merge develop branch

wangkuiyi-patch-2
dzhwinter 7 years ago
commit 5447046aee

@ -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()

@ -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):
@ -182,28 +183,27 @@ 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(
map(lambda x: x[0].reshape(data_shape), data)).astype(
"float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([-1, 1])
loss, acc, b_size = exe.run(
trainer_prog,
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost, batch_acc, batch_size])
iters += 1
num_samples += len(data)
train_pass_acc.add(value=acc, weight=b_size)
print(
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
% (pass_id, iters, loss, acc,
len(data) / (time.time() - ts))
) # The accuracy is the accumulation of batches, but not the current batch.
for batch_id, data in enumerate(train_reader()):
ts = time.time()
img_data = np.array(
map(lambda x: x[0].reshape(data_shape), data)).astype(
"float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([-1, 1])
loss, acc, b_size = exe.run(
trainer_prog,
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost, batch_acc, batch_size])
iters += 1
num_samples += len(data)
train_pass_acc.add(value=acc, weight=b_size)
print(
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, Speed = %.2f img/s"
% (pass_id, iters, loss, acc,
len(data) / (time.time() - ts))
) # The accuracy is the accumulation of batches, but not the current batch.
pass_elapsed = time.time() - start_time
pass_train_acc = train_pass_acc.eval()
@ -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

@ -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

@ -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}")

@ -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)

@ -4,18 +4,33 @@ 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(
extern_eigen3
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/RLovelett/eigen.git"
GIT_TAG 70661066beef694cadf6c304d0d07e0758825c10
PREFIX ${EIGEN_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
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"
GIT_TAG 70661066beef694cadf6c304d0d07e0758825c10
PREFIX ${EIGEN_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
endif()
if (${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/eigen3_dummy.c)

@ -34,7 +34,7 @@ SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR}/${MKLML_VER})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
@ -46,7 +46,7 @@ INCLUDE_DIRECTORIES(${MKLML_INC_DIR})
FILE(WRITE ${MKLML_DOWNLOAD_DIR}/CMakeLists.txt
"PROJECT(MKLML)\n"
"cmake_minimum_required(VERSION 3.0)\n"
"install(DIRECTORY ${MKLML_VER}\n"
"install(DIRECTORY ${MKLML_VER}/include ${MKLML_VER}/lib \n"
" DESTINATION ${MKLML_DST_DIR})\n")
ExternalProject_Add(

@ -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 "")

@ -0,0 +1,43 @@
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")

@ -69,6 +69,12 @@ if(NOT CBLAS_FOUND)
SRCS ${CBLAS_INSTALL_DIR}/lib ${CBLAS_INSTALL_DIR}/include
DSTS ${dst_dir} ${dst_dir}
)
elseif (WITH_MKLML)
set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/mklml")
copy(mklml_lib
SRCS ${MKLML_LIB} ${MKLML_IOMP_LIB} ${MKLML_INC_DIR}
DSTS ${dst_dir}/lib ${dst_dir}/lib ${dst_dir}
)
endif()
# paddle fluid module

@ -1 +1,2 @@
add_subdirectory(v2)
add_subdirectory(fluid)

@ -0,0 +1,49 @@
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})

@ -0,0 +1,2 @@
安装与使用
------------

@ -0,0 +1,2 @@
Build and Install
------------

@ -0,0 +1,231 @@
# go_op Design
## Introduction
The **go_op** allows user's of PaddlePaddle to run program blocks on a detached
thread. It works in conjuction with CSP operators (channel_send,
channel_receive, channel_open, channel_close, and select) to allow users to
concurrently process data and communicate easily between different threads.
## How to use it
```
channel = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
with fluid.Go():
# Send a tensor of value 99 to "channel" on a detached thread
tensor = fill_constant(shape=[1], dtype='int', value=99)
tensor.stop_gradient = True
fluid.channel_send(channel, tensor)
# Receive sent tensor from "channel" on the main thread
result = fill_constant(shape=[1], dtype='int', value=-1)
fluid.channel_recv(ch, result)
```
The go operator can be accessed by using the fluid.Go() control flow. This
will create a new sub block, where the user can add additional operators
to be ran on the thread.
**Note:** Since back propegation is currently not support in the go_op, users
should ensure that operators in the go block does not require gradient
calculations.
## How it Works
Similar to other control blocks, go_op will create a sub block and add it
as a child to the current block. Operators and variables defined in this
block will be added to the go sub_block.
In addition, the go operator will create a new child scope whose parent is
the global scope. Please refer to [block captures](#block-captures) for more
information.
When Paddle executor runs go_op, go_op will take the sub_block and pass it to
the executor.run method (along with a newly created local scope) on a detached
thread.
An example of the generated program description is shown below. Take note of
the **go_op** in particular. It is added as an operator in the current
block (in this example, block0). The **go_op** contains a `sub_block`
attribute, which points to the id of the block that will be executed in a
detached thread.
```
blocks {
idx: 0
parent_idx: -1
vars {
name: "return_value"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: INT64
}
}
}
}
vars {
name: "status_recv"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: BOOL
}
}
}
}
...
ops {
outputs {
parameter: "Out"
arguments: "channel"
}
type: "channel_create"
attrs {
name: "data_type"
type: INT
i: 7
}
attrs {
name: "capacity"
type: INT
i: 0
}
}
ops {
inputs {
parameter: "X"
arguments: "channel"
}
type: "go"
attrs {
name: "sub_block"
type: BLOCK
block_idx: 1
}
}
ops {
inputs {
parameter: "Channel"
arguments: "channel"
}
outputs {
parameter: "Out"
arguments: "return_value"
}
outputs {
parameter: "Status"
arguments: "status_recv"
}
type: "channel_recv"
}
...
}
blocks {
idx: 1
parent_idx: 0
vars {
name: "status"
type {
type: LOD_TENSOR
lod_tensor {
tensor {
data_type: BOOL
}
}
}
}
...
ops {
outputs {
parameter: "Out"
arguments: "fill_constant_1.tmp_0"
}
type: "fill_constant"
attrs {
name: "force_cpu"
type: BOOLEAN
b: false
}
attrs {
name: "value"
type: FLOAT
f: 99.0
}
attrs {
name: "shape"
type: INTS
ints: 1
}
attrs {
name: "dtype"
type: INT
i: 3
}
}
ops {
inputs {
parameter: "Channel"
arguments: "channel"
}
inputs {
parameter: "X"
arguments: "fill_constant_1.tmp_0"
}
outputs {
parameter: "Status"
arguments: "status"
}
type: "channel_send"
attrs {
name: "copy"
type: BOOLEAN
b: false
}
}
```
## Current Limitations
#### <a name="block-captures"></a>Scopes and block captures:
Paddle utilizes [scopes](./../concepts/scope.md) to store variables used in a
block. When a block is executed, a new local scope is created from the parent
scope (ie: scope derived from the parent block) and associated with the new
child block. After the block finishes executing, then the local scope and
all associated variables in the scope is deleted.
This works well in a single threaded scenario, however with introduction of
go_op, a child block may continue to execute even after the parent block has
exited. If the go_op tries to access variables located in the parent block's
scope, it may receive a segmentation fault because the parent scope may have
been deleted.
We need to implement block closures in order to prevent access to parent
scope variables from causing a segmentation fault. As a temporary workaround,
please ensure that all variables accessed in the go block is not destructed
before it is being accessed. Currently, the go_op will explicitly enforce
this requirement and raise an exception if a variable could not be found in
the scope.
Please refer to [Closure issue](https://github.com/PaddlePaddle/Paddle/issues/8502)
for more details.
#### Green Threads
Golang utilizes `green threads`, which is a mechnism for the runtime library to
manage multiple threads (instead of natively by the OS). Green threads usually
allows for faster thread creation and switching, as there is less overhead
when spawning these threads. For the first version of CSP, we only support
OS threads.
#### Backward Propegation:
go_op currently does not support backwards propagation. Please use go_op with
non training operators.

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@ -26,7 +26,7 @@ lookup of rows.
The following figure illustrates the multiplication of x with two
non-zero elements, or say, two symbols, and a lookup table W:
![lookup table](./lookup_table.png)
![lookup table](./src/lookup_table.png)
### The Backward Algorithm
@ -42,7 +42,7 @@ or some more sophisticated algorithms that rely on both W' and W:
$$W = f(W, W')$$
The following figure illustrates the backward pass of the lookup
operator: ![lookup table training](./lookup_table_training.png)
operator: ![lookup table training](./src/lookup_table_training.png)
## Distributed Storage Service

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@ -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>

@ -0,0 +1,2 @@
设计思想
------------

@ -0,0 +1,2 @@
Design
------------

@ -103,7 +103,7 @@ In computability theory, a system of data-manipulation rules, such as a programm
There are two ways to execute a Fluid program. When a program is executed, it creates a protobuf message [`ProgramDesc`](https://github.com/PaddlePaddle/Paddle/blob/a91efdde6910ce92a78e3aa7157412c4c88d9ee8/paddle/framework/framework.proto#L145) that describes the process and is conceptually like an [abstract syntax tree](https://en.wikipedia.org/wiki/Abstract_syntax_tree).
There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program.
There is a C++ class [`Executor`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/framework/executor.h), which runs a `ProgramDesc`, similar to how an interpreter runs a Python program.
Fluid is moving towards the direction of a compiler, which is explain in [fluid_compiler.md](fluid_compiler.md).

@ -0,0 +1,2 @@
开发标准
------------

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