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“c5f900f6201ca834d8a2a3469c967f5303051746”上不存在“source/libs/tdb/git@gitcode.net:taosdata/tdengine.git”
提交 79af7cc9 编写于 作者: Y Yancey1989
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Merge branch 'develop' of github.com:PaddlePaddle/Paddle into split_send_op

上级 081b7824 cffe1a91
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CMakeLists.txt
浏览文件 @ 79af7cc9
......@@ -36,6 +36,7 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF)
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
......@@ -180,6 +181,11 @@ if(WITH_GPU)
include(cuda)
endif(WITH_GPU)
if(WITH_AMD_GPU)
find_package(HIP)
include(hip)
endif(WITH_AMD_GPU)
if(WITH_MKLML)
list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
endif()
......
benchmark/cluster/vgg16/vgg16_fluid.py
浏览文件 @ 79af7cc9
......@@ -18,12 +18,13 @@ import sys
import time
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
import paddle.v2.fluid.core as core
import paddle.v2.fluid.profiler as profiler
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.profiler as profiler
import argparse
import functools
import os
from paddle.fluid import debuger
def str2bool(v):
......@@ -182,7 +183,6 @@ def main():
start_time = time.time()
num_samples = 0
train_pass_acc.reset()
with profiler.profiler("CPU", 'total') as prof:
for batch_id, data in enumerate(train_reader()):
ts = time.time()
img_data = np.array(
......@@ -254,9 +254,7 @@ def main():
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint,
pserver_prog)
print("starting server side startup")
exe.run(pserver_startup)
print("starting parameter server...")
exe.run(pserver_prog)
elif training_role == "TRAINER":
# Parameter initialization
......
benchmark/cluster/vgg16/vgg16_tf.py
浏览文件 @ 79af7cc9
......@@ -292,14 +292,18 @@ def run_benchmark(cluster_spec, server):
return np.mean(test_accs)
config = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1,
log_device_placement=True)
config.gpu_options.allow_growth = True
hooks = [tf.train.StopAtStepHook(last_step=1000000)]
with tf.train.MonitoredTrainingSession(
master=server.target, is_chief=(args.task_index == 0),
hooks=hooks) as sess:
master=server.target,
is_chief=(args.task_index == 0),
hooks=hooks,
config=config) as sess:
iters, num_samples, start_time = 0, 0, 0.0
for pass_id in range(args.num_passes):
# train
......
cmake/configure.cmake
浏览文件 @ 79af7cc9
......@@ -57,11 +57,7 @@ if(NOT WITH_GOLANG)
add_definitions(-DPADDLE_WITHOUT_GOLANG)
endif(NOT WITH_GOLANG)
if(NOT WITH_GPU)
add_definitions(-DHPPL_STUB_FUNC)
list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
else()
if(WITH_GPU)
add_definitions(-DPADDLE_WITH_CUDA)
FIND_PACKAGE(CUDA REQUIRED)
......@@ -84,7 +80,14 @@ else()
# Include cuda and cudnn
include_directories(${CUDNN_INCLUDE_DIR})
include_directories(${CUDA_TOOLKIT_INCLUDE})
endif(NOT WITH_GPU)
elseif(WITH_AMD_GPU)
add_definitions(-DPADDLE_WITH_HIP)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -D__HIP_PLATFORM_HCC__")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_HCC__")
else()
add_definitions(-DHPPL_STUB_FUNC)
list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
endif()
if (WITH_MKLML AND MKLML_IOMP_LIB)
message(STATUS "Enable Intel OpenMP with ${MKLML_IOMP_LIB}")
......
cmake/external/boost.cmake
浏览文件 @ 79af7cc9
......@@ -24,7 +24,7 @@ set(BOOST_PROJECT "extern_boost")
# So we use 1.41.0 here.
set(BOOST_VER "1.41.0")
set(BOOST_TAR "boost_1_41_0")
set(BOOST_URL "http://paddlepaddledeps.s3-website-us-west-1.amazonaws.com/${BOOST_TAR}.tar.gz")
set(BOOST_URL "http://paddlepaddledeps.bj.bcebos.com/${BOOST_TAR}.tar.gz")
set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
set(BOOST_DOWNLOAD_DIR "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
......
cmake/external/eigen.cmake
浏览文件 @ 79af7cc9
......@@ -4,7 +4,21 @@ SET(EIGEN_SOURCE_DIR ${THIRD_PARTY_PATH}/eigen3)
SET(EIGEN_INCLUDE_DIR ${EIGEN_SOURCE_DIR}/src/extern_eigen3)
INCLUDE_DIRECTORIES(${EIGEN_INCLUDE_DIR})
ExternalProject_Add(
if(WITH_AMD_GPU)
ExternalProject_Add(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/sabreshao/hipeigen.git"
GIT_TAG 0cba03ff9f8f9f70bbd92ac5857b031aa8fed6f9
PREFIX ${EIGEN_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
else()
ExternalProject_Add(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/RLovelett/eigen.git"
......@@ -15,7 +29,8 @@ ExternalProject_Add(
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
)
endif()
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/eigen3_dummy.c)
......
cmake/generic.cmake
浏览文件 @ 79af7cc9
......@@ -317,6 +317,82 @@ function(nv_test TARGET_NAME)
endif()
endfunction(nv_test)
function(hip_library TARGET_NAME)
if (WITH_AMD_GPU)
set(options STATIC static SHARED shared)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(hip_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(_sources ${hip_library_SRCS})
HIP_PREPARE_TARGET_COMMANDS(${TARGET_NAME} OBJ _generated_files _source_files ${_sources} HIPCC_OPTIONS ${_hipcc_options} HCC_OPTIONS ${_hcc_options} NVCC_OPTIONS ${_nvcc_options})
if(_source_files)
list(REMOVE_ITEM _sources ${_source_files})
endif()
if(hip_library_SRCS)
if (hip_library_SHARED OR hip_library_shared) # build *.so
add_library(${TARGET_NAME} SHARED ${_cmake_options} ${_generated_files} ${_sources})
set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP)
else()
add_library(${TARGET_NAME} STATIC ${_cmake_options} ${_generated_files} ${_sources})
set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE CXX)
target_link_libraries(${TARGET_NAME} /opt/rocm/hip/lib/libhip_hcc.so /opt/rocm/hip/lib/libhip_device.a)
find_fluid_modules(${TARGET_NAME})
endif()
if (hip_library_DEPS)
add_dependencies(${TARGET_NAME} ${hip_library_DEPS})
target_link_libraries(${TARGET_NAME} ${hip_library_DEPS})
endif()
# cpplint code style
foreach(source_file ${hip_library_SRCS})
string(REGEX REPLACE "\\.[^.]*$" "" source ${source_file})
if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
list(APPEND hip_library_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/${source}.h)
endif()
endforeach()
add_style_check_target(${TARGET_NAME} ${hip_library_SRCS} ${hip_library_HEADERS})
else(hip_library_SRCS)
if (hip_library_DEPS)
merge_static_libs(${TARGET_NAME} ${hip_library_DEPS})
else()
message(FATAL "Please specify source file or library in nv_library.")
endif()
endif(hip_library_SRCS)
endif()
endfunction(hip_library)
function(hip_binary TARGET_NAME)
if (WITH_AMD_GPU)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(hip_binary "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
hip_add_executable(${TARGET_NAME} ${hip_binary_SRCS})
if(hip_binary_DEPS)
target_link_libraries(${TARGET_NAME} ${hip_binary_DEPS})
add_dependencies(${TARGET_NAME} ${hip_binary_DEPS})
endif()
endif()
endfunction(hip_binary)
function(hip_test TARGET_NAME)
if (WITH_AMD_GPU AND WITH_TESTING)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(hip_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(_sources ${hip_test_SRCS})
HIP_PREPARE_TARGET_COMMANDS(${TARGET_NAME} OBJ _generated_files _source_files ${_sources} HIPCC_OPTIONS ${_hipcc_options} HCC_OPTIONS ${_hcc_options} NVCC_OPTIONS ${_nvcc_options})
if(_source_files)
list(REMOVE_ITEM _sources ${_source_files})
endif()
add_executable(${TARGET_NAME} ${_cmake_options} ${_generated_files} ${_sources})
set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP)
target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_test(${TARGET_NAME} ${TARGET_NAME})
endif()
endfunction(hip_test)
function(go_library TARGET_NAME)
set(options STATIC static SHARED shared)
set(oneValueArgs "")
......
cmake/hip.cmake 0 → 100644
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if(NOT WITH_AMD_GPU)
return()
endif()
include_directories("/opt/rocm/include")
include_directories("/opt/rocm/hipblas/include")
include_directories("/opt/rocm/hiprand/include")
include_directories("/opt/rocm/rocrand/include")
include_directories("/opt/rocm/rccl/include")
include_directories("/opt/rocm/thrust")
list(APPEND EXTERNAL_LIBS "-L/opt/rocm/lib/ -lhip_hcc")
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -fPIC -DPADDLE_WITH_HIP -std=c++14" )
if(WITH_DSO)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_USE_DSO")
endif(WITH_DSO)
if(WITH_DOUBLE)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_TYPE_DOUBLE")
endif(WITH_DOUBLE)
if(WITH_TESTING)
set(HIP_HCC_FLAGS "${HIP_HCC_FLAGS} -DPADDLE_WITH_TESTING")
endif(WITH_TESTING)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_DEBUG})
elseif(CMAKE_BUILD_TYPE STREQUAL "RelWithDebInfo")
list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_RELWITHDEBINFO})
elseif(CMAKE_BUILD_TYPE STREQUAL "MinSizeRel")
list(APPEND HIP_HCC_FLAGS ${CMAKE_CXX_FLAGS_MINSIZEREL})
endif()
if("x${HCC_HOME}" STREQUAL "x")
set(HCC_HOME "/opt/rocm/hcc")
endif()
set(CMAKE_HIP_LINK_EXECUTABLE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <FLAGS> <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES>")
set(CMAKE_HIP_CREATE_SHARED_LIBRARY "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -shared")
set(CMAKE_HIP_CREATE_SHARED_MODULE "${HIP_HIPCC_CMAKE_LINKER_HELPER} ${HCC_HOME} <CMAKE_CXX_LINK_FLAGS> <LINK_FLAGS> <OBJECTS> -o <TARGET> <LINK_LIBRARIES> -shared")
doc/CMakeLists.txt
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add_subdirectory(v2)
add_subdirectory(fluid)
doc/fluid/CMakeLists.txt 0 → 100644
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if(NOT DEFINED SPHINX_THEME)
set(SPHINX_THEME default)
endif()
if(NOT DEFINED SPHINX_THEME_DIR)
set(SPHINX_THEME_DIR)
endif()
# configured documentation tools and intermediate build results
set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
# Sphinx cache with pickled ReST documents
set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
# HTML output director
set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
configure_file(
"${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.en.in"
"${BINARY_BUILD_DIR_EN}/conf.py"
@ONLY)
sphinx_add_target(paddle_fluid_docs
html
${BINARY_BUILD_DIR_EN}
${SPHINX_CACHE_DIR_EN}
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_EN})
# configured documentation tools and intermediate build results
set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
# Sphinx cache with pickled ReST documents
set(SPHINX_CACHE_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_doctrees")
# HTML output directory
set(SPHINX_HTML_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/html")
configure_file(
"${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.cn.in"
"${BINARY_BUILD_DIR_CN}/conf.py"
@ONLY)
sphinx_add_target(paddle_fluid_docs_cn
html
${BINARY_BUILD_DIR_CN}
${SPHINX_CACHE_DIR_CN}
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_CN})
doc/fluid/build_and_install/index_en.rst 0 → 100644
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Build and Install
------------
doc/fluid/design/concurrent/select_op.md 0 → 100644
浏览文件 @ 79af7cc9
# select_op Design
## Introduction
In golang, the [**select**](https://golang.org/ref/spec#Select_statements)
statement lets a goroutine wait on multiple communication operations at the
same time. The **select** blocks until one of its cases can run, then
executes the case. If multiple cases are ready to run, then one case is
choosen at random to be executed.
With the introduction of CSP for Paddle, we mimic this behavior by
creating a ***select_op***.
## How to use it
The **select_op** is available as a c++ operator. However most users
will prefer to use the much simplier Python API.
- **fluid.Select()**: Creates a select operator and adds it to the current
block within the main program. Also creates a sub block and adds it to the
main program. This sub block is used to hold all variables and operators
used by the case statements.
Within the select block, users can add cases by
calling **select.case** or **select.default** method.
- **fluid.Select.case(channel_action, channel, result_variable)**: Represents
a fluid channel send/recv case. This method creates a SelectCase block
guard and adds it to the Select block. The arguments into this method tells
the select which channel operation to listen to.
- **fluid.Select.default()**: Represents the fluid default case. This default
case is executed if none of the channel send/recv cases are available to
execute.
**Example:**
```
ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
x = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
y = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=1)
while_cond = fill_constant(shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True)
while_op = While(cond=while_cond)
with while_op.block():
with fluid.Select() as select:
with select.case(fluid.channel_send, channel, x):
# Send x, then perform Fibonacci calculation on x and y
x_tmp = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
assign(input=x, output=x_tmp)
assign(input=y, output=x)
assign(elementwise_add(x=x_tmp, y=y), output=y)
with select.case(fluid.channel_recv, quit_channel, result2):
# Exit out of While loop
while_false = fill_constant(shape=[1], dtype=core.VarDesc.VarType.BOOL, value=False)
helper = layer_helper.LayerHelper('assign')
helper.append_op(
type='assign',
inputs={'X': [while_false]},
outputs={'Out': [while_cond]})
```
## How it Works
### Program Description
```
blocks {
idx: 0
...
// Create "case_to_execute" variable
ops {
outputs {
parameter: "Out"
arguments: "fill_constant_110.tmp_0"
}
type: "fill_constant"
attrs {
name: "force_cpu"
type: BOOLEAN
b: false
}
attrs {
name: "value"
type: FLOAT
f: -1.0
}
attrs {
name: "shape"
type: INTS
ints: 1
}
attrs {
name: "dtype"
type: INT
i: 2
}
}
// Create "select" operator.
// inputs:
// X: All input variables used by operators within the select block
// case_to_execute: Variable filled in by select_op when it determines
// which case to execute.
//
// outputs:
// Out: All output variables referenced by operators within select block.
//
// attrs:
// sub_block: The block id containing the select "cases"
// cases: Serialized list of all cases in the select op.
// Each case is serialized as: '<index>,<type>,<channel>,<value>'
// where type is 0 for default, 1 for send, and 2 for receive.
// No channel and values are needed for default cases.
ops {
inputs {
parameter: "X"
arguments: "fill_constant_103.tmp_0"
arguments: "fill_constant_104.tmp_0"
}
inputs {
parameter: "case_to_execute"
arguments: "fill_constant_110.tmp_0"
}
outputs {
parameter: "Out"
arguments: "fill_constant_110.tmp_0"
}
type: "select"
attrs {
name: "sub_block"
type: BLOCK
block_idx: 1
}
attrs {
name: "cases"
type: STRINGS
strings: "0,1,channel_101,fill_constant_109.tmp_0"
strings: "1,2,channel_102,fill_constant_108.tmp_0"
}
}
...
}
```
The python select API will add the **select_op** to the current block. In addition, it will
iterate through all it's case statements and add any input variables required by case statements
into **X**. It will also create a temp variable called **case_to_execute**. This variable is
filled in by the select_op after it has completed processing the case statements.
If there are no available cases to execute (ie: all cases are blocked on channel operations, and
there is no default statement), then the select_op will block the current thread. The thread will
unblock once there is a channel operation affecting one of the case statements, at which point, the
**select_op** will set the **case_to_execute** variable to the index of the case to execute.
Finally the select_op will call executor.run on the **sub_block**.
```
blocks {
idx: 1
parent_idx: 0
...
// Fill a tensor with the case index (ie: 0,1,2,3,ect.)
ops {
outputs {
parameter: "Out"
arguments: "fill_constant_111.tmp_0"
}
type: "fill_constant"
attrs {
name: "force_cpu"
type: BOOLEAN
b: false
}
attrs {
name: "value"
type: FLOAT
f: 0.0
}
attrs {
name: "shape"
type: INTS
ints: 1
}
attrs {
name: "dtype"
type: INT
i: 2
}
}
// Create an "equal" operator to compare the case index with the "case_to_execute"
// tensor (which was filled in by the select op).
ops {
inputs {
parameter: "X"
arguments: "fill_constant_111.tmp_0" // case 0
}
inputs {
parameter: "Y"
arguments: "fill_constant_110.tmp_0" // case_to_execute
}
outputs {
parameter: "Out"
arguments: "equal_0.tmp_0"
}
type: "equal"
attrs {
name: "axis"
type: INT
i: -1
}
}
// Use the output of the "equal" operator as a condition for the "conditional_block".
// If the condition evaluates to true, then execute the "sub_block" (which represents
// the select case's body)
ops {
inputs {
parameter: "Params"
}
inputs {
parameter: "X"
arguments: "equal_0.tmp_0"
}
outputs {
parameter: "Out"
}
outputs {
parameter: "Scope"
arguments: "_generated_var_0"
}
type: "conditional_block"
attrs {
name: "is_scalar_condition"
type: BOOLEAN
b: true
}
attrs {
name: "sub_block"
type: BLOCK
block_idx: 4
}
}
...
// Repeat the above operators for each case statements inside the select body
}
```
Cases are represented by a **conditional_block operator**, whose's condition is set as the output of
equal(**case_to_execute**, **case_index**). Since each case index is unique in this sub-block,
only one case will be executed.
### select_op flow
<p align="center">
<img src="./images/select_op_workflow.png"/><br/>
</p>
The select algorithm is inspired by golang's select routine. Please refer to
http://www.tapirgames.com/blog/golang-concurrent-select-implementation for more information.
## Backward Pass
TODO
doc/fluid/design/dynamic_rnn/rnn.md
浏览文件 @ 79af7cc9
......@@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i
## RNN Algorithm Implementation
<p align="center">
<img src="./images/rnn.jpg"/>
<img src="./rnn.jpg"/>
</p>
The above diagram shows an RNN unrolled into a full network.
......@@ -22,7 +22,7 @@ There are several important concepts here:
There could be local variables defined in each step-net. PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.
<p align="center">
<img src="./images/rnn.png"/><br/>
<img src="./rnn.png"/><br/>
Figure 2 illustrates the RNN's data flow
</p>
......@@ -49,7 +49,7 @@ or copy the memory value of the previous step to the current ex-memory variable.
### Usage in Python
For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/concepts/block.md).
We can define an RNN's step-net using a Block:
......@@ -93,7 +93,7 @@ For example, we could have a 2-level RNN, where the top level corresponds to par
The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.
<p align="center">
<img src="./images/2_level_rnn.png"/>
<img src="./2_level_rnn.png"/>
</p>
```python
......@@ -149,5 +149,5 @@ If the `output_all_steps` is set to False, it will only output the final time st
<p align="center">
<img src="images/rnn_2level_data.png"/>
<img src="./rnn_2level_data.png"/>
</p>
doc/fluid/design/index_cn.rst 0 → 100644
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设计思想
------------
doc/fluid/dev/index_cn.rst 0 → 100644
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开发标准
------------
doc/fluid/dev/index_en.rst 0 → 100644
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Development
------------
This is Development page
doc/fluid/getstarted/index_cn.rst 0 → 100644
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新手入门
------------
新手入门
doc/fluid/getstarted/index_en.rst 0 → 100644
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GET STARTED
------------
This is get started page
doc/fluid/howto/cluster/fluid_cluster_train_cn.md 0 → 100644
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# Fluid 分布式版本使用指南
本篇文章将说明如何在PaddlePaddle Fluid版本下进行分布式训练的配置和执行，以及将单机训练脚本改造成支持集群训练的版本
## 准备工作
* 可用的集群
包含一个或多个计算节点的集群，每一个节点都能够执行PaddlePaddle的训练任务且拥有唯一的IP地址，集群内的所有计算节点可以通过网络相互通信。
* 安装PaddlePaddle Fluid with Distribution版本
所有的计算节点上均需要按照分布式版本的PaddlePaddle, 在用于GPU等设备的机器上还需要额外安装好相应的驱动程序和CUDA的库。
**注意：**当前对外提供的PaddlePaddle版本并不支持分布式，需要通过源码重新编译。编译和安装方法参见[编译和安装指南](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html)。
cmake编译命令中需要将WITH_DISTRIBUTE设置为ON，下面是一个cmake编译指令示例：
``` bash
cmake .. -DWITH_DOC=OFF -DWITH_GPU=OFF -DWITH_DISTRIBUTE=ON -DWITH_SWIG_PY=ON -DWITH_PYTHON=ON
```
## 更新训练脚本
这里，我们以[Deep Learing 101](http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html)课程中的第一章 fit a line 为例，描述如何将单机训练脚本改造成支持集群训练的版本。
### 单机训练脚本示例
```python
import paddle.v2 as paddle
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_loss_value[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
exit(1)
```
我们创建了一个简单的全连接神经网络程序，并且通过Fluid的Executor执行了100次迭代,现在我们需要将该单机版本的程序更新为分布式版本的程序。
### 介绍Parameter Server
在非分布式版本的训练脚本中，只存在Trainer一种角色，它不仅处理常规的计算任务，也处理参数相关的计算、保存和优化任务。在分布式版本的训练过程中，由于存在多个Trainer节点进行同样的数据计算任务，因此需要有一个中心化的节点来统一处理参数相关的保存和分配。在PaddlePaddle中，我们称这样的节点为[Parameter Server](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/fluid/design/dist_train/parameter_server.md)
**因此，在分布式的Fluid环境中，我们有两个角色需要创建，分别是Parameter Server和Trainer。**
### 分布式训练
Fliud专门提供了工具[Distributed Transpiler](https://github.com/PaddlePaddle/Paddle/blob/ba65d54d9d3b41cd3c5171b00f476d4e60133ddb/doc/fluid/design/dist_train/distributed_architecture.md#distributed-transpiler)用于将单机版的训练程序转换为分布式版本的训练程序。工具背后的理念是找出程序的优化算子和梯度参数，将他们分隔为两部分，通过send/recv 操作算子进行连接,优化算子和梯度参数可以在优化器的minimize函数的返回值中获取到。
```python
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost)
```
将Distributed Transpiler、优化算子和梯度函数放在一个代码中如下：
```python
... #define the program, cost, and create sgd optimizer
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) #get optimize OPs and gradient parameters
t = fluid.DistributeTranspiler() # create the transpiler instance
# slice the program into 2 pieces with optimizer_ops and gradient parameters list, as well as pserver_endpoints, which is a comma separated list of [IP:PORT] and number of trainers
t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
... #create executor
# in pserver, run this
#current_endpoint here means current pserver IP:PORT you wish to run on
pserver_prog = t.get_pserver_program(current_endpoint)
pserver_startup = t.get_startup_program(current_endpoint, pserver_prog)
exe.run(pserver_startup)
exe.run(pserver_prog)
# in trainer, run this
... # define data reader
exe.run(fluid.default_startup_program())
for pass_id in range(100):
for data in train_reader():
exe.run(t.get_trainer_program())
```
### 分布式训练脚本运行说明
分布式任务的运行需要将表格中说明的多个参数进行赋值:
| 参数名 | 值类型 | 说明 | 示例 |
|:-------------|:------|:---------------------------------------|:-------------|
| trainer_id | int | 当前训练节点的ID，训练节点ID编号为0 - n-1， n为trainers的值 | 0/1/2/3 |
| pservers | str | parameter server 列表 | 127.0.0.1:6710,127.0.0.1:6711 |
| trainers | int | 训练节点的总个数，>0的数字 | 4 |
| server_endpoint | str | 当前所起的服务节点的IP:PORT | 127.0.0.1:8789 |
| training_role | str | 节点角色， TRAINER/PSERVER | PSERVER |
**注意：** ```training_role```是用来区分当前所起服务的角色的，用于训练程序中，用户可根据需要自行定义，其他参数为fluid.DistributeTranspiler的transpile函数所需要，需要在调用函数前进行定义，样例如下：
```python
t = fluid.DistributeTranspiler()
t.transpile(
optimize_ops,
params_grads,
trainer_id,
pservers=pserver,
trainers=trainers)
if training_role == "PSERVER":
pserver_prog = t.get_pserver_program(server_endpoint)
pserver_startup = t.get_startup_program(server_endpoint, pserver_prog)
```
### Demo
完整的demo代码位于Fluid的test目录下的[book](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/fluid/tests/book/test_fit_a_line.py)中。
第一步，进入demo代码所在目录：
```bash
cd /paddle/python/paddle/fluid/tests/book
```
第二步，启动Parameter Server：
```bash
PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.2 TRAINERS=2 POD_IP=192.168.1.2 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=PSERVER python test_fit_a_line.py
```
执行命令后请等待出现提示： ```Server listening on 192.168.1.2:6174 ```, 表示Paramter Server已经正常启动。
第三步，启动Trainer：
```bash
PADDLE_INIT_PORT=6174 PADDLE_INIT_PSERVERS=192.168.1.3 TRAINERS=2 POD_IP=192.168.1.3 PADDLE_INIT_TRAINER_ID=1 TRAINING_ROLE=TRAINER python test_fit_a_line.py
```
由于我们定义的Trainer的数量是2个，因此需要在另外一个计算节点上再启动一个Trainer。
现在我们就启动了一个包含一个Parameter Server和两个Trainer的分布式训练任务。
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进阶使用
------------
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HOW TO
------------
This is how to page
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PaddlePaddle Fluid
==========================
.. toctree::
:maxdepth: 1
getstarted/index_cn.rst
design/index_cn.rst
build_and_install/index_cn.rst
howto/index_cn.rst
dev/index_cn.rst
faq/index_cn.rst
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PaddlePaddle Fluid
==========================
.. toctree::
:maxdepth: 1
getstarted/index_en.rst
design/index_en.rst
build_and_install/index_en.rst
howto/index_en.rst
dev/index_en.rst
faq/index_en.rst
doc/v2/dev/write_docs_cn.rst
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......@@ -2,13 +2,14 @@
如何贡献文档
#############
PaddlePaddle的文档包括中英文两个部分。文档都是通过 ``cmake`` 驱动 ``sphinx`` 编译生成，也可以利用paddlepaddle.org工具来编译和预览文档。
PaddlePaddle的文档包括中英文两个部分。文档都是通过 ``cmake`` 驱动 ``sphinx`` 编译生成的，PaddlePaddle.org工具可以帮助我们实现这一编译过程，并提供更好的预览效果。
如何构建文档
============
PaddlePaddle的文档构建有两种方式，分别为使用paddlepaddle.org工具和不使用paddlepaddle.org工具，两种方式都有各自的优点，前者方便预览，后者方便开发者进行调试。这两种方式中又分别有使用docker和不使用docker的两种构建方法。
我们建议使用PaddlePaddle.org工具来构建文档。
使用PaddlePaddle.org工具
------------------------
......@@ -31,7 +32,7 @@ PaddlePaddle.org工具可以配合Docker使用，需要在系统里先安装好D
docker run -it -p 8000:8000 -v `pwd`:/var/content paddlepaddle/paddlepaddle.org:latest
注意: PaddlePaddle.org 会在 -v (volume) 指定的内容存储库运行命令
之后再用网页连到http://localhost:8000就可以在网页上生成需要的文档
之后再用网页连到 http://localhost:8000 就可以在网页上生成需要的文档
编译后的文件将被存储在工作目录 <paddlepaddle working directory>/.ppo_workspace/content。
如果不想使用Docker，你还可以通过运行Django框架直接激活工具的服务器。使用下面的命令来运行它。
......@@ -56,7 +57,7 @@ PaddlePaddle.org工具可以配合Docker使用，需要在系统里先安装好D
python manage.py runserver
工具服务器将读取环境变量 CONTENT_DIR 搜索代码库。请指定的PaddlePaddle工作目录给环境变量 CONTENT_DIR。
之后再用网页连到http://localhost:8000就可以在网页上生成需要的文档。
之后再用网页连到 http://localhost:8000 就可以在网页上生成需要的文档。
编译后的文件将被存储在工作目录 <paddlepaddle working directory>/.ppo_workspace/content。
想了解更多PaddlePaddle.org工具的详细信息，可以 `点击这里 <https://github.com/PaddlePaddle/PaddlePaddle.org/blob/develop/README.cn.md>`_ 。
......@@ -96,7 +97,7 @@ PaddlePaddle.org工具可以配合Docker使用，需要在系统里先安装好D
python -m SimpleHTTPServer 8088
在浏览器中输入http://localhost:8088就可以看到编译生成的中/英文的文档页面和英文的API页面,下图为生成的英文文档首页示例。注意，示例中由于使用了sphinx的原始主题，所以页面的风格与官网并不一致，但这并不影响开发者进行调试。
在浏览器中输入 http://localhost:8088 就可以看到编译生成的中/英文的文档页面和英文的API页面,下图为生成的英文文档首页示例。注意，示例中由于使用了sphinx的原始主题，所以页面的风格与官网并不一致，但这并不影响开发者进行调试。
.. image:: src/doc_en.png
:align: center
......
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......@@ -2,21 +2,20 @@
Contribute Documentation
########################
PaddlePaddle supports English documentation ``doc`` and Chinese documentation ``doc_cn``.
Both are compiled by `cmake`_ and `sphinx`_ , the compiled documentations will be stored under ``doc`` and ``doc_cn`` directories.
When using the PaddlePaddle.org to compile documentations, the compiled documentations will be stored under a consolidated directory: .ppo_workspace/content
PaddlePaddle's documentation includes both Chinese and English versions. The documentation is built using the ``cmake`` command to drive the ``sphinx`` compiler. The PaddlePaddle.org tool helps us to implement this compilation process and provides better preview results.
How to Build Documentations
============
How to build Documentation
===========================
We recommend using PaddlePaddle.org tool to build documentation
PaddlePaddle's documentation is built in two ways: using the PaddlePaddle.org tool and without using it. Both methods have their own advantages. The former facilitates previewing, while the latter facilitates debugging by the developer. We could choose to build the documentation with Docker or without it in each of the above ways.
We recommend using PaddlePaddle.org tool to build documentation.
Use PaddlePaddle.org tool
--------------
This is the recommended method to build documentation. It can compile documentation and preview the documentation in a web browser.
Using PaddlePaddle.org tool
-----------------------------
This is the recommended method to build documentation, because it can automatically compile the documentation and preview the documentation directly in a web page. Note that, although you can preview the documentation in other ways, its style may not be consistent with the official website. Compiling with the PaddlePaddle.org tool produces a preview that will be consistent with the official website documentation style.
The tool uses Docker, please install it on your system. Please check Docker official website on how to install Docker. You may use the following commands to activate the tool
The PaddlePaddle.org tool can be used with Docker and Docker needs to be installed first. Please refer to `Docker's official website <https://docs.docker.com/>`_ on how to install Docker. After installing Docker, you may use the following commands to activate the tool
.. code-block:: bash
......@@ -32,8 +31,8 @@ The tool uses Docker, please install it on your system. Please check Docker offi
# Please specify the working directory through -v
docker run -it -p 8000:8000 -v `pwd`:/var/content paddlepaddle/paddlepaddle.org:latest
Note: PaddlePaddle.org will read the content repos specified in the -v (volume) flag of the docker run command
Use a web browser and navigate to http://localhost:8000, click the buttons to compile the documentation
Note: PaddlePaddle.org will read the content repos specified in the -v (volume) flag of the docker run commands
Use a web browser and navigate to http://localhost:8000. Click the buttons to compile the documentation.
The compiled documentations will be stored in <paddlepaddle working directory>/.ppo_workspace/content
......@@ -58,19 +57,62 @@ If you don't wish to use Docker, you can also activate the tool through Django.
pip install -r requirements.txt
python manage.py runserver
Use a web browser and navigate to http://localhost:8000, click the buttons to compile the documentation
Specify the PaddlePaddle working directory for the environment variable CONTENT_DIR so that the tool could find where the working directory is.
Use a web browser and navigate to http://localhost:8000. Click the buttons to compile the documentation
The compiled documentations will be stored in <paddlepaddle working directory>/.ppo_workspace/content
If you want to learn more on the PaddlePaddle.org, please `click here <https://github.com/PaddlePaddle/PaddlePaddle.org/blob/develop/README.md>`_ 。
Please `click here <https://github.com/PaddlePaddle/PaddlePaddle.org/blob/develop/README.md>`_ for more information about the PaddlePaddle.org tool.
Manually Building the Documentation
-------------------------------------
Build PaddlePaddle's documentation with Docker，you need to install Docker first. Please refer to `Docker's official website <https://docs.docker.com/>`_ on how to install Docker. After Docker is installed, you could use the scripts in the source directory to build the documentation.
[TBD]
If you do not wish to use Docker, you can also use the following commands to directly build the PaddlePaddle documentation.
.. code-block:: bash
mkdir paddle
cd paddle
git clone https://github.com/PaddlePaddle/Paddle.git
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DWITH_GPU=OFF -DWITH_MKL=OFF -DWITH_DOC=ON
# If you only need to build documents, use the following commands
make -j $processors gen_proto_py
make -j $processors paddle_docs paddle_docs_cn
# If you only need to build APIs, use the following commands
make -j $processors gen_proto_py framework_py_proto
make -j $processors copy_paddle_pybind
make -j $processors paddle_api_docs
$processors indicates that as many processes as the CPU cores are started to compile in parallel. It should be set according to the number of CPU cores of your machine.
After the compilation is complete, enter the ``doc/v2`` directory. If you chose to build documents, it will generate ``cn/html/`` and ``en/html`` subdirectories under this directory. If you chose to build APIs，it will generate``api/en/html`` subdirectory. Please enter these directories respectively and execute the following commands:
.. code-block:: bash
python -m SimpleHTTPServer 8088
Use a web browser and navigate to http://localhost:8000, you could see the compiled Chinese/English documents page and the English APIs page. The following figure is an example of the built English documents home page. Note that due to the sphinx's original theme used in the example, the style of the page is not consistent with the official website, but this does not affect the developer's debugging.
How to write Documentations
============
.. image:: src/doc_en.png
:align: center
:scale: 60 %
PaddlePaddle uses `sphinx`_ to compile documentations，Please check sphinx official website for more detail.
How to write Documentation
===========================
PaddlePaddle uses `sphinx`_ to compile documentation，Please check sphinx official website for more detail.
How to update www.paddlepaddle.org
============================
===================================
Please create PRs and submit them to github, please check `Contribute Code <http://www.paddlepaddle.org/docs/develop/documentation/en/howto/dev/contribute_to_paddle_en.html>`_ 。
PaddlePaddle develop branch will update the documentation once the PR is merged. User may check latest `Chinese Docs <http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html>`_ and
......
doc/v2/faq/index_en.rst
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FAQ
====
This document provides answers to some of the frequently asked questions about PaddlePaddle. If you have a question that is not covered here, please go to `PaddlePaddle Community <https://github.com/PaddlePaddle/Paddle/issues>`_ , to find an answer or submit new `issue <https://github.com/PaddlePaddle/Paddle/issues/new>`_ , we will reply in time.
.. toctree::
:maxdepth: 1
......
doc/v2/getstarted/index_en.rst
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GET STARTED
============
If you want to quickly know how to use PaddlePaddle, please refer to the following guide:
.. toctree::
:maxdepth: 1
quickstart_en.rst
While using PaddlePaddle to build applications, please understand some basic concepts.
Here is an example of linear regression. It introduces workflow of PaddlePaddle, including data format, model configuration and training, etc.
.. toctree::
:maxdepth: 1
concepts/use_concepts_en.rst
doc/v2/howto/capi/index_en.rst
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C-API Prediction Library
C-API Inference Library
========================
After we train a neural network, we use it to do inference. Inference is the process of preparing input data and propagating it through the model to produce the result.
Compared with model training, prediction has the following features:
#. Inference does not require backpropagation and parameter updates, as required during training.
#. Labels are not needed in prediction.
#. Most of the time, predictions need to be integrated with the user system.
Therefore, the model prediction SDK needs to be designed separately and has the following features:
#. The predictive SDK does not include backpropagation and parameter updates to reduce the size of the SDK.
#. The predictive SDK needs a simple user interface for ease of use.
#. Since the input data may have a variety of structures, the format of the input data is clearly and compactly packaged.
#. In order to be compatible with user's system, the SDK's interface must conform to the C-standard interface.
PaddlePaddle provides C-API to solve the above problem. Following are the guidelines to use the C-API:
.. toctree::
:maxdepth: 1
......
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HOW TO
=======
========
PaddlePaddle provides the users the ability to flexibly set various command line parameters to control the model training and inference process. Please refer to the following instructions on using PaddlePaddle:
.. toctree::
:maxdepth: 1
cmd_parameter/index_cn.rst
PaddlePaddle supports distributed training tasks on fabric clusters, MPI clusters, and Kubernetes clusters. For detailed configuration and usage instructions, refer to:
.. toctree::
:maxdepth: 1
cluster/index_cn.rst
PaddlePaddle provides a C-API for inference. We provide the following guidelines for using the C-API:
.. toctree::
:maxdepth: 1
capi/index_cn.rst
PaddlePaddle supports a variety of flexible and efficient recurrent neural networks. For details, please refer to：
.. toctree::
:maxdepth: 1
rnn/index_cn.rst
How to use the built-in timing tool, nvprof, or nvvp to run performance analysis and tuning, please refer to：
.. toctree::
:maxdepth: 1
cmd_parameter/index_en.rst
cluster/index_en.rst
capi/index_en.rst
rnn/index_en.rst
optimization/gpu_profiling_en.rst
optimization/gpu_profiling_cn.rst
paddle/fluid/framework/concurrency_test.cc
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......@@ -150,7 +150,8 @@ void AddFibonacciSelect(Scope *scope, p::CPUPlace *place, ProgramDesc *program,
// Select block
AddOp("select", {{"X", {dataChanName, quitChanName}},
{"case_to_execute", {"caseToExecute"}}},
{}, {{"sub_block", casesBlock},
{{"Out", {}}},
{{"sub_block", casesBlock},
{"cases", std::vector<std::string>{case0Config, case1Config}}},
whileBlock);
......@@ -209,9 +210,8 @@ TEST(Concurrency, Go_Op) {
executor.Run(program, &scope, 0, true, true);
// After we call executor.run, the Go operator should do a channel_send to set
// the
// "result" variable to 99
// After we call executor.run, the Go operator should do a channel_send to
// set the "result" variable to 99.
auto *finalData = tensor.data<int>();
EXPECT_EQ(finalData[0], 99);
}
......
paddle/fluid/framework/executor.cc
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......@@ -14,12 +14,8 @@ limitations under the License. */
#include "paddle/fluid/framework/executor.h"
#include <set>
#include "gflags/gflags.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/feed_fetch_type.h"
#include "paddle/fluid/framework/lod_rank_table.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -40,14 +36,13 @@ namespace {
int kProgramId = -1;
} // namespace
struct ExecutorPrepareContext {
ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id)
ExecutorPrepareContext::ExecutorPrepareContext(
const framework::ProgramDesc& prog, size_t block_id)
: prog_(prog), block_id_(block_id) {}
const framework::ProgramDesc& prog_;
size_t block_id_;
std::vector<std::unique_ptr<OperatorBase>> ops_;
};
ExecutorPrepareContext::~ExecutorPrepareContext() {
VLOG(5) << "destroy ExecutorPrepareContext";
}
Executor::Executor(const platform::Place& place) : place_(place) {}
......@@ -101,9 +96,8 @@ static void CheckTensorNANOrInf(const std::string& name,
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id,
bool create_local_scope, bool create_vars) {
platform::RecordBlock b(block_id);
auto* ctx = Prepare(pdesc, block_id);
RunPreparedContext(ctx, scope, create_local_scope, create_vars);
delete ctx;
auto ctx = Prepare(pdesc, block_id);
RunPreparedContext(ctx.get(), scope, create_local_scope, create_vars);
}
// Check whether the block already has feed operators and feed_holder.
......@@ -191,7 +185,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
std::map<std::string, const LoDTensor*>& feed_targets,
std::map<std::string, LoDTensor*>& fetch_targets,
const std::string& feed_holder_name,
const std::string& fetch_holder_name) {
const std::string& fetch_holder_name, bool create_vars) {
platform::RecordBlock b(kProgramId);
bool has_feed_ops =
has_feed_operators(program.Block(0), feed_targets, feed_holder_name);
......@@ -261,7 +255,7 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
}
}
Run(*copy_program, scope, 0, true, true);
Run(*copy_program, scope, 0, create_vars, create_vars);
// obtain the data of fetch_targets from fetch_holder
for (auto* op : global_block->AllOps()) {
......@@ -274,15 +268,15 @@ void Executor::Run(const ProgramDesc& program, Scope* scope,
}
}
ExecutorPrepareContext* Executor::Prepare(const ProgramDesc& program,
int block_id) {
std::unique_ptr<ExecutorPrepareContext> Executor::Prepare(
const ProgramDesc& program, int block_id) {
auto* ctx = new ExecutorPrepareContext(program, block_id);
PADDLE_ENFORCE_LT(static_cast<size_t>(block_id), program.Size());
auto& block = program.Block(block_id);
for (auto& op_desc : block.AllOps()) {
ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc));
}
return ctx;
return std::unique_ptr<ExecutorPrepareContext>(ctx);
}
void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
......
paddle/fluid/framework/executor.h
浏览文件 @ 79af7cc9
......@@ -22,7 +22,16 @@ limitations under the License. */
namespace paddle {
namespace framework {
struct ExecutorPrepareContext;
struct ExecutorPrepareContext {
ExecutorPrepareContext(const framework::ProgramDesc& prog, size_t block_id);
~ExecutorPrepareContext();
const framework::ProgramDesc& prog_;
size_t block_id_;
std::vector<std::unique_ptr<OperatorBase>> ops_;
};
class Executor {
public:
// TODO(dzhwinter) : Do not rely on this function, it will be removed
......@@ -45,10 +54,11 @@ class Executor {
std::map<std::string, const LoDTensor*>& feed_targets,
std::map<std::string, LoDTensor*>& fetch_targets,
const std::string& feed_holder_name = "feed",
const std::string& fetch_holder_name = "fetch");
const std::string& fetch_holder_name = "fetch",
bool create_vars = true);
static ExecutorPrepareContext* Prepare(const ProgramDesc& program,
int block_id);
static std::unique_ptr<ExecutorPrepareContext> Prepare(
const ProgramDesc& program, int block_id);
void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
bool create_local_scope = true,
......
paddle/fluid/framework/mixed_vector.h
浏览文件 @ 79af7cc9
......@@ -176,7 +176,7 @@ class Vector {
// resize the vector
void resize(size_t size) {
if (size + 1 < capacity()) {
if (size + 1 <= capacity()) {
size_ = size;
} else {
MutableCPU();
......
paddle/fluid/framework/mixed_vector_test.cu
浏览文件 @ 79af7cc9
......@@ -104,3 +104,11 @@ TEST(mixed_vector, ForEach) {
for (auto& v : tmp) {
}
}
TEST(mixed_vector, Reserve) {
paddle::framework::Vector<int> vec;
vec.reserve(1);
vec.push_back(0);
vec.push_back(0);
vec.push_back(0);
}
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 79af7cc9
......@@ -12,6 +12,8 @@ function(op_library TARGET)
set(OP_LIBRARY ${TARGET} ${OP_LIBRARY} PARENT_SCOPE)
set(cc_srcs)
set(cu_srcs)
set(hip_cu_srcs)
set(miopen_hip_cc_srcs)
set(cu_cc_srcs)
set(cudnn_cu_cc_srcs)
set(CUDNN_FILE)
......@@ -36,10 +38,19 @@ function(op_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
list(APPEND cu_srcs ${TARGET}.cu)
endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.hip.cu)
list(APPEND hip_cu_srcs ${TARGET}.hip.cu)
endif()
string(REPLACE "_op" "_cudnn_op" CUDNN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc)
list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc)
endif()
if(WITH_AMD_GPU)
string(REPLACE "_op" "_miopen_op" MIOPEN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MIOPEN_FILE}.hip.cc)
list(APPEND miopen_hip_cc_srcs ${MIOPEN_FILE}.hip.cc)
endif()
endif()
if(WITH_MKLDNN)
string(REPLACE "_op" "_mkldnn_op" MKLDNN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MKLDNN_FILE}.cc)
......@@ -48,10 +59,14 @@ function(op_library TARGET)
endif()
else()
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.cu$")
if (${src} MATCHES ".*\\.hip.cu$")
list(APPEND hip_cu_srcs ${src})
elseif (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu.cc$")
list(APPEND cudnn_cu_cc_srcs ${src})
elseif(WITH_AMD_GPU AND ${src} MATCHES ".*_miopen_op.hip.cc$")
list(APPEND miopen_hip_cc_srcs ${src})
elseif(WITH_MKLDNN AND ${src} MATCHES ".*_mkldnn_op.cc$")
list(APPEND mkldnn_cc_srcs ${src})
elseif(${src} MATCHES ".*\\.cu.cc$")
......@@ -76,6 +91,9 @@ function(op_library TARGET)
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${mkldnn_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
elseif (WITH_AMD_GPU)
hip_library(${TARGET} SRCS ${cc_srcs} ${hip_cu_srcs} ${miopen_hip_cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
else()
cc_library(${TARGET} SRCS ${cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
......@@ -88,7 +106,7 @@ function(op_library TARGET)
endif()
endforeach()
# The registration of USE_OP, please refer to paddle/framework/op_registry.h.
# The registration of USE_OP, please refer to paddle/fluid/framework/op_registry.h.
# Note that it's enough to just adding one operator to pybind in a *_op.cc file.
# And for detail pybind information, please see generated paddle/pybind/pybind.h.
file(READ ${TARGET}.cc TARGET_CONTENT)
......@@ -114,7 +132,10 @@ function(op_library TARGET)
list(LENGTH cu_srcs cu_srcs_len)
list(LENGTH cu_cc_srcs cu_cc_srcs_len)
list(LENGTH mkldnn_cc_srcs mkldnn_cc_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0)
list(LENGTH hip_cu_srcs hip_cu_srcs_len)
list(LENGTH miopen_hip_cc_srcs miopen_hip_cc_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0 AND ${cu_srcs_len} EQUAL 0 AND ${cu_cc_srcs_len} EQUAL 0 AND
${hip_cu_srcs_len} EQUAL 0 AND ${miopen_hip_cc_srcs_len} EQUAL 0)
file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n")
set(pybind_flag 1)
endif()
......@@ -125,6 +146,11 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, CUDNN);\n")
endif()
# pybind USE_OP_DEVICE_KERNEL for MIOPEN
if (WITH_AMD_GPU AND ${miopen_hip_cc_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MIOPEN);\n")
endif()
# pybind USE_OP_DEVICE_KERNEL for MKLDNN
if (WITH_MKLDNN AND ${mkldnn_cc_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MKLDNN);\n")
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ 79af7cc9
......@@ -613,3 +613,14 @@ REGISTER_OP(swish, ops::ActivationOp, ops::SwishOpMaker, swish_grad,
ops::grad_functor<double>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL);
REGISTER_OP_CPU_KERNEL(relu,
ops::ActivationKernel<paddle::platform::CPUDeviceContext,
ops::ReluFunctor<float>>,
ops::ActivationKernel<paddle::platform::CPUDeviceContext,
ops::ReluFunctor<double>>);
REGISTER_OP_CPU_KERNEL(
relu_grad, ops::ActivationGradKernel<paddle::platform::CPUDeviceContext,
ops::ReluGradFunctor<float>>,
ops::ActivationGradKernel<paddle::platform::CPUDeviceContext,
ops::ReluGradFunctor<double>>);
paddle/fluid/operators/activation_op.cu
浏览文件 @ 79af7cc9
......@@ -14,6 +14,7 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
......@@ -31,3 +32,16 @@ namespace ops = paddle::operators;
ops::grad_functor<double>>);
FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CUDA_KERNEL);
REGISTER_OP_CUDA_KERNEL(
relu, ops::ActivationKernel<paddle::platform::CUDADeviceContext,
ops::ReluFunctor<float>>,
ops::ActivationKernel<paddle::platform::CUDADeviceContext,
ops::ReluFunctor<double>>,
ops::ActivationKernel<paddle::platform::CUDADeviceContext,
ops::ReluFunctor<paddle::platform::float16>>);
REGISTER_OP_CUDA_KERNEL(
relu_grad, ops::ActivationGradKernel<paddle::platform::CUDADeviceContext,
ops::ReluGradFunctor<float>>,
ops::ActivationGradKernel<paddle::platform::CUDADeviceContext,
ops::ReluGradFunctor<double>>);
paddle/fluid/operators/activation_op.h
浏览文件 @ 79af7cc9
......@@ -772,7 +772,6 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
__macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor); \
__macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor); \
__macro(exp, ExpFunctor, ExpGradFunctor); \
__macro(relu, ReluFunctor, ReluGradFunctor); \
__macro(tanh, TanhFunctor, TanhGradFunctor); \
__macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \
__macro(sqrt, SqrtFunctor, SqrtGradFunctor); \
......
paddle/fluid/operators/average_accumulates_op.cc 0 → 100644
浏览文件 @ 79af7cc9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/average_accumulates_op.h"
namespace paddle {
namespace operators {
template <>
void GetAccumulators<paddle::platform::CPUDeviceContext>(
const framework::ExecutionContext& ctx, int64_t& num_updates_,
int64_t& num_accumulates_, int64_t& old_num_accumulates_) {
auto* in_old_num_accumulates = ctx.Input<Tensor>("in_old_num_accumulates");
auto* in_num_accumulates = ctx.Input<Tensor>("in_num_accumulates");
auto* in_num_updates = ctx.Input<Tensor>("in_num_updates");
old_num_accumulates_ = in_old_num_accumulates->data<int64_t>()[0];
num_accumulates_ = in_num_accumulates->data<int64_t>()[0];
num_updates_ = in_num_updates->data<int64_t>()[0];
}
template <>
void SetAccumulators<paddle::platform::CPUDeviceContext>(
const framework::ExecutionContext& ctx, int64_t num_updates_,
int64_t num_accumulates_, int64_t old_num_accumulates_) {
auto* out_old_num_accumulates = ctx.Output<Tensor>("out_old_num_accumulates");
auto* out_num_accumulates = ctx.Output<Tensor>("out_num_accumulates");
auto* out_num_updates = ctx.Output<Tensor>("out_num_updates");
out_old_num_accumulates->data<int64_t>()[0] = old_num_accumulates_;
out_num_accumulates->data<int64_t>()[0] = num_accumulates_;
out_num_updates->data<int64_t>()[0] = num_updates_;
}
class AverageAccumulatesOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(
ctx->HasInput("param"),
"Input (param) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("in_sum_1"),
"Input (sum_1) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("in_sum_2"),
"Input (sum_2) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("in_sum_3"),
"Input (sum_3) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("in_num_accumulates"),
"Input (in_num_accumulates) of average_accumulates op should "
"not be null.");
PADDLE_ENFORCE(ctx->HasInput("in_old_num_accumulates"),
"Input (old_num_accumulates) of average_accumulates op "
"should not be null.");
PADDLE_ENFORCE(
ctx->HasInput("in_num_updates"),
"Input (num_updates) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("out_sum_1"),
"Output (sum_1) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("out_sum_2"),
"Output (sum_2) of average_accumulates op should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("out_sum_3"),
"Output (sum_3) of average_accumulates op should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("out_num_accumulates"),
"Output (num_accumulates) of average_accumulates op should "
"not be null.");
PADDLE_ENFORCE(ctx->HasOutput("out_old_num_accumulates"),
"Output (old_num_accumulates) of average_accumulates op "
"should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("out_num_updates"),
"Output (num_updates) of average_accumulates op should not be null.");
auto in_dim = ctx->GetInputDim("param");
ctx->SetOutputDim("out_sum_1", in_dim);
ctx->SetOutputDim("out_sum_2", in_dim);
ctx->SetOutputDim("out_sum_3", in_dim);
ctx->SetOutputDim("out_num_accumulates", {1});
ctx->SetOutputDim("out_old_num_accumulates", {1});
ctx->SetOutputDim("out_num_updates", {1});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("param")->type()),
ctx.GetPlace());
}
};
class AverageAccumulatesOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AverageAccumulatesOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("param", "(Tensor), The parameter to be accumulated.");
AddInput("in_sum_1",
"(Tensor), A tensor used to store the parameter "
"sums with the same shape as input(param).");
AddInput("in_sum_2",
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param). It is used to avoid loss of precision due to too "
"many sums.");
AddInput("in_sum_3",
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param).");
AddInput("in_num_accumulates",
"(Tensor<int64_t>), The accumulating times of current window with "
"shape [1].");
AddInput(
"in_old_num_accumulates",
"(Tensor<int64_t>), The accumulating times of previous window with "
"shape [1].");
AddInput("in_num_updates",
"(Tensor<int64_t>), The total number of batches used by trainning "
"before this batch with shape [1].");
AddOutput("out_sum_1",
"(Tensor), A tensor used to store the "
"parameter sums with the same shape as input(param).");
AddOutput("out_sum_2",
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param). It is used to avoid loss of precision due to too "
"many sums.");
AddOutput("out_sum_3",
"(Tensor), A auxiliary tensor to help "
"accumulating sums of parameter values with the same shape as "
"input(param).");
AddOutput(
"out_num_accumulates",
"(Tensor<int64_t>), The accumulating times of current window with "
"shape [1].");
AddOutput(
"out_old_num_accumulates",
"(Tensor<int64_t>) The accumulating times of previous window with "
"shape [1].");
AddOutput(
"out_num_updates",
"(Tensor<int64_t>), The total number of batches used by trainning "
"before this batch with shape [1].");
AddAttr<float>("average_window",
"(float, default 0) "
"The rate of average window size relative to num_updates.")
.SetDefault(0);
AddAttr<int64_t>("max_average_window",
"(int64_t) "
"Maximum size of average window. It suggests that the "
"number of mini-batches "
"in one pass is appropriate value to set.");
AddAttr<int64_t>("min_average_window",
"(int64_t, default 10000L) "
"Minimu size of average window.")
.SetDefault(10000L);
AddComment(R"DOC(
AverageAccumulates Operator.
Accumulate the sum of parameter whtin sliding window. The size of sliding window is
determined by 'average_window', 'max_average_window' and 'min_average_window'.
Memory was shared by Input(in_sum_1) and Output(out_sum_1) which acts as an accumulator 'sum_1'.
'sum_2', 'sum_3', 'num_accumulates', 'old_num_accumulates' and 'num_updates' were the same as 'sum_1'.
All the accumulators were inited to zero before training.
And for a mini-batch in training, accumulators were computed as below steps:
num_updates += 1
num_accumulates += 1
sum_1 += param
if num_updates % kMaxNumAccumulates == 0:
sum_2 += sum_1
sum_1 = 0
if num_accumulates >= min_average_window && num_accumulates >= min(max_average_window, num_updates * average_window):
sum_3 = sum_1 + sum_2
sum_1 = 0
sum_2 = 0
old_num_accumulates = num_accumulates
num_accumulates = 0
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(average_accumulates, ops::AverageAccumulatesOp,
ops::AverageAccumulatesOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
average_accumulates,
ops::AverageAccumulatesKernel<paddle::platform::CPUDeviceContext, float>,
ops::AverageAccumulatesKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/average_accumulates_op.cu 0 → 100644
浏览文件 @ 79af7cc9
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/average_accumulates_op.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace paddle {
namespace operators {
template <>
void GetAccumulators<paddle::platform::CUDADeviceContext>(
const framework::ExecutionContext& ctx, int64_t& num_updates_,
int64_t& num_accumulates_, int64_t& old_num_accumulates_) {
auto* in_old_num_accumulates = ctx.Input<Tensor>("in_old_num_accumulates");
auto* in_num_accumulates = ctx.Input<Tensor>("in_num_accumulates");
auto* in_num_updates = ctx.Input<Tensor>("in_num_updates");
auto stream = ctx.cuda_device_context().stream();
memory::Copy(platform::CPUPlace(), &old_num_accumulates_,
platform::CUDAPlace(), in_old_num_accumulates->data<int64_t>(),
sizeof(int64_t), stream);
memory::Copy(platform::CPUPlace(), &num_accumulates_, platform::CUDAPlace(),
in_num_accumulates->data<int64_t>(), sizeof(int64_t), stream);
memory::Copy(platform::CPUPlace(), &num_updates_, platform::CUDAPlace(),
in_num_updates->data<int64_t>(), sizeof(int64_t), stream);
}
template <>
void SetAccumulators<paddle::platform::CUDADeviceContext>(
const framework::ExecutionContext& ctx, int64_t num_updates_,
int64_t num_accumulates_, int64_t old_num_accumulates_) {
auto stream = ctx.cuda_device_context().stream();
auto* out_old_num_accumulates = ctx.Output<Tensor>("out_old_num_accumulates");
auto* out_num_accumulates = ctx.Output<Tensor>("out_num_accumulates");
auto* out_num_updates = ctx.Output<Tensor>("out_num_updates");
memory::Copy(platform::CUDAPlace(), out_old_num_accumulates->data<int64_t>(),
platform::CPUPlace(), &old_num_accumulates_, sizeof(int64_t),
stream);
memory::Copy(platform::CUDAPlace(), out_num_accumulates->data<int64_t>(),
platform::CPUPlace(), &num_accumulates_, sizeof(int64_t),
stream);
memory::Copy(platform::CUDAPlace(), out_num_updates->data<int64_t>(),
platform::CPUPlace(), &num_updates_, sizeof(int64_t), stream);
}
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
average_accumulates,
ops::AverageAccumulatesKernel<paddle::platform::CUDADeviceContext, float>,
ops::AverageAccumulatesKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/average_accumulates_op.h 0 → 100644
浏览文件 @ 79af7cc9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename DeviceContext>
void GetAccumulators(const framework::ExecutionContext& ctx,
int64_t& num_updates, int64_t& num_accumulates,
int64_t& old_num_accumulates);
template <typename DeviceContext>
void SetAccumulators(const framework::ExecutionContext& ctx,
int64_t num_updates, int64_t num_accumulates,
int64_t old_num_accumulates);
template <typename DeviceContext, typename T>
class AverageAccumulatesKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
// It is used to avoid loss of precision
static const int64_t kMaxNumAccumulates = 16384;
// Get accumulators from input
int64_t num_updates = 0;
int64_t num_accumulates = 0;
int64_t old_num_accumulates = 0;
GetAccumulators<DeviceContext>(ctx, num_updates, num_accumulates,
old_num_accumulates);
// Get attrs
float average_window = ctx.Attr<float>("average_window");
int64_t max_average_window = ctx.Attr<int64_t>("max_average_window");
int64_t min_average_window = ctx.Attr<int64_t>("min_average_window");
min_average_window =
std::min<int64_t>(min_average_window, max_average_window);
// Get inputs
auto* param = ctx.Input<Tensor>("param");
auto* in_sum_1 = ctx.Input<Tensor>("in_sum_1");
auto* in_sum_2 = ctx.Input<Tensor>("in_sum_2");
auto* in_sum_3 = ctx.Input<Tensor>("in_sum_3");
auto param_tensor = EigenVector<T>::Flatten(*param);
auto in_sum_1_tensor = EigenVector<T>::Flatten(*in_sum_1);
auto in_sum_2_tensor = EigenVector<T>::Flatten(*in_sum_2);
auto in_sum_3_tensor = EigenVector<T>::Flatten(*in_sum_3);
// Get outputs
auto* out_sum_1 = ctx.Output<Tensor>("out_sum_1");
auto* out_sum_2 = ctx.Output<Tensor>("out_sum_2");
auto* out_sum_3 = ctx.Output<Tensor>("out_sum_3");
auto out_sum_1_tensor = EigenVector<T>::Flatten(*out_sum_1);
auto out_sum_2_tensor = EigenVector<T>::Flatten(*out_sum_2);
auto out_sum_3_tensor = EigenVector<T>::Flatten(*out_sum_3);
// Compute
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
math::SetConstant<DeviceContext, T> constant_functor;
++num_updates;
++num_accumulates;
out_sum_1_tensor.device(place) = in_sum_1_tensor + param_tensor;
out_sum_2_tensor.device(place) = in_sum_2_tensor;
out_sum_3_tensor.device(place) = in_sum_3_tensor;
if (num_updates % kMaxNumAccumulates == 0) {
// Move the sum to a different buffer to avoid loss of precision due to
// too many sums.
out_sum_2_tensor.device(place) = in_sum_2_tensor + in_sum_1_tensor;
constant_functor(ctx.template device_context<DeviceContext>(), out_sum_1,
0.0);
}
if (num_accumulates >= min_average_window &&
num_accumulates >= std::min<int64_t>(max_average_window,
num_updates * average_window)) {
// Now the average window is too long, discard the old sum.
out_sum_3_tensor.device(place) = in_sum_1_tensor + in_sum_2_tensor;
constant_functor(ctx.template device_context<DeviceContext>(), out_sum_1,
0.0);
constant_functor(ctx.template device_context<DeviceContext>(), out_sum_2,
0.0);
old_num_accumulates = num_accumulates;
num_accumulates = 0;
}
// Set accumulators to output
SetAccumulators<DeviceContext>(ctx, num_updates, num_accumulates,
old_num_accumulates);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 79af7cc9
......@@ -80,6 +80,29 @@ class BatchNormOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("SavedVariance", {C});
ctx->ShareLoD("X", "Y");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("X")->type());
// For float or float16 input tensor, the type of the scale, bias, mean,
// and var tensors should both be float.
auto bn_param_type = framework::proto::VarType::FP32;
PADDLE_ENFORCE_EQ(bn_param_type,
framework::ToDataType(ctx.Input<Tensor>("Scale")->type()),
"Scale input should be of float type");
PADDLE_ENFORCE_EQ(bn_param_type,
framework::ToDataType(ctx.Input<Tensor>("Bias")->type()),
"Bias input should be of float type");
PADDLE_ENFORCE_EQ(bn_param_type,
framework::ToDataType(ctx.Input<Tensor>("Mean")->type()),
"Mean input should be of float type");
PADDLE_ENFORCE_EQ(bn_param_type, framework::ToDataType(
ctx.Input<Tensor>("Variance")->type()),
"Variance input should be of float type");
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -434,12 +457,39 @@ class BatchNormGradKernel<platform::CPUDeviceContext, T>
}
};
class BatchNormGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto *op = new framework::OpDesc();
op->SetType("batch_norm_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetInput("Scale", Input("Scale"));
op->SetInput("SavedMean", Output("SavedMean"));
op->SetInput("SavedVariance", Output("SavedVariance"));
op->SetAttrMap(Attrs());
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("Scale"), InputGrad("Scale"));
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
return std::unique_ptr<framework::OpDesc>(op);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
batch_norm_grad, ops::BatchNormGradOp);
REGISTER_OPERATOR(batch_norm, ops::BatchNormOp, ops::BatchNormOpMaker,
ops::BatchNormGradMaker);
REGISTER_OPERATOR(batch_norm_grad, ops::BatchNormGradOp);
REGISTER_OP_CPU_KERNEL(
batch_norm,
ops::BatchNormKernel<paddle::platform::CPUDeviceContext, float>);
......
paddle/fluid/operators/batch_norm_op.cu.cc
浏览文件 @ 79af7cc9
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <cfloat>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/cudnn_helper.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
......@@ -26,6 +27,8 @@ using Tensor = framework::Tensor;
using DataLayout = framework::DataLayout;
template <typename T>
using CudnnDataType = platform::CudnnDataType<T>;
template <typename T>
using BatchNormParamType = typename CudnnDataType<T>::BatchNormParamType;
void ExtractNCWHD(const framework::DDim &dims, const DataLayout &data_layout,
int *N, int *C, int *H, int *W, int *D) {
......@@ -104,8 +107,9 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
data_desc_, CudnnDataType<T>::type,
x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
// Note: PERSISTENT not implemented for inference
CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
bn_param_desc_, data_desc_, mode_));
bn_param_desc_, data_desc_, is_test ? CUDNN_BATCHNORM_SPATIAL : mode_));
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *bias = ctx.Input<Tensor>("Bias");
......@@ -118,15 +122,16 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
// alloc memory
y->mutable_data<T>(ctx.GetPlace());
mean_out->mutable_data<T>(ctx.GetPlace());
variance_out->mutable_data<T>(ctx.GetPlace());
saved_mean->mutable_data<T>(ctx.GetPlace());
saved_variance->mutable_data<T>(ctx.GetPlace());
mean_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
variance_out->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
saved_mean->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
saved_variance->mutable_data<BatchNormParamType<T>>(ctx.GetPlace());
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
math::SetConstant<platform::CUDADeviceContext, T> functor;
functor(dev_ctx, saved_mean, 0);
functor(dev_ctx, saved_variance, 0);
math::SetConstant<platform::CUDADeviceContext, BatchNormParamType<T>>
functor;
functor(dev_ctx, saved_mean, static_cast<BatchNormParamType<T>>(0));
functor(dev_ctx, saved_variance, static_cast<BatchNormParamType<T>>(0));
auto handle = dev_ctx.cudnn_handle();
......@@ -147,8 +152,10 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_, x->template data<T>(),
data_desc_, y->template mutable_data<T>(ctx.GetPlace()),
bn_param_desc_, scale->template data<T>(), bias->template data<T>(),
est_mean->template data<T>(), est_var->template data<T>(), epsilon));
bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
bias->template data<BatchNormParamType<T>>(),
est_mean->template data<BatchNormParamType<T>>(),
est_var->template data<BatchNormParamType<T>>(), epsilon));
} else {
// Run training mode.
// obtain running mean and running inv var, and see if we need to
......@@ -159,11 +166,16 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
handle, mode_, CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
data_desc_, x->template data<T>(), data_desc_,
y->template mutable_data<T>(ctx.GetPlace()), bn_param_desc_,
scale->template data<T>(), bias->template data<T>(), this_factor,
mean_out->template mutable_data<T>(ctx.GetPlace()),
variance_out->template mutable_data<T>(ctx.GetPlace()), epsilon,
saved_mean->template mutable_data<T>(ctx.GetPlace()),
saved_variance->template mutable_data<T>(ctx.GetPlace())));
scale->template data<BatchNormParamType<T>>(),
bias->template data<BatchNormParamType<T>>(), this_factor,
mean_out->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
variance_out->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
epsilon, saved_mean->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
saved_variance->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace())));
}
// clean when exit.
......@@ -270,9 +282,9 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
batch_norm,
ops::BatchNormKernel<paddle::platform::CUDADeviceContext, float>);
batch_norm, ops::BatchNormKernel<plat::CUDADeviceContext, float>,
ops::BatchNormKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
batch_norm_grad,
ops::BatchNormGradKernel<paddle::platform::CUDADeviceContext, float>);
batch_norm_grad, ops::BatchNormGradKernel<plat::CUDADeviceContext, float>);
paddle/fluid/operators/box_coder_op.cc
浏览文件 @ 79af7cc9
......@@ -126,6 +126,7 @@ width and height.
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker);
REGISTER_OPERATOR(box_coder, ops::BoxCoderOp, ops::BoxCoderOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(box_coder, ops::BoxCoderKernel<float>,
ops::BoxCoderKernel<double>);
paddle/fluid/operators/cross_entropy_op.h
浏览文件 @ 79af7cc9
......@@ -78,7 +78,7 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
for (int64_t i = 0; i < batch_size; ++i) {
PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num);
int64_t index = i * class_num + label_data[i];
dx_data[index] = -dy_data[i] / x_data[index];
dx_data[index] = math::TolerableValue<T>()(-dy_data[i] / x_data[index]);
}
}
}
......
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ 79af7cc9
if(WITH_DISTRIBUTE)
grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc grpc_server.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc
grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows)
set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor")
set_source_files_properties(test_serde.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS test_serde.cc DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc)
cc_test(serde_test SRCS test_serde.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
cares zlib protobuf sendrecvop_grpc)
endif()
paddle/fluid/operators/detail/bytebuffer_stream.h
浏览文件 @ 79af7cc9
......@@ -23,9 +23,107 @@ limitations under the License. */
#include "google/protobuf/io/coded_stream.h"
#include "google/protobuf/io/zero_copy_stream.h"
namespace grpc {
// A ZeroCopyInputStream that reads from grpc_byte_buffer
class GrpcBufferReader final
: public ::google::protobuf::io::ZeroCopyInputStream {
typedef void (CoreCodegenInterface::*OldReaderInitAPI)(
grpc_byte_buffer_reader* reader, grpc_byte_buffer* buffer);
typedef int (CoreCodegenInterface::*NewReaderInitAPI)(
grpc_byte_buffer_reader* reader, grpc_byte_buffer* buffer);
void ReaderInit(OldReaderInitAPI ptr, grpc_byte_buffer_reader* reader,
grpc_byte_buffer* buffer) {
(g_core_codegen_interface->*ptr)(reader, buffer);
}
void ReaderInit(NewReaderInitAPI ptr, grpc_byte_buffer_reader* reader,
grpc_byte_buffer* buffer) {
int result = (g_core_codegen_interface->*ptr)(reader, buffer);
(void)result;
}
public:
explicit GrpcBufferReader(grpc_byte_buffer* buffer)
: byte_count_(0), backup_count_(0) {
ReaderInit(&CoreCodegenInterface::grpc_byte_buffer_reader_init, &reader_,
buffer);
}
~GrpcBufferReader() override {
g_core_codegen_interface->grpc_byte_buffer_reader_destroy(&reader_);
}
bool Next(const void** data, int* size) override {
if (backup_count_ > 0) {
*data = GRPC_SLICE_START_PTR(slice_) + GRPC_SLICE_LENGTH(slice_) -
backup_count_;
GPR_CODEGEN_ASSERT(backup_count_ <= INT_MAX);
*size = (int)backup_count_;
backup_count_ = 0;
return true;
}
if (!g_core_codegen_interface->grpc_byte_buffer_reader_next(&reader_,
&slice_)) {
return false;
}
g_core_codegen_interface->grpc_slice_unref(slice_);
*data = GRPC_SLICE_START_PTR(slice_);
// On win x64, int is only 32bit
GPR_CODEGEN_ASSERT(GRPC_SLICE_LENGTH(slice_) <= INT_MAX);
byte_count_ += * size = (int)GRPC_SLICE_LENGTH(slice_);
return true;
}
void BackUp(int count) override { backup_count_ = count; }
bool Skip(int count) override {
const void* data;
int size;
while (Next(&data, &size)) {
if (size >= count) {
BackUp(size - count);
return true;
}
// size < count;
count -= size;
}
// error or we have too large count;
return false;
}
::google::protobuf::int64 ByteCount() const override {
return byte_count_ - backup_count_;
}
private:
int64_t byte_count_;
int64_t backup_count_;
grpc_byte_buffer_reader reader_;
grpc_slice slice_;
};
}; // namespace grpc
namespace paddle {
namespace operators {
namespace detail {
// Source provides a way for a particular RPC implementation to provide
// received data to ParseFrom.
class Source {
public:
virtual ~Source() {}
// Return the stream that contains the data to be parsed.
// Note that this method might be invoked more than once if
// ParseFrom needs to fall back to a more expensive parsing method.
// Every call must return a stream pointing at the beginning of
// the serialized RecvTensorResponse.
//
// Note that a subsequent call to contents() invalidates previous
// results of contents().
//
// Ownership of the returned stream is retained by the Source and
// should not be deleted by the caller.
virtual ::google::protobuf::io::ZeroCopyInputStream* contents() = 0;
};
// A ZeroCopyInputStream that reads from a grpc::ByteBuffer.
class GrpcByteBufferSource
......@@ -46,6 +144,43 @@ class GrpcByteBufferSource
::google::protobuf::int64 byte_count_;
};
class GrpcByteBufferSourceWrapper : public Source {
public:
explicit GrpcByteBufferSourceWrapper(GrpcByteBufferSource* source)
: source_(source) {}
::google::protobuf::io::ZeroCopyInputStream* contents() override {
return source_;
}
private:
GrpcByteBufferSource* source_;
};
class GrpcByteSource : public Source {
public:
explicit GrpcByteSource(grpc_byte_buffer* buffer) : buffer_(buffer) {}
~GrpcByteSource() override { DeleteStream(); }
typedef ::grpc::GrpcBufferReader Reader;
::google::protobuf::io::ZeroCopyInputStream* contents() override {
DeleteStream();
stream_ = new (&space_) Reader(buffer_);
return stream_;
}
private:
void DeleteStream() {
if (stream_) {
stream_->~Reader();
}
}
grpc_byte_buffer* buffer_; // Not owned
Reader* stream_ = nullptr; // Points into space_ if non-nullptr
char space_[sizeof(Reader)];
};
} // namespace detail
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ 79af7cc9
......@@ -13,7 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "grpc_client.h"
#include <sys/time.h>
#include "paddle/fluid/framework/threadpool.h"
namespace paddle {
namespace operators {
namespace detail {
......@@ -31,8 +33,9 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
framework::Async([var_name_val, p_ctx, ep_val, p_scope, time_out, ch, this] {
auto* var = p_scope->FindVar(var_name_val);
sendrecv::VariableMessage req;
SerializeToMessage(var_name_val, var, *p_ctx, &req);
::grpc::ByteBuffer req;
SerializeToByteBuffer(var_name_val, var, *p_ctx, &req);
// varhandle
VarHandle var_h;
......@@ -46,8 +49,11 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
s->Prepare(var_h, time_out);
s->response_call_back_ = NULL;
auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
auto call = std::move(s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req,
&cq_));
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
});
req_count_++;
......@@ -56,9 +62,19 @@ bool RPCClient::AsyncSendVariable(const std::string& ep,
}
void ProcGetResponse(const VarHandle& var_h,
const sendrecv::VariableMessage& ret_msg) {
auto* outvar = var_h.scope->FindVar(var_h.name);
DeserializeFromMessage(ret_msg, *var_h.ctx, outvar);
// const sendrecv::VariableMessage& ret_msg) {
const ::grpc::ByteBuffer& ret_msg) {
framework::Variable* outvar = NULL;
DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, outvar);
}
template <typename T>
void RequestToByteBuffer(const T& proto, ::grpc::ByteBuffer* result) {
::grpc::Slice slice(proto.ByteSizeLong());
proto.SerializeWithCachedSizesToArray(
const_cast<uint8_t*>(reinterpret_cast<const uint8_t*>(slice.begin())));
::grpc::ByteBuffer tmp(&slice, 1);
result->Swap(&tmp);
}
bool RPCClient::AsyncGetVariable(const std::string& ep,
......@@ -88,8 +104,13 @@ bool RPCClient::AsyncGetVariable(const std::string& ep,
s->Prepare(var_h, time_out);
s->response_call_back_ = ProcGetResponse;
auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_);
rpc->Finish(&s->reply_, &s->status_, (void*)s);
::grpc::ByteBuffer buf;
RequestToByteBuffer<sendrecv::VariableMessage>(req, &buf);
auto call = std::move(s->stub_g_.PrepareUnaryCall(
s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_));
call->StartCall();
call->Finish(&s->reply_, &s->status_, (void*)s);
});
req_count_++;
......
paddle/fluid/operators/detail/grpc_client.h
浏览文件 @ 79af7cc9
......@@ -25,6 +25,11 @@ limitations under the License. */
#include <string>
#include <vector>
#include <grpc++/generic/generic_stub.h>
#include <grpc++/grpc++.h>
#include <grpc++/support/byte_buffer.h>
#include <grpc++/support/slice.h>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
......@@ -49,15 +54,11 @@ struct VarHandle {
}
};
void ProcGetResponse(const VarHandle& var_h,
const sendrecv::VariableMessage& msg);
void ProcGetResponse(const VarHandle& var_h, const grpc::ByteBuffer& msg);
class BaseProcessor {
public:
explicit BaseProcessor(std::shared_ptr<grpc::Channel> ch) {
stub_ = sendrecv::SendRecvService::NewStub(ch);
context_ = NULL;
}
explicit BaseProcessor(std::shared_ptr<grpc::Channel> ch) { context_ = NULL; }
virtual ~BaseProcessor() {}
......@@ -82,19 +83,18 @@ class BaseProcessor {
virtual void Process() = 0;
std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
std::unique_ptr<grpc::ClientContext> context_;
grpc::Status status_;
VarHandle var_h_;
};
typedef std::function<void(const VarHandle&, const sendrecv::VoidMessage&)>
typedef std::function<void(const VarHandle&, const ::grpc::ByteBuffer&)>
RequestSendCallBack;
class SendProcessor : public BaseProcessor {
public:
explicit SendProcessor(std::shared_ptr<grpc::Channel> ch)
: BaseProcessor(ch) {}
: BaseProcessor(ch), stub_g_(ch) {}
virtual ~SendProcessor() {}
......@@ -104,17 +104,18 @@ class SendProcessor : public BaseProcessor {
}
}
sendrecv::VoidMessage reply_;
::grpc::GenericStub stub_g_;
::grpc::ByteBuffer reply_;
RequestSendCallBack response_call_back_ = NULL;
};
typedef std::function<void(const VarHandle&, const sendrecv::VariableMessage&)>
typedef std::function<void(const VarHandle&, const ::grpc::ByteBuffer&)>
RequestGetCallBack;
class GetProcessor : public BaseProcessor {
public:
explicit GetProcessor(std::shared_ptr<grpc::Channel> ch)
: BaseProcessor(ch) {}
: BaseProcessor(ch), stub_g_(ch) {}
virtual ~GetProcessor() {}
......@@ -124,30 +125,37 @@ class GetProcessor : public BaseProcessor {
}
}
sendrecv::VariableMessage reply_;
::grpc::ByteBuffer reply_;
::grpc::GenericStub stub_g_;
RequestGetCallBack response_call_back_ = ProcGetResponse;
};
class BatchBarrierProcessor : public BaseProcessor {
public:
explicit BatchBarrierProcessor(std::shared_ptr<grpc::Channel> ch)
: BaseProcessor(ch) {}
: BaseProcessor(ch) {
stub_ = sendrecv::SendRecvService::NewStub(ch);
}
virtual ~BatchBarrierProcessor() {}
virtual void Process() {}
sendrecv::VoidMessage reply_;
std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
};
class FetchBarrierProcessor : public BaseProcessor {
public:
explicit FetchBarrierProcessor(std::shared_ptr<grpc::Channel> ch)
: BaseProcessor(ch) {}
: BaseProcessor(ch) {
stub_ = sendrecv::SendRecvService::NewStub(ch);
}
virtual ~FetchBarrierProcessor() {}
virtual void Process() {}
sendrecv::VariableMessage reply_;
std::unique_ptr<sendrecv::SendRecvService::Stub> stub_;
};
class RPCClient {
......
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ 79af7cc9
......@@ -14,7 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/detail/grpc_server.h"
using grpc::ServerAsyncResponseWriter;
using ::grpc::ServerAsyncResponseWriter;
namespace paddle {
namespace operators {
......@@ -26,9 +26,10 @@ enum CallStatus { PROCESS = 0, FINISH };
// https://stackoverflow.com/questions/41732884/grpc-multiple-services-in-cpp-async-server
class RequestBase {
public:
explicit RequestBase(sendrecv::SendRecvService::AsyncService* service,
grpc::ServerCompletionQueue* cq)
: service_(service), cq_(cq), status_(PROCESS) {
explicit RequestBase(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq,
const platform::DeviceContext* dev_ctx)
: service_(service), cq_(cq), status_(PROCESS), dev_ctx_(dev_ctx) {
PADDLE_ENFORCE(cq_);
}
virtual ~RequestBase() {}
......@@ -42,55 +43,58 @@ class RequestBase {
}
protected:
grpc::ServerContext ctx_;
sendrecv::SendRecvService::AsyncService* service_;
grpc::ServerCompletionQueue* cq_;
::grpc::ServerContext ctx_;
GrpcService::AsyncService* service_;
::grpc::ServerCompletionQueue* cq_;
CallStatus status_;
const platform::DeviceContext* dev_ctx_;
};
typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
class RequestSend final : public RequestBase {
public:
explicit RequestSend(sendrecv::SendRecvService::AsyncService* service,
grpc::ServerCompletionQueue* cq,
SimpleBlockQueue<MessageWithName>* queue)
: RequestBase(service, cq), queue_(queue), responder_(&ctx_) {
service_->RequestSendVariable(&ctx_, &request_, &responder_, cq_, cq_,
this);
explicit RequestSend(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq,
framework::Scope* scope, ReceivedQueue* queue,
const platform::DeviceContext* dev_ctx)
: RequestBase(service, cq, dev_ctx), queue_(queue), responder_(&ctx_) {
request_.reset(new VariableResponse(scope, dev_ctx_));
int method_id = static_cast<int>(detail::GrpcMethod::kSendVariable);
service_->RequestAsyncUnary(method_id, &ctx_, request_.get(), &responder_,
cq_, cq_, this);
}
virtual ~RequestSend() {}
virtual std::string GetReqName() { return request_.varname(); }
virtual std::string GetReqName() { return request_->Varname(); }
virtual void Process() {
MessageWithName msg_with_name =
std::make_pair(request_.varname(), std::move(request_));
queue_->Push(std::move(msg_with_name));
responder_.Finish(reply_, grpc::Status::OK, this);
queue_->Push(std::make_pair(request_->Varname(), request_));
sendrecv::VoidMessage reply;
responder_.Finish(reply, ::grpc::Status::OK, this);
status_ = FINISH;
}
protected:
sendrecv::VariableMessage request_;
sendrecv::VoidMessage reply_;
SimpleBlockQueue<MessageWithName>* queue_;
std::shared_ptr<VariableResponse> request_;
ReceivedQueue* queue_;
ServerAsyncResponseWriter<sendrecv::VoidMessage> responder_;
};
class RequestGet final : public RequestBase {
public:
explicit RequestGet(sendrecv::SendRecvService::AsyncService* service,
grpc::ServerCompletionQueue* cq, framework::Scope* scope,
explicit RequestGet(GrpcService::AsyncService* service,
::grpc::ServerCompletionQueue* cq,
framework::Scope* scope,
const platform::DeviceContext* dev_ctx,
SimpleBlockQueue<MessageWithName>* queue)
: RequestBase(service, cq),
: RequestBase(service, cq, dev_ctx),
responder_(&ctx_),
scope_(scope),
dev_ctx_(dev_ctx),
queue_(queue) {
service_->RequestGetVariable(&ctx_, &request_, &responder_, cq_, cq_, this);
int method_id = static_cast<int>(detail::GrpcMethod::kGetVariable);
service_->RequestAsyncUnary(method_id, &ctx_, &request_, &responder_, cq_,
cq_, this);
}
virtual ~RequestGet() {}
......@@ -101,24 +105,26 @@ class RequestGet final : public RequestBase {
// proc request.
std::string var_name = request_.varname();
auto* var = scope_->FindVar(var_name);
::grpc::ByteBuffer reply;
if (var_name != FETCH_BARRIER_MESSAGE) {
SerializeToMessage(var_name, var, *dev_ctx_, &reply_);
SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply);
}
// TODO(gongwb): check var's info.
responder_.Finish(reply_, grpc::Status::OK, this);
responder_.Finish(reply, ::grpc::Status::OK, this);
status_ = FINISH;
MessageWithName msg_with_name =
// request name reply
std::make_pair(var_name, std::move(reply_));
if (var_name == FETCH_BARRIER_MESSAGE) {
sendrecv::VariableMessage msg;
MessageWithName msg_with_name = std::make_pair(var_name, msg);
queue_->Push(msg_with_name);
}
}
protected:
sendrecv::VariableMessage request_;
sendrecv::VariableMessage reply_;
ServerAsyncResponseWriter<sendrecv::VariableMessage> responder_;
ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
framework::Scope* scope_;
const platform::DeviceContext* dev_ctx_;
SimpleBlockQueue<MessageWithName>* queue_;
};
......@@ -133,8 +139,8 @@ void AsyncGRPCServer::WaitClientGet(int count) {
}
void AsyncGRPCServer::RunSyncUpdate() {
grpc::ServerBuilder builder;
builder.AddListeningPort(address_, grpc::InsecureServerCredentials());
::grpc::ServerBuilder builder;
builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials());
builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
builder.RegisterService(&service_);
......@@ -182,8 +188,8 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() {
if (is_shut_down_) {
return;
}
RequestSend* send =
new RequestSend(&service_, cq_send_.get(), &var_recv_queue_);
RequestSend* send = new RequestSend(&service_, cq_send_.get(), scope_,
&var_recv_queue_, dev_ctx_);
VLOG(4) << "Create RequestSend status:" << send->Status();
}
......@@ -198,7 +204,7 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() {
}
// FIXME(typhoonzero): change cq_name to enum.
void AsyncGRPCServer::HandleRequest(grpc::ServerCompletionQueue* cq,
void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq,
std::string cq_name,
std::function<void()> TryToRegisterNewOne) {
TryToRegisterNewOne();
......
paddle/fluid/operators/detail/grpc_server.h
浏览文件 @ 79af7cc9
......@@ -14,28 +14,31 @@ limitations under the License. */
#pragma once
#include <grpc++/grpc++.h>
#include <thread>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/simple_block_queue.h"
#include "paddle/fluid/operators/detail/grpc_service.h"
#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include <grpc++/grpc++.h>
#include <grpc/support/log.h>
#include <thread>
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/detail/simple_block_queue.h"
namespace paddle {
namespace operators {
namespace detail {
typedef std::pair<std::string, std::shared_ptr<VariableResponse>>
ReceivedMessage;
typedef SimpleBlockQueue<ReceivedMessage> ReceivedQueue;
typedef std::pair<std::string, sendrecv::VariableMessage> MessageWithName;
class RequestBase;
class AsyncGRPCServer final : public sendrecv::SendRecvService::Service {
class AsyncGRPCServer final {
public:
explicit AsyncGRPCServer(const std::string &address) : address_(address) {}
......@@ -50,14 +53,16 @@ class AsyncGRPCServer final : public sendrecv::SendRecvService::Service {
void SetDevCtx(const platform::DeviceContext *dev_ctx) { dev_ctx_ = dev_ctx; }
const MessageWithName Get() { return this->var_recv_queue_.Pop(); }
const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); }
void Push(const MessageWithName &msg) { this->var_recv_queue_.Push(msg); }
void Push(const std::string &msg_name) {
this->var_recv_queue_.Push(std::make_pair(msg_name, nullptr));
}
void ShutDown();
protected:
void HandleRequest(grpc::ServerCompletionQueue *cq, std::string cq_name,
void HandleRequest(::grpc::ServerCompletionQueue *cq, std::string cq_name,
std::function<void()> TryToRegisterNewOne);
void TryToRegisterNewSendOne();
void TryToRegisterNewGetOne();
......@@ -66,18 +71,19 @@ class AsyncGRPCServer final : public sendrecv::SendRecvService::Service {
private:
std::mutex cq_mutex_;
volatile bool is_shut_down_ = false;
std::unique_ptr<grpc::ServerCompletionQueue> cq_send_;
std::unique_ptr<grpc::ServerCompletionQueue> cq_get_;
std::unique_ptr<::grpc::ServerCompletionQueue> cq_send_;
std::unique_ptr<::grpc::ServerCompletionQueue> cq_get_;
sendrecv::SendRecvService::AsyncService service_;
std::unique_ptr<grpc::Server> server_;
GrpcService::AsyncService service_;
std::unique_ptr<::grpc::Server> server_;
std::string address_;
framework::Scope *scope_;
const platform::DeviceContext *dev_ctx_;
// received variable from RPC, operators fetch variable from this queue.
SimpleBlockQueue<MessageWithName> var_recv_queue_;
SimpleBlockQueue<MessageWithName> var_get_queue_;
ReceivedQueue var_recv_queue_;
// condition of the sub program
std::mutex barrier_mutex_;
......
paddle/fluid/operators/detail/grpc_service.h 0 → 100644
浏览文件 @ 79af7cc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <grpc++/impl/codegen/async_stream.h>
#include <grpc++/impl/codegen/async_unary_call.h>
#include <grpc++/impl/codegen/proto_utils.h>
#include <grpc++/impl/codegen/rpc_method.h>
#include <grpc++/impl/codegen/service_type.h>
#include <grpc++/impl/codegen/status.h>
#include <grpc++/impl/codegen/stub_options.h>
#include <grpc++/impl/codegen/sync_stream.h>
#include <grpc++/support/byte_buffer.h>
#include "paddle/fluid/operators/detail/variable_response.h"
// NOTE: This method was originally created by tensorflow
// (https://github.com/tensorflow/tensorflow/) we borrow this
// method and did some modifications so that we can parse gRPC
// requests without too much copying of the tensor data.
namespace grpc {
class CompletionQueue;
class Channel;
class RpcService;
class ServerCompletionQueue;
class ServerContext;
// Support parsing/unparsing of tensorflow::VariableResponse.
// Wire-format is identical to RecvVariableResponse.
template <>
class SerializationTraits<paddle::operators::detail::VariableResponse> {
public:
static Status Serialize(
const paddle::operators::detail::VariableResponse& msg,
grpc_byte_buffer** bp, bool* own_buffer) {
PADDLE_ENFORCE(false, "SerializationTraits::Serialize not implemented!");
return Status();
}
static Status Deserialize(grpc_byte_buffer* buffer,
paddle::operators::detail::VariableResponse* msg,
int max_message_size = INT_MAX) {
if (buffer == nullptr) {
return Status(StatusCode::INTERNAL, "No payload");
}
Status result = g_core_codegen_interface->ok();
if (result.ok()) {
paddle::operators::detail::GrpcByteSource source(buffer);
int ret = msg->Parse(&source);
if (ret != 0) {
result = Status(StatusCode::INTERNAL, "VariableResponse parse error");
}
}
g_core_codegen_interface->grpc_byte_buffer_destroy(buffer);
return result;
}
};
} // namespace grpc
namespace paddle {
namespace operators {
namespace detail {
enum class GrpcMethod {
kSendVariable,
kGetVariable,
};
static const int kGrpcNumMethods =
static_cast<int>(GrpcMethod::kGetVariable) + 1;
inline const char* GrpcMethodName(GrpcMethod id) {
switch (id) {
case GrpcMethod::kSendVariable:
return "/sendrecv.SendRecvService/SendVariable";
case GrpcMethod::kGetVariable:
return "/sendrecv.SendRecvService/GetVariable";
}
// Shouldn't be reached.
PADDLE_ENFORCE(false, "Invalid id: not found valid method name");
return nullptr;
}
class GrpcService final {
public:
class AsyncService : public ::grpc::Service {
public:
AsyncService() {
for (int i = 0; i < kGrpcNumMethods; ++i) {
AddMethod(new ::grpc::internal::RpcServiceMethod(
GrpcMethodName(static_cast<GrpcMethod>(i)),
::grpc::internal::RpcMethod::NORMAL_RPC, nullptr));
::grpc::Service::MarkMethodAsync(i);
}
}
virtual ~AsyncService() {}
// Make RequestAsyncUnary public for grpc_call.h
using ::grpc::Service::RequestAsyncUnary;
};
};
} // namespace detail
} // namespace operator
} // namespace paddle
paddle/fluid/operators/detail/send_recv.proto
浏览文件 @ 79af7cc9
......@@ -32,6 +32,9 @@ enum VarType {
SELECTED_ROWS = 1;
}
// NOTICE(gongwb):don't modify this proto if you are not
// not familar with how we serialize in sendrecvop_utils.h
// and deserilize it in variable_response.h.
message VariableMessage {
enum Type {
// Pod Types
......@@ -45,7 +48,6 @@ message VariableMessage {
}
message LodData { repeated int64 lod_data = 1; }
string varname = 1;
// TODO(Yancey1989): reference framework::proto::VarDesc::VarType
VarType type = 2;
......@@ -64,3 +66,5 @@ message VariableMessage {
}
message VoidMessage {}
message TestMessage { int64 test_1 = 1; }
paddle/fluid/operators/detail/sendrecvop_utils.cc
浏览文件 @ 79af7cc9
......@@ -13,61 +13,19 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include <sys/time.h>
#include <thread>
#include "google/protobuf/io/coded_stream.h"
#include "google/protobuf/io/zero_copy_stream.h"
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
#include "paddle/fluid/operators/detail/proto_encoder_helper.h"
#include "paddle/fluid/operators/detail/variable_response.h"
namespace paddle {
namespace operators {
namespace detail {
void SerializeToMessage(const std::string& name, const framework::Variable* var,
const platform::DeviceContext& ctx,
sendrecv::VariableMessage* msg) {
msg->set_varname(name);
std::ostringstream oss;
switch (framework::ToVarType(var->Type())) {
case framework::proto::VarType_Type_LOD_TENSOR:
msg->set_type(sendrecv::VarType::LOD_TENSOR);
framework::SerializeToStream(oss, var->Get<framework::LoDTensor>(), ctx);
break;
case framework::proto::VarType_Type_SELECTED_ROWS:
msg->set_type(sendrecv::VarType::SELECTED_ROWS);
framework::SerializeToStream(oss, var->Get<framework::SelectedRows>(),
ctx);
break;
default: {
PADDLE_THROW("Serialize does not support type: %s",
typeid(var->Type()).name());
break;
}
}
msg->set_serialized(oss.str());
}
void DeserializeFromMessage(const sendrecv::VariableMessage& msg,
const platform::DeviceContext& ctx,
framework::Variable* var) {
std::istringstream iss(msg.serialized());
switch (msg.type()) {
case sendrecv::VarType::LOD_TENSOR:
DeserializeFromStream(iss, var->GetMutable<framework::LoDTensor>(), ctx);
break;
case sendrecv::VarType::SELECTED_ROWS: {
DeserializeFromStream(iss, var->GetMutable<framework::SelectedRows>(),
ctx);
break;
}
default: {
PADDLE_THROW("Deserialize does not support type: %s",
typeid(var->Type()).name());
break;
}
}
}
void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
const platform::DeviceContext& ctx,
::grpc::ByteBuffer* msg) {
......@@ -123,6 +81,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
static_cast<const platform::CUDADeviceContext&>(ctx);
auto copy_size = tensor.memory_size();
payload = memory::Alloc(cpu, copy_size);
memory::Copy(cpu, payload,
boost::get<platform::CUDAPlace>(tensor.place()),
reinterpret_cast<const void*>(tensor.data<void>()),
......@@ -132,6 +91,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
platform::CPUPlace cpu;
memory::Free(cpu, backing);
};
#endif
} else {
payload = tensor.data<void>();
......@@ -219,80 +179,11 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
const platform::DeviceContext& ctx,
framework::Variable* var) {
sendrecv::VariableMessage meta;
GrpcByteBufferSource source;
source.Init(msg);
::google::protobuf::io::CodedInputStream input(&source);
// do zerocopy parsing
PADDLE_ENFORCE(meta.ParseFromCodedStream(&input));
PADDLE_ENFORCE(input.ConsumedEntireMessage());
// dims is needed by both tensor and selectedrows
std::vector<int> vecdims;
for (auto& d : meta.dims()) {
vecdims.push_back(d);
}
framework::DDim dims = framework::make_ddim(vecdims);
if (meta.type() == sendrecv::LOD_TENSOR) {
auto* tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(dims);
void* tensor_data = tensor->mutable_data(
ctx.GetPlace(),
paddle::operators::detail::ToTypeIndex(meta.data_type()));
framework::LoD lod;
for (int i = 0; i < meta.lod_level(); ++i) {
framework::Vector<size_t> v;
for (int j = 0; j < meta.lod(i).lod_data_size(); ++j) {
v.push_back(meta.lod(i).lod_data(j));
}
lod.push_back(v);
}
tensor->set_lod(lod);
// How to avoid copying and use the message buffer directly?
// Maybe need to find a way to release all memory except tensor content.
if (platform::is_gpu_place(ctx.GetPlace())) {
#ifdef PADDLE_WITH_CUDA
platform::CPUPlace cpu;
auto& gpu_dev_ctx = static_cast<const platform::CUDADeviceContext&>(ctx);
memory::Copy(boost::get<platform::CUDAPlace>(tensor->place()),
tensor_data, cpu,
reinterpret_cast<const void*>(meta.serialized().data()),
meta.serialized().size(), gpu_dev_ctx.stream());
ctx.Wait();
#endif
} else {
memcpy(tensor_data,
reinterpret_cast<const void*>(meta.serialized().data()),
meta.serialized().size());
}
} else if (meta.type() == sendrecv::SELECTED_ROWS) {
auto* slr = var->GetMutable<framework::SelectedRows>();
auto* tensor = slr->mutable_value();
int64_t* rows_data = slr->mutable_rows()->data();
tensor->Resize(dims);
void* tensor_data = tensor->mutable_data(
ctx.GetPlace(),
paddle::operators::detail::ToTypeIndex(meta.data_type()));
if (platform::is_gpu_place(ctx.GetPlace())) {
#ifdef PADDLE_WITH_CUDA
platform::CPUPlace cpu;
auto& gpu_dev_ctx = static_cast<const platform::CUDADeviceContext&>(ctx);
memory::Copy(boost::get<platform::CUDAPlace>(tensor->place()),
tensor_data, cpu,
reinterpret_cast<const void*>(meta.serialized().data()),
meta.serialized().size(), gpu_dev_ctx.stream());
ctx.Wait();
#endif
} else {
memcpy(tensor_data,
reinterpret_cast<const void*>(meta.serialized().data()),
meta.serialized().size());
}
// copy rows CPU data, GPU data will be copied lazly
memcpy(rows_data, reinterpret_cast<const void*>(meta.rows().data()),
meta.rows().size());
}
const framework::Scope* scope,
framework::Variable*& var) {
operators::detail::VariableResponse resp(scope, &ctx);
PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
var = resp.GetVar();
}
} // namespace detail
......
paddle/fluid/operators/detail/sendrecvop_utils.h
浏览文件 @ 79af7cc9
......@@ -21,6 +21,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
......@@ -36,21 +37,14 @@ namespace detail {
typedef void (*DestroyCallback)(void*);
void SerializeToMessage(const std::string& name, const framework::Variable* var,
const platform::DeviceContext& ctx,
sendrecv::VariableMessage* msg);
void DeserializeFromMessage(const sendrecv::VariableMessage& msg,
const platform::DeviceContext& ctx,
framework::Variable* var);
void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
const platform::DeviceContext& ctx,
::grpc::ByteBuffer* msg);
void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
const platform::DeviceContext& ctx,
framework::Variable* var);
const framework::Scope* scope,
framework::Variable*& var);
inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) {
switch (type) {
......
paddle/fluid/operators/detail/test_serde.cc
浏览文件 @ 79af7cc9
......@@ -16,11 +16,13 @@ limitations under the License. */
#include <string>
#include <thread>
#include <google/protobuf/text_format.h>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
#include "paddle/fluid/operators/detail/variable_response.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/printf.h"
......@@ -31,19 +33,21 @@ namespace operators = paddle::operators;
namespace math = paddle::operators::math;
namespace memory = paddle::memory;
void RunSerdeTestTensor(platform::Place place) {
// serialize var to ByteBuffer
framework::Variable var;
auto* tensor = var.GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim({4, 8, 4, 2}));
framework::LoD lod;
lod.push_back(framework::Vector<size_t>({1, 3, 8}));
tensor->set_lod(lod);
int tensor_numel = 4 * 8 * 4 * 2;
void RunSerdeTestSelectedRows(platform::Place place) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// serialize var to ByteBuffer
framework::Variable var;
auto* slr = var.GetMutable<framework::SelectedRows>();
auto* tensor = slr->mutable_value();
auto* rows = slr->mutable_rows();
tensor->Resize(framework::make_ddim({2, 10}));
tensor->mutable_data<float>(place);
math::set_constant(ctx, tensor, 31.9);
int tensor_numel = 2 * 10;
math::set_constant(ctx, tensor, 32.7);
rows->push_back(3);
rows->push_back(10);
::grpc::ByteBuffer msg;
operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg);
......@@ -56,62 +60,67 @@ void RunSerdeTestTensor(platform::Place place) {
for (const auto& s : slices) {
tmp.append(reinterpret_cast<const char*>(s.begin()), s.size());
}
sendrecv::VariableMessage varmsg;
EXPECT_TRUE(varmsg.ParseFromString(tmp));
EXPECT_EQ(varmsg.varname(), "myvar");
EXPECT_EQ(varmsg.type(), 0);
EXPECT_EQ(varmsg.dims()[0], 4);
EXPECT_EQ(varmsg.dims()[1], 8);
EXPECT_EQ(varmsg.dims()[2], 4);
EXPECT_EQ(varmsg.dims()[3], 2);
EXPECT_EQ(varmsg.lod_level(), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(0), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(1), 3);
EXPECT_EQ(varmsg.lod(0).lod_data(2), 8);
EXPECT_EQ(varmsg.type(), 1);
const float* tensor_data =
reinterpret_cast<const float*>(varmsg.serialized().data());
const int64_t* rows_data =
reinterpret_cast<const int64_t*>(varmsg.rows().data());
for (int i = 0; i < tensor_numel; ++i) {
EXPECT_FLOAT_EQ(tensor_data[i], 31.9);
EXPECT_FLOAT_EQ(tensor_data[i], 32.7);
}
EXPECT_EQ(rows_data[0], 3);
EXPECT_EQ(rows_data[1], 10);
// deserialize zero-copy
framework::Variable var2;
operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2);
auto tensor2 = var2.Get<framework::LoDTensor>();
// framework::Variable var2;
// operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2);
framework::Scope scope;
scope.Var("myvar");
operators::detail::VariableResponse resp(&scope, &ctx);
EXPECT_EQ(resp.Parse(msg), 0);
framework::Variable* var2 = resp.GetVar();
auto* slr2 = var2->GetMutable<framework::SelectedRows>();
auto* tensor2 = slr2->mutable_value();
auto* rows2 = slr2->mutable_rows();
float* tensor_data2 = nullptr;
framework::Tensor tmp_tensor;
if (platform::is_gpu_place(ctx.GetPlace())) {
platform::CPUPlace cpu;
framework::TensorCopy(tensor2, cpu, &tmp_tensor);
framework::TensorCopy(*tensor2, cpu, &tmp_tensor);
tensor_data2 = tmp_tensor.data<float>();
} else {
tensor_data2 = const_cast<float*>(tensor2.data<float>());
tensor_data2 = const_cast<float*>(tensor2->data<float>());
}
const int64_t* rows_data2 = rows2->data();
EXPECT_EQ(varmsg.lod_level(), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(0), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(1), 3);
EXPECT_EQ(varmsg.lod(0).lod_data(2), 8);
for (int i = 0; i < tensor_numel; ++i) EXPECT_FLOAT_EQ(tensor_data2[i], 31.9);
for (int i = 0; i < tensor_numel; ++i) {
EXPECT_FLOAT_EQ(tensor_data2[i], 32.7);
}
EXPECT_EQ(rows_data2[0], 3);
EXPECT_EQ(rows_data2[1], 10);
}
void RunSerdeTestSelectedRows(platform::Place place) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
void RunTestLodTensor(platform::Place place, int from_type = 0) {
// serialize var to ByteBuffer
framework::Variable var;
auto* slr = var.GetMutable<framework::SelectedRows>();
auto* tensor = slr->mutable_value();
auto* rows = slr->mutable_rows();
tensor->Resize(framework::make_ddim({2, 10}));
auto* tensor = var.GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim({4, 8, 4, 2}));
framework::LoD lod;
lod.push_back(framework::Vector<size_t>({1, 3, 8}));
tensor->set_lod(lod);
int tensor_numel = 4 * 8 * 4 * 2;
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
tensor->mutable_data<float>(place);
int tensor_numel = 2 * 10;
math::set_constant(ctx, tensor, 32.7);
rows->push_back(3);
rows->push_back(10);
math::set_constant(ctx, tensor, 31.9);
::grpc::ByteBuffer msg;
operators::detail::SerializeToByteBuffer("myvar", &var, ctx, &msg);
......@@ -126,61 +135,82 @@ void RunSerdeTestSelectedRows(platform::Place place) {
}
sendrecv::VariableMessage varmsg;
EXPECT_TRUE(varmsg.ParseFromString(tmp));
EXPECT_EQ(varmsg.varname(), "myvar");
EXPECT_EQ(varmsg.type(), 1);
EXPECT_EQ(varmsg.type(), 0);
EXPECT_EQ(varmsg.dims()[0], 4);
EXPECT_EQ(varmsg.dims()[1], 8);
EXPECT_EQ(varmsg.dims()[2], 4);
EXPECT_EQ(varmsg.dims()[3], 2);
EXPECT_EQ(varmsg.lod_level(), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(0), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(1), 3);
EXPECT_EQ(varmsg.lod(0).lod_data(2), 8);
const float* tensor_data =
reinterpret_cast<const float*>(varmsg.serialized().data());
const int64_t* rows_data =
reinterpret_cast<const int64_t*>(varmsg.rows().data());
for (int i = 0; i < tensor_numel; ++i) {
EXPECT_FLOAT_EQ(tensor_data[i], 32.7);
EXPECT_FLOAT_EQ(tensor_data[i], 31.9);
}
EXPECT_EQ(rows_data[0], 3);
EXPECT_EQ(rows_data[1], 10);
// message binary
std::string str;
varmsg.SerializeToString(&str);
// message bytebuffer
::grpc::Slice slices_2[1];
int num_slices = 1;
slices_2[0] = ::grpc::Slice(str.length());
memcpy(const_cast<uint8_t*>(slices_2[0].begin()), str.c_str(), str.length());
::grpc::ByteBuffer bytebuffer2(&slices_2[0], num_slices);
// deserialize zero-copy
framework::Variable var2;
operators::detail::DeserializeFromByteBuffer(msg, ctx, &var2);
framework::Scope scope;
scope.Var("myvar");
operators::detail::VariableResponse resp(&scope, &ctx);
if (from_type == 0) {
EXPECT_EQ(resp.Parse(msg), 0);
} else {
EXPECT_EQ(resp.Parse(bytebuffer2), 0);
}
auto* slr2 = var2.GetMutable<framework::SelectedRows>();
auto* tensor2 = slr2->mutable_value();
auto* rows2 = slr2->mutable_rows();
framework::Variable* var2 = resp.GetVar();
auto tensor2 = var2->Get<framework::LoDTensor>();
float* tensor_data2 = nullptr;
framework::Tensor tmp_tensor;
if (platform::is_gpu_place(ctx.GetPlace())) {
platform::CPUPlace cpu;
framework::TensorCopy(*tensor2, cpu, &tmp_tensor);
framework::TensorCopy(tensor2, cpu, &tmp_tensor);
tensor_data2 = tmp_tensor.data<float>();
} else {
tensor_data2 = const_cast<float*>(tensor2->data<float>());
tensor_data2 = const_cast<float*>(tensor2.data<float>());
}
const int64_t* rows_data2 = rows2->data();
for (int i = 0; i < tensor_numel; ++i) {
EXPECT_FLOAT_EQ(tensor_data2[i], 32.7);
}
EXPECT_EQ(rows_data2[0], 3);
EXPECT_EQ(rows_data2[1], 10);
EXPECT_EQ(varmsg.lod_level(), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(0), 1);
EXPECT_EQ(varmsg.lod(0).lod_data(1), 3);
EXPECT_EQ(varmsg.lod(0).lod_data(2), 8);
for (int i = 0; i < tensor_numel; ++i) EXPECT_FLOAT_EQ(tensor_data2[i], 31.9);
}
TEST(SelectedRows, CPU) {
TEST(LodTensor, Run) {
platform::CPUPlace place;
RunSerdeTestSelectedRows(place);
RunTestLodTensor(place);
RunTestLodTensor(place, 1);
#ifdef PADDLE_WITH_CUDA
platform::CUDAPlace gpu(0);
RunTestLodTensor(gpu);
RunTestLodTensor(gpu, 1);
#endif
}
TEST(SelectedRows, GPU) {
platform::CUDAPlace place;
RunSerdeTestSelectedRows(place);
}
TEST(Tensor, CPU) {
TEST(SelectedRows, Run) {
platform::CPUPlace place;
RunSerdeTestTensor(place);
}
RunSerdeTestSelectedRows(place);
TEST(Tensor, GPU) {
platform::CUDAPlace place;
RunSerdeTestTensor(place);
#ifdef PADDLE_WITH_CUDA
platform::CUDAPlace gpu;
RunSerdeTestSelectedRows(gpu);
#endif
}
paddle/fluid/operators/detail/variable_response.cc 0 → 100644
浏览文件 @ 79af7cc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/detail/variable_response.h"
#include <string.h>
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "paddle/fluid/operators/detail/sendrecvop_utils.h"
namespace paddle {
namespace operators {
namespace detail {
enum WireType {
WIRETYPE_VARINT = 0,
WIRETYPE_LENGTH_DELIMITED = 2,
};
inline int GetTagFieldNumber(uint32_t tag) { return tag >> 3; }
inline WireType GetTagWireType(uint32_t tag) {
return static_cast<WireType>(tag & 0x7);
}
bool ReadVarintSizeAsInt(::google::protobuf::io::CodedInputStream* input,
int* result) {
uint64_t v;
if (input->ReadVarint64(&v) && v <= static_cast<uint64_t>(INT_MAX)) {
*result = static_cast<int>(v);
return true;
} else {
return false;
}
}
bool ReadRaw(::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& dev_ctx, platform::Place place,
void* dest, int size) {
const void* data = NULL;
int size_to_write = 0;
if (platform::is_gpu_place(place)) {
#ifdef PADDLE_WITH_CUDA
auto& gpu_dev_ctx =
static_cast<const platform::CUDADeviceContext&>(dev_ctx);
platform::CPUPlace cpu;
char* p = reinterpret_cast<char*>(dest);
while (size > 0) {
if (!input->GetDirectBufferPointer(&data, &size_to_write)) {
return false;
}
memory::Copy(boost::get<platform::CUDAPlace>(place),
reinterpret_cast<void*>(p), cpu, data, size_to_write,
gpu_dev_ctx.stream());
p += size_to_write;
size -= size_to_write;
input->Skip(size_to_write);
}
gpu_dev_ctx.Wait();
#else
PADDLE_THROW("Unexpected branch");
#endif
return true;
}
char* p = reinterpret_cast<char*>(dest);
while (size > 0) {
if (!input->GetDirectBufferPointer(&data, &size_to_write)) {
return false;
}
// TODO(gongwb): can we avoid copy?
platform::CPUPlace cpu;
memory::Copy(cpu, reinterpret_cast<void*>(p), cpu, data, size_to_write);
p += size_to_write;
size -= size_to_write;
input->Skip(size_to_write);
}
return true;
}
bool VariableResponse::CopyLodTensorData(
::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, framework::DDim& dims, int length) {
auto var = scope_->FindVar(meta_.varname());
auto* tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(dims);
framework::LoD lod;
for (int i = 0; i < meta_.lod_level(); ++i) {
framework::Vector<size_t> v;
for (int j = 0; j < meta_.lod(i).lod_data_size(); ++j) {
v.push_back(meta_.lod(i).lod_data(j));
}
lod.push_back(v);
}
tensor->set_lod(lod);
void* tensor_data =
tensor->mutable_data(ctx.GetPlace(), ToTypeIndex(meta_.data_type()));
if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
return false;
}
return true;
}
inline framework::DDim GetDims(
const ::google::protobuf::RepeatedField<::google::protobuf::int64>& dims) {
std::vector<int> vecdims;
for (auto& d : dims) {
vecdims.push_back(d);
}
return framework::make_ddim(vecdims);
}
bool VariableResponse::CopySelectRowsTensorData(
::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, framework::DDim& dims, int length) {
auto var = scope_->FindVar(meta_.varname());
auto* slr = var->GetMutable<framework::SelectedRows>();
auto* tensor = slr->mutable_value();
tensor->Resize(dims);
void* tensor_data = tensor->mutable_data(
ctx.GetPlace(),
paddle::operators::detail::ToTypeIndex(meta_.data_type()));
if (!ReadRaw(input, ctx, tensor->place(), tensor_data, length)) {
return false;
}
return true;
}
bool VariableResponse::CopySelectRowsData(
::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, int length) {
auto var = scope_->FindVar(meta_.varname());
auto* slr = var->GetMutable<framework::SelectedRows>();
int64_t* rows_data = slr->mutable_rows()->data();
// copy rows CPU data, GPU data will be copied lazily.
platform::CPUPlace cpu;
if (!ReadRaw(input, ctx, cpu, rows_data, length)) {
return false;
}
return true;
}
bool ParseLodData(::google::protobuf::io::CodedInputStream* input,
std::vector<int64_t>* lod) {
while (true) {
auto p = input->ReadTagWithCutoff(127);
int tag = GetTagFieldNumber(p.first);
WireType wt = GetTagWireType(p.first);
if (!p.second) {
return (tag == 0);
}
switch (tag) {
case sendrecv::VariableMessage_LodData::kLodDataFieldNumber: {
uint64_t v;
if (wt == WIRETYPE_VARINT) {
if (!input->ReadVarint64(&v)) {
return false;
}
lod->push_back(v);
break;
}
if (wt == WIRETYPE_LENGTH_DELIMITED) {
int length = 0;
if (!input->ReadVarintSizeAsInt(&length)) {
return tag;
}
for (int i = 0; i < length; i++) {
uint64_t v;
if (!input->ReadVarint64(&v)) {
return false;
}
lod->push_back(v);
}
break;
}
return false;
}
default: { return false; }
}
}
return true;
}
int VariableResponse::Parse(const ::grpc::ByteBuffer& byte_buffer) {
GrpcByteBufferSource source;
source.Init(byte_buffer);
GrpcByteBufferSourceWrapper r(&source);
return Parse(&r);
}
int VariableResponse::Parse(Source* source) {
::google::protobuf::io::ZeroCopyInputStream* input_stream =
source->contents();
::google::protobuf::io::CodedInputStream input(input_stream);
input.SetTotalBytesLimit(INT_MAX, INT_MAX);
while (true) {
auto p = input.ReadTagWithCutoff(127);
int tag = GetTagFieldNumber(p.first);
WireType wt = GetTagWireType(p.first);
if (!p.second) {
if (tag != 0) {
return -1;
}
return 0;
}
switch (tag) {
case sendrecv::VariableMessage::kVarnameFieldNumber: {
uint32_t length;
if ((wt != WIRETYPE_LENGTH_DELIMITED) || !input.ReadVarint32(&length)) {
return tag;
}
std::string temp;
if (!input.ReadString(&temp, length)) {
return tag;
}
meta_.set_varname(temp);
break;
}
case sendrecv::VariableMessage::kTypeFieldNumber: {
uint64_t v;
if ((wt != WIRETYPE_VARINT) || !input.ReadVarint64(&v)) {
return tag;
}
meta_.set_type(static_cast<::sendrecv::VarType>(v));
break;
}
case sendrecv::VariableMessage::kDataTypeFieldNumber: {
uint64_t v = 0;
if ((wt != WIRETYPE_VARINT) || !input.ReadVarint64(&v)) {
return tag;
}
meta_.set_data_type(static_cast<::sendrecv::VariableMessage_Type>(v));
break;
}
case sendrecv::VariableMessage::kDimsFieldNumber: {
// not packed
if (wt == WIRETYPE_VARINT) {
uint64_t v;
if (!input.ReadVarint64(&v)) {
return tag;
}
meta_.add_dims(v);
break;
}
// packed
if (wt == WIRETYPE_LENGTH_DELIMITED) {
int length = 0;
if (!input.ReadVarintSizeAsInt(&length)) {
return tag;
}
for (int i = 0; i < length; i++) {
uint64_t v;
if (!input.ReadVarint64(&v)) {
return tag;
}
meta_.add_dims(v);
}
break;
}
return tag;
}
case sendrecv::VariableMessage::kLodLevelFieldNumber: {
uint64_t v = 0;
if ((wt != WIRETYPE_VARINT) || !input.ReadVarint64(&v)) {
return tag;
}
meta_.set_lod_level(static_cast<int64_t>(v));
break;
}
case sendrecv::VariableMessage::kLodFieldNumber: {
int length = 0;
if (wt != WIRETYPE_LENGTH_DELIMITED ||
!ReadVarintSizeAsInt(&input, &length)) {
return tag;
}
std::pair<::google::protobuf::io::CodedInputStream::Limit, int> p =
input.IncrementRecursionDepthAndPushLimit(length);
std::vector<int64_t> lod_data;
if (p.second < 0 || !ParseLodData(&input, &lod_data)) {
return tag;
}
if (!input.DecrementRecursionDepthAndPopLimit(p.first)) {
return false;
}
if (lod_data.size() == 0) {
break;
}
auto lod = meta_.add_lod();
for (uint32_t i = 0; i < lod_data.size(); i++) {
lod->add_lod_data(lod_data[i]);
}
break;
}
case sendrecv::VariableMessage::kSerializedFieldNumber: {
PADDLE_ENFORCE((meta_.type() == sendrecv::SELECTED_ROWS ||
meta_.type() == sendrecv::LOD_TENSOR) &&
meta_.varname() != "",
"meta info should be got first!");
int length = 0;
if (wt != WIRETYPE_LENGTH_DELIMITED ||
!ReadVarintSizeAsInt(&input, &length)) {
return tag;
}
framework::DDim dims = GetDims(meta_.dims());
if (meta_.type() == sendrecv::LOD_TENSOR) {
PADDLE_ENFORCE(meta_.lod_size() >= 0,
"lod info should be got first!");
if (!CopyLodTensorData(&input, *dev_ctx_, dims, length)) {
return tag;
}
break;
}
if (meta_.type() == sendrecv::SELECTED_ROWS) {
if (!CopySelectRowsTensorData(&input, *dev_ctx_, dims, length)) {
return tag;
}
break;
}
return tag;
}
case sendrecv::VariableMessage::kRowsFieldNumber: {
PADDLE_ENFORCE((meta_.type() == sendrecv::SELECTED_ROWS ||
meta_.type() == sendrecv::LOD_TENSOR) &&
meta_.varname() != "",
"meta info should be got first!");
int length = 0;
if (wt != WIRETYPE_LENGTH_DELIMITED ||
!ReadVarintSizeAsInt(&input, &length)) {
return tag;
}
if (!CopySelectRowsData(&input, *dev_ctx_, length)) {
return tag;
}
break;
}
default: {
// Unknown tag, return unknown error.
return -1;
}
}
}
return 0;
}
}; // namespace detail
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detail/variable_response.h 0 → 100644
浏览文件 @ 79af7cc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/send_recv.grpc.pb.h"
#include "paddle/fluid/operators/detail/send_recv.pb.h"
#include "google/protobuf/io/coded_stream.h"
#include "google/protobuf/io/zero_copy_stream.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/detail/bytebuffer_stream.h"
namespace paddle {
namespace operators {
namespace detail {
class VariableResponse {
public:
VariableResponse(const framework::Scope* scope,
const platform::DeviceContext* dev_ctx)
: scope_(scope), dev_ctx_(dev_ctx) {}
virtual ~VariableResponse() {}
// return:
// 0:ok.
// -1: unkown error.
// other: number of error field.
int Parse(Source* source);
// return:
// 0:ok.
// -1: unkown error.
// other: number of error field.
int Parse(const ::grpc::ByteBuffer& byte_buffer);
inline std::string Varname() { return meta_.varname(); }
// should call parse first.
framework::Variable* GetVar() { return scope_->FindVar(meta_.varname()); }
private:
bool CopySelectRowsTensorData(::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx,
framework::DDim& dims, int length);
bool CopySelectRowsData(::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx, int length);
bool CopyLodTensorData(::google::protobuf::io::CodedInputStream* input,
const platform::DeviceContext& ctx,
framework::DDim& dims, int length);
private:
const framework::Scope* scope_;
const platform::DeviceContext* dev_ctx_;
// only Skeleton
sendrecv::VariableMessage meta_;
};
}; // namespace detail
}; // namespace operators
}; // namespace paddle
paddle/fluid/operators/detection_map_op.cc
浏览文件 @ 79af7cc9
......@@ -188,8 +188,8 @@ The general steps are as follows. First, calculate the true positive and
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(detection_map, ops::DetectionMAPOp,
ops::DetectionMAPOpMaker);
REGISTER_OPERATOR(detection_map, ops::DetectionMAPOp, ops::DetectionMAPOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
detection_map, ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, float>,
ops::DetectionMAPOpKernel<paddle::platform::CPUPlace, double>);
paddle/fluid/operators/elementwise_add_op.cu
浏览文件 @ 79af7cc9
......@@ -14,19 +14,20 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/fluid/operators/elementwise_add_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_add,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseAddKernel<paddle::platform::CUDADeviceContext, int64_t>);
elementwise_add, ops::ElementwiseAddKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseAddKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseAddKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseAddKernel<plat::CUDADeviceContext, int64_t>,
ops::ElementwiseAddKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
elementwise_add_grad,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseAddGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseAddGradKernel<plat::CUDADeviceContext, int64_t>);
paddle/fluid/operators/iou_similarity_op.cc
浏览文件 @ 79af7cc9
......@@ -87,8 +87,9 @@ $$
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(iou_similarity, ops::IOUSimilarityOp,
ops::IOUSimilarityOpMaker);
REGISTER_OPERATOR(iou_similarity, ops::IOUSimilarityOp,
ops::IOUSimilarityOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
iou_similarity,
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 79af7cc9
......@@ -69,9 +69,7 @@ class ListenAndServOp : public framework::OperatorBase {
}
void Stop() override {
detail::MessageWithName term_msg;
term_msg.first = LISTEN_TERMINATE_MESSAGE;
rpc_service_->Push(term_msg);
rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
rpc_service_->ShutDown();
server_thread_->join();
}
......@@ -108,7 +106,7 @@ class ListenAndServOp : public framework::OperatorBase {
size_t recv_var_cnt = 0;
int batch_barrier = 0;
while (batch_barrier != fan_in) {
const detail::MessageWithName &v = rpc_service_->Get();
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
......@@ -121,12 +119,11 @@ class ListenAndServOp : public framework::OperatorBase {
} else {
VLOG(3) << "received grad: " << recv_var_name;
recv_var_cnt++;
auto *var = recv_scope.FindVar(recv_var_name);
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
detail::DeserializeFromMessage(v.second, dev_ctx, var);
if (var->IsType<framework::SelectedRows>()) {
sparse_vars.push_back(var);
}
......@@ -142,15 +139,15 @@ class ListenAndServOp : public framework::OperatorBase {
// should be global ops.
// NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads
// and this will still work.
std::vector<std::future<void>> fs;
// block0 contains only listen_and_serv op, start run from block1.
for (int blkid = 1; blkid < num_blocks - 1; ++blkid) {
fs.push_back(framework::Async([&executor, &program, &recv_scope,
blkid]() {
fs.push_back(
framework::Async([&executor, &program, &recv_scope, blkid]() {
int run_block = blkid; // thread local
try {
executor.Run(*program, &recv_scope, run_block,
false /*create_local_scope*/, false /*create_vars*/);
executor.Run(*program, &recv_scope, run_block, false, false);
} catch (std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
......@@ -160,8 +157,7 @@ class ListenAndServOp : public framework::OperatorBase {
// Run global block at final step, or block1 if there are only 2 blocks
if (num_blocks >= 2) {
try {
executor.Run(*program, &recv_scope, num_blocks - 1,
false /*create_local_scope*/, false /*create_vars*/);
executor.Run(*program, &recv_scope, num_blocks - 1, false, false);
} catch (std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
}
......@@ -180,6 +176,10 @@ class ListenAndServOp : public framework::OperatorBase {
rpc_service_->WaitClientGet(fan_in);
sparse_vars.clear();
} // while(true)
// for (int i = 0; i < num_blocks; ++i) {
// delete blk_ctx_list[i];
// }
}
protected:
......
paddle/fluid/operators/lrn_mkldnn_op.cc 0 → 100644
浏览文件 @ 79af7cc9
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/lrn_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
namespace {
template <typename T, typename... Args>
std::shared_ptr<T> insert_to_context(const std::string& key,
const MKLDNNDeviceContext& dev_ctx,
Args&&... args) {
auto p = std::static_pointer_cast<T, void>(dev_ctx.GetBlob(key));
if (!p) {
p = std::make_shared<T>(args...);
dev_ctx.SetBlob(key, std::static_pointer_cast<void, T>(p));
}
return p;
}
} // namespace
template <typename T>
class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(std::is_same<T, float>::value,
"MKLDNN LRN must use float data.");
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"MKLDNN LRN must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto x = ctx.Input<Tensor>("X");
auto out = ctx.Output<Tensor>("Out");
auto mid = ctx.Output<Tensor>("MidOut");
auto input_data = x->data<T>();
auto output_data = out->mutable_data<T>(ctx.GetPlace());
mid->mutable_data<T>(ctx.GetPlace());
const int n = ctx.Attr<int>("n");
const float alpha = ctx.Attr<float>("alpha");
const float beta = ctx.Attr<float>("beta");
const float k = ctx.Attr<float>("k");
const bool is_test = ctx.Attr<bool>("is_test");
auto e_mid = framework::EigenTensor<T, 4>::From(*mid);
e_mid = e_mid.constant(k);
auto dims = paddle::framework::vectorize2int(x->dims());
auto src_md = paddle::platform::MKLDNNMemDesc(
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto dst_md = paddle::platform::MKLDNNMemDesc(
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto forward_desc = mkldnn::lrn_forward::desc{mkldnn::prop_kind::forward,
mkldnn::lrn_across_channels,
src_md,
n,
alpha,
beta,
k};
auto src_memory_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine};
auto dst_memory = mkldnn::memory{{dst_md, mkldnn_engine},
static_cast<void*>(output_data)};
std::unique_ptr<mkldnn::lrn_forward> forward_op = nullptr;
if (!is_test) {
const std::string key = ctx.op().Output("Out");
const std::string key_src_memory = key + "@lrn_src_memory";
const std::string key_pd = key + "@lrn_pd";
const std::string key_workspace_memory = key + "@lrn_workspace_memory";
auto forward_pd = insert_to_context<mkldnn::lrn_forward::primitive_desc>(
key_pd, dev_ctx, forward_desc, mkldnn_engine);
auto src_memory = insert_to_context<mkldnn::memory>(
key_src_memory, dev_ctx, src_memory_pd);
src_memory->set_data_handle(
static_cast<void*>(const_cast<T*>(input_data)));
auto workspace_memory = insert_to_context<mkldnn::memory>(
key_workspace_memory, dev_ctx,
forward_pd->workspace_primitive_desc());
forward_op.reset(new mkldnn::lrn_forward{*forward_pd, *src_memory,
*workspace_memory, dst_memory});
} else {
auto forward_pd =
mkldnn::lrn_forward::primitive_desc{forward_desc, mkldnn_engine};
auto src_memory = mkldnn::memory{
src_memory_pd, static_cast<void*>(const_cast<T*>(input_data))};
auto workspace_memory =
mkldnn::memory{forward_pd.workspace_primitive_desc()};
forward_op.reset(new mkldnn::lrn_forward{forward_pd, src_memory,
workspace_memory, dst_memory});
}
std::vector<mkldnn::primitive> pipeline = {*forward_op};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
};
template <typename T>
class LRNMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(std::is_same<T, float>::value,
"MKLDNN LRN must use float data.");
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"MKLDNN LRN must use CPUPlace.");
auto x = ctx.Input<Tensor>("X");
auto out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
const std::string key = ctx.op().Input("Out");
const std::string key_src_memory = key + "@lrn_src_memory";
const std::string key_pd = key + "@lrn_pd";
const std::string key_workspace_memory = key + "@lrn_workspace_memory";
const int n = ctx.Attr<int>("n");
const float alpha = ctx.Attr<float>("alpha");
const float beta = ctx.Attr<float>("beta");
const float k = ctx.Attr<float>("k");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
auto x_grad_data = x_grad->mutable_data<T>(ctx.GetPlace());
auto out_grad_data = out_grad->data<T>();
auto dims = paddle::framework::vectorize2int(x->dims());
auto src_md = paddle::platform::MKLDNNMemDesc(
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto diff_src_md = paddle::platform::MKLDNNMemDesc(
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto diff_dst_md = paddle::platform::MKLDNNMemDesc(
dims, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw);
auto diff_dst_memory =
mkldnn::memory{{diff_dst_md, mkldnn_engine},
static_cast<void*>(const_cast<float*>(out_grad_data))};
auto diff_src_memory = mkldnn::memory{{diff_src_md, mkldnn_engine},
static_cast<void*>(x_grad_data)};
auto backward_desc = mkldnn::lrn_backward::desc{
mkldnn::lrn_across_channels, src_md, diff_src_md, n, alpha, beta, k};
auto forward_pd = dev_ctx.GetBlob(key_pd);
auto backward_pd = mkldnn::lrn_backward::primitive_desc{
backward_desc, mkldnn_engine,
*static_cast<mkldnn::lrn_forward::primitive_desc*>(forward_pd.get())};
std::shared_ptr<void> workspace_memory =
dev_ctx.GetBlob(key_workspace_memory);
auto src_memory = dev_ctx.GetBlob(key_src_memory);
auto backward_op = mkldnn::lrn_backward{
backward_pd, *static_cast<mkldnn::memory*>(src_memory.get()),
diff_dst_memory, *static_cast<mkldnn::memory*>(workspace_memory.get()),
diff_src_memory};
std::vector<mkldnn::primitive> pipeline = {backward_op};
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(lrn, MKLDNN, paddle::platform::CPUPlace,
ops::LRNMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(lrn_grad, MKLDNN, paddle::platform::CPUPlace,
ops::LRNMKLDNNGradOpKernel<float>);
paddle/fluid/operators/lrn_op.cc
浏览文件 @ 79af7cc9
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/lrn_op.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
......@@ -116,6 +119,26 @@ struct LRNGradFunctor<platform::CPUDeviceContext, T> {
template struct LRNGradFunctor<platform::CPUDeviceContext, float>;
template struct LRNGradFunctor<platform::CPUDeviceContext, double>;
namespace {
framework::OpKernelType GetExpectedLRNKernel(
const framework::ExecutionContext& ctx) {
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
layout_, library_);
}
} // namespace
class LRNOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -135,6 +158,11 @@ class LRNOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("MidOut", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetExpectedLRNKernel(ctx);
}
};
template <typename T>
......@@ -176,6 +204,17 @@ class LRNOpMaker : public framework::OpProtoAndCheckerMaker {
"beta is the power number.")
.SetDefault(0.75)
.GreaterThan(0.0);
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
AddAttr<bool>("is_test", "").SetDefault(false);
AddComment(R"DOC(
Local Response Normalization Operator.
......@@ -223,8 +262,12 @@ class LRNOpGrad : public framework::OperatorWithKernel {
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return GetExpectedLRNKernel(ctx);
}
};
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 79af7cc9
......@@ -6,6 +6,7 @@ function(math_library TARGET)
# But it handle split GPU/CPU code and link some common library.
set(cc_srcs)
set(cu_srcs)
set(hip_srcs)
set(math_common_deps device_context framework_proto)
set(multiValueArgs DEPS)
cmake_parse_arguments(math_library "${options}" "${oneValueArgs}"
......@@ -17,10 +18,15 @@ function(math_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu)
list(APPEND cu_srcs ${TARGET}.cu)
endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.hip.cu)
list(APPEND hip_srcs ${TARGET}.hip.cu)
endif()
list(LENGTH cc_srcs cc_srcs_len)
if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${math_library_DEPS} ${math_common_deps})
elseif (WITH_AMD_GPU)
hip_library(${TARGET} SRCS ${cc_srcs} ${hip_srcs} DEPS ${math_library_DEPS} ${math_common_deps})
elseif(${cc_srcs_len} GREATER 0)
cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${math_library_DEPS} ${math_common_deps})
endif()
......
paddle/fluid/operators/math/concat.hip.cu 0 → 100644
浏览文件 @ 79af7cc9
/* Copyright (c) 2018 paddlepaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <hip/hip_runtime.h>
paddle/fluid/operators/math/math_function.cc
浏览文件 @ 79af7cc9
......@@ -278,6 +278,7 @@ void axpy<platform::CPUDeviceContext, double>(
cblas_daxpy(n, alpha, x, 1, y, 1);
}
template struct SetConstant<platform::CPUDeviceContext, platform::float16>;
template struct SetConstant<platform::CPUDeviceContext, float>;
template struct SetConstant<platform::CPUDeviceContext, double>;
template struct SetConstant<platform::CPUDeviceContext, int>;
......
paddle/fluid/operators/math/math_function.cu
浏览文件 @ 79af7cc9
......@@ -348,6 +348,7 @@ void axpy<platform::CUDADeviceContext, double>(
&alpha, x, 1, y, 1));
}
template struct SetConstant<platform::CUDADeviceContext, platform::float16>;
template struct SetConstant<platform::CUDADeviceContext, float>;
template struct SetConstant<platform::CUDADeviceContext, double>;
template struct SetConstant<platform::CUDADeviceContext, int>;
......
paddle/fluid/operators/math/softmax.cu
浏览文件 @ 79af7cc9
......@@ -89,6 +89,7 @@ void SoftmaxGradCUDNNFunctor<T>::operator()(
XGrad->mutable_data<T>(context.GetPlace())));
}
template class SoftmaxCUDNNFunctor<platform::float16>;
template class SoftmaxCUDNNFunctor<float>;
template class SoftmaxCUDNNFunctor<double>;
template class SoftmaxGradCUDNNFunctor<float>;
......
paddle/fluid/operators/mine_hard_examples_op.cc
浏览文件 @ 79af7cc9
......@@ -324,8 +324,9 @@ MatchIndices elements with value -1.
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(mine_hard_examples, ops::MineHardExamplesOp,
ops::MineHardExamplesOpMaker);
REGISTER_OPERATOR(mine_hard_examples, ops::MineHardExamplesOp,
ops::MineHardExamplesOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
mine_hard_examples,
......
paddle/fluid/operators/prior_box_op.cc
浏览文件 @ 79af7cc9
......@@ -168,7 +168,9 @@ https://arxiv.org/abs/1512.02325.
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker);
REGISTER_OPERATOR(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);
paddle/fluid/operators/reader/CMakeLists.txt
浏览文件 @ 79af7cc9
......@@ -15,10 +15,12 @@ function(reader_library TARGET_NAME)
PARENT_SCOPE)
endfunction()
reader_library(open_files_op SRCS open_files_op.cc)
reader_library(create_random_data_generator_op SRCS create_random_data_generator_op.cc)
reader_library(create_shuffle_reader_op SRCS create_shuffle_reader_op.cc)
reader_library(create_batch_reader_op SRCS create_batch_reader_op.cc)
reader_library(create_recordio_file_reader_op SRCS create_recordio_file_reader_op.cc)
reader_library(create_double_buffer_reader_op SRCS create_double_buffer_reader_op.cc)
reader_library(create_multi_pass_reader_op SRCS create_multi_pass_reader_op.cc)
# Export local libraries to parent
set(READER_LIBRARY ${LOCAL_READER_LIBS} PARENT_SCOPE)
paddle/fluid/operators/reader/create_double_buffer_reader_op.cc
浏览文件 @ 79af7cc9
......@@ -124,10 +124,13 @@ class CreateDoubleBufferReaderOpMaker : public DecoratedReaderMakerBase {
};
void DoubleBufferReader::ReadNext(std::vector<framework::LoDTensor>* out) {
if (!HasNext()) {
PADDLE_THROW("There is no next data!");
}
if (local_buffer_.payloads_.empty()) {
buffer_->Receive(&local_buffer_);
}
*out = local_buffer_.payloads_;
local_buffer_.payloads_.clear();
if (local_buffer_.ctx_) {
......
paddle/fluid/operators/reader/create_multi_pass_reader_op.cc 0 → 100644
浏览文件 @ 79af7cc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/reader/reader_op_registry.h"
namespace paddle {
namespace operators {
namespace reader {
class MultiPassReader : public framework::DecoratedReader {
public:
MultiPassReader(ReaderBase* reader, int pass_num)
: DecoratedReader(reader), pass_num_(pass_num), pass_count_(0) {}
void ReadNext(std::vector<framework::LoDTensor>* out) override {
if (!HasNext()) {
PADDLE_THROW("There is no next data!");
}
reader_->ReadNext(out);
}
bool HasNext() const override {
if (reader_->HasNext()) {
return true;
} else {
++pass_count_;
if (pass_count_ >= pass_num_) {
return false;
} else {
reader_->ReInit();
return true;
}
}
}
void ReInit() override {
pass_count_ = 0;
reader_->ReInit();
}
private:
int pass_num_;
mutable int pass_count_;
};
class CreateMultiPassReaderOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
auto& out = detail::Ref(scope.FindVar(Output("Out")));
int pass_num = Attr<int>("pass_num");
out.GetMutable<framework::ReaderHolder>()->Reset(
new MultiPassReader(underlying_reader.Get(), pass_num));
}
};
class CreateMultiPassReaderOpMaker : public DecoratedReaderMakerBase {
public:
CreateMultiPassReaderOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
: DecoratedReaderMakerBase(op_proto, op_checker) {
AddAttr<int>("pass_num", "The number of pass to run.").GreaterThan(0);
AddComment(R"DOC(
CreateMultiPassReader Operator
This operator creates a multi-pass reader. A multi-pass reader
is used to yield data for several pass training continuously.
It takes the the number of pass to run as one of its attributes
('pass_num'), and maintains a pass counter to record how many
passes it has completed. When the underlying reader reach the EOF,
the multi-pass reader checks whether it has completed training
of the given number of pass. If not, the underlying reader will
be re-initialized and starts a new pass automatically.
)DOC");
}
};
} // namespace reader
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators::reader;
REGISTER_DECORATED_READER_OPERATOR(create_multi_pass_reader,
ops::CreateMultiPassReaderOp,
ops::CreateMultiPassReaderOpMaker);
paddle/fluid/operators/reader/open_files_op.cc 0 → 100644
浏览文件 @ 79af7cc9
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/operators/reader/reader_op_registry.h"
namespace paddle {
namespace operators {
namespace reader {
class MultipleReader : public framework::ReaderBase {
public:
MultipleReader(const std::vector<std::string>& file_names,
const std::vector<framework::DDim>& dims, size_t thread_num)
: file_names_(file_names), dims_(dims) {
prefetchers_.resize(thread_num);
StartNewScheduler();
}
void ReadNext(std::vector<framework::LoDTensor>* out) override;
bool HasNext() const override;
void ReInit() override;
~MultipleReader() { EndScheduler(); }
private:
void StartNewScheduler();
void EndScheduler();
void ScheduleThreadFunc();
void PrefetchThreadFunc(std::string file_name, size_t thread_idx);
std::vector<std::string> file_names_;
std::vector<framework::DDim> dims_;
std::thread scheduler_;
std::vector<std::thread> prefetchers_;
framework::Channel<size_t>* waiting_file_idx_;
framework::Channel<size_t>* available_thread_idx_;
framework::Channel<std::vector<framework::LoDTensor>>* buffer_;
mutable std::vector<framework::LoDTensor> local_buffer_;
};
void MultipleReader::ReadNext(std::vector<framework::LoDTensor>* out) {
if (!HasNext()) {
PADDLE_THROW("There is no next data!");
}
if (local_buffer_.empty()) {
buffer_->Receive(&local_buffer_);
}
*out = local_buffer_;
local_buffer_.clear();
}
bool MultipleReader::HasNext() const {
return local_buffer_.empty() ? buffer_->Receive(&local_buffer_) : true;
}
void MultipleReader::ReInit() {
EndScheduler();
local_buffer_.clear();
StartNewScheduler();
}
void MultipleReader::StartNewScheduler() {
size_t thread_num = prefetchers_.size();
waiting_file_idx_ = framework::MakeChannel<size_t>(file_names_.size());
available_thread_idx_ = framework::MakeChannel<size_t>(thread_num);
buffer_ =
framework::MakeChannel<std::vector<framework::LoDTensor>>(thread_num);
for (size_t i = 0; i < file_names_.size(); ++i) {
waiting_file_idx_->Send(&i);
}
waiting_file_idx_->Close();
for (size_t i = 0; i < thread_num; ++i) {
available_thread_idx_->Send(&i);
}
scheduler_ = std::thread([this] { ScheduleThreadFunc(); });
}
void MultipleReader::EndScheduler() {
available_thread_idx_->Close();
buffer_->Close();
waiting_file_idx_->Close();
if (scheduler_.joinable()) {
scheduler_.join();
}
delete buffer_;
delete available_thread_idx_;
delete waiting_file_idx_;
}
void MultipleReader::ScheduleThreadFunc() {
VLOG(5) << "MultipleReader schedule thread starts.";
size_t completed_thread_num = 0;
size_t thread_idx;
while (available_thread_idx_->Receive(&thread_idx)) {
std::thread& prefetcher = prefetchers_[thread_idx];
if (prefetcher.joinable()) {
prefetcher.join();
}
size_t file_idx;
if (waiting_file_idx_->Receive(&file_idx)) {
// Still have files to read. Start a new prefetch thread.
std::string file_name = file_names_[file_idx];
prefetcher = std::thread([this, file_name, thread_idx] {
PrefetchThreadFunc(file_name, thread_idx);
});
} else {
// No more file to read.
++completed_thread_num;
if (completed_thread_num == prefetchers_.size()) {
buffer_->Close();
break;
}
}
}
// If users invoke ReInit() when scheduler is running, it will close the
// 'avaiable_thread_idx_' and prefecther threads have no way to tell scheduler
// to release their resource. So a check is needed before scheduler ends.
for (auto& p : prefetchers_) {
if (p.joinable()) {
p.join();
}
}
VLOG(5) << "MultipleReader schedule thread terminates.";
}
void MultipleReader::PrefetchThreadFunc(std::string file_name,
size_t thread_idx) {
VLOG(5) << "The prefetch thread of file '" << file_name << "' starts.";
std::unique_ptr<framework::ReaderBase> reader =
CreateReaderByFileName(file_name, dims_);
while (reader->HasNext()) {
std::vector<framework::LoDTensor> ins;
reader->ReadNext(&ins);
if (!buffer_->Send(&ins)) {
VLOG(5) << "WARNING: The buffer channel has been closed. The prefetch "
"thread of file '"
<< file_name << "' will terminate.";
break;
}
}
if (!available_thread_idx_->Send(&thread_idx)) {
VLOG(5) << "WARNING: The available_thread_idx_ channel has been closed. "
"Fail to send thread_idx.";
}
VLOG(5) << "The prefetch thread of file '" << file_name << "' terminates.";
}
class OpenFilesOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& shape_concat = Attr<std::vector<int>>("shape_concat");
const auto& ranks = Attr<std::vector<int>>("ranks");
PADDLE_ENFORCE(!shape_concat.empty() && !ranks.empty());
PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0),
int(shape_concat.size()),
"The accumulate of all ranks should be equal to the "
"shape concat's length.");
const auto& file_names = Attr<std::vector<std::string>>("file_names");
PADDLE_ENFORCE(!file_names.empty(), "No file to be read!");
const size_t thread_num = Attr<int>("thread_num");
auto* out = scope.FindVar(Output("Out"))
->template GetMutable<framework::ReaderHolder>();
out->Reset(new MultipleReader(
file_names, RestoreShapes(shape_concat, ranks), thread_num));
}
};
class OpenFilesOpMaker : public FileReaderMakerBase {
public:
OpenFilesOpMaker(OpProto* op_proto, OpAttrChecker* op_checker)
: FileReaderMakerBase(op_proto, op_checker) {
AddAttr<std::vector<std::string>>("file_names", "Files to be read.");
AddAttr<int>("thread_num", "The maximal concurrent prefetch thread number.")
.GreaterThan(0);
AddComment(R"DOC(
OpenFiles Operator
An OpenFilesOp creates a MultipleReader, which is able to
read data multi-threaded from multiple files.
)DOC");
}
};
} // namespace reader
} // namespace operators
} // namespace paddle
namespace reader = paddle::operators::reader;
REGISTER_FILE_READER_OPERATOR(open_files, reader::OpenFilesOp,
reader::OpenFilesOpMaker);
paddle/fluid/operators/reader/reader_op_registry.cc
浏览文件 @ 79af7cc9
......@@ -36,6 +36,21 @@ std::unordered_map<std::string, FileReaderCreator>& FileReaderRegistry() {
return regs;
}
std::unique_ptr<framework::ReaderBase> CreateReaderByFileName(
const std::string& file_name, const std::vector<framework::DDim>& dims) {
size_t separator_pos = file_name.find_last_of(kFileFormatSeparator);
PADDLE_ENFORCE_NE(separator_pos, std::string::npos,
"File name illegal! A legal file name should be like: "
"[file_name].[file_format] (e.g., 'data_file.recordio').");
std::string filetype = file_name.substr(separator_pos + 1);
auto itor = FileReaderRegistry().find(filetype);
PADDLE_ENFORCE(itor != FileReaderRegistry().end(),
"No file reader registered for '%s' format.", filetype);
framework::ReaderBase* reader = (itor->second)(file_name, dims);
return std::unique_ptr<framework::ReaderBase>(reader);
}
FileReaderMakerBase::FileReaderMakerBase(
framework::OpProtoAndCheckerMaker::OpProto* op_proto,
framework::OpAttrChecker* op_checker)
......
paddle/fluid/operators/reader/reader_op_registry.h
浏览文件 @ 79af7cc9
......@@ -21,6 +21,8 @@ namespace paddle {
namespace operators {
namespace reader {
static constexpr char kFileFormatSeparator[] = ".";
using FileReaderCreator = std::function<framework::ReaderBase*(
const std::string&, const std::vector<framework::DDim>&)>;
......@@ -29,12 +31,15 @@ std::unordered_map<std::string, FileReaderCreator>& FileReaderRegistry();
template <typename Reader>
int RegisterFileReader(const std::string& filetype) {
FileReaderRegistry()[filetype] = [](
const std::string& fn, const std::vector<paddle::framework::DDim>& dim) {
return new Reader(fn, dim);
const std::string& fn, const std::vector<framework::DDim>& dims) {
return new Reader(fn, dims);
};
return 0;
}
std::unique_ptr<framework::ReaderBase> CreateReaderByFileName(
const std::string& file_name, const std::vector<framework::DDim>& dims);
extern std::vector<framework::DDim> RestoreShapes(
const std::vector<int>& shape_concat, const std::vector<int>& ranks);
......
paddle/fluid/operators/select_op.cc
浏览文件 @ 79af7cc9
......@@ -27,6 +27,7 @@ namespace operators {
static constexpr char kX[] = "X";
static constexpr char kCaseToExecute[] = "case_to_execute";
static constexpr char kOutputs[] = "Out";
static constexpr char kCases[] = "cases";
static constexpr char kCasesBlock[] = "sub_block";
......@@ -388,6 +389,10 @@ class SelectOpMaker : public framework::OpProtoAndCheckerMaker {
"(Int) The variable the sets the index of the case to execute, "
"after evaluating the channels being sent to and received from")
.AsDuplicable();
AddOutput(kOutputs,
"A set of variables, which will be assigned with values "
"generated by the operators inside the cases of Select Op.")
.AsDuplicable();
AddAttr<std::vector<std::string>>(kCases,
"(String vector) Serialized list of"
"all cases in the select op. Each"
......
paddle/fluid/operators/send_op.cc
浏览文件 @ 79af7cc9
......@@ -21,6 +21,7 @@ limitations under the License. */
#include <future>
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace operators {
......@@ -59,6 +60,9 @@ class SendOp : public framework::OperatorBase {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& ctx = *pool.Get(place);
// For profiling
platform::RecordEvent record_event(Type(), &ctx);
auto client_var_name = Output("RPCClient");
PADDLE_ENFORCE_NOT_NULL(scope.FindVar(client_var_name),
"Can not find variable '%s' in the scope.",
......@@ -68,7 +72,7 @@ class SendOp : public framework::OperatorBase {
for (size_t i = 0; i < ins.size(); i++) {
if (NeedSend(scope, ins[i])) {
VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
VLOG(2) << "sending " << ins[i] << " to " << epmap[i];
rpc_client->AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
} else {
VLOG(3) << "don't send no-initialied variable: " << ins[i];
......@@ -77,20 +81,20 @@ class SendOp : public framework::OperatorBase {
PADDLE_ENFORCE(rpc_client->Wait());
for (auto& ep : endpoints) {
VLOG(3) << "batch barrier, ep: " << ep;
VLOG(2) << "batch barrier, ep: " << ep;
rpc_client->AsyncSendBatchBarrier(ep);
}
PADDLE_ENFORCE(rpc_client->Wait());
if (outs.size() > 0) {
for (size_t i = 0; i < outs.size(); i++) {
VLOG(3) << "getting " << outs[i] << " from " << epmap[i];
VLOG(2) << "getting " << outs[i] << " from " << epmap[i];
rpc_client->AsyncGetVariable(epmap[i], ctx, scope, outs[i]);
}
PADDLE_ENFORCE(rpc_client->Wait());
// tell pservers that current trainer have called fetch
for (auto& ep : endpoints) {
VLOG(3) << "send fetch barrier, ep: " << ep;
VLOG(2) << "send fetch barrier, ep: " << ep;
rpc_client->AsyncSendFetchBarrier(ep);
}
PADDLE_ENFORCE(rpc_client->Wait());
......
paddle/fluid/operators/softmax_cudnn_op.cu.cc
浏览文件 @ 79af7cc9
......@@ -56,7 +56,9 @@ class SoftmaxGradCUDNNKernel : public framework::OpKernel<T> {
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(softmax, CUDNN, ::paddle::platform::CUDAPlace,
ops::SoftmaxCUDNNKernel<float>);
REGISTER_OP_KERNEL(softmax_grad, CUDNN, ::paddle::platform::CUDAPlace,
namespace plat = paddle::platform;
REGISTER_OP_KERNEL(softmax, CUDNN, plat::CUDAPlace,
ops::SoftmaxCUDNNKernel<float>,
ops::SoftmaxCUDNNKernel<plat::float16>);
REGISTER_OP_KERNEL(softmax_grad, CUDNN, plat::CUDAPlace,
ops::SoftmaxGradCUDNNKernel<float>);
paddle/fluid/operators/softmax_mkldnn_op.cc 0 → 100644
浏览文件 @ 79af7cc9
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "mkldnn.hpp"
#include "paddle/fluid/operators/softmax_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include <iostream>
namespace paddle {
namespace operators {
using paddle::framework::Tensor;
using paddle::platform::MKLDNNDeviceContext;
using paddle::platform::MKLDNNMemDesc;
using mkldnn::memory; // Note: paddle has also "memory" namespace
using mkldnn::primitive;
using mkldnn::softmax_forward;
using mkldnn::prop_kind;
using mkldnn::stream;
template <typename T>
class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
auto& dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
auto mkldnn_engine = dev_ctx.GetEngine();
const Tensor* input = ctx.Input<Tensor>("X");
Tensor* output = ctx.Output<Tensor>("Out");
PADDLE_ENFORCE(input->dims().size() == 2UL,
"The input of softmax op must be a 2D matrix.");
const T* input_data = input->data<T>();
// allocate memory for output
T* output_data = output->mutable_data<T>(ctx.GetPlace());
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
// MKL-DNN does support softmax over selected axis. Having 2D Tensor,
// we will make normalization after final eg. axis: 1
PADDLE_ENFORCE(((src_tz[0] == dst_tz[0]) && (src_tz[1] == dst_tz[1])),
"Softmax input and output dimensions should match");
// Same memory descriptor to be used for input and output
memory::dims softmax_tz = {src_tz[0], src_tz[1]};
// Currently only supports NC data format
// TODO(jczaja-intel): support more formats
auto softmax_md =
MKLDNNMemDesc({softmax_tz}, memory::f32, memory::format::nc);
// Normalization is made after innermost dimension eg. C out of NC
auto softmax_desc = softmax_forward::desc(prop_kind::forward_scoring,
softmax_md, 1 /*dim: C*/);
// create memory primitives
auto softmax_src_memory =
memory({softmax_md, mkldnn_engine}, (void*)input_data);
auto softmax_dst_memory =
memory({softmax_md, mkldnn_engine}, (void*)output_data);
auto softmax_prim_desc =
softmax_forward::primitive_desc(softmax_desc, mkldnn_engine);
auto softmax = softmax_forward(softmax_prim_desc, softmax_src_memory,
softmax_dst_memory);
std::vector<primitive> pipeline{softmax};
stream(stream::kind::eager).submit(pipeline).wait();
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace,
ops::SoftmaxMKLDNNKernel<float>);
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 79af7cc9
......@@ -13,7 +13,13 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/softmax_op.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
......@@ -38,26 +44,32 @@ class SoftmaxOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
// choose cudnn kernel if the runtime supported.
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
bool runtime_cudnn_support = false;
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
auto& dev_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
runtime_cudnn_support = dev_ctx.cudnn_handle() != nullptr ? true : false;
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
framework::LibraryType library_ = framework::LibraryType::kPlain;
if (use_cudnn && runtime_cudnn_support) {
library_ = framework::LibraryType::kCUDNN;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
}
#endif
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("X")->type());
if (input_data_type == framework::proto::VarType::FP16) {
PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
"float16 can only be used when CUDNN is used");
}
std::string data_format = ctx.Attr<std::string>("data_format");
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
framework::StringToDataLayout(data_format), library_);
return framework::OpKernelType(input_data_type, ctx.GetPlace(),
framework::StringToDataLayout(data_format),
library_);
}
};
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
......@@ -77,6 +89,9 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddComment(R"DOC(
Softmax Operator.
......@@ -119,19 +134,12 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
// choose cudnn kernel if the runtime supported.
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
bool runtime_cudnn_support = false;
framework::LibraryType library_{framework::LibraryType::kPlain};
#ifdef PADDLE_WITH_CUDA
if (platform::is_gpu_place(ctx.GetPlace())) {
auto& dev_ctx =
ctx.template device_context<platform::CUDADeviceContext>();
runtime_cudnn_support = dev_ctx.cudnn_handle() != nullptr ? true : false;
}
#endif
framework::LibraryType library_ = framework::LibraryType::kPlain;
if (use_cudnn && runtime_cudnn_support) {
if (platform::CanCUDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kCUDNN;
}
#endif
std::string data_format = ctx.Attr<std::string>("data_format");
return framework::OpKernelType(
framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace(),
......
paddle/fluid/operators/target_assign_op.cc
浏览文件 @ 79af7cc9
......@@ -153,8 +153,8 @@ template struct NegTargetAssignFunctor<platform::CPUDeviceContext, float,
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(target_assign, ops::TargetAssignOp,
ops::TargetAssignOpMaker);
REGISTER_OPERATOR(target_assign, ops::TargetAssignOp, ops::TargetAssignOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
target_assign,
ops::TargetAssignKernel<paddle::platform::CPUDeviceContext, int, float>,
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 79af7cc9
......@@ -49,7 +49,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place device_context)
cc_library(device_tracer SRCS device_tracer.cc DEPS profiler_proto ${GPU_CTX_DEPS})
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ 79af7cc9
......@@ -86,7 +86,8 @@ class CudnnDataType<float16> {
public:
static const cudnnDataType_t type = CUDNN_DATA_HALF;
// The scaling param type is float for HALF and FLOAT tensors
typedef const float ScalingParamType;
using ScalingParamType = const float;
using BatchNormParamType = float;
static ScalingParamType* kOne() {
static ScalingParamType v = 1.0;
return &v;
......@@ -101,7 +102,8 @@ template <>
class CudnnDataType<float> {
public:
static const cudnnDataType_t type = CUDNN_DATA_FLOAT;
typedef const float ScalingParamType;
using ScalingParamType = const float;
using BatchNormParamType = float;
static ScalingParamType* kOne() {
static ScalingParamType v = 1.0;
return &v;
......@@ -116,7 +118,8 @@ template <>
class CudnnDataType<double> {
public:
static const cudnnDataType_t type = CUDNN_DATA_DOUBLE;
typedef const double ScalingParamType;
using ScalingParamType = const double;
using BatchNormParamType = double;
static ScalingParamType* kOne() {
static ScalingParamType v = 1.0;
return &v;
......
paddle/fluid/platform/device_context.cc
浏览文件 @ 79af7cc9
......@@ -10,43 +10,45 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/device_context.h"
#include <unordered_set>
#include "paddle/fluid/memory/memory.h"
namespace paddle {
namespace platform {
DeviceContextPool* DeviceContextPool::pool = nullptr;
const platform::DeviceContext* DeviceContextPool::Get(
const platform::Place& place) {
platform::DeviceContext* DeviceContextPool::Get(const platform::Place& place) {
auto it = device_contexts_.find(place);
if (it == device_contexts_.end()) {
PADDLE_THROW(
"'Place' is not supported, Please re-compile with WITH_GPU "
"option");
}
return it->second;
return it->second.get();
}
DeviceContextPool::DeviceContextPool(
const std::vector<platform::Place>& places) {
PADDLE_ENFORCE_GT(places.size(), 0);
for (size_t i = 0; i < places.size(); i++) {
if (platform::is_cpu_place(places[i])) {
using PtrType = std::unique_ptr<DeviceContext>;
std::unordered_set<Place, PlaceHash> set;
for (auto& p : places) {
set.insert(p);
}
for (auto& p : set) {
if (platform::is_cpu_place(p)) {
#ifdef PADDLE_WITH_MKLDNN
device_contexts_.emplace(places[i],
new platform::MKLDNNDeviceContext(
boost::get<platform::CPUPlace>(places[i])));
device_contexts_.emplace(
p, PtrType(new MKLDNNDeviceContext(boost::get<CPUPlace>(p))));
#else
device_contexts_.emplace(places[i],
new platform::CPUDeviceContext(
boost::get<platform::CPUPlace>(places[i])));
device_contexts_.emplace(
p, PtrType(new CPUDeviceContext(boost::get<CPUPlace>(p))));
#endif
} else if (platform::is_gpu_place(places[i])) {
} else if (platform::is_gpu_place(p)) {
#ifdef PADDLE_WITH_CUDA
device_contexts_.emplace(places[i],
new platform::CUDADeviceContext(
boost::get<platform::CUDAPlace>(places[i])));
device_contexts_.emplace(
p, PtrType(new CUDADeviceContext(boost::get<CUDAPlace>(p))));
#else
PADDLE_THROW(
"'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
......@@ -159,6 +161,7 @@ CUDADeviceContext::~CUDADeviceContext() {
Place CUDADeviceContext::GetPlace() const { return place_; }
void CUDADeviceContext::Wait() const {
std::lock_guard<std::mutex> guard(mutex_);
PADDLE_ENFORCE(cudaStreamSynchronize(stream_));
PADDLE_ENFORCE(cudaGetLastError());
}
......
paddle/fluid/platform/device_context.h
浏览文件 @ 79af7cc9
......@@ -103,6 +103,7 @@ class CUDADeviceContext : public DeviceContext {
std::unique_ptr<Eigen::GpuDevice> eigen_device_;
std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
mutable std::mutex mutex_;
cudaStream_t stream_;
cudnnHandle_t cudnn_handle_;
cublasHandle_t cublas_handle_;
......@@ -159,7 +160,7 @@ class DeviceContextPool {
}
/*! \brief Return handle of single device context. */
const platform::DeviceContext* Get(const platform::Place& place);
platform::DeviceContext* Get(const platform::Place& place);
template <typename Place>
const typename DefaultDeviceContextType<Place>::TYPE* GetByPlace(
......@@ -172,19 +173,8 @@ class DeviceContextPool {
private:
static DeviceContextPool* pool;
constexpr static int LEFT_SHIFT = 8;
struct Hash {
std::hash<int> hash_;
size_t operator()(const platform::Place& place) const {
int pre_hash = place.which() << LEFT_SHIFT;
if (platform::is_gpu_place(place)) {
pre_hash += boost::get<platform::CUDAPlace>(place).GetDeviceId();
}
return hash_(pre_hash);
}
};
std::unordered_map<const platform::Place, const platform::DeviceContext*,
Hash>
std::unordered_map<const platform::Place,
std::unique_ptr<platform::DeviceContext>, PlaceHash>
device_contexts_;
DISABLE_COPY_AND_ASSIGN(DeviceContextPool);
};
......
paddle/fluid/platform/float16.h
浏览文件 @ 79af7cc9
......@@ -600,7 +600,7 @@ HOSTDEVICE inline bool operator>=(const float16& a, const float16& b) {
// Arithmetic operators for float16 on ARMv8.2-A CPU
#elif defined(PADDLE_WITH_NATIVE_FP16)
HOST inline float16 operator+(const float16& a, const float16& b) {
inline float16 operator+(const float16& a, const float16& b) {
float16 res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -616,7 +616,7 @@ HOST inline float16 operator+(const float16& a, const float16& b) {
return res;
}
HOST inline float16 operator-(const float16& a, const float16& b) {
inline float16 operator-(const float16& a, const float16& b) {
float16 res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -632,7 +632,7 @@ HOST inline float16 operator-(const float16& a, const float16& b) {
return res;
}
HOST inline float16 operator*(const float16& a, const float16& b) {
inline float16 operator*(const float16& a, const float16& b) {
float16 res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -648,7 +648,7 @@ HOST inline float16 operator*(const float16& a, const float16& b) {
return res;
}
HOST inline float16 operator/(const float16& a, const float16& b) {
inline float16 operator/(const float16& a, const float16& b) {
float16 res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -664,7 +664,7 @@ HOST inline float16 operator/(const float16& a, const float16& b) {
return res;
}
HOST inline float16 operator-(const float16& a) {
inline float16 operator-(const float16& a) {
float16 res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -679,27 +679,27 @@ HOST inline float16 operator-(const float16& a) {
return res;
}
HOST inline float16& operator+=(float16& a, const float16& b) {
inline float16& operator+=(float16& a, const float16& b) {
a = a + b;
return a;
}
HOST inline float16& operator-=(float16& a, const float16& b) {
inline float16& operator-=(float16& a, const float16& b) {
a = a - b;
return a;
}
HOST inline float16& operator*=(float16& a, const float16& b) {
inline float16& operator*=(float16& a, const float16& b) {
a = a * b;
return a;
}
HOST inline float16& operator/=(float16& a, const float16& b) {
inline float16& operator/=(float16& a, const float16& b) {
a = a / b;
return a;
}
HOST inline bool operator==(const float16& a, const float16& b) {
inline bool operator==(const float16& a, const float16& b) {
uint16_t res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -715,11 +715,9 @@ HOST inline bool operator==(const float16& a, const float16& b) {
return (res & 0xffff) != 0;
}
HOST inline bool operator!=(const float16& a, const float16& b) {
return !(a == b);
}
inline bool operator!=(const float16& a, const float16& b) { return !(a == b); }
HOST inline bool operator<(const float16& a, const float16& b) {
inline bool operator<(const float16& a, const float16& b) {
uint16_t res;
asm volatile(
"ld1 {v1.h}[0], [%[a_ptr]]\n"
......@@ -735,7 +733,7 @@ HOST inline bool operator<(const float16& a, const float16& b) {
return (res & 0xffff) != 0;
}
HOST inline bool operator<=(const float16& a, const float16& b) {
inline bool operator<=(const float16& a, const float16& b) {
uint16_t res;
asm volatile(
"ld1 {v1.h}[0], [%[a_ptr]]\n"
......@@ -751,7 +749,7 @@ HOST inline bool operator<=(const float16& a, const float16& b) {
return (res & 0xffff) != 0;
}
HOST inline bool operator>(const float16& a, const float16& b) {
inline bool operator>(const float16& a, const float16& b) {
uint16_t res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -767,7 +765,7 @@ HOST inline bool operator>(const float16& a, const float16& b) {
return (res & 0xffff) != 0;
}
HOST inline bool operator>=(const float16& a, const float16& b) {
inline bool operator>=(const float16& a, const float16& b) {
uint16_t res;
asm volatile(
"ld1 {v0.h}[0], [%[a_ptr]]\n"
......@@ -785,69 +783,69 @@ HOST inline bool operator>=(const float16& a, const float16& b) {
// Arithmetic operators for float16, software emulated on other CPU
#else
HOST inline float16 operator+(const float16& a, const float16& b) {
inline float16 operator+(const float16& a, const float16& b) {
return float16(float(a) + float(b));
}
HOST inline float16 operator-(const float16& a, const float16& b) {
inline float16 operator-(const float16& a, const float16& b) {
return float16(float(a) - float(b));
}
HOST inline float16 operator*(const float16& a, const float16& b) {
inline float16 operator*(const float16& a, const float16& b) {
return float16(float(a) * float(b));
}
HOST inline float16 operator/(const float16& a, const float16& b) {
inline float16 operator/(const float16& a, const float16& b) {
return float16(float(a) / float(b));
}
HOST inline float16 operator-(const float16& a) {
inline float16 operator-(const float16& a) {
float16 res;
res.x = a.x ^ 0x8000;
return res;
}
HOST inline float16& operator+=(float16& a, const float16& b) {
inline float16& operator+=(float16& a, const float16& b) {
a = float16(float(a) + float(b));
return a;
}
HOST inline float16& operator-=(float16& a, const float16& b) {
inline float16& operator-=(float16& a, const float16& b) {
a = float16(float(a) - float(b));
return a;
}
HOST inline float16& operator*=(float16& a, const float16& b) {
inline float16& operator*=(float16& a, const float16& b) {
a = float16(float(a) * float(b));
return a;
}
HOST inline float16& operator/=(float16& a, const float16& b) {
inline float16& operator/=(float16& a, const float16& b) {
a = float16(float(a) / float(b));
return a;
}
HOST inline bool operator==(const float16& a, const float16& b) {
inline bool operator==(const float16& a, const float16& b) {
return float(a) == float(b);
}
HOST inline bool operator!=(const float16& a, const float16& b) {
inline bool operator!=(const float16& a, const float16& b) {
return float(a) != float(b);
}
HOST inline bool operator<(const float16& a, const float16& b) {
inline bool operator<(const float16& a, const float16& b) {
return float(a) < float(b);
}
HOST inline bool operator<=(const float16& a, const float16& b) {
inline bool operator<=(const float16& a, const float16& b) {
return float(a) <= float(b);
}
HOST inline bool operator>(const float16& a, const float16& b) {
inline bool operator>(const float16& a, const float16& b) {
return float(a) > float(b);
}
HOST inline bool operator>=(const float16& a, const float16& b) {
inline bool operator>=(const float16& a, const float16& b) {
return float(a) >= float(b);
}
#endif
......
paddle/fluid/platform/place.h
浏览文件 @ 79af7cc9
......@@ -65,6 +65,18 @@ bool is_cpu_place(const Place &);
bool places_are_same_class(const Place &, const Place &);
bool is_same_place(const Place &, const Place &);
struct PlaceHash {
std::size_t operator()(const Place &p) const {
constexpr size_t num_dev_bits = 4;
std::hash<int> ihash;
size_t dev_id = 0;
if (is_gpu_place(p)) {
dev_id = boost::get<CUDAPlace>(p).device;
}
return ihash(dev_id << num_dev_bits | p.which());
}
};
std::ostream &operator<<(std::ostream &, const Place &);
template <typename Visitor>
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 79af7cc9
if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method
${GLOB_OP_LIB})
else()
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method
......@@ -6,4 +12,5 @@ if(WITH_PYTHON)
if(NOT APPLE AND NOT ANDROID)
target_link_libraries(paddle_pybind rt)
endif(NOT APPLE AND NOT ANDROID)
endif(WITH_AMD_GPU)
endif(WITH_PYTHON)
paddle/fluid/recordio/header.cc
浏览文件 @ 79af7cc9
......@@ -29,8 +29,8 @@ Header::Header(uint32_t num, uint32_t sum, Compressor c, uint32_t cs)
bool Header::Parse(std::istream& is) {
uint32_t magic;
size_t read_size =
is.readsome(reinterpret_cast<char*>(&magic), sizeof(uint32_t));
is.read(reinterpret_cast<char*>(&magic), sizeof(uint32_t));
size_t read_size = is.gcount();
if (read_size < sizeof(uint32_t)) {
return false;
}
......
paddle/fluid/recordio/scanner.cc
浏览文件 @ 79af7cc9
......@@ -28,6 +28,7 @@ Scanner::Scanner(const std::string &filename) {
}
void Scanner::Reset() {
stream_->clear();
stream_->seekg(0, std::ios::beg);
ParseNextChunk();
}
......
paddle/scripts/docker/build.sh 100644 → 100755
浏览文件 @ 79af7cc9
......@@ -35,8 +35,9 @@ function cmake_gen() {
-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release}
${PYTHON_FLAGS}
-DWITH_DSO=ON
-DWITH_DOC=OFF
-DWITH_DOC=${WITH_DOC:-OFF}
-DWITH_GPU=${WITH_GPU:-OFF}
-DWITH_AMD_GPU=${WITH_AMD_GPU:-OFF}
-DWITH_DISTRIBUTE=${WITH_DISTRIBUTE:-OFF}
-DWITH_MKL=${WITH_MKL:-ON}
-DWITH_AVX=${WITH_AVX:-OFF}
......@@ -50,6 +51,7 @@ function cmake_gen() {
-DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON}
-DWITH_TESTING=${WITH_TESTING:-ON}
-DWITH_FAST_BUNDLE_TEST=ON
-DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
========================================
EOF
......@@ -60,8 +62,9 @@ EOF
-DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE:-Release} \
${PYTHON_FLAGS} \
-DWITH_DSO=ON \
-DWITH_DOC=OFF \
-DWITH_DOC=${WITH_DOC:-OFF} \
-DWITH_GPU=${WITH_GPU:-OFF} \
-DWITH_AMD_GPU=${WITH_AMD_GPU:-OFF} \
-DWITH_DISTRIBUTE=${WITH_DISTRIBUTE:-OFF} \
-DWITH_MKL=${WITH_MKL:-ON} \
-DWITH_AVX=${WITH_AVX:-OFF} \
......@@ -74,6 +77,7 @@ EOF
-DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON} \
-DWITH_TESTING=${WITH_TESTING:-ON} \
-DWITH_FAST_BUNDLE_TEST=ON \
-DCMAKE_MODULE_PATH=/opt/rocm/hip/cmake \
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
}
......
paddle/scripts/tools/build_docs/build_docs.sh 已删除 100755 → 0
浏览文件 @ 081b7824
#!/bin/bash
docker run --rm \
-v $(git rev-parse --show-toplevel):/paddle \
-e "WITH_GPU=OFF" \
-e "WITH_AVX=ON" \
-e "WITH_DOC=ON" \
-e "WOBOQ=ON" \
${1:-"paddlepaddle/paddle:latest-dev"}
python/paddle/fluid/concurrency.py
浏览文件 @ 79af7cc9
......@@ -12,7 +12,8 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from layers.control_flow import BlockGuard, Select
from layers.control_flow import BlockGuard, equal
from .framework import Operator
from layer_helper import LayerHelper, unique_name
from layers import fill_constant
import core
......@@ -75,6 +76,185 @@ class Go(BlockGuard):
attrs={'sub_block': go_block})
class SelectCase(object):
DEFAULT = 0
SEND = 1
RECEIVE = 2
def __init__(self,
case_idx,
case_to_execute,
channel_action_fn=None,
channel=None,
value=None):
self.helper = LayerHelper('conditional_block')
self.main_program = self.helper.main_program
self.is_scalar_condition = True
self.case_to_execute = case_to_execute
self.idx = case_idx
# Since we aren't going to use the `channel_send` or `channel_recv`
# functions directly, we just need to capture the name.
self.action = (self.SEND
if channel_action_fn.__name__ == ('channel_send') else
self.RECEIVE) if channel_action_fn else self.DEFAULT
self.value = value
self.channel = channel
def __enter__(self):
self.block = self.main_program.create_block()
def construct_op(self):
main_program = self.helper.main_program
cases_block = main_program.current_block()
inner_outputs = set()
input_set = set()
params = set()
for op in self.block.ops:
# Iterate over all operators, get all the inputs
# and add as input to the SelectCase operator.
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs:
input_set.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
param_list = [
cases_block.var(each_name) for each_name in params
if each_name not in input_set
]
# Iterate over all operators, get all the outputs
# add to the output list of SelectCase operator only if
# they exist in the parent block.
out_vars = []
for inner_out_name in inner_outputs:
if inner_out_name in cases_block.vars:
out_vars.append(cases_block.var(inner_out_name))
# First, create an op that will determine whether or not this is the
# conditional variable to execute.
should_execute_block = equal(
fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=self.idx),
self.case_to_execute)
step_scope = cases_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
cases_block.append_op(
type='conditional_block',
inputs={'X': [should_execute_block],
'Params': param_list},
outputs={'Out': out_vars,
'Scope': [step_scope]},
attrs={
'sub_block': self.block,
'is_scalar_condition': self.is_scalar_condition
})
return '%s,%s,%s,%s' % (self.idx, self.action, self.channel.name
if self.channel else '', self.value.name
if self.value else '')
def __exit__(self, exc_type, exc_val, exc_tb):
self.main_program.rollback()
if exc_type is not None:
return False # re-raise exception
return True
class Select(BlockGuard):
def __init__(self, name=None):
self.helper = LayerHelper('select', name=name)
self.cases = []
super(Select, self).__init__(self.helper.main_program)
self.case_to_execute = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=-1)
def __enter__(self):
super(Select, self).__enter__()
return self
def case(self, channel_action_fn, channel, value):
"""Create a new block for this condition.
"""
select_case = SelectCase(
len(self.cases), self.case_to_execute, channel_action_fn, channel,
value)
self.cases.append(select_case)
return select_case
def default(self):
"""Create a default case block for this condition.
"""
default_case = SelectCase(len(self.cases), self.case_to_execute)
self.cases.append(default_case)
return default_case
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
# Create a select op and another block to wrap its
# case blocks.
select_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(select_block.parent_idx)
# Construct each case op, inside the newly created select block.
serialized_cases = []
for case in self.cases:
serialized_cases.append(case.construct_op())
intermediate = set()
params = set()
for case_block in select_block.ops:
if case_block.attrs and 'sub_block' in case_block.attrs:
for each_op in case_block.attrs['sub_block'].ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
out_list = [
parent_block.var(var_name) for var_name in parent_block.vars
if var_name in intermediate
]
X = [select_block.var_recursive(x_name) for x_name in params]
# Needs to be used by `equal` inside the cases block.
X.append(self.case_to_execute)
# Construct the select op.
parent_block.append_op(
type='select',
inputs={'X': X,
'case_to_execute': self.case_to_execute},
attrs={'sub_block': select_block,
'cases': serialized_cases},
outputs={'Out': out_list})
return super(Select, self).__exit__(exc_type, exc_val, exc_tb)
def make_channel(dtype, capacity=0):
"""
Helps implementation of a concurrent program by creating a "channel" of
......@@ -131,7 +311,7 @@ def make_channel(dtype, capacity=0):
return channel
def channel_send(channel, value, copy=False):
def channel_send(channel, value, is_copy=False):
"""
Sends a value through a channel variable. Used by an unbuffered or buffered
channel to pass data from within or to a concurrent Go block, where
......@@ -141,8 +321,8 @@ def channel_send(channel, value, copy=False):
channel (Variable|Channel): Channel variable created using
`make_channel`.
value (Variable): Value to send to channel
copy (bool): Copy data while channel send. If False, then data
is moved. The input cannot be used after move.
is_copy (bool): Copy data while channel send. If False, then data
is moved. The input cannot be used after move. (default False)
Returns:
Variable: The boolean status on whether or not the channel
successfully sent the passed value.
......@@ -166,7 +346,7 @@ def channel_send(channel, value, copy=False):
X = value
if copy is True:
if is_copy is True:
copied_X = helper.create_variable(
name=unique_name.generate(value.name + '_copy'),
type=value.type,
......
python/paddle/fluid/debuger.py
浏览文件 @ 79af7cc9
......@@ -16,7 +16,6 @@ import sys
import re
from graphviz import GraphPreviewGenerator
import proto.framework_pb2 as framework_pb2
import paddle.fluid.core as core
_vartype2str_ = [
"UNK",
......@@ -126,7 +125,6 @@ def pprint_block_codes(block_desc, show_backward=False):
def is_var_backward(var_desc):
return "@GRAD" in var_desc.name
#print(type(block_desc))
if type(block_desc) is not framework_pb2.BlockDesc:
block_desc = framework_pb2.BlockDesc.FromString(
block_desc.serialize_to_string())
......
python/paddle/fluid/distribute_transpiler.py
浏览文件 @ 79af7cc9
......@@ -20,6 +20,7 @@ from layer_helper import LayerHelper
from distributed_spliter import *
import math
from . import core
import debuger
class VarBlock:
......@@ -289,6 +290,7 @@ class DistributeTranspiler:
dtype=v.dtype,
shape=v.shape)
recv_inputs.append(var)
# step3
optimize_block = pserver_program.create_block(0)
# step 4
......@@ -563,6 +565,8 @@ class DistributeTranspiler:
orig_var_name = ""
if suff_idx >= 0:
orig_var_name = varname[:suff_idx]
else:
orig_var_name = varname
return orig_var_name
def _append_pserver_ops(self, optimize_block, opt_op, endpoint,
......@@ -577,7 +581,8 @@ class DistributeTranspiler:
grad_block = None
for g in self.param_grad_ep_mapping[endpoint]["grads"]:
if same_or_split_var(
self._orig_varname(g.name), opt_op.input(key)[0]):
self._orig_varname(g.name),
self._orig_varname(opt_op.input(key)[0])):
grad_block = g
break
if not grad_block:
......@@ -748,7 +753,7 @@ class DistributeTranspiler:
param_names = [
p.name for p in self.param_grad_ep_mapping[endpoint]["params"]
]
if op.input("Param") in param_names:
if op.input("Param")[0] in param_names:
return True
else:
for n in param_names:
......
python/paddle/fluid/executor.py
浏览文件 @ 79af7cc9
......@@ -235,66 +235,17 @@ class Executor(object):
tensor.set_lod(lod)
return tensor
def run(self,
program=None,
feed=None,
fetch_list=None,
feed_var_name='feed',
fetch_var_name='fetch',
scope=None,
return_numpy=True,
use_program_cache=False):
""" Run program by this Executor. Feed data by feed map, fetch result by fetch_list.
Python executor takes a program, add feed operators and fetch operators to this program according
to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides
the variables(or names) that user want to get after program run. Note: the executor will run all
operators in the program but not only the operators dependent by the fetch_list
:param program: the program that need to run, if not provied, then default_main_program will be used.
:param feed: feed variable map, e.g. {"image": ImageData, "label": LableData}
:param fetch_list: a list of variable or variable names that user want to get, run will return them according
to this list.
:param feed_var_name: the name for the input variable of feed Operator.
:param fetch_var_name: the name for the output variable of feed Operator.
:param scope: the scope used to run this program, you can switch it to different scope. default is global_scope
:param return_numpy: if convert the fetched tensor to numpy
:param use_program_cache: set use_program_cache to true if program not changed compare to the last step.
:return: result according to fetch_list.
"""
if feed is None:
feed = {}
if not isinstance(feed, dict):
raise TypeError("feed should be a map")
if fetch_list is None:
fetch_list = []
if program is None:
program = default_main_program()
def _get_program_cache(self, program_cache_key):
return self.program_caches.get(program_cache_key, None)
if not isinstance(program, Program):
raise TypeError()
if scope is None:
scope = global_scope()
program_cache = None
program_cache_key = get_program_cache_key(feed, fetch_list)
def _add_program_cache(self, program_cache_key, program):
self.program_caches[program_cache_key] = program
if use_program_cache:
# find program cache by cache_key
program_cache = self.program_caches.get(program_cache_key, None)
# TODO(qiao): Should check program_cache and program are exactly the same.
else:
self.program_caches.pop(program_cache_key, None)
if program_cache is None:
program_cache = program.clone()
if use_program_cache:
self.program_caches[program_cache_key] = program_cache
def _add_feed_fetch_ops(self, program, feed, fetch_list, feed_var_name,
fetch_var_name):
tmp_program = program.clone()
global_block = program_cache.global_block()
global_block = tmp_program.global_block()
if feed_var_name in global_block.vars:
feed_var = global_block.var(feed_var_name)
......@@ -323,8 +274,7 @@ class Executor(object):
attrs={'col': i})
# append fetch_operators
if not has_fetch_operators(global_block, fetch_list,
fetch_var_name):
if not has_fetch_operators(global_block, fetch_list, fetch_var_name):
for i, var in enumerate(fetch_list):
assert isinstance(var, Variable) or isinstance(var, str), (
"Wrong type for fetch_list[%s]: %s" % (i, type(var)))
......@@ -334,8 +284,11 @@ class Executor(object):
outputs={'Out': [fetch_var]},
attrs={'col': i})
return tmp_program
def _feed_data(self, program, feed, feed_var_name, scope):
# feed var to framework
for op in program_cache.global_block().ops:
for op in program.global_block().ops:
if op.desc.type() == 'feed':
feed_target_name = op.desc.output('Out')[0]
cur_feed = feed[feed_target_name]
......@@ -346,11 +299,79 @@ class Executor(object):
else:
break
self.executor.run(program_cache.desc, scope, 0, True, True)
def _fetch_data(self, fetch_list, fetch_var_name, scope):
outs = [
core.get_fetch_variable(scope, fetch_var_name, i)
for i in xrange(len(fetch_list))
]
return outs
def run(self,
program=None,
feed=None,
fetch_list=None,
feed_var_name='feed',
fetch_var_name='fetch',
scope=None,
return_numpy=True,
use_program_cache=False):
""" Run program by this Executor. Feed data by feed map, fetch result by fetch_list.
Python executor takes a program, add feed operators and fetch operators to this program according
to feed map and fetch_list. Feed map provides input data for the program. fetch_list provides
the variables(or names) that user want to get after program run. Note: the executor will run all
operators in the program but not only the operators dependent by the fetch_list
:param program: the program that need to run, if not provied, then default_main_program will be used.
:param feed: feed variable map, e.g. {"image": ImageData, "label": LableData}
:param fetch_list: a list of variable or variable names that user want to get, run will return them according
to this list.
:param feed_var_name: the name for the input variable of feed Operator.
:param fetch_var_name: the name for the output variable of feed Operator.
:param scope: the scope used to run this program, you can switch it to different scope. default is global_scope
:param return_numpy: if convert the fetched tensor to numpy
:param use_program_cache: set use_program_cache to true if program not changed compare to the last step.
:return: result according to fetch_list.
"""
if feed is None:
feed = {}
if not isinstance(feed, dict):
raise TypeError("feed should be a map")
if fetch_list is None:
fetch_list = []
if program is None:
program = default_main_program()
if not isinstance(program, Program):
raise TypeError()
if scope is None:
scope = global_scope()
cache_key = get_program_cache_key(feed, fetch_list)
if use_program_cache:
cached_program = self._get_program_cache(cache_key)
if cached_program is None:
cached_program = self._add_feed_fetch_ops(
program=program,
feed=feed,
fetch_list=fetch_list,
feed_var_name=feed_var_name,
fetch_var_name=fetch_var_name)
self._add_program_cache(cache_key, cached_program)
program = cached_program
else:
self.program_caches.pop(cache_key, None)
program = self._add_feed_fetch_ops(
program=program,
feed=feed,
fetch_list=fetch_list,
feed_var_name=feed_var_name,
fetch_var_name=fetch_var_name)
self._feed_data(program, feed, feed_var_name, scope)
self.executor.run(program.desc, scope, 0, True, True)
outs = self._fetch_data(fetch_list, fetch_var_name, scope)
if return_numpy:
outs = as_numpy(outs)
return outs
python/paddle/fluid/framework.py
浏览文件 @ 79af7cc9
......@@ -918,6 +918,24 @@ class Block(object):
name=v.name)
self.vars[new_p.name] = new_p
def clone_variable(self, var):
"""
Clone a variable into current block.
Args:
var: the variable to be cloned.
Returns:
The new variable cloned from 'var' in current block.
"""
assert isinstance(var, Variable)
return self.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
class Program(object):
def __init__(self):
......
python/paddle/fluid/layer_helper.py
浏览文件 @ 79af7cc9
......@@ -399,6 +399,9 @@ class LayerHelper(object):
if isinstance(act, basestring):
act = {'type': act}
tmp = self.create_tmp_variable(dtype=input_var.dtype)
if 'use_mkldnn' in self.kwargs:
act['use_mkldnn'] = self.kwargs.get('use_mkldnn')
act_type = act.pop('type')
self.append_op(
type=act_type,
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 79af7cc9
......@@ -16,7 +16,7 @@ import contextlib
from layer_function_generator import autodoc
from tensor import assign, fill_constant
from .. import core
from ..framework import Program, Variable, Operator, Block
from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ops import logical_and, logical_not, logical_or
......@@ -29,7 +29,6 @@ __all__ = [
'WhileGuard',
'While',
'Switch',
'Select',
'lod_rank_table',
'max_sequence_len',
'topk',
......@@ -1212,186 +1211,6 @@ class Switch(object):
return True
class SelectCase(object):
DEFAULT = 0
SEND = 1
RECEIVE = 2
def __init__(self,
case_idx,
case_to_execute,
channel_action_fn=None,
channel=None,
value=None):
self.helper = LayerHelper('conditional_block')
self.main_program = self.helper.main_program
self.is_scalar_condition = True
self.case_to_execute = case_to_execute
self.idx = case_idx
# Since we aren't going to use the `channel_send` or `channel_recv`
# functions directly, we just need to capture the name.
self.action = (self.SEND
if channel_action_fn.__name__ == ('channel_send') else
self.RECEIVE) if channel_action_fn else (self.DEFAULT)
self.value = value
self.channel = channel
def __enter__(self):
self.block = self.main_program.create_block()
def construct_op(self):
main_program = self.helper.main_program
cases_block = main_program.current_block()
inner_outputs = set()
input_set = set()
params = set()
for op in self.block.ops:
# Iterate over all operators, get all the inputs
# and add as input to the SelectCase operator.
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in inner_outputs:
input_set.add(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
inner_outputs.add(out_var_name)
param_list = [
cases_block.var(each_name) for each_name in params
if each_name not in input_set
]
# Iterate over all operators, get all the outputs
# add to the output list of SelectCase operator only if
# they exist in the parent block.
out_vars = []
for inner_out_name in inner_outputs:
if inner_out_name in cases_block.vars:
out_vars.append(cases_block.var(inner_out_name))
# First, create an op that will determine whether or not this is the
# conditional variable to execute.
should_execute_block = equal(
fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=self.idx),
self.case_to_execute)
step_scope = cases_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
cases_block.append_op(
type='conditional_block',
inputs={'X': [should_execute_block],
'Params': param_list},
outputs={'Out': out_vars,
'Scope': [step_scope]},
attrs={
'sub_block': self.block,
'is_scalar_condition': self.is_scalar_condition
})
return '%s,%s,%s,%s' % (self.idx, self.action, self.channel.name
if self.channel else '', self.value.name
if self.value else '')
def __exit__(self, exc_type, exc_val, exc_tb):
self.main_program.rollback()
if exc_type is not None:
return False # re-raise exception
return True
class Select(BlockGuard):
def __init__(self, name=None):
self.helper = LayerHelper('select', name=name)
self.cases = []
super(Select, self).__init__(self.helper.main_program)
self.case_to_execute = fill_constant(
shape=[1], dtype=core.VarDesc.VarType.INT32, value=-1)
def __enter__(self):
super(Select, self).__enter__()
return self
def case(self, channel_action_fn, channel, value):
"""Create a new block for this condition.
"""
select_case = SelectCase(
len(self.cases), self.case_to_execute, channel_action_fn, channel,
value)
self.cases.append(select_case)
return select_case
def default(self):
"""Create a default case block for this condition.
"""
default_case = SelectCase(len(self.cases), self.case_to_execute)
self.cases.append(default_case)
return default_case
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
return False
# Create a select op and another block to wrap its
# case blocks.
select_block = self.helper.main_program.current_block()
parent_block = self.helper.main_program.block(select_block.parent_idx)
# Construct each case op, inside the newly created select block.
serialized_cases = []
for case in self.cases:
serialized_cases.append(case.construct_op())
intermediate = set()
params = set()
for case_block in select_block.ops:
if case_block.attrs and 'sub_block' in case_block.attrs:
for each_op in case_block.attrs['sub_block'].ops:
assert isinstance(each_op, Operator)
for iname in each_op.input_names:
for in_var_name in each_op.input(iname):
if in_var_name not in intermediate:
params.add(in_var_name)
for oname in each_op.output_names:
for out_var_name in each_op.output(oname):
intermediate.add(out_var_name)
# TODO(varunarora): Figure out if defining output is needed.
out_list = [
parent_block.var(var_name) for var_name in parent_block.vars
if var_name in intermediate
]
X = [select_block.var_recursive(x_name) for x_name in params]
# Needs to be used by `equal` inside the cases block.
X.append(self.case_to_execute)
# Construct the select op.
parent_block.append_op(
type='select',
inputs={'X': X,
'case_to_execute': self.case_to_execute},
attrs={'sub_block': select_block,
'cases': serialized_cases},
outputs={})
return super(Select, self).__exit__(exc_type, exc_val, exc_tb)
class IfElseBlockGuard(object):
def __init__(self, is_true, ifelse):
if not isinstance(ifelse, IfElse):
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 79af7cc9
......@@ -129,13 +129,11 @@ def detection_output(loc,
prior_box_var=prior_box_var,
target_box=loc,
code_type='decode_center_size')
old_shape = scores.shape
scores = ops.reshape(x=scores, shape=(-1, old_shape[-1]))
scores = nn.softmax(input=scores)
scores = ops.reshape(x=scores, shape=old_shape)
scores = nn.transpose(scores, perm=[0, 2, 1])
nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
helper.append_op(
type="multiclass_nms",
......@@ -475,6 +473,7 @@ def ssd_loss(location,
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
gt_label = ops.reshape(x=gt_label, shape=gt_label.shape + (1, ))
gt_label.stop_gradient = True
target_label, _ = target_assign(
gt_label, matched_indices, mismatch_value=background_label)
# 2.2. Compute confidence loss.
......@@ -482,10 +481,12 @@ def ssd_loss(location,
confidence = __reshape_to_2d(confidence)
target_label = tensor.cast(x=target_label, dtype='int64')
target_label = __reshape_to_2d(target_label)
target_label.stop_gradient = True
conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
# 3. Mining hard examples
conf_loss = ops.reshape(x=conf_loss, shape=(num, num_prior))
conf_loss.stop_gradient = True
neg_indices = helper.create_tmp_variable(dtype='int32')
dtype = matched_indices.dtype
updated_matched_indices = helper.create_tmp_variable(dtype=dtype)
......@@ -695,6 +696,8 @@ def multi_box_head(inputs,
outputs={"Boxes": box,
"Variances": var},
attrs=attrs, )
box.stop_gradient = True
var.stop_gradient = True
return box, var
def _reshape_with_axis_(input, axis=1):
......
python/paddle/fluid/layers/io.py
浏览文件 @ 79af7cc9
......@@ -21,7 +21,8 @@ from ..executor import global_scope
__all__ = [
'data', 'BlockGuardServ', 'ListenAndServ', 'Send', 'open_recordio_file',
'read_file', 'create_shuffle_reader', 'create_double_buffer_reader'
'open_files', 'read_file', 'create_shuffle_reader',
'create_double_buffer_reader', 'create_multi_pass_reader'
]
......@@ -287,6 +288,36 @@ def open_recordio_file(filename, shapes, lod_levels, dtypes):
startup_var)
def open_files(filenames, thread_num, shapes, lod_levels, dtypes):
dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
shape_concat = []
ranks = []
for shape in shapes:
shape_concat.extend(shape)
ranks.append(len(shape))
var_name = unique_name('multiple_reader')
startup_blk = default_startup_program().current_block()
startup_var = startup_blk.create_var(name=var_name)
startup_blk.append_op(
type='open_files',
outputs={'Out': [startup_var]},
attrs={
'shape_concat': shape_concat,
'lod_levels': lod_levels,
'ranks': ranks,
'file_names': filenames,
'thread_num': thread_num
})
startup_var.desc.set_dtypes(dtypes)
startup_var.persistable = True
return _copy_reader_var_(default_main_program().current_block(),
startup_var)
def __create_decorated_reader__(op_type, reader, attrs):
var_name = unique_name(op_type)
startup_blk = default_startup_program().current_block()
......@@ -314,6 +345,11 @@ def create_double_buffer_reader(reader, place=None):
attrs)
def create_multi_pass_reader(reader, pass_num):
return __create_decorated_reader__('create_multi_pass_reader', reader,
{'pass_num': int(pass_num)})
def read_file(file_obj):
helper = LayerHelper('read_file')
out = [
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 79af7cc9
......@@ -82,6 +82,7 @@ def fc(input,
num_flatten_dims=1,
param_attr=None,
bias_attr=None,
use_mkldnn=False,
act=None,
name=None):
"""
......@@ -163,8 +164,11 @@ def fc(input,
inputs={"X": input_var,
"Y": w},
outputs={"Out": tmp},
attrs={"x_num_col_dims": num_flatten_dims,
"y_num_col_dims": 1})
attrs={
"x_num_col_dims": num_flatten_dims,
"y_num_col_dims": 1,
'use_mkldnn': use_mkldnn
})
mul_results.append(tmp)
# sum
......@@ -1117,12 +1121,14 @@ def conv2d(input,
filter_size,
stride=1,
padding=0,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
use_mkldnn=False,
act=None):
act=None,
name=None):
"""
**Convlution2D Layer**
......@@ -1183,6 +1189,9 @@ def conv2d(input,
padding(int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
......@@ -1193,6 +1202,8 @@ def conv2d(input,
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution and \
......@@ -1233,6 +1244,7 @@ def conv2d(input,
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
......@@ -1262,6 +1274,7 @@ def conv2d(input,
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn,
'use_mkldnn': use_mkldnn
......@@ -1670,7 +1683,9 @@ def conv2d_transpose(input,
stride=1,
dilation=1,
param_attr=None,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
"""
**Convlution2D transpose layer**
......@@ -1739,8 +1754,10 @@ def conv2d_transpose(input,
dilation_H = dilation_W = dilation. Default: dilation = 1.
param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -1793,12 +1810,12 @@ def conv2d_transpose(input,
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
out = helper.create_tmp_variable(dtype=input.dtype)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type='conv2d_transpose',
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': out},
outputs={'Output': pre_bias},
attrs={
'strides': stride,
'paddings': padding,
......@@ -1806,6 +1823,8 @@ def conv2d_transpose(input,
'use_cudnn': use_cudnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
out = helper.append_activation(pre_act)
return out
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 79af7cc9
......@@ -69,6 +69,7 @@ __all__ = [
'gaussian_random_batch_size_like',
'cumsum',
'scatter',
'sum',
] + __activations__
for _OP in set(__all__):
......
python/paddle/fluid/optimizer.py
浏览文件 @ 79af7cc9
......@@ -13,7 +13,7 @@
# limitations under the License.
from collections import defaultdict
from paddle.fluid.framework import Program
import framework
import layers
from backward import append_backward
......@@ -23,9 +23,11 @@ from initializer import Constant
from layer_helper import LayerHelper
from regularizer import append_regularization_ops
from clip import append_gradient_clip_ops, error_clip_callback
from contextlib import contextmanager
__all__ = [
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Adadelta'
'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad',
'Adadelta', 'ModelAverage'
]
......@@ -121,7 +123,12 @@ class Optimizer(object):
"""
pass
def _add_accumulator(self, name, param, dtype=None, fill_value=0.0):
def _add_accumulator(self,
name,
param,
dtype=None,
fill_value=0.0,
shape=None):
"""Utility function to add an accumulator for a parameter
Args:
......@@ -135,17 +142,19 @@ class Optimizer(object):
param.name in self._accumulators[name]):
raise Exception("Accumulator {} already exists for parameter {}".
format(name, param.name))
if shape == None:
shape = param.shape
assert isinstance(self.helper, LayerHelper)
var = self.helper.create_global_variable(
name=unique_name.generate(name),
persistable=True,
dtype=dtype or param.dtype,
type=param.type,
shape=param.shape)
shape=shape)
self.helper.set_variable_initializer(
var, initializer=Constant(value=float(fill_value)))
self._accumulators[name][param.name] = var
return var
def _get_accumulator(self, name, param):
"""Utility function to fetch an accumulator for a parameter
......@@ -797,3 +806,143 @@ Adamax = AdamaxOptimizer
DecayedAdagrad = DecayedAdagradOptimizer
Adadelta = AdadeltaOptimizer
RMSProp = RMSPropOptimizer
class ModelAverage(Optimizer):
"""Accumulate the average of parameters whtin sliding window. The average
result will be saved in temporary variables which can be applied to
parameter variables of current model by calling 'apply()' method. And the
'restore()' method is used to restored the parameter values of current model.
The size of average window is determined by average_window_rate,
min_average_window, max_average_window and current update times.
Args:
params_grads: A list of parameter-grad variable pairs.
average_window_rate: The rate of average window.
min_average_window: The minimum size of average window.
max_average_window: The maximum size of average window.
Examples:
...
optimizer = fluid.optimizer.Momentum()
_, params_grads = optimizer.minimize(cost)
model_average = fluid.optimizer.ModelAverage(params_grads, 0.15,
min_average_window=10000,
max_average_window=20000)
for pass_id in range(args.pass_num):
for data in train_reader():
exe.run(fluid.default_main_program()...)
with model_average.apply(exe):
for data in test_reader():
exe.run(inference_program...)
"""
def __init__(self,
params_grads,
average_window_rate,
min_average_window=10000,
max_average_window=10000,
**kwargs):
super(ModelAverage, self).__init__(0.0, **kwargs)
self.average_window = average_window_rate
self.min_average_window = min_average_window
self.max_average_window = max_average_window
self.params_grads = params_grads
for param, grad in self.params_grads:
if grad is not None:
self._append_average_accumulate_op(param)
self.apply_program = Program()
block = self.apply_program.global_block()
with program_guard(main_program=self.apply_program):
for param_grad in self.params_grads:
if param_grad[1] is not None:
self._add_average_apply_op(block, param_grad)
self.restore_program = Program()
block = self.restore_program.global_block()
with program_guard(main_program=self.restore_program):
for param_grad in self.params_grads:
if param_grad[1] is not None:
self._add_average_restore_op(block, param_grad)
def _add_average_apply_op(self, block, param_grad):
param = block.clone_variable(param_grad[0])
grad = block.clone_variable(param_grad[1])
sum_1 = block.clone_variable(self._get_accumulator('sum_1', param))
sum_2 = block.clone_variable(self._get_accumulator('sum_2', param))
sum_3 = block.clone_variable(self._get_accumulator('sum_3', param))
num_accumulates = block.clone_variable(
self._get_accumulator('num_accumulates', param))
old_num_accumulates = block.clone_variable(
self._get_accumulator('old_num_accumulates', param))
num_updates = block.clone_variable(
self._get_accumulator('num_updates', param))
# backup param value to grad
layers.assign(input=param, output=grad)
# param = (sum_1 + sum_2 + sum_3) / (num_accumulates + old_num_accumulates)
tmp = layers.sum(x=[num_accumulates, old_num_accumulates])
sum = layers.sum(x=[sum_1, sum_2, sum_3])
tmp = layers.cast(x=tmp, dtype='float32')
sum = layers.cast(x=sum, dtype='float32')
layers.elementwise_div(x=sum, y=tmp, out=param)
def _add_average_restore_op(self, block, param_grad):
param = block.clone_variable(param_grad[0])
grad = block.clone_variable(param_grad[1])
layers.assign(input=grad, output=param)
def _append_average_accumulate_op(self, param):
self.helper = LayerHelper("average_accumulate")
sum_1 = self._add_accumulator('sum_1', param)
sum_2 = self._add_accumulator('sum_2', param)
sum_3 = self._add_accumulator('sum_3', param)
num_accumulates = self._add_accumulator(
'num_accumulates', param, dtype='int64', shape=[1])
old_num_accumulates = self._add_accumulator(
'old_num_accumulates', param, dtype='int64', shape=[1])
num_updates = self._add_accumulator(
'num_updates', param, dtype='int64', shape=[1])
self.helper.append_op(
type='average_accumulates',
inputs={
"param": param,
"in_sum_1": sum_1,
"in_sum_2": sum_2,
"in_sum_3": sum_3,
"in_num_accumulates": num_accumulates,
"in_old_num_accumulates": old_num_accumulates,
"in_num_updates": num_updates
},
outputs={
"out_sum_1": sum_1,
"out_sum_2": sum_2,
"out_sum_3": sum_3,
"out_num_accumulates": num_accumulates,
"out_old_num_accumulates": old_num_accumulates,
"out_num_updates": num_updates,
},
attrs={
"average_window": self.average_window,
"min_average_window": self.min_average_window,
"max_average_window": self.max_average_window,
})
@contextmanager
def apply(self, executor, need_restore=True):
"""Apply average values to parameters of current model.
"""
executor.run(self.apply_program)
try:
yield
finally:
if need_restore:
self.restore(executor)
def restore(self, executor):
"""Restore parameter values of current model.
"""
executor.run(self.restore_program)
python/paddle/fluid/tests/unittests/.gitignore
浏览文件 @ 79af7cc9
mnist.recordio
mnist_0.recordio
mnist_1.recordio
mnist_2.recordio
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @ 79af7cc9
......@@ -14,6 +14,7 @@
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from scipy.special import expit
......@@ -212,18 +213,39 @@ class TestRound(OpTest):
class TestRelu(OpTest):
def setUp(self):
self.op_type = "relu"
x = np.random.uniform(-1, 1, [11, 17]).astype("float32")
self.dtype = np.float32
self.init_dtype()
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
self.inputs = {'X': x}
self.outputs = {'Out': np.maximum(self.inputs['X'], 0)}
out = np.maximum(x, 0)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
if self.dtype == np.float16:
return
self.check_grad(['X'], 'Out', max_relative_error=0.007)
def init_dtype(self):
pass
class TestFP16Relu(TestRelu):
def init_dtype(self):
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestBRelu(OpTest):
def setUp(self):
......
python/paddle/fluid/tests/unittests/test_batch_norm_op.py
浏览文件 @ 79af7cc9
......@@ -31,6 +31,37 @@ def get_backward_op(scope, op, no_grad_set):
return backward_op
def _reference_testing(x, scale, offset, mean, var, epsilon, data_format):
x_shape = x.shape
if len(x_shape) == 2:
if data_format == "NCHW":
x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1))
else:
x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
if data_format == "NCHW":
n, c, h, w = x.shape
mean_tile = np.reshape(mean, (1, c, 1, 1))
mean_tile = np.tile(mean_tile, (n, 1, h, w))
var_tile = np.reshape(var, (1, c, 1, 1))
var_tile = np.tile(var_tile, (n, 1, h, w))
normalized = (x - mean_tile) / np.sqrt(var_tile + epsilon)
scale_tile = np.reshape(scale, (1, c, 1, 1))
scale_tile = np.tile(scale_tile, (n, 1, h, w))
offset_tile = np.reshape(offset, (1, c, 1, 1))
offset_tile = np.reshape(offset_tile, (1, c, 1, 1))
y = normalized * scale_tile + offset_tile
elif data_format == "NHWC":
normalized = (x - mean) / np.sqrt(var + epsilon)
y = normalized * scale + offset
else:
raise ValueError("Unknown data order.")
if len(x_shape) == 2:
y = np.reshape(y, x_shape)
return y
def _reference_training(x, scale, offset, epsilon, data_format):
x_shape = x.shape
if len(x_shape) == 2:
......@@ -155,11 +186,159 @@ def set_output_grad(scope, outputs, place, feed_dict=None):
__set_tensor__(output, data)
class TestBatchNormOp(OpTest):
class TestBatchNormOpInference(OpTest):
def setUp(self):
self.dtype = np.float32
def __assert_close(self, tensor, np_array, msg, atol=1e-4):
self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
def test_python(self):
def check_with_place(self, place, data_layout, dtype, shape):
epsilon = 0.00001
if len(shape) == 2:
x_shape = shape
c = x_shape[1]
else:
n, h, w, c = shape[0], shape[1], shape[2], shape[3]
if data_layout == "NHWC":
x_shape = [n, h, w, c]
elif data_layout == "NCHW":
x_shape = [n, c, h, w]
else:
raise ValueError("Unknown data layout.")
scale_shape = [c]
x_val = np.random.random_sample(x_shape).astype(dtype)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
bias_val = np.random.random_sample(scale_shape).astype(np.float32)
mean = np.zeros(scale_shape).astype(np.float32)
variance = np.ones(scale_shape).astype(np.float32)
y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
epsilon, data_layout).astype(dtype)
scope = core.Scope()
# create input
x_tensor = create_or_get_tensor(scope, "x_val",
OpTest.np_dtype_to_fluid_dtype(x_val),
place)
scale_tensor = create_or_get_tensor(
scope, "scale_val",
OpTest.np_dtype_to_fluid_dtype(scale_val), place)
bias_tensor = create_or_get_tensor(
scope, "bias_val", OpTest.np_dtype_to_fluid_dtype(bias_val), place)
mean_tensor = create_or_get_tensor(scope, "mean",
OpTest.np_dtype_to_fluid_dtype(mean),
place)
variance_tensor = create_or_get_tensor(
scope, "variance", OpTest.np_dtype_to_fluid_dtype(variance), place)
# create output
y_tensor = create_or_get_tensor(scope, "y_out", None, place)
saved_mean_tensor = create_or_get_tensor(scope, "saved_mean", None,
place)
saved_variance_tensor = create_or_get_tensor(scope, "saved_variance",
None, place)
mean_out_tensor = mean_tensor
variance_out_tensor = variance_tensor
batch_norm_op = Operator(
"batch_norm",
# inputs
X="x_val",
Scale="scale_val",
Bias="bias_val",
Mean="mean",
Variance="variance",
# outputs
Y="y_out",
MeanOut="mean",
VarianceOut="variance",
SavedMean="saved_mean",
SavedVariance="saved_variance",
# attrs
is_test=True,
data_layout=data_layout,
epsilon=epsilon)
batch_norm_op.run(scope, place)
# check inference result
self.__assert_close(
y_tensor,
y_out,
"inference output are different at " + str(place) + ", " +
data_layout + ", " + str(np.dtype(dtype)) +
str(np.array(y_tensor)) + str(y_out),
atol=1e-3)
def test_check_output(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
places.append(core.CUDAPlace(0))
for place in places:
for data_format in ["NCHW", "NHWC"]:
self.check_with_place(place, data_format, self.dtype,
[2, 3, 4, 5])
self.check_with_place(place, data_format, self.dtype, [2, 3])
class TestFP16BatchNormOpInference(TestBatchNormOpInference):
def setUp(self):
self.dtype = np.float16
def test_check_output(self):
places = []
if core.is_compiled_with_cuda() and core.op_support_gpu("batch_norm"):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
places.append(place)
for place in places:
for data_format in ["NCHW", "NHWC"]:
self.check_with_place(place, data_format, self.dtype,
[2, 3, 4, 5])
self.check_with_place(place, data_format, self.dtype, [2, 3])
class TestBatchNormOpTraining(OpTest):
def __assert_close(self, tensor, np_array, msg, atol=1e-4):
self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
def test_python_testing(self):
data_format = "NHWC"
epsilon = 0.00001
n, h, w, c = 2, 3, 4, 5
x_shape = [n, h, w, c]
scale_shape = [c]
x_val = np.random.random_sample(x_shape).astype(np.float32)
scale_val = np.random.random_sample(scale_shape).astype(np.float32)
bias_val = np.random.random_sample(scale_shape).astype(np.float32)
mean = np.zeros(scale_shape).astype(np.float32)
variance = np.ones(scale_shape).astype(np.float32)
y_out = _reference_testing(x_val, scale_val, bias_val, mean, variance,
epsilon, "NHWC")
# running N, C, H, W case
# should produce the same results
x_shape2 = [n, c, h, w]
x_val2 = np.transpose(x_val, (0, 3, 1, 2))
y_out2 = _reference_testing(x_val2, scale_val, bias_val, mean, variance,
epsilon, "NCHW")
# transfer (N, C, H, W) back to (N, H, W, C)
y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
self.__assert_close(y_out, y_out2_trans, "inference output")
print 'python: NHWC, NCHW, inference checking passed'
def test_python_training(self):
data_format = "NHWC"
epsilon = 0.00001
momentum = 0.9
......@@ -197,7 +376,7 @@ class TestBatchNormOp(OpTest):
# transfer (N, C, H, W) back to (N, H, W, C)
y_out2_trans = np.transpose(y_out2, (0, 2, 3, 1))
self.__assert_close(y_out, y_out2_trans, "batch variance")
self.__assert_close(y_out, y_out2_trans, "batch output")
print 'python: NHWC, NCHW, forward checking passed'
# test backward now
......
python/paddle/fluid/tests/unittests/test_elementwise_add_op.py
浏览文件 @ 79af7cc9
......@@ -13,158 +13,243 @@
# limitations under the License.
import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
class TestElementwiseOp(OpTest):
class TestElementwiseAddOp(OpTest):
def setUp(self):
self.op_type = "elementwise_add"
self.dtype = np.float32
self.axis = -1
self.init_dtype()
self.init_input_output()
self.init_axis()
self.inputs = {
'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
'X': OpTest.np_dtype_to_fluid_dtype(self.x),
'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
}
self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['Y'])}
self.attrs = {'axis': self.axis}
self.outputs = {'Out': self.out}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
if self.dtype == np.float16:
return
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.005)
def test_check_grad_ingore_x(self):
if self.dtype == np.float16:
return
self.check_grad(
['Y'], 'Out', max_relative_error=0.005, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
if self.dtype == np.float16:
return
self.check_grad(
['X'], 'Out', max_relative_error=0.005, no_grad_set=set('Y'))
def init_input_output(self):
self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
self.out = np.add(self.x, self.y)
class TestElementwiseAddOp_scalar(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(1).astype(np.float32)
}
self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
def init_dtype(self):
pass
def init_axis(self):
pass
class TestElementwiseAddOp_scalar2(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(1, 1).astype(np.float32)
}
self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']}
class TestFP16ElementwiseAddOp(TestElementwiseAddOp):
def init_dtype(self):
self.dtype = np.float16
class TestElementwiseAddOp_Vector(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.random((32, )).astype("float32"),
'Y': np.random.random((32, )).astype("float32")
}
self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['Y'])}
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestElementwiseAddOp_broadcast_0(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(2).astype(np.float32)
}
class TestElementwiseAddOp_scalar(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y
self.attrs = {'axis': 0}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(2, 1, 1)
}
class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y
class TestElementwiseAddOp_broadcast_1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(3).astype(np.float32)
}
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 1)
}
class TestElementwiseAddOp_scalar2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1, 1).astype(self.dtype)
self.out = self.x + self.y
class TestElementwiseAddOp_broadcast_2(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(4).astype(np.float32)
}
class TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(1, 1).astype(self.dtype)
self.out = self.x + self.y
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 1, 4)
}
class TestElementwiseAddOp_Vector(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.random((32, )).astype(self.dtype)
self.y = np.random.random((32, )).astype(self.dtype)
self.out = np.add(self.x, self.y)
class TestElementwiseAddOp_broadcast_3(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
'Y': np.random.rand(3, 4).astype(np.float32)
}
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 4, 1)
}
class TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.random((32, )).astype(self.dtype)
self.y = np.random.random((32, )).astype(self.dtype)
self.out = np.add(self.x, self.y)
class TestElementwiseAddOp_broadcast_4(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4, 5).astype(np.float32),
'Y': np.random.rand(2, 1).astype(np.float32)
}
class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(2).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1)
self.attrs = {'axis': 0}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(2, 1, 1, 1)
}
def init_axis(self):
self.axis = 0
class TestElementwiseAddOp_rowwise_add_0(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 3, 4).astype(np.float32),
'Y': np.random.rand(3, 4).astype(np.float32)
}
class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(2).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1)
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 3, 4)
}
def init_axis(self):
self.axis = 0
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseOp):
def setUp(self):
self.op_type = "elementwise_add"
self.inputs = {
'X': np.random.rand(2, 1).astype(np.float32),
'Y': np.random.rand(1).astype(np.float32)
}
class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 1)
self.attrs = {'axis': 1}
self.outputs = {
'Out': self.inputs['X'] + self.inputs['Y'].reshape(1, 1)
}
def init_axis(self):
self.axis = 1
class TestFP16ElementwiseAddOp_broadcast_1(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 1)
def init_axis(self):
self.axis = 1
class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 4)
class TestFP16ElementwiseAddOp_broadcast_2(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1, 4)
class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4, 1)
def init_axis(self):
self.axis = 1
class TestFP16ElementwiseAddOp_broadcast_3(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4, 1)
def init_axis(self):
self.axis = 1
class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(2, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1, 1)
def init_axis(self):
self.axis = 0
class TestFP16ElementwiseAddOp_broadcast_4(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4, 5).astype(self.dtype)
self.y = np.random.rand(2, 1).astype(self.dtype)
self.out = self.x + self.y.reshape(2, 1, 1, 1)
def init_axis(self):
self.axis = 0
class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4)
def init_axis(self):
self.axis = 1
class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 3, 4).astype(self.dtype)
self.y = np.random.rand(3, 4).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 3, 4)
def init_axis(self):
self.axis = 1
class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 1).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1)
def init_axis(self):
self.axis = 1
class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp):
def init_input_output(self):
self.x = np.random.rand(2, 1).astype(self.dtype)
self.y = np.random.rand(1).astype(self.dtype)
self.out = self.x + self.y.reshape(1, 1)
def init_axis(self):
self.axis = 1
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_executor_and_mul.py
浏览文件 @ 79af7cc9
......@@ -16,7 +16,6 @@ import unittest
import numpy
import paddle.fluid.core as core
from paddle.fluid.executor import Executor
from paddle.fluid.layers import mul, data
......
python/paddle/fluid/tests/unittests/test_lrn_op.py
浏览文件 @ 79af7cc9
......@@ -87,5 +87,15 @@ class TestLRNOp(OpTest):
self.check_grad(['X'], 'Out', max_relative_error=0.01)
class TestLRNMKLDNNOp(TestLRNOp):
def get_attrs(self):
attrs = TestLRNOp.get_attrs(self)
attrs['use_mkldnn'] = True
return attrs
def test_check_output(self):
self.check_output(atol=0.002)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_multi_pass_reader.py 0 → 100644
浏览文件 @ 79af7cc9
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle.fluid as fluid
import paddle.v2 as paddle
import paddle.v2.dataset.mnist as mnist
class TestMultipleReader(unittest.TestCase):
def setUp(self):
self.batch_size = 64
self.pass_num = 3
# Convert mnist to recordio file
with fluid.program_guard(fluid.Program(), fluid.Program()):
data_file = paddle.batch(mnist.train(), batch_size=self.batch_size)
feeder = fluid.DataFeeder(
feed_list=[
fluid.layers.data(
name='image', shape=[784]),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
self.num_batch = fluid.recordio_writer.convert_reader_to_recordio_file(
'./mnist.recordio', data_file, feeder)
def test_main(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
data_file = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[(-1, 784), (-1, 1)],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
data_file = fluid.layers.create_multi_pass_reader(
reader=data_file, pass_num=self.pass_num)
img, label = fluid.layers.read_file(data_file)
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
batch_count = 0
while not data_file.eof():
img_val, = exe.run(fetch_list=[img])
batch_count += 1
self.assertLessEqual(img_val.shape[0], self.batch_size)
data_file.reset()
self.assertEqual(batch_count, self.num_batch * self.pass_num)
python/paddle/fluid/tests/unittests/test_multiple_reader.py 0 → 100644
浏览文件 @ 79af7cc9
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import paddle.fluid as fluid
import paddle.v2 as paddle
import paddle.v2.dataset.mnist as mnist
from shutil import copyfile
class TestMultipleReader(unittest.TestCase):
def setUp(self):
self.batch_size = 64
# Convert mnist to recordio file
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(mnist.train(), batch_size=self.batch_size)
feeder = fluid.DataFeeder(
feed_list=[ # order is image and label
fluid.layers.data(
name='image', shape=[784]),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
self.num_batch = fluid.recordio_writer.convert_reader_to_recordio_file(
'./mnist_0.recordio', reader, feeder)
copyfile('./mnist_0.recordio', './mnist_1.recordio')
copyfile('./mnist_0.recordio', './mnist_2.recordio')
def main(self, thread_num):
file_list = [
'./mnist_0.recordio', './mnist_1.recordio', './mnist_2.recordio'
]
with fluid.program_guard(fluid.Program(), fluid.Program()):
data_files = fluid.layers.open_files(
filenames=file_list,
thread_num=thread_num,
shapes=[(-1, 784), (-1, 1)],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(data_files)
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
else:
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
batch_count = 0
while not data_files.eof():
img_val, = exe.run(fetch_list=[img])
batch_count += 1
self.assertLessEqual(img_val.shape[0], self.batch_size)
data_files.reset()
self.assertEqual(batch_count, self.num_batch * 3)
def test_main(self):
self.main(thread_num=3) # thread number equals to file number
self.main(thread_num=10) # thread number is larger than file number
self.main(thread_num=2) # thread number is less than file number
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @ 79af7cc9
......@@ -29,15 +29,20 @@ class TestSoftmaxOp(OpTest):
def setUp(self):
self.op_type = "softmax"
self.use_cudnn = False
self.inputs = {
'X': np.random.uniform(0.1, 1, [10, 10]).astype("float32")
}
self.outputs = {
'Out': np.apply_along_axis(stable_softmax, 1, self.inputs['X'])
self.use_mkldnn = False
self.dtype = np.float32
self.init_kernel_type()
x = np.random.uniform(0.1, 1, [10, 10]).astype(self.dtype)
out = np.apply_along_axis(stable_softmax, 1, x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
self.attrs = {
'use_cudnn': self.use_cudnn,
'use_mkldnn': self.use_mkldnn
}
self.attrs = {'use_cudnn': self.use_cudnn, }
def init_op_type(self):
def init_kernel_type(self):
pass
def test_check_output(self):
......@@ -48,6 +53,8 @@ class TestSoftmaxOp(OpTest):
self.check_output()
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.use_cudnn:
place = core.CUDAPlace(0)
self.check_grad_with_place(
......@@ -57,8 +64,25 @@ class TestSoftmaxOp(OpTest):
class TestSoftmaxCUDNNOp(TestSoftmaxOp):
def init_op_type(self):
def init_kernel_type(self):
self.use_cudnn = True
class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
def init_kernel_type(self):
self.use_cudnn = True
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
def init_kernel_type(self):
self.use_mkldnn = True
if __name__ == "__main__":
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 79af7cc9
......@@ -2747,17 +2747,17 @@ def img_pool_layer(input,
.. math::
w & = 1 + \\frac{ceil(input\_width + 2 * padding - pool\_size)}{stride}
w & = 1 + ceil(\\frac{input\_width + 2 * padding - pool\_size}{stride})
h & = 1 + \\frac{ceil(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y}
h & = 1 + ceil(\\frac{input\_height + 2 * padding\_y - pool\_size\_y}{stride\_y})
- ceil_mode=False:
.. math::
w & = 1 + \\frac{floor(input\_width + 2 * padding - pool\_size)}{stride}
w & = 1 + floor(\\frac{input\_width + 2 * padding - pool\_size}{stride})
h & = 1 + \\frac{floor(input\_height + 2 * padding\_y - pool\_size\_y)}{stride\_y}
h & = 1 + floor(\\frac{input\_height + 2 * padding\_y - pool\_size\_y}{stride\_y})
The example usage is:
......
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