,
real* maskData) {
int num_kernels = pooledH * pooledW * channels * frameCnt;
int blocks = (num_kernels + 1024 - 1) / 1024;
dim3 threads(1024, 1);
......@@ -92,7 +100,8 @@ void hl_maxpool_forward(const int frameCnt,
paddingH,
paddingW,
tgtData,
tgtStride);
tgtStride,
maskData);
CHECK_SYNC("hl_maxpool_forward failed");
}
......
paddle/framework/CMakeLists.txt
浏览文件 @ d9642cb3
......@@ -38,9 +38,9 @@ py_proto_compile(framework_py_proto SRCS framework.proto)
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto/
COMMENT "Copy generated python proto into directory paddle/v2/framework/proto."
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/proto
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/fluid/proto/
COMMENT "Copy generated python proto into directory paddle/v2/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS net_op)
......
paddle/framework/backward.cc
浏览文件 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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&> {
......@@ -117,7 +124,7 @@ int64_t DDim::operator[](int idx) const {
return boost::apply_visitor(DynamicConstIndexer(idx), var);
}
int64_t DDim::size() const { return arity(*this); }
int DDim::size() const { return arity(*this); }
bool DDim::operator==(DDim d) const {
if (var.which() != d.getVar().which()) {
......
paddle/framework/ddim.h
浏览文件 @ d9642cb3
......@@ -71,7 +71,7 @@ struct DDim {
DDim operator*(DDim d) const;
int64_t size() const;
int size() const;
};
/**
......@@ -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/lod_rank_table.cc
浏览文件 @ d9642cb3
......@@ -31,6 +31,7 @@ void LoDRankTable::Reset(const LoD& lod, size_t level) {
TableItem item;
item.index = i;
item.length = vec[i + 1] - vec[i];
VLOG(10) << "Add item to rank table " << item.index << " " << item.length;
items_.emplace_back(item);
}
// NOTE(yuyang18):
......
paddle/framework/lod_tensor.cc
浏览文件 @ d9642cb3
......@@ -27,6 +27,20 @@
namespace paddle {
namespace framework {
std::ostream& operator<<(std::ostream& os, const LoD& lod) {
os << "{";
for (auto& v : lod) {
os << "{";
for (auto& i : v) {
os << i << ",";
}
os << "}";
}
os << "}";
return os;
}
LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end) {
LoD new_lod;
new_lod.reserve(level_end - level_begin);
......@@ -136,37 +150,35 @@ void LoDTensor::ShrinkInLevel(size_t level, size_t elem_begin,
ShareDataWith(Slice(begin, end));
}
void GetFineGrainedLoDLength(const LoD& lod, size_t start_idx, size_t end_idx,
std::vector<std::vector<size_t>>* lod_length,
size_t* start_offset) {
lod_length->clear();
PADDLE_ENFORCE(start_idx < lod.size() - 1,
"start_idx should be >= 0 and < lod.size() - 1.");
PADDLE_ENFORCE(end_idx < lod.size(),
"end_idx should be >= 0 and < lod.size().");
PADDLE_ENFORCE_LE(start_idx, end_idx,
"start_idx should be less than end_idx.");
for (size_t level_idx = 0; level_idx < lod.size(); ++level_idx) {
using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
LoDAndOffset GetSubLoDAndAbsoluteOffset(const LoD& lod, size_t start_idx,
size_t end_idx, size_t start_level) {
LoD sub_lod;
for (size_t level_idx = start_level; level_idx < lod.size(); ++level_idx) {
PADDLE_ENFORCE_LE(start_idx, end_idx);
PADDLE_ENFORCE_LT(end_idx, lod[level_idx].size());
std::vector<size_t> level_lens;
for (size_t i = start_idx; i < end_idx; ++i) {
level_lens.push_back(lod[level_idx][i + 1] - lod[level_idx][i]);
}
lod_length->emplace_back(level_lens);
sub_lod.emplace_back(level_lens);
start_idx = lod[level_idx][start_idx];
end_idx = lod[level_idx][end_idx];
}
*start_offset = start_idx;
return LoDAndOffset{sub_lod, {start_idx, end_idx}};
}
void AppendLoD(LoD* lod, const std::vector<std::vector<size_t>>& lod_length) {
PADDLE_ENFORCE_EQ(
lod->size(), lod_length.size(),
void AppendLoD(LoD* lod, const LoD& lod_length) {
PADDLE_ENFORCE(
lod->empty() || lod->size() == lod_length.size(),
"The lod_length should has the same size with the appended lod.");
if (lod->empty()) {
*lod = LoD(lod_length.size(), std::vector<size_t>({0}));
}
for (size_t i = 0; i < lod->size(); ++i) {
auto& level = (*lod)[i];
if (level.empty()) {
level.push_back(0);
}
for (size_t len : lod_length[i]) {
level.push_back(level.back() + len);
}
......
paddle/framework/lod_tensor.h
浏览文件 @ d9642cb3
......@@ -56,6 +56,8 @@ using Vector = thrust::host_vector<
*/
using LoD = std::vector<Vector<size_t>>;
std::ostream& operator<<(std::ostream& os, const LoD& lod);
/*
* Slice levels from a LoD.
* NOTE the lowest level should always be the absolute offsets of the underlying
......@@ -181,11 +183,10 @@ LoDTensor LodExpand(const LoDTensor& source, const LoD& lod, size_t level,
return tensor;
}
void GetFineGrainedLoDLength(const LoD& lod, size_t start_idx, size_t end_idx,
std::vector<std::vector<size_t>>* lod_length,
size_t* start_offset);
std::pair<LoD, std::pair<size_t, size_t>> GetSubLoDAndAbsoluteOffset(
const LoD& lod, size_t start_idx, size_t end_idx, size_t start_level);
void AppendLoD(LoD* lod, const std::vector<std::vector<size_t>>& lod_length);
void AppendLoD(LoD* lod, const LoD& lod_length);
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor_test.cc
浏览文件 @ d9642cb3
......@@ -146,43 +146,44 @@ TEST(LodExpand, test) {
TEST(LoD, GetFineGrainedLoDLength) {
LoD lod;
lod.push_back(std::vector<size_t>{0, 2, 4, 5});
lod.push_back(std::vector<size_t>{0, 1, 6, 8, 10, 11});
lod.push_back(std::vector<size_t>({0, 2, 4, 5}));
lod.push_back(std::vector<size_t>({0, 1, 6, 8, 10, 11}));
lod.push_back(
std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26, 29});
std::vector<size_t>({0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26, 29}));
std::vector<std::vector<size_t>> lod_length;
size_t start_offset;
paddle::framework::GetFineGrainedLoDLength(lod, 1, 2, &lod_length,
&start_offset);
auto lod_and_offset =
paddle::framework::GetSubLoDAndAbsoluteOffset(lod, 1, 2, 0);
LoD lod_length = lod_and_offset.first;
size_t start_offset = lod_and_offset.second.first;
size_t end_offset = lod_and_offset.second.second;
std::vector<std::vector<size_t>> expected;
LoD expected;
expected.push_back(std::vector<size_t>{2});
expected.push_back(std::vector<size_t>{2, 2});
expected.push_back(std::vector<size_t>{2, 3, 4, 2});
EXPECT_EQ(lod_length, expected);
EXPECT_EQ(start_offset, 15UL);
EXPECT_EQ(end_offset, 26UL);
}
TEST(LoD, AppendLoD) {
std::vector<std::vector<size_t>> lod_lens;
lod_lens.push_back(std::vector<size_t>{2});
lod_lens.push_back(std::vector<size_t>{2, 2});
lod_lens.push_back(std::vector<size_t>{2, 3, 4, 2});
LoD lod_lens;
lod_lens.push_back(std::vector<size_t>({2}));
lod_lens.push_back(std::vector<size_t>({2, 2}));
lod_lens.push_back(std::vector<size_t>({2, 3, 4, 2}));
LoD origin;
origin.push_back(std::vector<size_t>{0, 2});
origin.push_back(std::vector<size_t>{0, 1, 6});
origin.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15});
origin.push_back(std::vector<size_t>({0, 2}));
origin.push_back(std::vector<size_t>({0, 1, 6}));
origin.push_back(std::vector<size_t>({0, 2, 5, 7, 10, 12, 15}));
paddle::framework::AppendLoD(&origin, lod_lens);
LoD expected;
expected.push_back(std::vector<size_t>{0, 2, 4});
expected.push_back(std::vector<size_t>{0, 1, 6, 8, 10});
expected.push_back(std::vector<size_t>({0, 2, 4}));
expected.push_back(std::vector<size_t>({0, 1, 6, 8, 10}));
expected.push_back(
std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26});
std::vector<size_t>({0, 2, 5, 7, 10, 12, 15, 17, 20, 24, 26}));
EXPECT_EQ(origin, expected);
}
......
paddle/framework/op_desc.cc
浏览文件 @ d9642cb3
......@@ -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/op_registry.h
浏览文件 @ d9642cb3
......@@ -92,8 +92,7 @@ struct OpKernelRegistrarFunctor<PlaceType, false, I, KernelTypes...> {
void operator()(const char* op_type) const {
using T = typename KERNEL_TYPE::ELEMENT_TYPE;
OperatorWithKernel::OpKernelKey key(ToDataType(std::type_index(typeid(T))),
PlaceType());
OpKernelType key(ToDataType(std::type_index(typeid(T))), PlaceType());
OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE);
constexpr auto size = std::tuple_size<std::tuple<KernelTypes...>>::value;
......
paddle/framework/operator.cc
浏览文件 @ d9642cb3
......@@ -254,8 +254,7 @@ std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
return res;
}
std::ostream& operator<<(std::ostream& os,
const OperatorWithKernel::OpKernelKey& kernel_key) {
std::ostream& operator<<(std::ostream& os, const OpKernelType& kernel_key) {
os << "place[" << kernel_key.place_ << "]:data_type[" << kernel_key.data_type_
<< "]";
return os;
......@@ -432,7 +431,7 @@ void OperatorWithKernel::Run(const Scope& scope,
// check if op[type] have kernel for kernel_key
OpKernelMap& kernels = kernels_iter->second;
auto kernel_key = OpKernelKey(IndicateDataType(ctx), dev_ctx);
auto kernel_key = GetKernelType(ctx);
auto kernel_iter = kernels.find(kernel_key);
if (kernel_iter == kernels.end()) {
......@@ -440,6 +439,41 @@ void OperatorWithKernel::Run(const Scope& scope,
}
kernel_iter->second->Compute(ctx);
// throws errors if have.
dev_ctx.Finish();
}
OpKernelType OperatorWithKernel::GetKernelType(
const ExecutionContext& ctx) const {
return OpKernelType(IndicateDataType(ctx), ctx.device_context());
}
DataType OperatorWithKernel::IndicateDataType(
const ExecutionContext& ctx) const {
auto& scope = ctx.scope();
int data_type = -1;
for (auto& input : this->inputs_) {
for (auto& ipt_name : input.second) {
auto* var = scope.FindVar(ipt_name);
if (var != nullptr) {
const Tensor* t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
t = &(var->Get<SelectedRows>().value());
}
if (t != nullptr) {
int tmp = static_cast<int>(ToDataType(t->type()));
PADDLE_ENFORCE(tmp == data_type || data_type == -1,
"DataType of Paddle Op %s must be the same.", Type());
data_type = tmp;
}
}
}
}
PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
return static_cast<DataType>(data_type);
}
} // namespace framework
......
paddle/framework/operator.h
浏览文件 @ d9642cb3
......@@ -345,27 +345,10 @@ class OpKernel : public OpKernelBase {
using ELEMENT_TYPE = T;
};
class OperatorWithKernel : public OperatorBase {
public:
struct OpKernelKey {
platform::Place place_;
DataType data_type_;
OpKernelKey(DataType data_type, platform::Place place)
: place_(place), data_type_(data_type) {}
OpKernelKey(DataType data_type, const platform::DeviceContext& dev_ctx)
: place_(dev_ctx.GetPlace()), data_type_(data_type) {}
bool operator==(const OpKernelKey& o) const {
return platform::places_are_same_class(place_, o.place_) &&
data_type_ == o.data_type_;
}
};
struct OpKernelHash {
struct OpKernelType {
struct Hash {
std::hash<int> hash_;
size_t operator()(const OpKernelKey& key) const {
size_t operator()(const OpKernelType& key) const {
int place = key.place_.which();
int data_type = static_cast<int>(key.data_type_);
int pre_hash = data_type << NUM_PLACE_TYPE_LIMIT_IN_BIT |
......@@ -374,9 +357,26 @@ class OperatorWithKernel : public OperatorBase {
}
};
platform::Place place_;
DataType data_type_;
OpKernelType(DataType data_type, platform::Place place)
: place_(place), data_type_(data_type) {}
OpKernelType(DataType data_type, const platform::DeviceContext& dev_ctx)
: place_(dev_ctx.GetPlace()), data_type_(data_type) {}
bool operator==(const OpKernelType& o) const {
return platform::places_are_same_class(place_, o.place_) &&
data_type_ == o.data_type_;
}
};
class OperatorWithKernel : public OperatorBase {
public:
using OpKernelMap =
std::unordered_map<OpKernelKey, std::unique_ptr<OpKernelBase>,
OpKernelHash>;
std::unordered_map<OpKernelType, std::unique_ptr<OpKernelBase>,
OpKernelType::Hash>;
OperatorWithKernel(const std::string& type, const VariableNameMap& inputs,
const VariableNameMap& outputs, const AttributeMap& attrs)
......@@ -404,40 +404,15 @@ class OperatorWithKernel : public OperatorBase {
}
protected:
virtual OpKernelType GetKernelType(const ExecutionContext& ctx) const;
private:
// indicate kernel DataType by input data. Defaultly all input data must be
// same.
virtual DataType IndicateDataType(const ExecutionContext& ctx) const {
auto& scope = ctx.scope();
int data_type = -1;
for (auto& input : this->inputs_) {
for (auto& ipt_name : input.second) {
auto* var = scope.FindVar(ipt_name);
if (var != nullptr) {
const Tensor* t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
} else if (var->IsType<SelectedRows>()) {
t = &(var->Get<SelectedRows>().value());
}
if (t != nullptr) {
int tmp = static_cast<int>(ToDataType(t->type()));
PADDLE_ENFORCE(tmp == data_type || data_type == -1,
"DataType of Paddle Op %s must be the same.",
Type());
data_type = tmp;
}
}
}
}
PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input");
return static_cast<DataType>(data_type);
}
DataType IndicateDataType(const ExecutionContext& ctx) const;
};
std::ostream& operator<<(std::ostream& os,
const OperatorWithKernel::OpKernelKey& kernel_key);
std::ostream& operator<<(std::ostream& os, const OpKernelType& kernel_key);
extern bool OpSupportGPU(const std::string& op_type);
......
paddle/framework/operator_test.cc
浏览文件 @ d9642cb3
......@@ -114,8 +114,8 @@ class OpWithKernelTest : public OperatorWithKernel {
protected:
void InferShape(framework::InferShapeContext* ctx) const override {}
DataType IndicateDataType(const ExecutionContext& ctx) const override {
return DataType::FP32;
OpKernelType GetKernelType(const ExecutionContext& ctx) const override {
return OpKernelType(DataType::FP32, ctx.device_context());
}
};
......
paddle/framework/scope.cc
浏览文件 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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/tensor_impl.h
浏览文件 @ d9642cb3
......@@ -52,7 +52,7 @@ struct SizeOfTypeFunctor<HEAD, TAIL...> {
};
static inline size_t SizeOfType(std::type_index type) {
SizeOfTypeFunctor<int, float, double, int16_t, int64_t> functor;
SizeOfTypeFunctor<int, float, double, int16_t, int64_t, bool> functor;
size_t size = functor(type);
PADDLE_ENFORCE(size != 0UL, "Cannot get size of type %s", type.name());
return size;
......
paddle/framework/var_desc.cc
浏览文件 @ d9642cb3
......@@ -45,7 +45,8 @@ void VarDescBind::SetLoDLevel(int32_t lod_level) {
desc_.mutable_tensor_array()->set_lod_level(lod_level);
break;
default:
PADDLE_THROW("Tensor type=%d does not support LoDLevel", desc_.type());
PADDLE_THROW("Tensor type=%d does not support LoDLevel",
desc_.tensor_array().lod_level());
}
}
......@@ -56,7 +57,8 @@ int32_t VarDescBind::GetLodLevel() const {
case VarDesc::LOD_TENSOR_ARRAY:
return desc_.tensor_array().lod_level();
default:
PADDLE_THROW("Tensor type=%d does not support LoDLevel", desc_.type());
PADDLE_THROW("Tensor type=%d does not support LoDLevel",
desc_.tensor_array().lod_level());
}
}
......
paddle/framework/var_type.h
浏览文件 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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/ConvOp.h
浏览文件 @ d9642cb3
......@@ -61,6 +61,7 @@ public:
// function arguments
strides_ = config.get<std::vector<size_t>>("strides");
paddings_ = config.get<std::vector<size_t>>("paddings");
dilations_ = config.get<std::vector<size_t>>("dilations");
groups_ = config.get<size_t>("groups");
// number of inputs and outputs
......@@ -118,6 +119,7 @@ protected:
std::vector<size_t> strides_;
std::vector<size_t> paddings_;
std::vector<size_t> dilations_;
/// Group size, refer to grouped convolution in
/// Alex Krizhevsky's paper: when group=2, the first half of the
......@@ -133,6 +135,10 @@ protected:
inline int paddingW() const { return paddings_[1]; }
inline int dilationH() const { return dilations_[0]; }
inline int dilationW() const { return dilations_[1]; }
// A temporary memory in convolution calculation.
MemoryHandlePtr memory_;
......
paddle/function/ConvOpTest.h
浏览文件 @ d9642cb3
......@@ -79,45 +79,59 @@ void Convolution(const std::string& conv1,
if (outputChannels < inputChannels) continue;
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
for (size_t dilation : {1, 3}) {
if (padding >= filterSize) break;
size_t filterS = (filterSize - 1) * dilation + 1;
// NNPACK only supports stride = 1 if batchSize > 1
if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
batchSize > 1 && stride > 1)
break;
if (inputSize + 2 * padding < filterS) break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize << " stride=" << stride
<< " padding=" << padding;
if ((conv1 == "NaiveConv-CPU" || conv2 == "NaiveConv-CPU" ||
conv1 == "NNPACKConv-CPU" ||
conv2 == "NNPACKConv-CPU") &&
dilation > 1)
break;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("algo", (std::string) "auto"));
// NNPACK only supports stride = 1 if batchSize > 1
if ((conv1 == "NNPACKConv-CPU" ||
conv2 == "NNPACKConv-CPU") &&
batchSize > 1 && stride > 1)
break;
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
size_t outputSize =
(inputSize - filterS + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize
<< " stride=" << stride << " padding=" << padding;
function(test, input, filter, output);
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> dilations = {dilation, dilation};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)1)
.set("algo", (std::string) "auto"));
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
function(test, input, filter, output);
}
}
}
}
......@@ -144,6 +158,7 @@ void Convolution2(const std::string& conv1,
for (size_t outputChannels : {7}) {
size_t stride = 1;
size_t padding = 0;
size_t dilation = 1;
size_t outputHeight =
(inputHeight - filterHeight + 2 * padding + stride) /
stride;
......@@ -162,6 +177,7 @@ void Convolution2(const std::string& conv1,
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> dilations = {dilation, dilation};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
......@@ -169,6 +185,7 @@ void Convolution2(const std::string& conv1,
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("dilations", dilations)
.set("algo", (std::string) "auto"));
TensorShape input{
......@@ -223,6 +240,7 @@ void DepthwiseConvolution(const std::string& conv1,
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> dilations = {1, 1};
size_t groups = inputChannels;
Compare2Function<DType1, DType2> test(
conv1,
......@@ -231,6 +249,7 @@ void DepthwiseConvolution(const std::string& conv1,
.set("paddings", paddings)
.set("strides", strides)
.set("groups", groups)
.set("dilations", dilations)
.set("algo", (std::string) "auto"));
TensorShape input{
......
paddle/function/FunctionTest.h
浏览文件 @ d9642cb3
......@@ -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/GemmConvOp.cpp
浏览文件 @ d9642cb3
......@@ -100,7 +100,9 @@ public:
strideH(),
strideW(),
paddingH(),
paddingW());
paddingW(),
dilationH(),
dilationW());
} else {
colData = inputData + g * inputOffset;
}
......@@ -223,7 +225,9 @@ public:
strideH(),
strideW(),
paddingH(),
paddingW());
paddingW(),
dilationH(),
dilationW());
}
}
inputGrad += inputChannels * inputHeight * inputWidth;
......@@ -310,7 +314,9 @@ public:
strideH(),
strideW(),
paddingH(),
paddingW());
paddingW(),
dilationH(),
dilationW());
} else {
colData = inputData + g * inputOffset;
}
......
paddle/function/Im2Col.h
浏览文件 @ d9642cb3
......@@ -78,7 +78,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth);
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1);
};
template <ColFormat Format, DeviceType Device, class T>
......@@ -91,7 +93,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth);
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1);
};
} // namespace paddle
paddle/function/Im2ColOp.cpp
浏览文件 @ d9642cb3
......@@ -31,7 +31,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -47,8 +49,8 @@ public:
int c_im = c / filterWidth / filterHeight;
for (int h = 0; h < outputHeight; ++h) {
for (int w = 0; w < outputWidth; ++w) {
int imRowIdx = h * strideHeight + hOffset;
int imColIdx = w * strideWidth + wOffset;
int imRowIdx = h * strideHeight + hOffset * dilationHeight;
int imColIdx = w * strideWidth + wOffset * dilationWidth;
if ((imRowIdx - paddingHeight) < 0 ||
(imRowIdx - paddingHeight) >= inputHeight ||
(imColIdx - paddingWidth) < 0 ||
......@@ -81,7 +83,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -97,8 +101,8 @@ public:
int c_im = c / filterWidth / filterHeight;
for (int h = 0; h < outputHeight; ++h) {
for (int w = 0; w < outputWidth; ++w) {
int imRowIdx = h * strideHeight + hOffset;
int imColIdx = w * strideWidth + wOffset;
int imRowIdx = h * strideHeight + hOffset * dilationHeight;
int imColIdx = w * strideWidth + wOffset * dilationWidth;
if ((imRowIdx - paddingHeight) >= 0 &&
(imRowIdx - paddingHeight) < inputHeight &&
(imColIdx - paddingWidth) >= 0 &&
......@@ -134,7 +138,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -147,9 +153,10 @@ public:
for (int channel = 0; channel < inputChannels; ++channel) {
for (int filterH = 0; filterH < filterHeight; ++filterH) {
for (int filterW = 0; filterW < filterWidth; ++filterW) {
int imRowOffset =
outputH * strideHeight + filterH - paddingHeight;
int imColOffset = outputW * strideWidth + filterW - paddingWidth;
int imRowOffset = outputH * strideHeight +
filterH * dilationHeight - paddingHeight;
int imColOffset = outputW * strideWidth +
filterW * dilationWidth - paddingWidth;
int colDataOffset =
(((outputH * outputWidth + outputW) * inputChannels +
channel) *
......@@ -189,7 +196,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -202,9 +211,10 @@ public:
for (int channel = 0; channel < inputChannels; ++channel) {
for (int filterH = 0; filterH < filterHeight; ++filterH) {
for (int filterW = 0; filterW < filterWidth; ++filterW) {
int imRowOffset =
outputH * strideHeight + filterH - paddingHeight;
int imColOffset = outputW * strideWidth + filterW - paddingWidth;
int imRowOffset = outputH * strideHeight +
filterH * dilationHeight - paddingHeight;
int imColOffset = outputW * strideWidth +
filterW * dilationWidth - paddingWidth;
int colDataOffset =
(((outputH * outputWidth + outputW) * inputChannels +
channel) *
......
paddle/function/Im2ColOpGpu.cu
浏览文件 @ d9642cb3
......@@ -28,6 +28,8 @@ __global__ void im2col(const T* data_im,
int strideW,
int paddingH,
int paddingW,
int dilationH,
int dilationW,
int height_col,
int width_col,
T* data_col) {
......@@ -44,8 +46,8 @@ __global__ void im2col(const T* data_im,
data_col += (channel_out * height_col + h_out) * width_col + w_out;
for (int i = 0; i < blockH; ++i) {
for (int j = 0; j < blockW; ++j) {
int rIdx = int(h_in + i);
int cIdx = int(w_in + j);
int rIdx = int(h_in + i * dilationH);
int cIdx = int(w_in + j * dilationW);
if ((rIdx - (int)paddingH) >= (int)height ||
(rIdx - (int)paddingH) < 0 ||
(cIdx - (int)paddingW) >= (int)width ||
......@@ -77,7 +79,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -102,6 +106,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth,
colData);
......@@ -121,6 +127,8 @@ __global__ void col2im(size_t n,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t dilationH,
size_t dilationW,
size_t height_col,
size_t width_col,
T* data_im) {
......@@ -131,23 +139,34 @@ __global__ void col2im(size_t n,
int w = int(index % width);
int h = int((index / width) % height);
int c = int(index / (width * height));
int filterH = (blockH - 1) * dilationH + 1;
int filterW = (blockW - 1) * dilationW + 1;
if ((w - (int)paddingW) >= 0 &&
(w - (int)paddingW) < (width - 2 * paddingW) &&
(h - (int)paddingH) >= 0 && (h - paddingH) < (height - 2 * paddingH)) {
// compute the start and end of the output
int w_col_start =
(w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1;
(w < (int)filterW) ? 0 : (w - int(filterW)) / (int)strideW + 1;
int w_col_end = min((int)(w / (int)strideW + 1), (int)(width_col));
int h_col_start =
(h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1;
(h < (int)filterH) ? 0 : (h - (int)filterH) / (int)strideH + 1;
int h_col_end = min(int(h / strideH + 1), int(height_col));
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
// the col location: [c * width * height + h_out, w_out]
int c_col = int(c * blockH * blockW) +
(h - h_col * (int)strideH) * (int)blockW +
(w - w_col * (int)strideW);
val += data_col[(c_col * height_col + h_col) * width_col + w_col];
int h_k = (h - h_col * strideH);
int w_k = (w - w_col * strideW);
if (h_k % dilationH == 0 && w_k % dilationW == 0) {
h_k /= dilationH;
w_k /= dilationW;
int c_col =
(((c * blockH + h_k) * blockW + w_k) * height_col + h_col) *
width_col +
w_col;
val += data_col[c_col];
}
}
}
h -= paddingH;
......@@ -173,7 +192,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -205,6 +226,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth,
imData);
......@@ -229,6 +252,8 @@ __global__ void im2colOCF(const T* imData,
int strideWidth,
int paddingHeight,
int paddingWidth,
int dilationHeight,
int dilationWidth,
int outputHeight,
int outputWidth) {
int swId = blockIdx.x;
......@@ -237,8 +262,10 @@ __global__ void im2colOCF(const T* imData,
channelId += blockDim.z) {
for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) {
int widthOffset = idx + swId * strideWidth - paddingWidth;
int heightOffset = idy + shId * strideHeight - paddingHeight;
int widthOffset =
idx * dilationHeight + swId * strideWidth - paddingWidth;
int heightOffset =
idy * dilationWidth + shId * strideHeight - paddingHeight;
int imOffset = widthOffset + heightOffset * inputWidth +
channelId * inputHeight * inputWidth;
......@@ -273,7 +300,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -312,6 +341,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth);
CHECK_SYNC("Im2ColFunctor GPU failed");
......@@ -330,6 +361,8 @@ __global__ void col2imOCF(T* imData,
int strideWidth,
int paddingHeight,
int paddingWidth,
int dilationHeight,
int dilationWidth,
int outputHeight,
int outputWidth) {
int swId = blockIdx.x;
......@@ -338,8 +371,10 @@ __global__ void col2imOCF(T* imData,
channelId += blockDim.z) {
for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) {
int widthOffset = idx + swId * strideWidth - paddingWidth;
int heightOffset = idy + shId * strideHeight - paddingHeight;
int widthOffset =
idx * dilationWidth + swId * strideWidth - paddingWidth;
int heightOffset =
idy * dilationHeight + shId * strideHeight - paddingHeight;
int imOffset = widthOffset + heightOffset * inputWidth +
channelId * inputHeight * inputWidth;
......@@ -372,7 +407,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -411,6 +448,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth);
CHECK_SYNC("Col2ImFunctor GPU failed");
......
paddle/function/Im2ColTest.cpp
浏览文件 @ d9642cb3
......@@ -29,82 +29,98 @@ void TestIm2ColFunctor() {
for (size_t filterWidth : {3, 7}) {
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (inputHeight <= filterHeight || inputWidth <= filterWidth)
break;
if (padding >= filterHeight || padding >= filterWidth) break;
size_t outputHeight =
(inputHeight - filterHeight + 2 * padding + stride) /
stride;
size_t outputWidth =
(inputWidth - filterWidth + 2 * padding + stride) / stride;
TensorShape imShape =
TensorShape({channels, inputHeight, inputWidth});
TensorShape colShape1 = TensorShape({channels,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
TensorShape colShape2 = TensorShape({outputHeight,
outputWidth,
channels,
filterHeight,
filterWidth});
size_t height = channels * filterHeight * filterWidth;
size_t width = outputHeight * outputWidth;
VectorPtr input1 = Vector::create(imShape.getElements(), false);
VectorPtr input2 = Vector::create(imShape.getElements(), false);
MatrixPtr output1 = Matrix::create(height, width, false, false);
MatrixPtr output2 = Matrix::create(width, height, false, false);
input1->uniform(0.001, 1);
input2->copyFrom(*input1);
Im2ColFunctor<kCFO, Device, T> im2Col1;
Im2ColFunctor<kOCF, Device, T> im2Col2;
im2Col1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding);
im2Col2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding);
// The transposition of the result of ColFormat == kCFO
// is equal to the result of ColFormat == kOCF.
MatrixPtr test;
output2->transpose(test, true);
autotest::TensorCheckErr(*output1, *test);
Col2ImFunctor<kCFO, Device, T> col2Im1;
Col2ImFunctor<kOCF, Device, T> col2Im2;
col2Im1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding);
col2Im2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding);
autotest::TensorCheckErr(*input1, *input2);
for (size_t dilation : {1, 3}) {
size_t filterSizeH = (filterHeight - 1) * dilation + 1;
size_t filterSizeW = (filterWidth - 1) * dilation + 1;
if (inputHeight + 2 * padding < filterSizeH ||
inputWidth + 2 * padding < filterSizeW)
break;
if (padding >= filterSizeH || padding >= filterSizeW) break;
size_t outputHeight =
(inputHeight - filterSizeH + 2 * padding) / stride + 1;
size_t outputWidth =
(inputWidth - filterSizeW + 2 * padding) / stride + 1;
TensorShape imShape =
TensorShape({channels, inputHeight, inputWidth});
TensorShape colShape1 = TensorShape({channels,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
TensorShape colShape2 = TensorShape({outputHeight,
outputWidth,
channels,
filterHeight,
filterWidth});
size_t height = channels * filterHeight * filterWidth;
size_t width = outputHeight * outputWidth;
VectorPtr input1 =
Vector::create(imShape.getElements(), false);
VectorPtr input2 =
Vector::create(imShape.getElements(), false);
MatrixPtr output1 =
Matrix::create(height, width, false, false);
MatrixPtr output2 =
Matrix::create(width, height, false, false);
input1->uniform(0.001, 1);
input2->copyFrom(*input1);
Im2ColFunctor<kCFO, Device, T> im2Col1;
Im2ColFunctor<kOCF, Device, T> im2Col2;
im2Col1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding,
dilation,
dilation);
im2Col2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding,
dilation,
dilation);
// The transposition of the result of ColFormat == kCFO
// is equal to the result of ColFormat == kOCF.
MatrixPtr test;
output2->transpose(test, true);
autotest::TensorCheckErr(*output1, *test);
Col2ImFunctor<kCFO, Device, T> col2Im1;
Col2ImFunctor<kOCF, Device, T> col2Im2;
col2Im1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding,
dilation,
dilation);
col2Im2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding,
dilation,
dilation);
autotest::TensorCheckErr(*input1, *input2);
}
}
}
}
......
paddle/function/ScaleSubRegionOp.cpp 0 → 100644
浏览文件 @ d9642cb3
/* 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 @ d9642cb3
/* 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 @ d9642cb3
/* 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 @ d9642cb3
/* 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/CMakeLists.txt
浏览文件 @ d9642cb3
......@@ -85,9 +85,49 @@ if(MOBILE_INFERENCE)
gradientmachines/GradientMachineMode.cpp
gradientmachines/MultiGradientMachine.cpp)
# Remove useless layers
# Remove layers that used in training
list(REMOVE_ITEM GSERVER_SOURCES
layers/RecurrentLayerGroup.cpp)
layers/RecurrentLayerGroup.cpp
layers/CostLayer.cpp
layers/MultiBoxLossLayer.cpp
layers/WarpCTCLayer.cpp
layers/CTCLayer.cpp
layers/LinearChainCTC.cpp
layers/PrintLayer.cpp)
list(REMOVE_ITEM GSERVER_SOURCES
layers/OuterProdLayer.cpp
layers/SumToOneNormLayer.cpp
layers/ConvShiftLayer.cpp
layers/InterpolationLayer.cpp
layers/AgentLayer.cpp
layers/DotMulOperator.cpp
layers/GruStepLayer.cpp
layers/LstmStepLayer.cpp
layers/ConvexCombinationLayer.cpp
layers/Conv3DLayer.cpp
layers/DeConv3DLayer.cpp
layers/CropLayer.cpp
layers/CrossEntropyOverBeam.cpp
layers/DataNormLayer.cpp
layers/FeatureMapExpandLayer.cpp
layers/HierarchicalSigmoidLayer.cpp
layers/MultinomialSampler.cpp
layers/NCELayer.cpp
layers/KmaxSeqScoreLayer.cpp
layers/MDLstmLayer.cpp
layers/MultiplexLayer.cpp
layers/PadLayer.cpp
layers/Pool3DLayer.cpp
layers/ResizeLayer.cpp
layers/RotateLayer.cpp
layers/RowConvLayer.cpp
layers/RowL2NormLayer.cpp
layers/SamplingIdLayer.cpp
layers/ScaleShiftLayer.cpp
layers/SelectiveFullyConnectedLayer.cpp
layers/SpatialPyramidPoolLayer.cpp
layers/BilinearInterpLayer.cpp
layers/ClipLayer.cpp)
endif()
if(WITH_GPU)
......
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @ d9642cb3
......@@ -16,7 +16,6 @@ limitations under the License. */
#include "NeuralNetwork.h"
#include "hl_gpu.h"
#include "paddle/gserver/layers/AgentLayer.h"
#include "paddle/utils/CustomStackTrace.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
......@@ -28,6 +27,7 @@ limitations under the License. */
#ifndef PADDLE_MOBILE_INFERENCE
#include "MultiNetwork.h"
#include "RecurrentGradientMachine.h"
#include "paddle/gserver/layers/AgentLayer.h"
#endif
namespace paddle {
......@@ -192,9 +192,11 @@ void NeuralNetwork::init(const ModelConfig& config,
void NeuralNetwork::connect(LayerPtr agentLayer,
LayerPtr realLayer,
int height) {
#ifndef PADDLE_MOBILE_INFERENCE
AgentLayer* agent = dynamic_cast<AgentLayer*>(agentLayer.get());
CHECK_NOTNULL(agent);
agent->setRealLayer(realLayer, height);
#endif
}
void NeuralNetwork::connect(std::string agentLayerName,
......
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ d9642cb3
......@@ -79,6 +79,10 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
for (int i = 0; i < config_.inputs_size(); i++) {
std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
std::vector<size_t> dilations = {(size_t)dilationY_[i],
(size_t)dilation_[i]};
bool useDilation = ((size_t)dilationY_[i] > 1 || (size_t)dilation_[i] > 1);
// Convolution Layer uses the GemmConv function by default.
convType = "GemmConv";
......@@ -97,13 +101,14 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
if ((filterSize_[i] == filterSizeY_[i]) &&
(filterSize_[i] == 3 || filterSize_[i] == 4) &&
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2)) {
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2) &&
!useDilation) {
convType = "NeonDepthwiseConv";
}
#endif
}
if (FLAGS_use_nnpack && !isDeconv_) {
if (FLAGS_use_nnpack && !isDeconv_ && !useDilation) {
createFunction(forward_,
"NNPACKConv",
FuncConfig()
......@@ -117,6 +122,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
......@@ -124,6 +130,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
......@@ -131,6 +138,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)groups_[i]));
}
}
......
paddle/gserver/layers/Layer.cpp
浏览文件 @ d9642cb3
......@@ -98,6 +98,7 @@ ClassRegistrar<Layer, LayerConfig> Layer::registrar_;
LayerPtr Layer::create(const LayerConfig& config) {
std::string type = config.type();
#ifndef PADDLE_MOBILE_INFERENCE
// NOTE: As following types have illegal character '-',
// they can not use REGISTER_LAYER to registrar.
// Besides, to fit with old training models,
......@@ -106,7 +107,6 @@ LayerPtr Layer::create(const LayerConfig& config) {
return LayerPtr(new MultiClassCrossEntropy(config));
else if (type == "rank-cost")
return LayerPtr(new RankingCost(config));
#ifndef PADDLE_MOBILE_INFERENCE
else if (type == "auc-validation")
return LayerPtr(new AucValidation(config));
else if (type == "pnpair-validation")
......
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
浏览文件 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -60,18 +60,16 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() {
}
CHECK(wgtVal_) << "should have been initialized";
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
auto targetDim = wgtVal_->getDims();
auto srcFmt = hasNoSpatial_ ? format::io : format::ihwo;
auto srcFmt = targetDim.size() == 2 ? format::io : format::ihwo;
wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
hasInitedWgt_ = true;
}
void MKLDNNFcLayer::convertWeightsToPaddle() {
CHECK(wgtVal_) << "should have been initialized";
bool hasNoSpatial_ = ih_ == 1 && iw_ == 1;
auto targetDim = wgtVal_->getDims();
auto dstFmt = hasNoSpatial_ ? format::io : format::ihwo;
auto dstFmt = targetDim.size() == 2 ? format::io : format::ihwo;
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
......@@ -86,8 +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 @ d9642cb3
......@@ -181,21 +181,17 @@ void MKLDNNLayer::resetInValue(
auto extPD = MKLDNNMatrix::createPrimitiveDesc(
{bs_, ic_, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[inputIdx]->getOutputValue();
in = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), in != nullptr);
if (in == nullptr || in->getFormat() == format::nc) {
in = MKLDNNMatrix::create(extPD, inMat);
}
extInVal_ = isPaddleFormat(in->getFormat()) ? in : nullptr;
if (in->getFormat() == format::nc) {
CHECK(ih_ == 1 && iw_ == 1);
extInVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), extInVal_ != nullptr);
if (extInVal_ == nullptr || extInVal_->getFormat() == format::nc) {
extInVal_ = MKLDNNMatrix::create(extPD, inMat);
}
in = extInVal_;
if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
return;
}
// need create reorder
in = MKLDNNMatrix::create(*intPD);
extInVal_ = extInVal_ ? extInVal_ : MKLDNNMatrix::create(extPD, inMat);
cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
}
......@@ -291,7 +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 @ d9642cb3
......@@ -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/MaxPoolWithMaskLayer.cpp 0 → 100644
浏览文件 @ d9642cb3
/* 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 "MaxPoolWithMaskLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
namespace paddle {
bool MaxPoolWithMaskLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
PoolLayer::init(layerMap, parameterMap);
setOutput("mask", &mask_);
return true;
}
size_t MaxPoolWithMaskLayer::getSize() {
CHECK_EQ(inputLayers_.size(), 1UL);
size_t layerSize = 0;
outputY_ = outputSize(imgSizeY_,
sizeY_,
confPaddingY_,
strideY_,
/* caffeMode */ false);
outputX_ = outputSize(imgSize_,
sizeX_,
confPadding_,
stride_,
/* caffeMode */ false);
layerSize = outputX_ * outputY_ * channels_;
getOutput().setFrameHeight(outputY_);
getOutput().setFrameWidth(outputX_);
return layerSize;
}
void MaxPoolWithMaskLayer::forward(PassType passType) {
size_t size = getSize();
MatrixPtr inputV = inputLayers_[0]->getOutputValue();
int batchSize = inputV->getHeight();
resetOutput(batchSize, size);
MatrixPtr outV = getOutputValue();
CHECK_EQ(size, outV->getWidth());
resetSpecifyOutput(mask_,
batchSize,
size,
/* isValueClean */ false,
/* isGradClean */ true);
MatrixPtr maskV = mask_.value;
outV->maxPoolForward(*inputV,
imgSizeY_,
imgSize_,
channels_,
sizeX_,
sizeY_,
strideY_,
stride_,
outputY_,
outputX_,
confPaddingY_,
confPadding_,
maskV);
}
void MaxPoolWithMaskLayer::backward(const UpdateCallback& callback) {
(void)callback;
if (NULL == getInputGrad(0)) {
return;
}
MatrixPtr outGrad = getOutputGrad();
MatrixPtr inputV = inputLayers_[0]->getOutputValue();
MatrixPtr outV = getOutputValue();
MatrixPtr inputGrad = inputLayers_[0]->getOutputGrad();
inputGrad->maxPoolBackward(*inputV,
imgSizeY_,
imgSize_,
*outGrad,
*outV,
sizeX_,
sizeY_,
strideY_,
stride_,
outputY_,
outputX_,
1,
1,
confPaddingY_,
confPadding_);
}
} // namespace paddle
paddle/operators/fill_constant_op.h → paddle/gserver/layers/MaxPoolWithMaskLayer.h
浏览文件 @ d9642cb3
......@@ -13,25 +13,28 @@ 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 <vector>
#include "PoolLayer.h"
#include "paddle/math/Matrix.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));
}
/**
* @brief Basic parent layer of different kinds of pooling
*/
class MaxPoolWithMaskLayer : public PoolLayer {
protected:
Argument mask_;
public:
explicit MaxPoolWithMaskLayer(const LayerConfig& config)
: PoolLayer(config) {}
size_t getSize();
void forward(PassType passType) override;
void backward(const UpdateCallback& callback = nullptr) override;
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
};
} // namespace operators
} // namespace paddle
paddle/gserver/layers/PoolLayer.cpp
浏览文件 @ d9642cb3
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "PoolLayer.h"
#include "MaxPoolWithMaskLayer.h"
#include "PoolProjectionLayer.h"
#include "paddle/utils/Logging.h"
#ifdef PADDLE_WITH_CUDA
......@@ -44,7 +45,6 @@ bool PoolLayer::init(const LayerMap& layerMap,
strideY_ = conf.has_stride_y() ? conf.stride_y() : conf.stride();
confPaddingY_ = conf.has_padding_y() ? conf.padding_y() : conf.padding();
outputY_ = conf.has_output_y() ? conf.output_y() : conf.output_x();
return true;
}
......@@ -57,6 +57,8 @@ Layer* PoolLayer::create(const LayerConfig& config) {
} else if (CudnnPoolLayer::typeCheck(pool)) {
return new CudnnPoolLayer(config);
#endif
} else if (pool == "max-pool-with-mask") {
return new MaxPoolWithMaskLayer(config);
} else {
LOG(FATAL) << "Unknown pool type: " << pool;
return nullptr;
......
paddle/gserver/layers/ROIPoolLayer.cpp 0 → 100644
浏览文件 @ d9642cb3
/* 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, static_cast<size_t>(1));
size_t roiWidth = std::max(roiEndW - roiStartW + 1, static_cast<size_t>(1));
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, static_cast<size_t>(0)), height_);
wstart = std::min(
std::max(wstart + roiStartW, static_cast<size_t>(0)), width_);
hend = std::min(std::max(hend + roiStartH, static_cast<size_t>(0)),
height_);
wend = std::min(std::max(wend + roiStartW, static_cast<size_t>(0)),
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 @ d9642cb3
/* 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 @ d9642cb3
/* 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 @ d9642cb3
/* 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/CMakeLists.txt
浏览文件 @ d9642cb3
# gserver pacakge unittests
add_simple_unittest(test_LinearChainCRF)
add_simple_unittest(test_MultinomialSampler)
add_simple_unittest(test_RecurrentLayer)
if(NOT MOBILE_INFERENCE)
add_simple_unittest(test_MultinomialSampler)
endif()
function(gserver_test TARGET)
add_unittest_without_exec(${TARGET}
${TARGET}.cpp
......@@ -24,6 +27,7 @@ gserver_test(test_ConvUnify)
gserver_test(test_BatchNorm)
gserver_test(test_KmaxSeqScore)
gserver_test(test_Expand)
gserver_test(test_MaxPoolingWithMaskOutput)
########## test_Mkldnn layers and activations ##########
if(WITH_MKLDNN)
......@@ -48,7 +52,7 @@ if(WITH_PYTHON)
endif()
############### test_WarpCTCLayer #######################
if(NOT WITH_DOUBLE)
if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
add_unittest_without_exec(test_WarpCTCLayer
test_WarpCTCLayer.cpp)
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ d9642cb3
......@@ -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
......@@ -434,7 +434,7 @@ void testConvLayer(const string& type, bool trans, bool useGpu) {
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
int dilation = 1;
int dilation = 2;
if (type == "cudnn_conv") {
#if CUDNN_VERSION >= 6000
dilation = 2;
......@@ -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) {
......@@ -1234,6 +1234,7 @@ void testPoolLayer2(const string& poolType, bool trans, bool useGpu) {
TEST(Layer, PoolLayer) {
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false);
testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false);
testPoolLayer("max-pool-with-mask", /* trans= */ false, /* useGpu= */ false);
#ifdef PADDLE_WITH_CUDA
testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true);
......@@ -1242,6 +1243,7 @@ TEST(Layer, PoolLayer) {
testPoolLayer("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer2("cudnn-avg-pool", /* trans= */ false, /* useGpu= */ true);
testPoolLayer("max-pool-with-mask", /* trans= */ false, /* useGpu= */ true);
#endif
}
......@@ -1995,7 +1997,7 @@ 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 +2058,43 @@ 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 +2397,38 @@ 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 @ d9642cb3
......@@ -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/gserver/tests/test_MaxPoolingWithMaskOutput.cpp 0 → 100644
浏览文件 @ d9642cb3
/* 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 <string>
#include <vector>
#include "LayerGradUtil.h"
#include "paddle/math/MathUtils.h"
#include "paddle/testing/TestUtil.h"
using namespace paddle;
void setPoolConfig(TestConfig* config,
PoolConfig* pool,
const string& poolType) {
(*config).biasSize = 0;
(*config).layerConfig.set_type("pool");
(*config).layerConfig.set_num_filters(1);
int kw = 3, kh = 3;
int pw = 0, ph = 0;
int sw = 2, sh = 2;
pool->set_pool_type(poolType);
pool->set_channels(1);
pool->set_size_x(kw);
pool->set_size_y(kh);
pool->set_start(0);
pool->set_padding(pw);
pool->set_padding_y(ph);
pool->set_stride(sw);
pool->set_stride_y(sh);
int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
pool->set_output_x(ow);
pool->set_output_y(oh);
}
void doOneMaxPoolingWithMaskOutputTest(MatrixPtr& inputMat,
const string& poolType,
bool use_gpu,
MatrixPtr& maskMat) {
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 25, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
pool->set_img_size(5);
pool->set_img_size_y(5);
setPoolConfig(&config, pool, poolType);
config.layerConfig.set_size(pool->output_x() * pool->output_y() *
pool->channels());
config.layerConfig.set_name("MaxPoolWithMask");
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
initDataLayer(config,
&dataLayers,
&datas,
&layerMap,
"MaxPoolWithMask",
1,
false,
use_gpu);
dataLayers[0]->getOutputValue()->copyFrom(*inputMat);
FLAGS_use_gpu = use_gpu;
std::vector<ParameterPtr> parameters;
LayerPtr maxPoolingWithMaskOutputLayer;
initTestLayer(config, &layerMap, &parameters, &maxPoolingWithMaskOutputLayer);
maxPoolingWithMaskOutputLayer->forward(PASS_GC);
checkMatrixEqual(maxPoolingWithMaskOutputLayer->getOutput("mask").value,
maskMat);
}
TEST(Layer, maxPoolingWithMaskOutputLayerFwd) {
bool useGpu = false;
MatrixPtr inputMat;
MatrixPtr maskMat;
real inputData[] = {0.1, 0.1, 0.5, 0.5, 1.1, 0.2, 0.2, 0.6, 0.1,
0.1, 0.3, 0.3, 0.7, 0.1, 0.1, 0.4, 0.4, 0.8,
0.8, 0.1, 1.0, 2.0, 3.0, 0.0, 9.0};
real maskData[] = {12, 4, 22, 24};
inputMat = Matrix::create(1, 25, false, useGpu);
maskMat = Matrix::create(1, 4, false, useGpu);
inputMat->setData(inputData);
maskMat->setData(maskData);
doOneMaxPoolingWithMaskOutputTest(
inputMat, "max-pool-with-mask", useGpu, maskMat);
#ifdef PADDLE_WITH_CUDA
useGpu = true;
inputMat = Matrix::create(1, 25, false, useGpu);
maskMat = Matrix::create(1, 4, false, useGpu);
inputMat->copyFrom(inputData, 25);
maskMat->copyFrom(maskData, 4);
doOneMaxPoolingWithMaskOutputTest(
inputMat, "max-pool-with-mask", useGpu, maskMat);
#endif
}
paddle/math/BaseMatrix.cu
浏览文件 @ d9642cb3
......@@ -1902,5 +1902,52 @@ void BaseMatrixT<real>::sumOfProducts(BaseMatrixT& b,
}
template class BaseMatrixT<real>;
#ifndef PADDLE_MOBILE_INFERENCE
template class BaseMatrixT<int>;
#else
template <>
void BaseMatrixT<int>::zero() {
applyUnary(unary::Zero<int>());
}
template <>
void BaseMatrixT<int>::assign(int p) {
applyUnary(unary::Assign<int>(p));
}
template <>
void BaseMatrixT<int>::isEqualTo(BaseMatrixT& b, int value) {
applyBinary(binary::IsEqual<int>(value), b);
}
template <>
void BaseMatrixT<int>::neg() {
applyUnary(unary::Neg<int>());
}
template <>
void BaseMatrixT<int>::abs2() {
applyUnary(unary::Abs<int>());
}
template <>
void BaseMatrixT<int>::add(int p) {
applyUnary(unary::Add<int>(p));
}
template <>
void BaseMatrixT<int>::add(int p1, int p2) {
applyUnary(unary::Add2<int>(p1, p2));
}
template <>
void BaseMatrixT<int>::applyL1(int learningRate, int decayRate) {
applyUnary(unary::ApplyL1<int>(learningRate * decayRate));
}
#endif
} // namespace paddle
paddle/math/CMakeLists.txt
浏览文件 @ d9642cb3
......@@ -25,6 +25,19 @@ else()
message(STATUS "Compile with MKLDNNMatrix")
endif()
if(MOBILE_INFERENCE)
list(REMOVE_ITEM MATH_SOURCES
${CMAKE_CURRENT_SOURCE_DIR}/SIMDFunctions.cpp)
# Remove sparse
list(REMOVE_ITEM MATH_HEADERS
${CMAKE_CURRENT_SOURCE_DIR}/CpuSparseMatrix.h
${CMAKE_CURRENT_SOURCE_DIR}/SparseMatrix.h
${CMAKE_CURRENT_SOURCE_DIR}/SparseRowMatrix.h)
list(REMOVE_ITEM MATH_SOURCES
${CMAKE_CURRENT_SOURCE_DIR}/CpuSparseMatrix.cpp
${CMAKE_CURRENT_SOURCE_DIR}/SparseMatrix.cpp
${CMAKE_CURRENT_SOURCE_DIR}/SparseRowMatrix.cpp)
endif()
set(MATH_SOURCES
"${PADDLE_SOURCE_DIR}/paddle/math/BaseMatrix.cu"
"${PADDLE_SOURCE_DIR}/paddle/math/TrainingAlgorithmOp.cu"
......
paddle/math/CpuSparseMatrix.h
浏览文件 @ d9642cb3
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#ifndef PADDLE_MOBILE_INFERENCE
#include <cstddef>
#include "Matrix.h"
......@@ -309,3 +312,57 @@ private:
using Matrix::subMatrix;
};
} // namespace paddle
#else
#include "Matrix.h"
namespace paddle {
class CpuSparseMatrix : public Matrix {
public:
CpuSparseMatrix(size_t height,
size_t width,
size_t nnz, /* used to allocate space */
SparseValueType valueType = FLOAT_VALUE,
SparseFormat format = SPARSE_CSR,
bool trans = false)
: Matrix(NULL, height, width, trans, false) {}
CpuSparseMatrix(real* data,
int* rows,
int* cols,
size_t height,
size_t width,
size_t nnz,
SparseValueType valueType,
SparseFormat format,
bool trans)
: Matrix(NULL, height, width, trans, false) {}
real* getValue() const { return nullptr; }
size_t getColStartIdx(size_t i) const { return 0; }
size_t getRowStartIdx(size_t i) const { return 0; }
size_t getColNum(size_t i) const { return 0; }
int* getRowCols(size_t i) const { return nullptr; }
CpuSparseMatrixPtr getTmpSparseMatrix(size_t height, size_t width) {
return nullptr;
}
void resize(size_t newHeight,
size_t newWidth,
size_t newNnz, /* used to allocate space */
SparseValueType valueType,
SparseFormat format) {}
void resize(size_t newHeight, size_t newWidth) {}
MatrixPtr getTranspose() { return nullptr; }
void setRow(size_t row,
size_t colNum,
const unsigned int* cols,
const real* values) {}
};
} // namespace paddle
#endif
paddle/math/MKLDNNMatrix.cpp
浏览文件 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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 @ d9642cb3
......@@ -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/Matrix.cpp
浏览文件 @ d9642cb3
......@@ -451,6 +451,7 @@ void GpuMatrix::addSharedBias(Matrix& b, real scale) {
}
void GpuMatrix::collectBias(Matrix& a, real scale) {
#ifdef PADDLE_WITH_CUDA
CHECK_EQ(getHeight(), (size_t)1);
CHECK_EQ(width_, a.getWidth());
GpuSparseMatrix* sMatPtr = dynamic_cast<GpuSparseMatrix*>(&a);
......@@ -461,6 +462,7 @@ void GpuMatrix::collectBias(Matrix& a, real scale) {
hl_sparse_matrix_s A_d = sMatPtr->sMatrix_.get();
hl_sparse_matrix_column_sum(data, A_d, sMatPtr->getHeight(), width_, scale);
}
#endif
}
void GpuMatrix::collectSharedBias(Matrix& a, real scale) {
......@@ -552,6 +554,7 @@ void GpuMatrix::mul(const GpuSparseMatrix& a,
const GpuMatrix& b,
real scaleAB,
real scaleT) {
#ifdef PADDLE_WITH_CUDA
CHECK(isContiguous());
CHECK(b.isContiguous());
CHECK(b.useGpu_ == true) << "Matrix type are not equal";
......@@ -578,12 +581,14 @@ void GpuMatrix::mul(const GpuSparseMatrix& a,
b.height_,
scaleAB,
scaleT);
#endif
}
void GpuMatrix::mul(const GpuMatrix& a,
const GpuSparseMatrix& b,
real scaleAB,
real scaleT) {
#ifdef PADDLE_WITH_CUDA
CHECK(isContiguous());
CHECK(a.isContiguous());
CHECK(a.useGpu_ == true) << "Matrix type are not equal";
......@@ -622,6 +627,7 @@ void GpuMatrix::mul(const GpuMatrix& a,
scaleAB,
scaleT);
}
#endif
}
/* this = a*b */
......@@ -1028,15 +1034,23 @@ void GpuMatrix::maxPoolForward(Matrix& inputMat,
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
MatrixPtr maskMatP) {
CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal";
real* inputData = inputMat.getData();
real* maskData = NULL;
size_t frameNum = inputMat.getHeight();
CHECK(imgSizeH * imgSizeW * channels == inputMat.getWidth());
CHECK(height_ == inputMat.getHeight());
CHECK(width_ == outputH * outputW * channels);
if (maskMatP != NULL) {
CHECK(maskMatP->useGpu_ == true) << "Matrix type are not equal";
CHECK(outputH * outputW * channels == maskMatP->getWidth());
maskData = maskMatP->getData();
}
hl_maxpool_forward(frameNum,
inputData,
channels,
......@@ -1051,7 +1065,8 @@ void GpuMatrix::maxPoolForward(Matrix& inputMat,
paddingH,
paddingW,
data_,
getStride());
getStride(),
maskData);
}
void GpuMatrix::maxPoolBackward(Matrix& inputMat,
......@@ -1548,6 +1563,7 @@ void GpuMatrix::bilinearBackward(const Matrix& out,
}
void GpuMatrix::multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
#ifdef PADDLE_WITH_CUDA
GpuMatrix* outputPtr = dynamic_cast<GpuMatrix*>(&output);
auto labelPtr = dynamic_cast<GpuSparseMatrix*>(&label);
......@@ -1563,9 +1579,11 @@ void GpuMatrix::multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label) {
hl_sparse_matrix_s mat_d = labelPtr->sMatrix_.get();
hl_matrix_multi_binary_cross_entropy(
output_d, entropy_d, mat_d, height_, outputPtr->width_);
#endif
}
void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) {
#ifdef PADDLE_WITH_CUDA
GpuMatrix* outputPtr = dynamic_cast<GpuMatrix*>(&output);
auto labelPtr = dynamic_cast<GpuSparseMatrix*>(&label);
......@@ -1581,6 +1599,7 @@ void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) {
hl_sparse_matrix_s mat_d = labelPtr->sMatrix_.get();
hl_matrix_multi_binary_cross_entropy_bp(
output_d, grad_d, mat_d, height_, width_);
#endif
}
void GpuMatrix::vol2Col(real* dataSrc,
......@@ -1973,9 +1992,11 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
MatrixPtr maskMatP) {
real* inputData = inputMat.getData();
real* outData = data_;
real* maskData = NULL;
size_t num = inputMat.getHeight();
size_t inLength = imgSizeH * imgSizeW;
size_t outLength = outputH * outputW;
......@@ -1984,6 +2005,11 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
CHECK_EQ(channels * outLength, this->getWidth());
size_t outStride = getStride();
if (maskMatP != NULL) {
maskData = maskMatP->getData();
CHECK_EQ(channels * outLength, maskMatP->getWidth());
}
/* initialize the data_ */
for (size_t i = 0; i < height_; i++) {
for (size_t j = 0; j < width_; j++) {
......@@ -2005,10 +2031,21 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
int wstart = pw * strideW - paddingW;
int wend = std::min(wstart + sizeX, imgSizeW);
wstart = std::max(wstart, 0);
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
outData[ph * outputW + pw] = std::max(
outData[ph * outputW + pw], inputData[h * imgSizeW + w]);
if (maskData == NULL) {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
outData[ph * outputW + pw] = std::max(
outData[ph * outputW + pw], inputData[h * imgSizeW + w]);
}
}
} else {
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
if (outData[ph * outputW + pw] < inputData[h * imgSizeW + w]) {
outData[ph * outputW + pw] = inputData[h * imgSizeW + w];
maskData[ph * outputW + pw] = h * imgSizeW + w;
}
}
}
}
}
......@@ -2016,6 +2053,8 @@ void CpuMatrix::maxPoolForward(Matrix& inputMat,
// compute offset
inputData += inLength;
outData += outLength;
if (maskData != NULL) maskData += outLength;
}
}
}
......@@ -3226,6 +3265,7 @@ template void CpuMatrix::mul<CpuMatrix, CacheRowCpuMatrix>(CpuSparseMatrix* a,
real scaleAB,
real scaleT);
#ifndef PADDLE_MOBILE_INFERENCE
void SharedCpuMatrix::mul(CpuSparseMatrix* a,
CpuMatrix* b,
real scaleAB,
......@@ -3354,6 +3394,7 @@ void SharedCpuMatrix::initBlock(int blockNum) {
}
}
#endif
/* Add a (column) vector b to matrix a, column by column */
void CpuMatrix::addColumnVector(const Matrix& b) {
BaseMatrix::addColVector(const_cast<Matrix&>(b));
......
paddle/math/Matrix.h
浏览文件 @ d9642cb3
......@@ -861,7 +861,8 @@ public:
/**
* Pooling forward operation, pick out the largest element
* in the sizeX of value
* in the sizeX of value, if the maskMatP is not NULL, it will
* also caculate the location indices.
*/
virtual void maxPoolForward(Matrix& inputMat,
size_t imgSizeH,
......@@ -874,7 +875,8 @@ public:
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW) {
size_t paddingW,
MatrixPtr maskMatP = NULL) {
LOG(FATAL) << "Not implemeted";
}
......@@ -1426,7 +1428,8 @@ public:
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW);
size_t paddingW,
MatrixPtr maskMatP);
void maxPoolBackward(Matrix& image,
size_t imgSizeH,
......@@ -1697,7 +1700,8 @@ public:
size_t outputH,
size_t outputW,
size_t paddingH,
size_t paddingW);
size_t paddingW,
MatrixPtr maskMatP);
void maxPoolBackward(Matrix& image,
size_t imgSizeH,
......@@ -2066,6 +2070,7 @@ public:
class SharedCpuMatrix : public CpuMatrix {
public:
#ifndef PADDLE_MOBILE_INFERENCE
/* blockNum is number of partitions of the matrix */
SharedCpuMatrix(int blockNum, size_t height, size_t width, bool trans = false)
: CpuMatrix(height, width, trans) {
......@@ -2111,6 +2116,7 @@ private:
ThreadLocal<CpuMatrixPtr> localBuf_;
ThreadLocal<std::vector<int>> localBufRows_;
ThreadLocal<std::vector<int>> blockSeq_;
#endif
};
typedef struct { unsigned int col; } sparse_non_value_t;
......
paddle/math/SparseMatrix.h
浏览文件 @ d9642cb3
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#ifndef PADDLE_MOBILE_INFERENCE
#include <cstddef>
#include "CpuSparseMatrix.h"
#include "Matrix.h"
......@@ -237,3 +240,47 @@ private:
};
} // namespace paddle
#else
#include "CpuSparseMatrix.h"
namespace paddle {
class GpuSparseMatrix : public Matrix {
public:
GpuSparseMatrix(size_t height,
size_t width,
size_t nnz, /* used to allocate space */
SparseValueType valueType = FLOAT_VALUE,
SparseFormat format_ = SPARSE_CSR,
bool trans = false)
: Matrix(NULL, height, width, trans, false) {}
GpuSparseMatrix(real* value,
int* rows,
int* cols,
size_t height,
size_t width,
size_t nnz,
SparseValueType valueType,
SparseFormat format,
bool trans)
: Matrix(NULL, height, width, trans, true) {}
void resize(size_t newHeight,
size_t newWidth,
size_t newNnz, /* used to allocate space */
SparseValueType valueType,
SparseFormat format) {}
void resize(size_t newHeight, size_t newWidth) {}
MatrixPtr getTranspose() { return nullptr; }
void setRow(size_t row,
size_t colNum,
const unsigned int* cols,
const real* values) {}
};
} // namespace paddle
#endif
paddle/math/SparseRowMatrix.h
浏览文件 @ d9642cb3
......@@ -14,6 +14,8 @@ limitations under the License. */
#pragma once
#ifndef PADDLE_MOBILE_INFERENCE
#include <gflags/gflags.h>
#include <string.h>
#include <algorithm>
......@@ -313,3 +315,27 @@ private:
};
} // namespace paddle
#else
namespace paddle {
class SparseRowCpuMatrix : public CpuMatrix {
public:
void reserveStore() {}
void clearIndices() {}
};
class SparsePrefetchRowCpuMatrix : public SparseRowCpuMatrix {
public:
void setupIndices() {}
void addRows(MatrixPtr input) {}
void addRows(IVectorPtr ids) {}
};
class SparseAutoGrowRowCpuMatrix : public SparseRowCpuMatrix {};
class CacheRowCpuMatrix : public SparseAutoGrowRowCpuMatrix {};
class SparseRowIdsCpuMatrix : public CpuMatrix {};
} // namespace paddle
#endif
paddle/math/tests/CMakeLists.txt
浏览文件 @ d9642cb3
......@@ -3,8 +3,10 @@
add_simple_unittest(test_ExecViaCpu)
add_simple_unittest(test_SIMDFunctions)
add_simple_unittest(test_TrainingAlgorithm)
add_simple_unittest(test_SparseMatrix)
add_simple_unittest(test_RowBuffer)
if(NOT MOBILE_INFERENCE)
add_simple_unittest(test_SparseMatrix)
endif()
# TODO(yuyang18): Refactor TestUtil.cpp. Remove this cross module reference.
add_unittest(test_matrixCompare
......
paddle/math/tests/TensorCheck.h
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