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提交 43ba24e0 编写于 作者: D dongzhihong
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Merge remote-tracking branch 'origin/develop' into add_op_gradient

上级 7c0cb0c7 ffbb0be2
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Showing with 4455 addition and 2434 deletion
.gitignore
浏览文件 @ 43ba24e0
......@@ -24,4 +24,5 @@ cmake-build-*
python/paddle/v2/framework/core.so
CMakeFiles
cmake_install.cmake
paddle/.timestamp
python/paddlepaddle.egg-info/
.travis.yml
浏览文件 @ 43ba24e0
......@@ -37,8 +37,8 @@ before_install:
- if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
# Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
# protobuf version.
- pip install numpy wheel 'protobuf==3.1' sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit requests==2.9.2 LinkChecker
- pip install rarfile nltk==3.2.2 scipy==0.19.0 recordio matplotlib Pillow
- pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt
- pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker
- curl https://glide.sh/get | bash
- eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
- go get -u github.com/alecthomas/gometalinter
......
CMakeLists.txt
浏览文件 @ 43ba24e0
......@@ -14,8 +14,8 @@
cmake_minimum_required(VERSION 3.0)
set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake")
set(PROJ_ROOT ${CMAKE_CURRENT_SOURCE_DIR})
set(PROJ_BINARY_ROOT ${CMAKE_CURRENT_BINARY_DIR})
set(PADDLE_SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR})
set(PADDLE_BINARY_DIR ${CMAKE_CURRENT_BINARY_DIR})
include(system)
......@@ -36,8 +36,8 @@ include(simd)
################################ Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." ${AVX_FOUND})
option(WITH_MKLML "Compile PaddlePaddle with mklml package." ${AVX_FOUND})
option(WITH_MKLDNN "Compile PaddlePaddle with mkl-dnn support." OFF)
option(WITH_MKLML "Compile PaddlePaddle with mklml package." OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" ON)
option(WITH_SWIG_PY "Compile PaddlePaddle with inference api" ON)
......@@ -121,8 +121,8 @@ include(version) # set PADDLE_VERSION
include(coveralls) # set code coverage
include_directories("${PROJ_ROOT}")
include_directories("${PROJ_ROOT}/paddle/cuda/include")
include_directories("${PADDLE_SOURCE_DIR}")
include_directories("${PADDLE_SOURCE_DIR}/paddle/cuda/include")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/proto")
include_directories("${CMAKE_CURRENT_BINARY_DIR}/go/pserver/client/c")
include_directories(${Boost_INCLUDE_DIRS})
......@@ -144,7 +144,7 @@ if(WITH_GPU)
endif(WITH_GPU)
if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIBRARY} ${MKLDNN_IOMP_LIB})
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB} ${MKLDNN_IOMP_LIB})
endif()
if(USE_NNPACK)
......@@ -164,10 +164,12 @@ if(WITH_GOLANG)
add_subdirectory(go)
endif(WITH_GOLANG)
set(PADDLE_PYTHON_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}/python/build")
add_subdirectory(paddle)
if(WITH_PYTHON)
add_subdirectory(python)
endif()
if(WITH_DOC)
add_subdirectory(doc)
endif()
Dockerfile
浏览文件 @ 43ba24e0
......@@ -64,13 +64,28 @@ RUN pip install --upgrade pip && \
pip install -U sphinx-rtd-theme==0.1.9 recommonmark && \
pip install pre-commit 'requests==2.9.2' 'ipython==5.3.0' && \
pip install 'ipykernel==4.6.0' 'jupyter==1.0.0' && \
pip install rarfile
pip install opencv-python rarfile 'scipy>=0.19.0' 'nltk>=3.2.2'
# To fix https://github.com/PaddlePaddle/Paddle/issues/1954, we use
# the solution in https://urllib3.readthedocs.io/en/latest/user-guide.html#ssl-py2
RUN apt-get install -y libssl-dev libffi-dev
RUN pip install certifi urllib3[secure]
# TODO(qijun) The template library Eigen doesn't work well with GCC 5
# coming with the default Docker image, so we switch to use GCC 4.8
# by default. And I will check Eigen library later.
RUN ln -sf gcc-4.8 /usr/bin/gcc && \
ln -sf gcc-ar-4.8 /usr/bin/gcc-ar && \
ln -sf gcc-nm-4.8 /usr/bin/gcc-nm && \
ln -sf gcc-ranlib-4.8 /usr/bin/gcc-ranlib && \
ln -sf gcc-4.8 /usr/bin/x86_64-linux-gnu-gcc && \
ln -sf gcc-ar-4.8 /usr/bin/x86_64-linux-gnu-gcc-ar && \
ln -sf gcc-nm-4.8 /usr/bin/x86_64-linux-gnu-gcc-nm && \
ln -sf gcc-ranlib-4.8 /usr/bin/x86_64-linux-gnu-gcc-ranlib && \
ln -sf g++-4.8 /usr/bin/g++ && \
ln -sf g++-4.8 /usr/bin/x86_64-linux-gnu-g++
# Install woboq_codebrowser to /woboq
RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \
(cd /woboq \
......
cmake/configure.cmake
浏览文件 @ 43ba24e0
......@@ -129,7 +129,7 @@ if(WITH_GOLANG)
add_custom_command(OUTPUT ${CMAKE_BINARY_DIR}/glide
COMMAND env GOPATH=${GOPATH} ${GLIDE} install
COMMAND touch ${CMAKE_BINARY_DIR}/glide
DEPENDS ${PROJ_ROOT}/go/glide.lock
DEPENDS ${PADDLE_SOURCE_DIR}/go/glide.lock
WORKING_DIRECTORY "${PADDLE_IN_GOPATH}/go"
)
......
cmake/cpplint.cmake
浏览文件 @ 43ba24e0
......@@ -52,7 +52,7 @@ macro(add_style_check_target TARGET_NAME)
if(SOURCES_LIST)
add_custom_command(TARGET ${TARGET_NAME} POST_BUILD
COMMAND "${PYTHON_EXECUTABLE}" "${PROJ_ROOT}/paddle/scripts/cpplint.py"
COMMAND "${PYTHON_EXECUTABLE}" "${PADDLE_SOURCE_DIR}/paddle/scripts/cpplint.py"
"--filter=${STYLE_FILTER}"
${SOURCES_LIST}
COMMENT "cpplint: Checking source code style"
......
cmake/flags.cmake
浏览文件 @ 43ba24e0
......@@ -9,10 +9,12 @@ function(CheckCompilerCXX11Flag)
if(${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 4.8)
message(FATAL_ERROR "Unsupported GCC version. GCC >= 4.8 required.")
endif()
# TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem.
# Use Debug mode instead for now.
if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9)
set(CMAKE_BUILD_TYPE "Debug" CACHE STRING "" FORCE)
if(NOT ANDROID)
# TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem.
# Use Debug mode instead for now.
if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9)
set(CMAKE_BUILD_TYPE "Debug" CACHE STRING "" FORCE)
endif()
endif()
elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang")
# cmake >= 3.0 compiler id "AppleClang" on Mac OS X, otherwise "Clang"
......
cmake/generic.cmake
浏览文件 @ 43ba24e0
......@@ -411,7 +411,7 @@ function(py_test TARGET_NAME)
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_test(NAME ${TARGET_NAME}
COMMAND env PYTHONPATH=${PADDLE_PYTHON_PACKAGE_DIR}
COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python
python2 ${py_test_SRCS}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
endif()
......
cmake/package.cmake
浏览文件 @ 43ba24e0
......@@ -12,7 +12,7 @@ set(CPACK_PACKAGE_DESCRIPTION "")
set(CPACK_DEBIAN_PACKAGE_DEPENDS "libpython2.7-dev, libstdc++6, python-pip, curl, libgfortran3, python-pip-whl")
set(CPACK_DEBIAN_PACKAGE_SECTION Devel)
set(CPACK_DEBIAN_PACKAGE_VERSION ${PADDLE_VERSION})
set(CPACK_DEBIAN_PACKAGE_CONTROL_EXTRA "${PROJ_ROOT}/paddle/scripts/deb/postinst")
set(CPACK_DEBIAN_PACKAGE_CONTROL_EXTRA "${PADDLE_SOURCE_DIR}/paddle/scripts/deb/postinst")
#set(CPACK_GENERATOR "DEB")
# Start cpack
include (CMakePackageConfigHelpers)
......
cmake/util.cmake
浏览文件 @ 43ba24e0
......@@ -141,8 +141,8 @@ endmacro()
function(create_resources res_file output_file)
add_custom_command(
OUTPUT ${output_file}
COMMAND python ARGS ${PROJ_ROOT}/cmake/make_resource.py ${res_file} ${output_file}
DEPENDS ${res_file} ${PROJ_ROOT}/cmake/make_resource.py)
COMMAND python ARGS ${PADDLE_SOURCE_DIR}/cmake/make_resource.py ${res_file} ${output_file}
DEPENDS ${res_file} ${PADDLE_SOURCE_DIR}/cmake/make_resource.py)
endfunction()
......
cmake/version.cmake
浏览文件 @ 43ba24e0
......@@ -4,7 +4,7 @@ set(tmp_version "HEAD")
while ("${PADDLE_VERSION}" STREQUAL "")
execute_process(
COMMAND ${GIT_EXECUTABLE} describe --tags --abbrev=0 ${tmp_version}
WORKING_DIRECTORY ${PROJ_ROOT}
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
OUTPUT_VARIABLE GIT_TAG_NAME
RESULT_VARIABLE GIT_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
......
doc/design/auto_gradient_check.md 0 → 100644
浏览文件 @ 43ba24e0
## Auto Gradient Checker Design
## Backgraound：
- Operator forward computing is easy to check if the result is right because it has a clear definition. **But** backpropagation is a notoriously difficult algorithm to debug and get right:
- 1. you should get the right backpropagation formula according to the forward computation.
- 2. you should implement it right in CPP.
- 3. it's difficult to prepare test data.
- Auto gradient check gets a numeric gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
- 1. numeric gradient checker only need forward operator.
- 2. user only need to prepare the input data for forward Operator.
## Mathematical Theory
The following two document from stanford has a detailed explanation of how to get numeric gradient and why it's useful.
- [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
- [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
## Numeric Gradient Implementation
### Python Interface
```python
def get_numeric_gradient(op,
input_values,
output_name,
input_to_check,
delta=0.005,
local_scope=None):
"""
Get Numeric Gradient for an operator's input.
:param op: C++ operator instance, could be an network
:param input_values: The input variables. Should be an dictionary, key is
variable name. Value is numpy array.
:param output_name: The final output variable name.
:param input_to_check: The input variable need to get gradient.
:param delta: The perturbation value for numeric gradient method. The
smaller delta is, the more accurate result will get. But if that delta is
too small, it could occur numerical stability problem.
:param local_scope: The local scope used for get_numeric_gradient.
:return: The gradient array in numpy format.
"""
```
### Explaination:
- Why need `output_name`
- One Operator may have multiple Output, you can get independent gradient from each Output. So user should set one output to calculate.
- Why need `input_to_check`
- One operator may have multiple inputs. Gradient Op can calculate the gradient of these Inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
### Core Algorithm Implementation
```python
# we only compute gradient of one element each time.
# we use a for loop to compute the gradient of every element.
for i in xrange(tensor_size):
# get one input element throw it's index i.
origin = tensor_to_check.get_float_element(i)
# add delta to it, run op and then get the sum of the result tensor.
x_pos = origin + delta
tensor_to_check.set_float_element(i, x_pos)
y_pos = get_output()
# plus delta to this element, run op and get the sum of the result tensor.
x_neg = origin - delta
tensor_to_check.set_float_element(i, x_neg)
y_neg = get_output()
# restore old value
tensor_to_check.set_float_element(i, origin)
# compute the gradient of this element and store it into a numpy array.
gradient_flat[i] = (y_pos - y_neg) / delta / 2
# reshape the gradient result to the shape of the source tensor.
return gradient_flat.reshape(tensor_to_check.get_dims())
```
## Auto Graident Checker Framework
Each Operator Kernel has three kinds of Gradient:
- 1. Numeric Gradient
- 2. CPU Operator Gradient
- 3. GPU Operator Gradient(if supported)
Numeric Gradient Only relies on forward Operator. So we use Numeric Gradient as the reference value.
- 1. calculate the numeric gradient.
- 2. calculate CPU kernel Gradient with the backward Operator and compare it with the numeric gradient.
- 3. calculate GPU kernel Gradient with the backward Operator and compare it with the numeric gradient.(if support GPU)
#### Python Interface
```python
def check_grad(self,
forward_op,
input_vars,
inputs_to_check,
output_name,
no_grad_set=None,
only_cpu=False,
max_relative_error=0.005):
"""
:param forward_op: used to create backward_op
:param input_vars: numpy value of input variable. The following
computation will use these variables.
:param inputs_to_check: inputs var names that should check gradient.
:param output_name: output name that used to
:param max_relative_error: The relative tolerance parameter.
:param no_grad_set: used when create backward ops
:param only_cpu: only compute and check gradient on cpu kernel.
:return:
"""
```
### How to check if two numpy array is close enough?
if `abs_numeric_grad` is nearly zero, then use abs error for numeric_grad, not relative
```python
numeric_grad = ...
operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
abs_numeric_grad = numpy.abs(numeric_grad)
# if abs_numeric_grad is nearly zero, then use abs error for numeric_grad, not relative
# error.
abs_numeric_grad[abs_numeric_grad < 1e-3] = 1
diff_mat = numpy.abs(abs_numeric_grad - operator_grad) / abs_numeric_grad
max_diff = numpy.max(diff_mat)
```
#### Notes：
1，The Input data for auto gradient checker should be reasonable to avoid numeric problem.
#### Refs:
- [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
- [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
doc/getstarted/build_and_install/docker_install_cn.rst
浏览文件 @ 43ba24e0
......@@ -74,13 +74,13 @@ PaddlePaddle发布新版本的时候都会发布对应版本的生产镜像以
.. code-block:: bash
docker run -it --rm paddlepaddle/paddle:0.10.0-dev /bin/bash
docker run -it --rm -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /bin/bash
或者，可以以后台进程方式运行容器：
.. code-block:: bash
docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0-dev
docker run -d -p 2202:22 -p 8888:8888 -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /usr/sbin/sshd -D
然后用密码 :code:`root` SSH进入容器：
......
doc/templates/conf.py.cn.in
浏览文件 @ 43ba24e0
......@@ -13,7 +13,7 @@
# serve to show the default.
import sys
import os, subprocess
sys.path.insert(0, os.path.abspath('@PROJ_ROOT@/python'))
sys.path.insert(0, os.path.abspath('@PADDLE_SOURCE_DIR@/python'))
import shlex
from recommonmark import parser, transform
import paddle
......@@ -24,7 +24,7 @@ AutoStructify = transform.AutoStructify
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
templates_path = ["@PROJ_ROOT@/doc_theme/templates"]
templates_path = ["@PADDLE_SOURCE_DIR@/doc_theme/templates"]
# -- General configuration ------------------------------------------------
......@@ -120,7 +120,7 @@ html_theme = 'sphinx_rtd_theme'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['@PROJ_ROOT@/doc_theme/static']
html_static_path = ['@PADDLE_SOURCE_DIR@/doc_theme/static']
# Output file base name for HTML help builder.
htmlhelp_basename = project + 'doc'
......
doc/templates/conf.py.en.in
浏览文件 @ 43ba24e0
......@@ -13,7 +13,7 @@
# serve to show the default.
import sys
import os, subprocess
sys.path.insert(0, os.path.abspath('@PROJ_ROOT@/python'))
sys.path.insert(0, os.path.abspath('@PADDLE_SOURCE_DIR@/python'))
import shlex
from recommonmark import parser, transform
import paddle
......@@ -25,7 +25,7 @@ AutoStructify = transform.AutoStructify
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
templates_path = ["@PROJ_ROOT@/doc_theme/templates"]
templates_path = ["@PADDLE_SOURCE_DIR@/doc_theme/templates"]
# -- General configuration ------------------------------------------------
......@@ -120,7 +120,7 @@ html_theme = 'sphinx_rtd_theme'
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['@PROJ_ROOT@/doc_theme/static']
html_static_path = ['@PADDLE_SOURCE_DIR@/doc_theme/static']
# Output file base name for HTML help builder.
htmlhelp_basename = project + 'doc'
......
go/pserver/client/c/test/test_train.py
浏览文件 @ 43ba24e0
......@@ -17,12 +17,10 @@ def main():
# network config
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x,
param_attr=paddle.attr.Param(
name='w', learning_rate=1e-3),
param_attr=paddle.attr.Param(name='w'),
size=1,
act=paddle.activation.Linear(),
bias_attr=paddle.attr.Param(
name='b', learning_rate=1e-3))
bias_attr=paddle.attr.Param(name='b'))
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.mse_cost(input=y_predict, label=y)
......
paddle/api/CMakeLists.txt
浏览文件 @ 43ba24e0
......@@ -19,9 +19,9 @@ add_library(paddle_api STATIC ${API_SOURCES})
add_dependencies(paddle_api paddle_proto paddle_trainer_lib)
INCLUDE(${SWIG_USE_FILE})
INCLUDE_DIRECTORIES(${PROJ_ROOT}/paddle)
INCLUDE_DIRECTORIES(${PADDLE_SOURCE_DIR}/paddle)
FILE(GLOB PY_PADDLE_PYTHON_FILES ${PROJ_ROOT}/paddle/py_paddle/*.py)
FILE(GLOB PY_PADDLE_PYTHON_FILES ${PADDLE_SOURCE_DIR}/paddle/py_paddle/*.py)
SET_SOURCE_FILES_PROPERTIES(Paddle.i PROPERTIES CPLUSPLUS ON)
......@@ -79,16 +79,16 @@ SWIG_LINK_LIBRARIES(swig_paddle
${START_END}
)
add_custom_command(OUTPUT ${PROJ_ROOT}/paddle/py_paddle/_swig_paddle.so
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/swig_paddle.py ${PROJ_ROOT}/paddle/py_paddle
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/_swig_paddle.so ${PROJ_ROOT}/paddle/py_paddle
add_custom_command(OUTPUT ${PADDLE_SOURCE_DIR}/paddle/py_paddle/_swig_paddle.so
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/swig_paddle.py ${PADDLE_SOURCE_DIR}/paddle/py_paddle
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/_swig_paddle.so ${PADDLE_SOURCE_DIR}/paddle/py_paddle
COMMAND ${CMAKE_COMMAND} -E touch .timestamp
WORKING_DIRECTORY ${PROJ_ROOT}/paddle
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
DEPENDS _swig_paddle
)
# TODO(yuyang18) : make wheel name calculated by cmake
add_custom_target(python_api_wheel ALL DEPENDS ${PROJ_ROOT}/paddle/py_paddle/_swig_paddle.so)
add_custom_target(python_api_wheel ALL DEPENDS ${PADDLE_SOURCE_DIR}/paddle/py_paddle/_swig_paddle.so)
if(WITH_TESTING)
IF(NOT PY_PIP_FOUND)
......
paddle/api/ParameterUpdater.cpp
浏览文件 @ 43ba24e0
......@@ -41,7 +41,7 @@ ParameterUpdater *ParameterUpdater::createNewRemoteUpdater(
config->m->getConfig(), pserverSpec, useEtcd));
return updater;
#else
throw UnsupportError();
throw UnsupportError("not compiled with WITH_GOLANG");
#endif
}
......
paddle/capi/Arguments.cpp
浏览文件 @ 43ba24e0
......@@ -90,6 +90,18 @@ paddle_error paddle_arguments_set_ids(paddle_arguments args,
return kPD_NO_ERROR;
}
paddle_error paddle_arguments_set_frame_shape(paddle_arguments args,
uint64_t ID,
uint64_t frameHeight,
uint64_t frameWidth) {
if (args == nullptr) return kPD_NULLPTR;
auto a = castArg(args);
if (ID >= a->args.size()) return kPD_OUT_OF_RANGE;
a->args[ID].setFrameHeight(frameHeight);
a->args[ID].setFrameWidth(frameWidth);
return kPD_NO_ERROR;
}
paddle_error paddle_arguments_set_sequence_start_pos(paddle_arguments args,
uint64_t ID,
uint32_t nestedLevel,
......
paddle/capi/arguments.h
浏览文件 @ 43ba24e0
......@@ -111,6 +111,20 @@ PD_API paddle_error paddle_arguments_set_ids(paddle_arguments args,
uint64_t ID,
paddle_ivector ids);
/**
* @brief paddle_arguments_set_frame_shape Set the fram size of one argument
* in array, which index is `ID`.
* @param [in] args arguments array
* @param [in] ID array index
* @param [in] frameHeight maximum height of input images
* @param [in] frameWidth maximum width of input images
* @return paddle_error
*/
PD_API paddle_error paddle_arguments_set_frame_shape(paddle_arguments args,
uint64_t ID,
uint64_t frameHeight,
uint64_t frameWidth);
/**
* @brief PDArgsSetSequenceStartPos Set sequence start position vector of one
* argument in array, which index is `ID`.
......
paddle/capi/examples/model_inference/common/common.h
浏览文件 @ 43ba24e0
......@@ -3,18 +3,21 @@
#include <stdio.h>
#include <stdlib.h>
#define CHECK(stmt) \
do { \
paddle_error __err__ = stmt; \
if (__err__ != kPD_NO_ERROR) { \
fprintf(stderr, "Invoke paddle error %d \n" #stmt, __err__); \
exit(__err__); \
} \
#define CHECK(stmt) \
do { \
paddle_error __err__ = stmt; \
if (__err__ != kPD_NO_ERROR) { \
fprintf(stderr, "Invoke paddle error %d in " #stmt "\n", __err__); \
exit(__err__); \
} \
} while (0)
void* read_config(const char* filename, long* size) {
FILE* file = fopen(filename, "r");
if (file == NULL) return NULL;
if (file == NULL) {
fprintf(stderr, "Open %s error\n", filename);
return NULL;
}
fseek(file, 0L, SEEK_END);
*size = ftell(file);
fseek(file, 0L, SEEK_SET);
......
paddle/capi/gradient_machine.cpp
浏览文件 @ 43ba24e0
......@@ -54,6 +54,31 @@ paddle_error paddle_gradient_machine_create_for_inference(
return kPD_NO_ERROR;
}
paddle_error paddle_gradient_machine_create_for_inference_with_parameters(
paddle_gradient_machine* machine, void* mergedModel, uint64_t size) {
if (mergedModel == nullptr) return kPD_NULLPTR;
std::istringstream is(std::string(static_cast<char*>(mergedModel), size));
int64_t modelConfigSize = 0;
is.read((char*)(&modelConfigSize), sizeof(modelConfigSize));
std::string modelConfigProtobuf;
modelConfigProtobuf.resize(modelConfigSize);
is.read(&modelConfigProtobuf[0], modelConfigSize);
paddle::TrainerConfig config;
if (!config.ParseFromString(modelConfigProtobuf) || !config.IsInitialized()) {
return kPD_PROTOBUF_ERROR;
}
auto ptr = new paddle::capi::CGradientMachine();
ptr->machine.reset(paddle::GradientMachine::create(
config.model_config(), CREATE_MODE_TESTING, {paddle::PARAMETER_VALUE}));
std::vector<paddle::ParameterPtr>& parameters = ptr->machine->getParameters();
for (auto& para : parameters) {
para->load(is);
}
*machine = ptr;
return kPD_NO_ERROR;
}
paddle_error paddle_gradient_machine_destroy(paddle_gradient_machine machine) {
delete cast(machine);
return kPD_NO_ERROR;
......
paddle/capi/gradient_machine.h
浏览文件 @ 43ba24e0
......@@ -36,6 +36,18 @@ typedef void* paddle_gradient_machine;
PD_API paddle_error paddle_gradient_machine_create_for_inference(
paddle_gradient_machine* machine, void* modelConfigProtobuf, int size);
/**
* @brief Create a gradient machine used for model inference, using config with
* parameters which is generated by `paddle merge_model`.
* @param [out] machine that used for model inference.
* @param [in] mergedModel
* @param [in] size
* @return paddle_error
*/
PD_API paddle_error
paddle_gradient_machine_create_for_inference_with_parameters(
paddle_gradient_machine* machine, void* mergedModel, uint64_t size);
/**
* @brief Load parameter from disk.
* @param machine Gradient Machine.
......
paddle/capi/tests/CMakeLists.txt
浏览文件 @ 43ba24e0
......@@ -10,5 +10,5 @@ target_include_directories(capi_test_gradientMachine PUBLIC
${PADDLE_CAPI_INC_PATH})
target_link_libraries(capi_test_gradientMachine paddle_capi)
add_test(NAME capi_test_gradientMachine
COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine
WORKING_DIRECTORY ${PROJ_ROOT}/paddle/capi/tests)
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests)
paddle/framework/CMakeLists.txt
浏览文件 @ 43ba24e0
......@@ -7,7 +7,7 @@ cc_library(tensor SRCS tensor.cc DEPS ddim place paddle_memory device_context)
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc details/lod_tensor.cc DEPS ddim place tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
......@@ -15,26 +15,27 @@ cc_test(variable_test SRCS variable_test.cc)
cc_library(scope SRCS scope.cc)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
proto_library(attribute_proto SRCS attribute.proto)
proto_library(op_proto SRCS op_proto.proto DEPS attribute_proto)
proto_library(op_desc SRCS op_desc.proto DEPS attribute_proto)
cc_test(op_proto_test SRCS op_proto_test.cc DEPS op_proto protobuf)
cc_test(op_desc_test SRCS op_desc_test.cc DEPS op_desc protobuf)
proto_library(framework_proto SRCS framework.proto)
cc_library(attribute SRCS attribute.cc DEPS op_desc op_proto)
cc_library(attribute SRCS attribute.cc DEPS framework_proto)
cc_library(operator SRCS operator.cc DEPS op_desc device_context tensor scope attribute)
cc_library(operator SRCS operator.cc DEPS framework_proto device_context tensor scope attribute)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS op_proto operator)
cc_library(op_registry SRCS op_registry.cc DEPS op_desc grad_op_builder)
cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator)
cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder)
cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry)
cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op)
py_proto_compile(framework_py_proto SRCS attribute.proto op_proto.proto op_desc.proto)
py_proto_compile(framework_py_proto SRCS framework.proto)
# Generate an empty __init__.py to make framework_py_proto as a valid python module.
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/v2/framework/proto/
COMMENT "Copy generated python proto into directory paddle/v2/framework/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
cc_library(backward SRCS backward.cc DEPS net_op)
cc_test(backward_test SRCS backward_test.cc DEPS backward)
......@@ -43,12 +44,16 @@ if(WITH_PYTHON)
cc_library(paddle_pybind SHARED
SRCS pybind.cc
DEPS pybind python backward
fc_op
sgd_op
add_op
mul_op
rowwise_add_op
sigmoid_op
softmax_op
mean_op
cross_entropy_op
recurrent_op
uniform_random_op
gaussian_random_op
fill_zeros_like_op)
endif(WITH_PYTHON)
paddle/framework/attribute.cc
浏览文件 @ 43ba24e0
......@@ -44,7 +44,7 @@ AttrType AttrTypeID<std::vector<std::string>>() {
return STRINGS;
}
Attribute GetAttrValue(const AttrDesc& attr_desc) {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
case paddle::framework::AttrType::INT: {
return attr_desc.i();
......
paddle/framework/attribute.h
浏览文件 @ 43ba24e0
......@@ -14,16 +14,15 @@ limitations under the License. */
#pragma once
#include <boost/variant.hpp>
#include <functional>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include "paddle/framework/attribute.pb.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/variant.h"
namespace paddle {
namespace framework {
......@@ -37,7 +36,7 @@ typedef std::unordered_map<std::string, Attribute> AttributeMap;
template <typename T>
AttrType AttrTypeID();
Attribute GetAttrValue(const AttrDesc& attr_desc);
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
// check whether a value(attribute) fit a certain limit
template <typename T>
......
paddle/framework/backward.cc
浏览文件 @ 43ba24e0
......@@ -21,15 +21,24 @@
namespace paddle {
namespace framework {
static bool AllInSet(const std::vector<std::string>& names,
const std::string& suffix,
const std::unordered_set<std::string>& set) {
template <typename Map, typename T>
static void ForEachVarName(Map& names, T callback) {
for (auto& name : names) {
if (set.find(name + suffix) == set.end()) {
return false;
for (auto& n : name.second) {
if (callback(n)) return;
}
}
return true;
}
static bool AllInSet(
const std::map<std::string, std::vector<std::string>>& names,
const std::string& suffix, const std::unordered_set<std::string>& set) {
bool all_in_set = true;
ForEachVarName(names, [&all_in_set, &set, &suffix](const std::string& n) {
all_in_set = set.find(n + suffix) != set.end();
return !all_in_set;
});
return all_in_set;
}
static std::shared_ptr<OperatorBase> NOP() {
......@@ -68,10 +77,11 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
// Then all input gradients cannot be computed at all, and we put them into
// `no_grad_names` set. Return an NOP.
if (AllInSet(forwardOp.outputs_, kGradVarSuffix, no_grad_names)) {
for (auto& name : forwardOp.inputs_) {
// Mark all input is not need
no_grad_names.insert(name + kGradVarSuffix);
}
ForEachVarName(forwardOp.inputs_,
[&no_grad_names](const std::string& name) -> bool {
no_grad_names.insert(GradVarName(name));
return false;
});
return NOP();
}
......@@ -93,9 +103,11 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
auto fwd = *it;
auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id);
net->AddOp(bwd);
for (auto& out : bwd->outputs_) {
dup_output_ops[out].emplace_back(local_op_id);
}
ForEachVarName(bwd->outputs_,
[&dup_output_ops, local_op_id](const std::string& out) {
dup_output_ops[out].emplace_back(local_op_id);
return false;
});
}
// Get unique ID for this method.
auto uid = uniq_id++;
......@@ -117,7 +129,7 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
insert_position.push_back(
{dup_op.back(),
OpRegistry::CreateOp(
"add", {dup_outputs}, {name},
"add", {{"X", {dup_outputs}}}, {{"Out", {name}}},
{{"input_format",
std::vector<int>{0, static_cast<int>(dup_outputs.size())}}})});
}
......@@ -131,24 +143,30 @@ std::shared_ptr<OperatorBase> BackwardRecursive(
} else {
std::shared_ptr<OperatorBase> grad_op = OpRegistry::CreateGradOp(forwardOp);
for (std::string& grad_input : grad_op->inputs_) {
ForEachVarName(grad_op->inputs_, [&no_grad_names,
&net](std::string& grad_input) {
if (no_grad_names.count(grad_input)) {
std::string prefix =
grad_input.substr(0, grad_input.size() - kGradVarSuffix.size());
// +1 for \0
std::string prefix = grad_input.substr(
0, grad_input.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1);
grad_input = prefix + kZeroVarSuffix;
// If part of input gradient of that operator is not calculated, fill
// zero variables to that input gradient.
net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {prefix},
{grad_input}, {}));
net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {{"Src", {prefix}}},
{{"Dst", {grad_input}}}, {}));
}
}
return false;
});
for (std::string& grad_output : grad_op->outputs_) {
if (no_grad_names.count(grad_output)) {
grad_output = kEmptyVarName;
}
}
ForEachVarName(grad_op->outputs_,
[&no_grad_names](std::string& grad_output) {
if (no_grad_names.count(grad_output)) {
grad_output = kEmptyVarName;
}
return false;
});
if (net->ops_.empty()) { // Current no aux op is added to network
return grad_op;
......@@ -167,7 +185,7 @@ std::shared_ptr<OperatorBase> Backward(
std::unordered_set<std::string> no_grad_names;
no_grad_names.reserve(no_grad_vars.size());
no_grad_names.insert(kEmptyVarName + kGradVarSuffix);
no_grad_names.insert(std::string(kEmptyVarName) + kGradVarSuffix);
for (auto& name : no_grad_vars) {
no_grad_names.insert(name + kGradVarSuffix);
......
paddle/framework/backward_test.cc
浏览文件 @ 43ba24e0
......@@ -30,6 +30,7 @@ using DeviceContext = platform::DeviceContext;
class EmptyOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope, const DeviceContext &dev_ctx) const override {}
};
......@@ -38,9 +39,9 @@ class RowWiseAddOpMaker : public OpProtoAndCheckerMaker {
public:
RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input X of Add").IgnoreGradient();
AddInput("b", "Bias of Add").IgnoreGradient();
AddOutput("Out", "Out of Add").IgnoreGradient();
AddInput("X", "Input X of Add").AsNoGradient();
AddInput("b", "Bias of Add").AsNoGradient();
AddOutput("Out", "Out of Add").AsNoGradient();
AddComment("Add Op");
}
};
......@@ -49,8 +50,8 @@ class MulOpMaker : public OpProtoAndCheckerMaker {
public:
MulOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("A", "A");
AddInput("B", "B");
AddInput("X", "A");
AddInput("Y", "B");
AddOutput("Out", "Out");
AddComment("Mul");
}
......@@ -61,7 +62,7 @@ class SigmoidOpMaker : public OpProtoAndCheckerMaker {
SigmoidOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "X");
AddOutput("Y", "Y");
AddOutput("Out", "Y");
AddComment("Sigmoid");
}
};
......@@ -71,21 +72,25 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker {
NoGradOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "X input");
AddOutput("Y", "Y output");
AddOutput("Out", "Y output");
AddComment("NoGradOp, same input output. no Grad");
}
};
class FcOp : public operators::NetOp {
public:
void Init() override {
AddOp(OpRegistry::CreateOp("mul", {Input("X"), Input("W")},
{Output("mul_result")}, {}));
auto b_name = Input("b");
FcOp(const std::string &type, const VarNameMap &inputs,
const VarNameMap &outputs, const AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
AddOp(OpRegistry::CreateOp("mul",
{{"X", {Input("X")}}, {"Y", {Input("W")}}},
{{"Out", {Output("mul_result")}}}, {}));
auto input_b = Inputs("b");
std::string before_act = "mul_result";
if (b_name != kEmptyVarName) {
AddOp(OpRegistry::CreateOp("rowwise_add", {Output("mul_result"), b_name},
{Output("add_result")}, {}));
if (input_b.size() != 0) {
AddOp(OpRegistry::CreateOp(
"rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}},
{{"Out", {Output("add_result")}}}, {}));
before_act = "add_result";
} else {
auto out_varname = Output("add_result");
......@@ -94,8 +99,8 @@ class FcOp : public operators::NetOp {
}
}
AddOp(OpRegistry::CreateOp("sigmoid", {Output(before_act)}, {Output("Out")},
{}));
AddOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}},
{{"Out", {Output("Out")}}}, {}));
CompleteAddOp(false);
}
};
......@@ -107,8 +112,8 @@ class FcOpMaker : public OpProtoAndCheckerMaker {
AddInput("X", "x");
AddInput("W", "w");
AddInput("b", "b");
AddOutput("mul_result", "").SetTemporary();
AddOutput("add_result", "").SetTemporary();
AddOutput("mul_result", "").AsIntermediate();
AddOutput("add_result", "").AsIntermediate();
AddOutput("Out", "");
AddComment("");
}
......@@ -139,7 +144,7 @@ class AddOpMaker : public OpProtoAndCheckerMaker {
public:
AddOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "x").SetMultiple();
AddInput("X", "x").AsDuplicable();
AddOutput("Y", "y");
AddComment("");
}
......@@ -165,27 +170,24 @@ REGISTER_OP(many_output_op, f::EmptyOp, f::ManyOutputOpMaker);
REGISTER_GRADIENT_OP(many_output_op, many_output_op_grad, f::EmptyOp);
TEST(Backward, simple_op_grad) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::OpRegistry::CreateGradOp(*fwd);
ASSERT_EQ(4UL, gop->inputs_.size());
ASSERT_EQ(f::kEmptyVarName, gop->inputs_[0]);
ASSERT_EQ(1UL, gop->inputs_.size());
ASSERT_EQ("rowwise_add_grad", gop->type_);
ASSERT_EQ("X" + f::kGradVarSuffix, gop->outputs_[0]);
ASSERT_EQ("b" + f::kGradVarSuffix, gop->outputs_[1]);
ASSERT_EQ("X" + f::kGradVarSuffix, gop->Output("X" + f::kGradVarSuffix));
ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X")));
ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b")));
}
TEST(Backward, simple_op_not_need_grad) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
ASSERT_NE(fwd, nullptr);
auto gop = f::Backward(*fwd, {"X"});
ASSERT_EQ(std::find(gop->outputs_.begin(), gop->outputs_.end(),
"X" + f::kGradVarSuffix),
gop->outputs_.end());
auto gop = f::Backward(*fwd, {"x"});
ASSERT_EQ(gop->Output(f::GradVarName("X")), f::kEmptyVarName);
auto no_input_gop = f::Backward(*fwd, {"X", "b"});
auto no_input_gop = f::Backward(*fwd, {"x", "b"});
ASSERT_NE(no_input_gop, nullptr);
ASSERT_TRUE(no_input_gop->IsNetOp());
ASSERT_EQ(0UL,
......@@ -193,8 +195,12 @@ TEST(Backward, simple_op_not_need_grad) {
}
TEST(Backward, net_fc_backward_normal) {
std::shared_ptr<f::OperatorBase> fwd = f::OpRegistry::CreateOp(
"fc", {"X", "w", "b"}, {"mul_result", "add_result", "out"}, {});
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_res"}},
{"add_result", {"add_re"}},
{"Out", {"out"}}},
{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
......@@ -216,8 +222,11 @@ TEST(Backward, net_fc_backward_normal) {
TEST(Backward, net_fc_backward_not_have_b) {
std::shared_ptr<f::OperatorBase> fwd =
f::OpRegistry::CreateOp("fc", {"X", "w", f::kEmptyVarName},
{"mul_result", "add_result", "tmp"}, {});
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {}}},
{{"mul_result", {"mul_res"}},
{"add_result", {"add_res"}},
{"Out", {"tmp"}}},
{});
ASSERT_NE(fwd, nullptr);
std::shared_ptr<f::OperatorBase> gop = f::Backward(*fwd, {});
ASSERT_TRUE(gop->IsNetOp());
......@@ -236,38 +245,49 @@ TEST(Backward, net_fc_backward_not_have_b) {
TEST(Backward, net_input_of_network_not_need_grad) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("fc", {"X", "W1", "b1"},
{"mul_tmp_0", "add_tmp_0", "hidden0"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"hidden0", "W2", "b2"},
{"mul_tmp_1", "add_tmp_1", "hidden1"}, {}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x"}}, {"W", {"W1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_tmp_0"}},
{"add_result", {"add_tmp_0"}},
{"Out", {"hidden0"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_tmp_1"}},
{"add_result", {"add_tmp_1"}},
{"Out", {"hidden1"}}},
{}));
net.CompleteAddOp();
auto bwd = Backward(net, {"X"}); // X@GRAD is not need.
auto bwd = Backward(net, {"x"}); // x@GRAD is not need.
ASSERT_TRUE(bwd->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(bwd.get());
std::unordered_set<std::string> all_output = std::unordered_set<std::string>(
bwd_net->outputs_.begin(), bwd_net->outputs_.end());
all_output.erase(f::kEmptyVarName);
auto output_vars = bwd_net->OutputVars(true);
std::unordered_set<std::string> all_outputs =
std::unordered_set<std::string>(output_vars.begin(), output_vars.end());
all_outputs.erase(f::kEmptyVarName);
for (auto &out : {"W1", "b1", "hidden0", "W2", "b2"}) {
ASSERT_NE(all_output.find(out + f::kGradVarSuffix), all_output.end());
ASSERT_NE(all_outputs.find(f::GradVarName(out)), all_outputs.end());
}
// Not Generated X
ASSERT_EQ(all_output.find("X" + f::kGradVarSuffix), all_output.end());
ASSERT_EQ(all_outputs.find(f::GradVarName("X")), all_outputs.end());
ASSERT_EQ(2UL, bwd_net->ops_.size());
ASSERT_TRUE(bwd_net->ops_[1]->IsNetOp());
auto first_fc_grad = static_cast<ops::NetOp *>(bwd_net->ops_[1].get());
ASSERT_EQ(3UL, first_fc_grad->ops_.size());
ASSERT_EQ(f::kEmptyVarName,
first_fc_grad->ops_[2]->Output("A" + f::kGradVarSuffix));
first_fc_grad->ops_[2]->Output(f::GradVarName("X")));
}
TEST(Backward, net_shared_weight) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("mul", {"X", "W"}, {"Out"}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {"Out", "W"}, {"FinalOut"}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}},
{{"Out", {"out"}}}, {}));
net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}},
{{"Out", {"FinalOut"}}}, {}));
net.CompleteAddOp();
auto bwd = f::Backward(net, {});
......@@ -278,31 +298,37 @@ TEST(Backward, net_shared_weight) {
}
TEST(Backward, op_register_grad_not_for_network) {
auto fwd = f::OpRegistry::CreateOp(
"fc", {"X", "W", "b"}, {"mul_out", "add_out", "out1"},
{{"temporary_index", std::vector<int>{0, 1}}});
auto fwd =
f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}},
{{"mul_result", {"mul_out"}},
{"add_result", {"add_out"}},
{"Out", {"out1"}}},
{{"temporary_index", std::vector<int>{0, 1}}});
ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet);
}
TEST(Backward, op_all_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto backward = f::Backward(*fwd, {"X", "b"});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"x", "b"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
TEST(Backward, op_all_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("rowwise_add", {"X", "b"}, {"Out"}, {});
auto backward = f::Backward(*fwd, {"Out"});
auto fwd = f::OpRegistry::CreateOp(
"rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"out"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
ASSERT_TRUE(net->ops_.empty());
}
TEST(Backward, op_part_of_output_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("many_output_op", {"X"}, {"Y", "Z"}, {});
auto fwd = f::OpRegistry::CreateOp("many_output_op", {{"x", {"X"}}},
{{"y", {"Y"}}, {"z", {"Z"}}}, {});
auto backward = f::Backward(*fwd, {"Z"});
ASSERT_TRUE(backward->IsNetOp());
auto net = static_cast<ops::NetOp *>(backward.get());
......@@ -310,60 +336,77 @@ TEST(Backward, op_part_of_output_are_not_need) {
auto &fill_zero = *net->ops_[0];
ASSERT_EQ("fill_zeros_like", fill_zero.type_);
ASSERT_EQ(1UL, fill_zero.inputs_.size());
ASSERT_EQ("Z", fill_zero.inputs_[0]);
ASSERT_EQ(1UL, fill_zero.outputs_.size());
ASSERT_EQ("Z" + f::kZeroVarSuffix, fill_zero.outputs_[0]);
ASSERT_EQ(1UL, fill_zero.Inputs("Src").size());
ASSERT_EQ("Z", fill_zero.Input("Src"));
ASSERT_EQ(1UL, fill_zero.Outputs("Dst").size());
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix, fill_zero.Output("Dst"));
auto &d_many_out = *net->ops_[1];
ASSERT_EQ("many_output_op_grad", d_many_out.type_);
ASSERT_EQ(1UL + 2UL + 2UL, d_many_out.inputs_.size()); // I/O/OG
ASSERT_EQ("Z" + f::kZeroVarSuffix, d_many_out.Input("z" + f::kGradVarSuffix));
ASSERT_EQ("Y" + f::kGradVarSuffix, d_many_out.Input("y" + f::kGradVarSuffix));
ASSERT_EQ("X" + f::kGradVarSuffix,
d_many_out.Output("x" + f::kGradVarSuffix));
ASSERT_EQ(std::string("Z") + f::kZeroVarSuffix,
d_many_out.Input(f::GradVarName("z")));
ASSERT_EQ(f::GradVarName("Y"), d_many_out.Input(f::GradVarName("y")));
ASSERT_EQ(f::GradVarName("X"), d_many_out.Output(f::GradVarName("x")));
}
TEST(Backward, op_part_of_input_are_not_need) {
auto fwd = f::OpRegistry::CreateOp("mul", {"a", "b"}, {"out"}, {});
auto fwd = f::OpRegistry::CreateOp("mul", {{"X", {"a"}}, {"Y", {"b"}}},
{{"Out", {"out"}}}, {});
auto backward = f::Backward(*fwd, {"a"});
auto &grad_mul = *backward;
ASSERT_EQ(grad_mul.type_, "mul_grad");
ASSERT_EQ(grad_mul.inputs_.size(), 2UL + 1UL + 1UL);
ASSERT_EQ(grad_mul.outputs_.size(), 2UL);
ASSERT_EQ(grad_mul.Output("A" + f::kGradVarSuffix), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output("B" + f::kGradVarSuffix), "b" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("Out" + f::kGradVarSuffix),
"out" + f::kGradVarSuffix);
ASSERT_EQ(grad_mul.Input("A"), "a");
ASSERT_EQ(grad_mul.Input("B"), "b");
ASSERT_EQ(grad_mul.Output(f::GradVarName("X")), f::kEmptyVarName);
ASSERT_EQ(grad_mul.Output(f::GradVarName("Y")), f::GradVarName("b"));
ASSERT_EQ(grad_mul.Input(f::GradVarName("Out")), f::GradVarName("out"));
ASSERT_EQ(grad_mul.Input("X"), "a");
ASSERT_EQ(grad_mul.Input("Y"), "b");
ASSERT_EQ(grad_mul.Input("Out"), "out");
}
TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
ops::NetOp net;
net.AddOp(f::OpRegistry::CreateOp("fc", {"x1", "w1", "b1"},
{"mul_out1", "add_out1", "out1"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"out1", "w2", "b2"},
{"mul_out2", "tmp_out2", "out2"}, {}));
net.AddOp(f::OpRegistry::CreateOp("fc", {"out2", "w3", "b3"},
{"mul_out3", "tmp_out3", "out3"}, {}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"x1"}}, {"W", {"w1"}}, {"b", {"b1"}}},
{{"mul_result", {"mul_out1"}},
{"add_result", {"add_out1"}},
{"Out", {"out1"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}},
{{"mul_result", {"mul_out2"}},
{"add_result", {"tmp_out2"}},
{"Out", {"out2"}}},
{}));
net.AddOp(f::OpRegistry::CreateOp(
"fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}},
{{"mul_result", {"mul_out3"}},
{"add_result", {"tmp_out3"}},
{"Out", {"out3"}}},
{}));
net.CompleteAddOp();
auto backward = f::Backward(net, {"mul_out2", "tmp_out2", "out2"});
ASSERT_TRUE(backward->IsNetOp());
auto bwd_net = static_cast<ops::NetOp *>(backward.get());
ASSERT_EQ(bwd_net->ops_.size(), 3UL);
auto &grad_fc = *bwd_net->ops_[0];
EXPECT_EQ(grad_fc.inputs_.size(),
3UL /* external input number */
const char *all = paddle::operators::NetOp::kAll;
EXPECT_EQ(grad_fc.inputs_[all].size(),
2UL /* external input number */
+ 1UL /* external output number*/
+ 1UL /* number of gradient of external output*/
+ 2U /* internal variable number*/);
EXPECT_EQ(grad_fc.outputs_.size(), 2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add */
+ 1UL /* input number of sigmod */);
EXPECT_EQ(bwd_net->ops_[1]->inputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->outputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_.size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_.size(), 0UL);
EXPECT_EQ(grad_fc.outputs_[all].size(),
2UL /* input number of mul*/
+ 2UL /* input number of rowwise_add
*/
+ 1UL /* input number of sigmod */);
EXPECT_EQ(bwd_net->ops_[1]->inputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[1]->outputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->inputs_[all].size(), 0UL);
EXPECT_EQ(bwd_net->ops_[2]->outputs_[all].size(), 0UL);
}
paddle/framework/ddim.cc
浏览文件 @ 43ba24e0
......@@ -283,6 +283,5 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
DDim::DDim(std::initializer_list<int> init_list) {
*this = make_ddim(init_list);
}
} // namespace framework
} // namespace paddle
paddle/framework/ddim.h
浏览文件 @ 43ba24e0
......@@ -14,13 +14,12 @@ limitations under the License. */
#pragma once
#include <boost/variant.hpp>
#include <initializer_list>
#include <stdexcept>
#include <vector>
#include "paddle/framework/dim.h"
#include "paddle/platform/enforce.h"
#include "unsupported/Eigen/CXX11/Tensor"
#include "paddle/platform/variant.h"
namespace paddle {
namespace framework {
......
paddle/framework/details/lod_tensor.cc 已删除 100644 → 0
浏览文件 @ 7c0cb0c7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/lod_tensor.h"
#include <memory>
namespace paddle {
namespace framework {
namespace details {
using LOD = LODTensor::LOD;
std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level_begin,
size_t level_end) {
auto new_lod = std::make_shared<LOD>();
new_lod->reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
new_lod->emplace_back(lod[i]);
}
return new_lod;
}
std::shared_ptr<LOD> SliceLOD(const LOD &lod, size_t level, size_t elem_begin,
size_t elem_end, bool tensor_shared) {
// slice the lod.
auto new_lod = std::make_shared<LOD>();
new_lod->reserve(lod.size() - level);
auto start = lod.at(level)[elem_begin];
auto end = lod.at(level)[elem_end];
for (auto it = lod.begin() + level; it != lod.end(); it++) {
auto it_begin = std::find(it->begin(), it->end(), start);
auto it_end = std::find(it_begin, it->end(), end);
PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
new_lod->emplace_back(it_begin, it_end + 1);
if (!tensor_shared) {
// reset offset if tensor is copyed and sliced.
std::transform(new_lod->back().begin(), new_lod->back().end(),
new_lod->back().begin(),
[start](int v) { return v - start; });
PADDLE_ENFORCE(new_lod->back().front() == 0, "error in slice LOD");
}
}
return new_lod;
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/framework/attribute.proto → paddle/framework/framework.proto
浏览文件 @ 43ba24e0
......@@ -15,9 +15,6 @@ limitations under the License. */
syntax = "proto2";
package paddle.framework;
// Attribute Type for paddle's Op.
// Op contains many attributes. Each type of attributes could be different.
// The AttrType will be shared between AttrDesc and AttrProto.
enum AttrType {
INT = 0;
FLOAT = 1;
......@@ -25,4 +22,61 @@ enum AttrType {
INTS = 3;
FLOATS = 4;
STRINGS = 5;
}
\ No newline at end of file
}
// OpDesc describes an instance of a C++ framework::OperatorBase
// derived class type.
message OpDesc {
message Attr {
required string name = 1;
required AttrType type = 2;
optional int32 i = 3;
optional float f = 4;
optional string s = 5;
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
};
message Var {
required string parameter = 1;
repeated string arguments = 2;
};
required string type = 3;
repeated Var inputs = 1;
repeated Var outputs = 2;
repeated Attr attrs = 4;
};
// OpProto describes a C++ framework::OperatorBase derived class.
message OpProto {
// VarProto describes the C++ type framework::Variable.
message Var {
required string name = 1;
required string comment = 2;
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool no_gradient = 5 [ default = false ];
}
// AttrProto describes the C++ type Attribute.
message Attr {
required string name = 1;
required AttrType type = 2;
required string comment = 3;
// If that attribute is generated, it means the Paddle third
// language binding has responsibility to fill that
// attribute. End-User should not set that attribute.
optional bool generated = 4 [ default = false ];
}
required string type = 1;
repeated Var inputs = 2;
repeated Var outputs = 3;
repeated Attr attrs = 4;
required string comment = 5;
}
paddle/framework/grad_op_builder.cc
浏览文件 @ 43ba24e0
......@@ -13,90 +13,52 @@ express or implied. See the License for the specific language governing
permissions and limitations under the License. */
#include "paddle/framework/grad_op_builder.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
class OpRegistry;
using VarIndexMap = std::unordered_map<std::string, int>;
enum class OpArgType { IN, OUT };
static std::vector<int>* GetOpFormat(OperatorBase* op, const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
}
static const std::vector<int>* GetOpFormat(const OperatorBase* op,
const OpArgType& type) {
std::string key = type == OpArgType::IN ? "input_format" : "output_format";
return op->attrs_.count(key)
? &boost::get<std::vector<int>>(op->attrs_.at(key))
: nullptr;
}
static void TransOpArg(const OperatorBase* src_op, OperatorBase* dst_op,
const OpArgType& src_type, const OpArgType& dst_type,
int& idx, bool is_grad) {
const std::vector<std::string>& src_inout =
static void TransOpArg(const OperatorBase* src_op,
OperatorBase::VarNameMap* vars,
const OpArgType& src_type, bool is_grad) {
const auto& src_inout =
src_type == OpArgType::IN ? src_op->inputs_ : src_op->outputs_;
const std::vector<int>* src_format = GetOpFormat(src_op, src_type);
auto& dst_inout = *vars;
std::vector<std::string>& dst_inout =
dst_type == OpArgType::IN ? dst_op->inputs_ : dst_op->outputs_;
std::vector<int>* dst_format = GetOpFormat(dst_op, dst_type);
const OpProto& proto = OpRegistry::protos().at(src_op->type_);
const OpProto& proto = OpProtos().at(src_op->type_);
const auto& src_arg_list =
src_type == OpArgType::IN ? proto.inputs() : proto.outputs();
for (const auto& arg : src_arg_list) {
std::string src_name = arg.name();
std::string dst_name = is_grad ? src_name + kGradVarSuffix : src_name;
(*dst_op->in_out_idxs_)[dst_name] = idx++;
int src_arg_idx = src_op->in_out_idxs_->at(src_name);
int src_begin =
src_format == nullptr ? src_arg_idx : src_format->at(src_arg_idx);
int src_end = src_format == nullptr ? src_arg_idx + 1
: src_format->at(src_arg_idx + 1);
for (int i = src_begin; i < src_end; ++i) {
std::string s =
is_grad ? src_inout[i] + kGradVarSuffix
: (arg.ignore_gradient() ? kEmptyVarName : src_inout[i]);
dst_inout.emplace_back(s);
}
if (dst_format != nullptr) {
dst_format->push_back(dst_inout.size());
if (arg.no_gradient() && !is_grad) continue;
const std::string src_name = arg.name();
std::string dst_name = is_grad ? GradVarName(src_name) : src_name;
dst_inout[dst_name].reserve(src_inout.at(src_name).size());
for (auto& var_name : src_inout.at(src_name)) {
std::string s = is_grad ? GradVarName(var_name) : var_name;
dst_inout[dst_name].emplace_back(s);
}
}
}
OperatorBase* BuildGradOp(const OperatorBase* op) {
std::string grad_op_type = OpRegistry::grad_ops().at(op->type_);
OperatorBase* grad_op = OpRegistry::op_creators().at(grad_op_type)();
grad_op->type_ = grad_op_type;
grad_op->attrs_ = op->attrs_;
grad_op->attrs_.erase("input_format");
grad_op->attrs_.erase("output_format");
if (GetOpFormat(op, OpArgType::IN) != nullptr) {
grad_op->attrs_["output_format"] = std::vector<int>({0});
}
if (GetOpFormat(op, OpArgType::IN) != nullptr ||
GetOpFormat(op, OpArgType::OUT) != nullptr) {
grad_op->attrs_["input_format"] = std::vector<int>({0});
}
grad_op->in_out_idxs_.reset(new VarIndexMap());
int in_idx = 0;
int out_idx = 0;
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::IN, in_idx, false); // I
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, false); // G
TransOpArg(op, grad_op, OpArgType::OUT, OpArgType::IN, in_idx, true); // OG
TransOpArg(op, grad_op, OpArgType::IN, OpArgType::OUT, out_idx, true); // IG
return grad_op;
auto gop_type_it = OpRegistry::grad_ops().find(op->type_);
PADDLE_ENFORCE(gop_type_it != OpRegistry::grad_ops().end(),
"Operator %s do not register gradient type", op->type_);
auto& grad_op_type = gop_type_it->second;
OperatorBase::VarNameMap inputs;
OperatorBase::VarNameMap outputs;
TransOpArg(op, &inputs, OpArgType::IN, false); // I
TransOpArg(op, &inputs, OpArgType::OUT, false); // O
TransOpArg(op, &inputs, OpArgType::OUT, true); // OG
TransOpArg(op, &outputs, OpArgType::IN, true); // IG
auto gop_it = OpRegistry::op_creators().find(grad_op_type);
PADDLE_ENFORCE(gop_it != OpRegistry::op_creators().end(),
"Operator %s 's Gradient %s's creator cannot be found",
op->type_, grad_op_type);
return gop_it->second(grad_op_type, inputs, outputs, op->attrs_);
}
} // namespace framework
......
paddle/framework/grad_op_builder_test.cc
浏览文件 @ 43ba24e0
......@@ -10,6 +10,7 @@ namespace framework {
class NOP : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void InferShape(const Scope &scope) const override {}
void Run(const Scope &scope,
const platform::DeviceContext &dev_ctx) const override {}
......@@ -20,10 +21,10 @@ class MutiInOutOpMaker : public OpProtoAndCheckerMaker {
MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple();
AddInput("In2_mult", "a multiple input").AsDuplicable();
AddInput("In3", "another single input");
AddOutput("Out1", "a single output");
AddOutput("Out2_mult", "a multiple output").SetMultiple();
AddOutput("Out2_mult", "a multiple output").AsDuplicable();
AddComment("test op with multiple inputs and outputs");
}
};
......@@ -33,10 +34,10 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("In1", "a single input");
AddInput("In2_mult", "a multiple input").SetMultiple().IgnoreGradient();
AddInput("In3_mult", "another multiple input").SetMultiple();
AddOutput("Out1_mult", "a multiple output").SetMultiple();
AddOutput("Out2", "a single output").IgnoreGradient();
AddInput("In2_mult", "a multiple input").AsDuplicable().AsNoGradient();
AddInput("In3_mult", "another multiple input").AsDuplicable();
AddOutput("Out1_mult", "a multiple output").AsDuplicable();
AddOutput("Out2", "a single output").AsNoGradient();
AddComment("op with inputs and outputs ignored in gradient calculating");
}
};
......@@ -47,18 +48,18 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker {
namespace f = paddle::framework;
TEST(GradOpBuilder, AddTwo) {
std::shared_ptr<f::OperatorBase> add_op(
f::OpRegistry::CreateOp("add_two", {"x", "y"}, {"out"}, {}));
std::shared_ptr<f::OperatorBase> add_op(f::OpRegistry::CreateOp(
"add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_add_op =
f::OpRegistry::CreateGradOp(*add_op);
EXPECT_EQ(static_cast<int>(grad_add_op->inputs_.size()), 4);
EXPECT_EQ(static_cast<int>(grad_add_op->outputs_.size()), 2);
EXPECT_EQ(grad_add_op->inputs_.size(), 4UL);
EXPECT_EQ(grad_add_op->outputs_.size(), 2UL);
EXPECT_EQ(grad_add_op->Input("X"), "x");
EXPECT_EQ(grad_add_op->Input("Y"), "y");
EXPECT_EQ(grad_add_op->Input("Out"), "out");
EXPECT_EQ(grad_add_op->Input("Out@GRAD"), "out@GRAD");
EXPECT_EQ(grad_add_op->Output("X@GRAD"), "x@GRAD");
EXPECT_EQ(grad_add_op->Output("Y@GRAD"), "y@GRAD");
EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x"));
EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y"));
}
REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker);
......@@ -67,15 +68,15 @@ REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker);
REGISTER_GRADIENT_OP(io_ignored, io_ignored_grad, f::NOP);
TEST(GradOpBuilder, MutiInOut) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 4, 5}},
{"output_format", std::vector<int>{0, 1, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"mult_io", {"in1", "in2_1", "in2_2", "in2_3", "in3"},
{"out1", "out2_1", "out2_2"}, attrs));
"mult_io", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2", "in2_3"}},
{"In3", {"in3"}}},
{{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
ASSERT_EQ(grad_test_op->inputs_.size(), 3UL + 2UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({"in2_1", "in2_2", "in2_3"}));
......@@ -83,55 +84,49 @@ TEST(GradOpBuilder, MutiInOut) {
EXPECT_EQ(grad_test_op->Input("Out1"), "out1");
EXPECT_EQ(grad_test_op->Inputs("Out2_mult"),
std::vector<std::string>({"out2_1", "out2_2"}));
EXPECT_EQ(grad_test_op->Input("Out1" + f::kGradVarSuffix),
"out1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Inputs("Out2_mult" + f::kGradVarSuffix),
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")),
f::GradVarName("out1"));
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")),
std::vector<std::string>(
{"out2_1" + f::kGradVarSuffix, "out2_2" + f::kGradVarSuffix}));
{f::GradVarName("out2_1"), f::GradVarName("out2_2")}));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
"in1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
std::vector<std::string>({"in2_1" + f::kGradVarSuffix,
"in2_2" + f::kGradVarSuffix,
"in2_3" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Output("In3" + f::kGradVarSuffix),
"in3" + f::kGradVarSuffix);
ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>({f::GradVarName("in2_1"),
f::GradVarName("in2_2"),
f::GradVarName("in2_3")}));
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3"));
}
TEST(GradOpBuilder, IOIgnoredInGradient) {
f::AttributeMap attrs{{"input_format", std::vector<int>{0, 1, 3, 5}},
{"output_format", std::vector<int>{0, 2, 3}}};
std::shared_ptr<f::OperatorBase> test_op(f::OpRegistry::CreateOp(
"io_ignored", {"in1", "in2_1", "in2_2", "in3_1", "in3_2"},
{"out1_1", "out1_2", "out2"}, attrs));
"io_ignored", {{"In1", {"in1"}},
{"In2_mult", {"in2_1", "in2_2"}},
{"In3_mult", {"in3_1", "in3_2"}}},
{{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {}));
std::shared_ptr<f::OperatorBase> grad_test_op =
f::OpRegistry::CreateGradOp(*test_op);
// 'In2' and 'Out2' are ignored in gradient calculating
ASSERT_EQ(grad_test_op->inputs_.size(), 5UL + 3UL + 3UL);
ASSERT_EQ(grad_test_op->inputs_.size(), 2UL + 1UL + 2UL);
EXPECT_EQ(grad_test_op->Input("In1"), "in1");
EXPECT_EQ(grad_test_op->Inputs("In2_mult"),
std::vector<std::string>({f::kEmptyVarName, f::kEmptyVarName}));
EXPECT_EQ(grad_test_op->Inputs("In3_mult"),
std::vector<std::string>({"in3_1", "in3_2"}));
EXPECT_EQ(grad_test_op->Inputs("Out1_mult"),
std::vector<std::string>({"out1_1", "out1_2"}));
EXPECT_EQ(grad_test_op->Input("Out2"), f::kEmptyVarName);
EXPECT_EQ(grad_test_op->Inputs("Out1_mult" + f::kGradVarSuffix),
EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")),
std::vector<std::string>(
{"out1_1" + f::kGradVarSuffix, "out1_2" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Input("Out2" + f::kGradVarSuffix),
"out2" + f::kGradVarSuffix);
{f::GradVarName("out1_1"), f::GradVarName("out1_2")}));
EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")),
f::GradVarName("out2"));
ASSERT_EQ(grad_test_op->outputs_.size(), 5UL);
EXPECT_EQ(grad_test_op->Output("In1" + f::kGradVarSuffix),
"in1" + f::kGradVarSuffix);
EXPECT_EQ(grad_test_op->Outputs("In2_mult" + f::kGradVarSuffix),
ASSERT_EQ(grad_test_op->outputs_.size(), 3UL);
EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1"));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")),
std::vector<std::string>(
{"in2_1" + f::kGradVarSuffix, "in2_2" + f::kGradVarSuffix}));
EXPECT_EQ(grad_test_op->Outputs("In3_mult" + f::kGradVarSuffix),
{f::GradVarName("in2_1"), f::GradVarName("in2_2")}));
EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")),
std::vector<std::string>(
{"in3_1" + f::kGradVarSuffix, "in3_2" + f::kGradVarSuffix}));
{f::GradVarName("in3_1"), f::GradVarName("in3_2")}));
}
paddle/framework/lod_tensor.cc
浏览文件 @ 43ba24e0
......@@ -19,32 +19,59 @@
namespace paddle {
namespace framework {
LODTensor LODTensor::SliceShared(size_t level_begin, size_t level_end) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
// slice levels just need to update LOD info, each level will contains the
// whole tensor_, so no need to modify tensor_.
return LODTensor(tensor_, new_lod);
LODTensor::LOD LODTensor::LOD::SliceLevels(size_t level_begin,
size_t level_end) const {
LOD new_lod;
new_lod.reserve(level_end - level_begin);
for (size_t i = level_begin; i < level_end; i++) {
new_lod.emplace_back(at(i));
}
return new_lod;
}
LODTensor LODTensor::SliceShared(size_t level, size_t elem_begin,
size_t elem_end) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
PADDLE_ENFORCE(elem_begin < NumElements(level),
"element begin [%d] out of range [%d]", elem_begin,
NumElements(level));
PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
"element end [%d] out of range [%d]", elem_end,
NumElements(level));
auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
true /*tensor_shared*/);
// slice elements just need to update LOD info, because offsets are not
// changed, so the original tensor_ can be reused.
return LODTensor(tensor_, new_lod);
LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin,
size_t elem_end) const {
// slice the lod.
LOD new_lod;
new_lod.reserve(size() - level);
auto start = this->at(level)[elem_begin];
auto end = this->at(level)[elem_end];
for (auto it = this->begin() + level; it != this->end(); it++) {
auto it_begin = std::find(it->begin(), it->end(), start);
auto it_end = std::find(it_begin, it->end(), end);
PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info");
PADDLE_ENFORCE(it_end != it->end(), "error in parsing lod info");
new_lod.emplace_back(it_begin, it_end + 1);
// reset offset if tensor is copyed and sliced.
std::transform(new_lod.back().begin(), new_lod.back().end(),
new_lod.back().begin(),
[start](int v) { return v - start; });
PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD");
}
PADDLE_ENFORCE_LE(new_lod.size(), this->size());
return new_lod;
}
bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) {
if (a.size() != b.size()) {
return false;
}
for (size_t i = 0; i < a.size(); i++) {
const auto& a_level = a[i];
const auto& b_level = b[i];
if (a_level.size() != b_level.size()) {
return false;
}
for (size_t j = 0; j < a_level.size(); j++) {
if (a_level[j] != b_level[j]) {
return false;
}
}
}
return true;
}
} // namespace framework
......
paddle/framework/lod_tensor.h
浏览文件 @ 43ba24e0
......@@ -15,7 +15,7 @@
#pragma once
#include <memory>
#if (!PADDLE_ONLY_CPU)
#if !defined(PADDLE_ONLY_CPU)
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#endif
......@@ -31,30 +31,29 @@ namespace framework {
* LODTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
class LODTensor {
class LODTensor : public Tensor {
public:
// Level save offsets of each unit.
#ifdef PADDLE_ONLY_CPU
using Level = std::vector<size_t>;
template <typename T>
using Vector = std::vector<T>;
#else
using Level = thrust::device_vector<size_t>;
template <typename T>
using Vector = thrust::host_vector<T>;
#endif
// LOD stores offsets of each level of units, the largest units level first,
// LoD stores offsets of each level of units, the largest units level first,
// then the smaller units level. Each Level stores the offsets of units in
// Tesor.
typedef std::vector<Level> LOD;
class LOD : public std::vector<Vector<size_t>> {
public:
LOD SliceLevels(size_t level_begin, size_t level_end) const;
LOD SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) const;
};
LODTensor() {}
LODTensor(const std::shared_ptr<Tensor> &tensor,
const std::shared_ptr<LOD> &lod) {
Reset(tensor, lod);
}
explicit LODTensor(const LOD &lod) : lod_(lod) {}
void Reset(const std::shared_ptr<Tensor> &tensor,
const std::shared_ptr<LOD> &lod) {
tensor_ = tensor;
lod_start_pos_ = lod;
}
virtual Tensor *Clone() const { return new LODTensor(lod_); }
/*
* Get a element from LOD.
......@@ -65,16 +64,14 @@ class LODTensor {
PADDLE_ENFORCE(elem < NumElements(level),
"element begin [%d] out of range [%d]", elem,
NumElements(level));
return (*lod_start_pos_)[level][elem];
return (lod_)[level][elem];
}
/*
* Number of LODTensor's levels, each level has units of data, for example,
* in the sentence's view, article, paragraph, sentence are 3 levels.
*/
size_t NumLevels() const {
return lod_start_pos_ ? lod_start_pos_->size() : 0UL;
}
size_t NumLevels() const { return lod_.size(); }
/*
* Number of elements in a level.
*/
......@@ -82,64 +79,71 @@ class LODTensor {
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
// the last offset is the end of last element
return lod_start_pos_->at(level).size() - 1;
return lod_[level].size() - 1;
}
/*
* Slice of levels[level_begin:level_end], with tensor copied.
*/
template <typename T>
LODTensor SliceCopied(size_t level_begin, size_t level_end,
const platform::Place &dst_place) const;
/*
* Slice of levels[level_begin:level_end], with tensor shared.
*/
LODTensor SliceShared(size_t level_begin, size_t level_end) const;
/*
* Slice of elements of a level, [elem_begin: elem_end], with tensor copied.
* @note: low performance in slice lod_start_pos_.
*/
template <typename T>
LODTensor SliceCopied(size_t level, size_t elem_begin, size_t elem_end,
const platform::Place &dst_place) const;
LODTensor SliceLevels(size_t level_begin, size_t level_end) const;
/*
* Slice of elements of a level, [elem_begin: elem_end], with tensor shared.
* @note: low performance in slice lod_start_pos_.
*/
LODTensor SliceShared(size_t level, size_t elem_begin, size_t elem_end) const;
/*
* Copy other's lod_start_pos_, to share LOD info.
* @note: the LOD info should not be changed.
* @note: low performance in slice lod_.
*/
void ShareLOD(const LODTensor &other) {
lod_start_pos_ = other.lod_start_pos_;
}
template <typename T>
LODTensor SliceInLevel(size_t level, size_t elem_begin,
size_t elem_end) const;
/*
* Copy other's lod_start_pos_'s content, free to mutate.
* Copy other's lod_'s content, free to mutate.
*/
void CopyLOD(const LODTensor &other) {
lod_start_pos_ = std::make_shared<LOD>(*other.lod_start_pos_);
}
void CopyLOD(const LODTensor &other) { lod_ = other.lod_; }
/*
* Determine whether LODTensor has a valid LOD info.
*/
bool HasLOD() const { return bool(lod_start_pos_); }
LOD *lod() const { return lod_start_pos_.get(); }
const LOD &lod() const { return lod_; }
LOD *mutable_lod() { return &lod_; }
std::shared_ptr<Tensor> &tensor() { return tensor_; }
Tensor *raw_tensor() { return tensor_.get(); }
virtual ~LODTensor() {}
private:
std::shared_ptr<LOD> lod_start_pos_;
std::shared_ptr<Tensor> tensor_;
LOD lod_;
};
bool operator==(const LODTensor::LOD &a, const LODTensor::LOD &b);
template <typename T>
LODTensor LODTensor::SliceLevels(size_t level_begin, size_t level_end) const {
auto new_lod = lod_.SliceLevels(level_begin, level_end);
// slice levels just need to update LOD info, each level will contains the
// whole tensor_, so no need to modify tensor_.
LODTensor new_tensor(new_lod);
new_tensor.ShareDataWith<T>(*this);
return new_tensor;
}
template <typename T>
LODTensor LODTensor::SliceInLevel(size_t level, size_t elem_begin,
size_t elem_end) const {
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
PADDLE_ENFORCE(elem_begin < NumElements(level),
"element begin [%d] out of range [%d]", elem_begin,
NumElements(level));
PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
"element end [%d] out of range [%d]", elem_end,
NumElements(level));
auto new_lod = lod_.SliceInLevel(level, elem_begin, elem_end);
// slice elements just need to update LOD info, because offsets are not
// changed, so the original tensor_ can be reused.
LODTensor new_tensor(new_lod);
new_tensor.ShareDataWith<T>(*this);
return new_tensor;
}
} // namespace framework
} // namespace paddle
#include "paddle/framework/lod_tensor_impl.h"
paddle/framework/lod_tensor_impl.h 已删除 100644 → 0
浏览文件 @ 7c0cb0c7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/details/lod_tensor.h"
namespace paddle {
namespace framework {
template <typename T>
LODTensor LODTensor::SliceCopied(size_t level_begin, size_t level_end,
const platform::Place &dst_place) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
auto new_lod = details::SliceLOD(*lod_start_pos_, level_begin, level_end);
auto new_tensor = std::make_shared<Tensor>();
new_tensor->CopyFrom<T>(*tensor_, dst_place);
return LODTensor(new_tensor, new_lod);
}
template <typename T>
LODTensor LODTensor::SliceCopied(size_t level, size_t elem_begin,
size_t elem_end,
const platform::Place &dst_place) const {
PADDLE_ENFORCE(HasLOD(), "has no LOD info, can't be sliced.");
PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level,
NumLevels());
PADDLE_ENFORCE(elem_begin < NumElements(level),
"element begin [%d] out of range [%d]", elem_begin,
NumElements(level));
PADDLE_ENFORCE(elem_end < NumElements(level) + 1,
"element end [%d] out of range [%d]", elem_end,
NumElements(level));
auto new_lod = details::SliceLOD(*lod_start_pos_, level, elem_begin, elem_end,
false /*tensor_shared*/);
auto start_idx = new_lod->front().front();
auto end_idx = new_lod->front().back() - 1 /*the next element's start*/;
auto sliced_tensor = tensor_->Slice<T>(start_idx, end_idx);
auto new_tensor = std::make_shared<Tensor>();
new_tensor->CopyFrom<T>(sliced_tensor, dst_place);
return LODTensor(new_tensor, new_lod);
}
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor_test.cc
浏览文件 @ 43ba24e0
......@@ -15,6 +15,7 @@
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <algorithm>
#include <memory>
namespace paddle {
......@@ -29,22 +30,28 @@ class LODTensorTester : public ::testing::Test {
// 0 10 20
// 0 5 10 15 20
// 0 2 5 7 10 12 15 20
auto lod = std::make_shared<LODTensor::LOD>();
lod->push_back(std::vector<size_t>{0, 10, 20});
lod->push_back(std::vector<size_t>{0, 5, 10, 15, 20});
lod->push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
LODTensor::LOD lod;
lod.push_back(std::vector<size_t>{0, 10, 20});
lod.push_back(std::vector<size_t>{0, 5, 10, 15, 20});
lod.push_back(std::vector<size_t>{0, 2, 5, 7, 10, 12, 15, 17, 20});
auto tensor = std::make_shared<Tensor>();
tensor->Resize({20 /*batch size*/, 128 /*dim*/});
ASSERT_EQ(lod.size(), 3UL);
tensor.Resize({20 /*batch size*/, 128 /*dim*/});
// malloc memory
tensor->mutable_data<float>(place);
tensor.mutable_data<float>(place);
lod_tensor.reset(new LODTensor(lod));
lod_tensor->Resize({20 /*batch size*/, 128 /*dim*/});
lod_tensor->Reset(tensor, lod);
lod_tensor->ShareDataWith<float>(tensor);
// lod_tensor->ShareDataWith<Tensor>(tensor);
}
protected:
std::unique_ptr<LODTensor> lod_tensor;
platform::CPUPlace place;
Tensor tensor;
};
TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); }
......@@ -55,110 +62,54 @@ TEST_F(LODTensorTester, NumElements) {
ASSERT_EQ(lod_tensor->NumElements(2), 8UL);
}
TEST_F(LODTensorTester, SliceShared_Level) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
auto new_lod_tensor = lod_tensor->SliceShared(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
auto new_lod_tensor = lod_tensor->SliceShared(level, level + 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1),
lod_tensor->NumElements(level + 1));
ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
}
}
TEST_F(LODTensorTester, SliceCopied_Level) {
TEST_F(LODTensorTester, SliceLevels) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
auto new_lod_tensor =
lod_tensor->SliceCopied<float>(level, level + 1, place);
auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level));
// ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
// TODO(superjom) add tensor comparation here.
// ASSERT_EQ(new_lod_tensor, *lod_tensor);
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
auto new_lod_tensor =
lod_tensor->SliceCopied<float>(level, level + 2, place);
auto new_lod_tensor = lod_tensor->SliceLevels<float>(level, level + 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1),
lod_tensor->NumElements(level + 1));
// ASSERT_EQ(new_lod_tensor.tensor(), lod_tensor->tensor());
// TODO(superjom) add tensor comparation here.
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
}
}
TEST_F(LODTensorTester, SliceShared_Element) {
size_t level = 0;
auto new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
level = 1;
new_lod_tensor = lod_tensor->SliceShared(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
}
TEST_F(LODTensorTester, SliceCopied_Element) {
TEST_F(LODTensorTester, SliceInLevel) {
size_t level = 0;
auto new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
ASSERT_EQ(new_lod_tensor.NumLevels(), 3UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
auto new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
level = 1;
new_lod_tensor = lod_tensor->SliceCopied<float>(level, 0, 2, place);
new_lod_tensor = lod_tensor->SliceInLevel<float>(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_NE(new_lod_tensor.raw_tensor(), lod_tensor->raw_tensor());
level = 1;
// LOD is
// 0 5 10
// 0 2 5 7 10
new_lod_tensor = lod_tensor->SliceCopied<float>(level, 1, 3, place);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.lod_element(0, 0), 0UL);
ASSERT_EQ(new_lod_tensor.lod_element(0, 1), 5UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 0), 0UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 1), 2UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 2), 5UL);
ASSERT_EQ(new_lod_tensor.lod_element(1, 3), 7UL);
// TODO(superjom) compare the content of these tensors
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor->data<float>());
}
TEST_F(LODTensorTester, ShareLOD) {
LODTensor new_lod_tensor;
new_lod_tensor.ShareLOD(*lod_tensor);
new_lod_tensor.CopyLOD(*lod_tensor);
ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod());
}
TEST_F(LODTensorTester, CopyLOD) {
LODTensor new_lod_tensor;
new_lod_tensor.CopyLOD(*lod_tensor);
ASSERT_NE(new_lod_tensor.lod(), lod_tensor->lod());
bool equals = std::equal(lod_tensor->lod().begin(), lod_tensor->lod().end(),
new_lod_tensor.lod().begin());
ASSERT_TRUE(equals);
}
} // namespace framework
......
paddle/framework/op_desc.proto 已删除 100644 → 0
浏览文件 @ 7c0cb0c7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
syntax = "proto2";
package paddle.framework;
import "attribute.proto";
// AttrDesc is used to describe Attributes of an Operator. It contain's
// name, type, and value of Attribute.
//
// e.g, for scale=3.0: name=scala, type=AttrType.FLOAT, value=3.0
message AttrDesc {
required string name = 1;
required AttrType type = 2;
optional int32 i = 3;
optional float f = 4;
optional string s = 5;
repeated int32 ints = 6;
repeated float floats = 7;
repeated string strings = 8;
};
// Protocol Message to describe an Operator.
//
// In PaddlePaddle, Operator is used to do a certain computation such
// as "add", "sub", "cosine", etc.
// (1) Operator needs to know the input and output variable names.
// (2) Some ops may have special attributes such as "scale" in "CosineOp".
//
// 3rd-party language can build this proto message and call
// AddOp(const OpDesc& op_desc) of Paddle core to create an Operator.
message OpDesc {
// input names of this Operator.
repeated string inputs = 1;
// output names of this Operator.
repeated string outputs = 2;
// type of this Operator, such as "add", "sub", "fc".
required string type = 3;
// Attributes of this Operator. e.g., scale=3.0 in cosine op.
repeated AttrDesc attrs = 4;
};
\ No newline at end of file
paddle/framework/op_proto.proto 已删除 100644 → 0
浏览文件 @ 7c0cb0c7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
// Protocol Message for 3rd-party language binding.
//
// Paddle Python package will use `OpProto` to generate op creation methods.
// The op creation methods take user's input and generate `OpDesc` proto
// message,
// then pass `OpDesc` to C++ side and create Op pointer.
//
syntax = "proto2";
package paddle.framework;
import "attribute.proto";
// Attribute protocol message for 3rd-party language binding.
// It will store the Op support what attribute and what type.
message AttrProto {
// Supported attribute name. e.g. `scale` for cosine op.
required string name = 1;
// Supported attribute type.
required AttrType type = 2;
// Supported attribute comments. It helps 3rd-party language generate
// doc-string.
required string comment = 3;
// If that attribute is generated, it means the Paddle third language
// binding has responsibility to fill that attribute. End-User should
// not set that attribute.
optional bool generated = 4 [ default = false ];
}
// Input or output message for 3rd-party language binding.
// It contains parameter name and its comments.
message VarProto {
// Input or output name in that op creation function.
// e.g. `cos(a, b, output, ...)`, "a", "b", "output" are names.
required string name = 1;
// The comment for that input. It helps 3rd-party language generate
// doc-string.
required string comment = 2;
// Is that input/output could be a list or not.
// If so, that Op should write a attributed named `input_format` or
// `output_format`.
//
// e.g.
// If the op is a fc op, the inputs are `X`, `W`, `b`. The `X` and `W`
// could be multiple, so the multiple of `X` and `W` is True, and OpDesc
// will hold a attribute of them.
//
// The Op desc of same fc could be
// {
// "type": "fc",
// "input": ["X1", "X2", "W1", "W2", "b"],
// "output": "fc.out",
// "attrs" : {
// "input_format": [0, 2, 4, 5]
// }
// }
//
optional bool multiple = 3 [ default = false ];
// It marks that output is a temporary output. That output is not used by
// user, but used by other op internally as input. If other op is not use
// that output, it could be optimized early.
//
// Attribute temporary_index will be set in OpDesc if there is some
// outputs are temporary.
//
// output = [ "xxx.out1", "xxx.tmp", "xxx.out2"],
// attrs = {
// "temporary_index": [1]
// }
optional bool temporary = 4 [ default = false ];
// The gradient of operator can be ignored immediately
// e.g. operator AddOp, y = x1 + x2, the gradient of dy/dx1, dy/dx2
// can be ignored for the future optimized on graph.
optional bool ignore_gradient = 6;
}
// Op protocol message for 3rd-party language binding.
// It contains all information for generating op creation method.
message OpProto {
// The input information to generate op creation method.
repeated VarProto inputs = 1;
// The output information to generate op creation method.
repeated VarProto outputs = 2;
// The attribute information to generate op creation method.
repeated AttrProto attrs = 3;
// The comments for that Op. It helps 3rd-party language generate
// doc-string. The whole documentation of that Op is generated by comment,
// inputs, outputs, attrs together.
required string comment = 4;
// The type of that Op.
required string type = 5;
}
paddle/framework/op_proto_test.cc 已删除 100644 → 0
浏览文件 @ 7c0cb0c7
#include <gtest/gtest.h>
#include <paddle/framework/op_proto.pb.h>
TEST(TestOpProto, ALL) {
paddle::framework::OpProto proto;
{
auto ipt = proto.mutable_inputs()->Add();
*ipt->mutable_name() = "a";
*ipt->mutable_comment() = "the one input of cosine op";
}
{
auto ipt = proto.mutable_inputs()->Add();
*ipt->mutable_name() = "b";
*ipt->mutable_comment() = "the other input of cosine op";
}
{
auto opt = proto.mutable_outputs()->Add();
*opt->mutable_name() = "output";
*opt->mutable_comment() = "the output of cosine op";
}
{
auto attr = proto.mutable_attrs()->Add();
*attr->mutable_name() = "scale";
attr->set_type(paddle::framework::AttrType::FLOAT);
*attr->mutable_comment() = "the scale attribute of cosine op";
}
proto.set_type("cos");
*proto.mutable_comment() = "cosine op, output = scale * cos(a, b)";
ASSERT_TRUE(proto.IsInitialized());
}
\ No newline at end of file
paddle/framework/op_registry.h
浏览文件 @ 43ba24e0
此差异已折叠。
paddle/framework/op_registry_test.cc
浏览文件 @ 43ba24e0
......@@ -7,6 +7,7 @@ namespace paddle {
namespace framework {
class CosineOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
void InferShape(const Scope& scope) const override {}
......@@ -27,6 +28,7 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
class MyTestOp : public OperatorBase {
public:
using OperatorBase::OperatorBase;
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {}
......@@ -36,8 +38,8 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
public:
MyTestOpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("input", "input of cosine op").SetMultiple();
AddOutput("output", "output of cosine op").SetTemporary();
AddInput("input", "input of cosine op").AsDuplicable();
AddOutput("output", "output of cosine op").AsIntermediate();
auto my_checker = [](int i) {
PADDLE_ENFORCE(i % 2 == 0, "'test_attr' must be even!");
};
......@@ -49,6 +51,15 @@ class MyTestOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
} // namespace framework
} // namespace paddle
static void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::OpDesc::Var* var) {
var->set_parameter(param_name);
for (auto& arg_name : arguments) {
var->add_arguments(arg_name);
}
}
REGISTER_OP(cos_sim, paddle::framework::CosineOp,
paddle::framework::CosineOpProtoAndCheckerMaker);
REGISTER_OP(my_test_op, paddle::framework::MyTestOp,
......@@ -57,8 +68,8 @@ REGISTER_OP(my_test_op, paddle::framework::MyTestOp,
TEST(OpRegistry, CreateOp) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("cos_sim");
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
float scale = 3.3;
auto attr = op_desc.mutable_attrs()->Add();
......@@ -78,8 +89,8 @@ TEST(OpRegistry, CreateOp) {
TEST(OpRegistry, IllegalAttr) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("cos_sim");
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
......@@ -103,8 +114,8 @@ TEST(OpRegistry, IllegalAttr) {
TEST(OpRegistry, DefaultValue) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("cos_sim");
op_desc.add_inputs("aa");
op_desc.add_outputs("bb");
BuildVar("input", {"aa"}, op_desc.add_inputs());
BuildVar("output", {"bb"}, op_desc.add_outputs());
ASSERT_TRUE(op_desc.IsInitialized());
......@@ -116,20 +127,11 @@ TEST(OpRegistry, DefaultValue) {
ASSERT_EQ(op->GetAttr<float>("scale"), 1.0);
}
static void SetInputFormat(paddle::framework::OpDesc* desc) {
auto attr = desc->add_attrs();
attr->set_name("input_format");
attr->set_type(paddle::framework::INTS);
attr->mutable_ints()->Add(0);
attr->mutable_ints()->Add(1);
}
TEST(OpRegistry, CustomChecker) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("my_test_op");
op_desc.add_inputs("ii");
op_desc.add_outputs("oo");
SetInputFormat(&op_desc);
BuildVar("input", {"ii"}, op_desc.add_inputs());
BuildVar("output", {"oo"}, op_desc.add_outputs());
// attr 'test_attr' is not set
bool caught = false;
......@@ -169,7 +171,6 @@ TEST(OpRegistry, CustomChecker) {
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_i(4);
SetInputFormat(&op_desc);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::platform::CPUDeviceContext dev_ctx;
paddle::framework::Scope scope;
......
paddle/framework/operator.cc
浏览文件 @ 43ba24e0
......@@ -12,9 +12,9 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include "paddle/framework/operator.h"
#include <algorithm>
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
......@@ -33,84 +33,139 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
const std::string& OperatorBase::Input(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_,
"Input Output Indices could not be nullptr");
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
name);
if (attrs_.count("input_format") == 0) {
return inputs_.at((size_t)it->second);
} else {
const auto& input_format = GetAttr<std::vector<int>>("input_format");
int idx = input_format[it->second];
return inputs_.at((size_t)idx);
static std::unordered_map<std::string, OpProto>* g_op_protos = nullptr;
std::unordered_map<std::string, OpProto>& OpProtos() {
if (g_op_protos == nullptr) {
g_op_protos = new std::unordered_map<std::string, OpProto>();
}
return *g_op_protos;
}
std::vector<std::string> OperatorBase::Inputs(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "IO Idx could not be nullptr");
auto input_format = GetAttr<std::vector<int>>("input_format");
auto offset = in_out_idxs_->at(name);
PADDLE_ENFORCE(input_format.at(static_cast<size_t>(offset) + 1) <=
static_cast<int>(inputs_.size()),
"Input Out Of Range");
return std::vector<std::string>{
inputs_.begin() + input_format.at(offset),
inputs_.begin() + input_format.at(offset + 1)};
const std::string& OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_EQ(ins.size(), 1UL,
"Op %s input %s should contain only one variable", type_,
name);
return ins[0];
}
const std::string& OperatorBase::Output(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "InOut Indice could not be nullptr");
auto it = in_out_idxs_->find(name);
PADDLE_ENFORCE(it != in_out_idxs_->end(), "no key [%s] in in_out_idxs_",
const std::vector<std::string>& OperatorBase::Inputs(
const std::string& name) const {
auto it = inputs_.find(name);
PADDLE_ENFORCE(it != inputs_.end(), "Op %s do not have input %s", type_,
name);
if (attrs_.count("output_format") == 0) {
return outputs_.at((size_t)it->second);
} else {
const auto& output_format = GetAttr<std::vector<int>>("output_format");
int idx = output_format[it->second];
return outputs_.at((size_t)idx);
}
return it->second;
}
const std::string& OperatorBase::Output(const std::string& name) const {
auto& outs = Outputs(name);
PADDLE_ENFORCE_EQ(outs.size(), 1UL,
"Op %s output %s should contain only one variable", type_,
name);
return outs[0];
}
std::vector<std::string> OperatorBase::Outputs(const std::string& name) const {
PADDLE_ENFORCE_NOT_NULL(in_out_idxs_, "InOut Indice could not be nullptr");
auto output_format = GetAttr<std::vector<int>>("output_format");
auto offset = in_out_idxs_->at(name);
PADDLE_ENFORCE(output_format.at(static_cast<size_t>(offset) + 1) <=
static_cast<int>(outputs_.size()),
"Output Out of Range");
return std::vector<std::string>{
outputs_.begin() + output_format.at(offset),
outputs_.begin() + output_format.at(offset + 1)};
const std::vector<std::string>& OperatorBase::Outputs(
const std::string& name) const {
auto it = outputs_.find(name);
PADDLE_ENFORCE(it != outputs_.end(), "Op %s does not have output %s", type_,
name);
return it->second;
}
std::string OperatorBase::DebugString() const {
std::stringstream ss;
ss << "Op(" << type_ << "), inputs:(";
for (size_t i = 0; i < inputs_.size(); ++i) {
ss << inputs_[i];
if (i != inputs_.size() - 1) {
ss << "Op(" << type_ << "), inputs:{";
for (auto it = inputs_.begin(); it != inputs_.end();) {
auto& input = *it;
ss << input.first << "[";
for (size_t i = 0; i < input.second.size(); ++i) {
ss << input.second[i];
if (i != input.second.size() - 1) {
ss << ", ";
}
}
ss << "]";
++it;
if (it != inputs_.end()) {
ss << ", ";
}
}
ss << "), outputs:(";
for (size_t i = 0; i < outputs_.size(); ++i) {
ss << outputs_[i];
if (i != outputs_.size() - 1) {
ss << "}, outputs:{";
for (auto it = outputs_.begin(); it != outputs_.end();) {
auto& output = *it;
ss << output.first << "[";
for (size_t i = 0; i < output.second.size(); ++i) {
ss << output.second[i];
if (i != output.second.size() - 1) {
ss << ", ";
}
}
ss << "]";
++it;
if (it != outputs_.end()) {
ss << ", ";
}
}
ss << ").";
ss << "}.";
return ss.str();
}
void OperatorBase::Rename(const std::string& old_name,
const std::string& new_name) {
std::replace(inputs_.begin(), inputs_.end(), old_name, new_name);
std::replace(outputs_.begin(), outputs_.end(), old_name, new_name);
for (auto& input : inputs_) {
std::replace(input.second.begin(), input.second.end(), old_name, new_name);
}
for (auto& output : outputs_) {
std::replace(output.second.begin(), output.second.end(), old_name,
new_name);
}
}
OperatorBase::OperatorBase(const std::string& type,
const OperatorBase::VarNameMap& inputs,
const OperatorBase::VarNameMap& outputs,
const AttributeMap& attrs)
: type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs) {
static std::atomic<size_t> gUniqId(0UL);
for (auto& output : outputs_) {
for (auto& output_name : output.second) {
if (output_name == kTempVarName) {
output_name += type_;
output_name += "@";
output_name += std::to_string(gUniqId.fetch_add(1));
}
}
}
}
std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
std::vector<std::string> ret_val;
if (has_intermediate) {
// push all outputs into ret_val
for (auto& o : outputs_) {
ret_val.reserve(ret_val.size() + o.second.size());
ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
}
return ret_val;
}
auto it = OpProtos().find(type_);
PADDLE_ENFORCE(
it != OpProtos().end(),
"Operator %s not registered, cannot figure out intermediate outputs",
type_);
// get all OpProto::Var for outputs
for (auto& o : it->second.outputs()) {
// ignore all intermediate output
if (o.intermediate()) continue;
auto out = outputs_.find(o.name());
if (out != outputs_.end()) {
ret_val.reserve(ret_val.size() + out->second.size());
ret_val.insert(ret_val.end(), out->second.begin(), out->second.end());
}
}
return ret_val;
}
} // namespace framework
......
paddle/framework/operator.h
浏览文件 @ 43ba24e0
......@@ -15,42 +15,43 @@ limitations under the License. */
#pragma once
#include <algorithm>
#include <boost/variant.hpp>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/framework/attribute.h"
#include "paddle/framework/op_desc.pb.h"
#include "paddle/framework/op_proto.pb.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/place.h"
#include "paddle/platform/variant.h"
#include "paddle/utils/Error.h"
namespace paddle {
namespace framework {
/// If a variable is a empty variable, that name will be used.
const std::string kEmptyVarName = "@EMPTY@";
constexpr char kEmptyVarName[] = "@EMPTY@";
/// If a variable is a temporary variable, that name will be set in Python,
/// but it will be convert to a unique name in scope after OpCreator.
const std::string kTempVarName = "@TEMP@";
constexpr char kTempVarName[] = "@TEMP@";
/// If a variable's name has a certain suffix, it means that the
/// variable is the gradient of another varibale.
/// e.g. Variable "x@GRAD" is the gradient of varibale "x".
const std::string kGradVarSuffix = "@GRAD";
constexpr char kGradVarSuffix[] = "@GRAD";
/// Variables with this suffix are supposed to be filled up with zeros.
const std::string kZeroVarSuffix = "@ZERO";
constexpr char kZeroVarSuffix[] = "@ZERO";
inline std::string GradVarName(const std::string& var_name) {
return var_name + kGradVarSuffix;
}
extern std::unordered_map<std::string, OpProto>& OpProtos();
class OperatorBase;
class InferShapeContext;
class ExecutionContext;
......@@ -63,6 +64,15 @@ class ExecutionContext;
*/
class OperatorBase {
public:
using VarNameMap = std::map<std::string, std::vector<std::string>>;
OperatorBase(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs);
OperatorBase(const OperatorBase& o) = delete;
OperatorBase& operator=(const OperatorBase& o) = delete;
OperatorBase(OperatorBase&& o) = delete;
virtual ~OperatorBase() {}
template <typename T>
......@@ -74,10 +84,6 @@ class OperatorBase {
virtual std::string DebugString() const;
/// Init will be called after CreateOperator, you can put some initialization
/// logic here.
virtual void Init() {}
/// InferShape infer the size of Variables used by this Operator with
/// information inside scope
virtual void InferShape(const Scope& scope) const = 0;
......@@ -95,15 +101,19 @@ class OperatorBase {
//! Get a input with argument's name described in `op_proto`
const std::string& Input(const std::string& name) const;
//! Get a input which has multiple variables.
//! TODO add a vector_view to prevent memory copy.
std::vector<std::string> Inputs(const std::string& name) const;
const std::vector<std::string>& Inputs(const std::string& name) const;
//! Get a output with argument's name described in `op_proto`
const std::string& Output(const std::string& name) const;
//! Get an output which has multiple variables.
//! TODO add a vector_view to prevent memory copy.
std::vector<std::string> Outputs(const std::string& name) const;
const std::vector<std::string>& Outputs(const std::string& name) const;
virtual std::vector<std::string> OutputVars(bool has_intermediate) const;
std::string Type() const { return type_; }
const AttributeMap& Attrs() const { return attrs_; }
public:
std::string type_;
......@@ -111,30 +121,25 @@ class OperatorBase {
// I (Inputs)
// O (Outputs)
// OG (Output Gradients)
std::vector<std::string> inputs_;
VarNameMap inputs_;
// NOTE: in case of OpGrad, outputs_ contains
// IG (Inputs Gradients)
std::vector<std::string> outputs_;
VarNameMap outputs_;
AttributeMap attrs_;
// store the arguments' offset described in op_desc.
std::shared_ptr<std::unordered_map<std::string, int>> in_out_idxs_;
};
class OperatorContext {
class InferShapeContext {
public:
OperatorContext(const OperatorBase* op, const Scope& scope)
: op_(*op), scope_(scope) {}
InferShapeContext(const OperatorBase& op, const Scope& scope)
: op_(op), scope_(scope) {}
size_t InputSize() const { return op_.inputs_.size(); }
size_t OutputSize() const { return op_.outputs_.size(); }
const Variable* InputVar(const size_t index) const {
return scope_.FindVar(op_.inputs_.at(index));
size_t InputSize(const std::string& name) const {
return op_.Inputs(name).size();
}
Variable* OutputVar(const size_t index) const {
return scope_.FindVar(op_.outputs_.at(index));
size_t OutputSize(const std::string& name) const {
return op_.Outputs(name).size();
}
const Variable* InputVar(const std::string& name) const {
......@@ -166,27 +171,9 @@ class OperatorContext {
return res;
}
template <typename T>
const T* Input(const size_t index) const {
auto var = InputVar(index);
PADDLE_ENFORCE_NOT_NULL(var, "Input(%d) should not be nullptr", index);
return &var->Get<T>();
}
template <typename T>
T* Output(const size_t index) const {
auto var = OutputVar(index);
PADDLE_ENFORCE_NOT_NULL(
var,
"Output(%d) not be nullptr, which means variable [%s] does not "
"exist in scope",
index, op_.outputs_[index]);
return var->GetMutable<T>();
}
template <typename T>
const T* Input(const std::string& name) const {
auto var = InputVar(name);
auto* var = InputVar(name);
PADDLE_ENFORCE_NOT_NULL(var, "Input(%s) should not be nullptr", name);
return &var->Get<T>();
}
......@@ -234,12 +221,6 @@ class OperatorContext {
const Scope& scope_;
};
class InferShapeContext : public OperatorContext {
public:
InferShapeContext(const OperatorBase* op, const Scope& scope)
: OperatorContext(op, scope) {}
};
template <typename T>
struct EigenDeviceConverter;
......@@ -255,11 +236,11 @@ struct EigenDeviceConverter<platform::GPUPlace> {
};
#endif
class ExecutionContext : public OperatorContext {
class ExecutionContext : public InferShapeContext {
public:
ExecutionContext(const OperatorBase* op, const Scope& scope,
ExecutionContext(const OperatorBase& op, const Scope& scope,
const platform::DeviceContext* device_context)
: OperatorContext(op, scope), device_context_(device_context) {}
: InferShapeContext(op, scope), device_context_(device_context) {}
template <typename PlaceType,
typename DeviceType =
......@@ -268,6 +249,10 @@ class ExecutionContext : public OperatorContext {
platform::Place GetPlace() const { return device_context_->GetPlace(); }
const platform::DeviceContext* device_context() const {
return device_context_;
}
const platform::DeviceContext* device_context_;
};
......@@ -310,14 +295,18 @@ class OperatorWithKernel : public OperatorBase {
using OpKernelMap =
std::unordered_map<OpKernelKey, std::unique_ptr<OpKernel>, OpKernelHash>;
OperatorWithKernel(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void InferShape(const Scope& scope) const override {
InferShape(InferShapeContext(this, scope));
InferShape(InferShapeContext(*this, scope));
}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const final {
auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx));
opKernel->Compute(ExecutionContext(this, scope, &dev_ctx));
opKernel->Compute(ExecutionContext(*this, scope, &dev_ctx));
}
static std::unordered_map<std::string /* op_type */, OpKernelMap>&
......
paddle/framework/operator_test.cc
浏览文件 @ 43ba24e0
......@@ -23,20 +23,22 @@ static int op_run_num = 0;
class OpWithoutKernelTest : public OperatorBase {
public:
void Init() override { x = 1; }
OpWithoutKernelTest(const std::string& type, const VarNameMap& inputs,
const VarNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs), x(1) {}
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
op_run_num++;
ASSERT_EQ((int)inputs_.size(), 1);
ASSERT_EQ((int)outputs_.size(), 1);
ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
++op_run_num;
ASSERT_EQ(static_cast<int>(inputs_.size()), 1);
ASSERT_EQ(static_cast<int>(outputs_.size()), 1);
ASSERT_EQ(scope.FindVar(inputs_.at("input")[0]), nullptr);
ASSERT_EQ(x, 1);
ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
ASSERT_NE(scope.FindVar(outputs_.at("output")[0]), nullptr);
}
public:
float x = 0;
int x{0};
};
class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
......@@ -54,14 +56,24 @@ class OpeWithoutKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
} // namespace framework
} // namespace paddle
static void BuildVar(const std::string& param_name,
std::initializer_list<const char*> arguments,
paddle::framework::OpDesc::Var* var) {
var->set_parameter(param_name);
for (auto& arg_name : arguments) {
*var->mutable_arguments()->Add() = arg_name;
}
}
REGISTER_OP(test_operator, paddle::framework::OpWithoutKernelTest,
paddle::framework::OpeWithoutKernelTestProtoAndCheckerMaker);
TEST(OperatorBase, all) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("test_operator");
*op_desc.mutable_inputs()->Add() = "IN1";
*op_desc.mutable_outputs()->Add() = "OUT1";
BuildVar("input", {"IN1"}, op_desc.add_inputs());
BuildVar("output", {"OUT1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......@@ -97,6 +109,9 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker {
static int cpu_kernel_run_num = 0;
class OpWithKernelTest : public OperatorWithKernel {
public:
using OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {}
};
......@@ -113,33 +128,15 @@ class CPUKernelTest : public OpKernel {
}
};
// multiple inputs test
class OperatorMultiInputsTest : public OperatorBase {
public:
void Init() override { x = 1; }
void InferShape(const Scope& scope) const override {}
void Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const override {
ASSERT_EQ(scope.FindVar(inputs_[0]), nullptr);
ASSERT_EQ(x, 1);
ASSERT_NE(scope.FindVar(outputs_[0]), nullptr);
ASSERT_EQ(Input("x"), "IN1");
ASSERT_EQ(Input("y"), "OUT1");
}
public:
float x = 0;
};
class OpKernelTestMultiInputsProtoAndCheckerMaker
: public OpProtoAndCheckerMaker {
public:
OpKernelTestMultiInputsProtoAndCheckerMaker(OpProto* proto,
OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("xs", "inputs of test op").SetMultiple();
AddInput("xs", "inputs of test op").AsDuplicable();
AddInput("k", "input of test op");
AddOutput("ys", "outputs of test op").SetMultiple();
AddOutput("ys", "outputs of test op").AsDuplicable();
AddAttr<float>("scale", "scale of cosine op")
.SetDefault(1.0)
.LargerThan(0.0);
......@@ -196,8 +193,9 @@ REGISTER_OP_CPU_KERNEL(op_with_kernel,
TEST(OpKernel, all) {
paddle::framework::OpDesc op_desc;
op_desc.set_type("op_with_kernel");
*op_desc.mutable_inputs()->Add() = "IN1";
*op_desc.mutable_outputs()->Add() = "OUT1";
BuildVar("x", {"IN1"}, op_desc.add_inputs());
BuildVar("y", {"OUT1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
......@@ -223,32 +221,15 @@ TEST(OpKernel, multi_inputs) {
OpDesc op_desc;
op_desc.set_type("op_multi_inputs_with_kernel");
*op_desc.mutable_inputs()->Add() = "x0";
*op_desc.mutable_inputs()->Add() = "x1";
*op_desc.mutable_inputs()->Add() = "x2";
*op_desc.mutable_inputs()->Add() = "k0";
*op_desc.mutable_outputs()->Add() = "y0";
*op_desc.mutable_outputs()->Add() = "y1";
BuildVar("xs", {"x0", "x1", "x2"}, op_desc.add_inputs());
BuildVar("k", {"k0"}, op_desc.add_inputs());
BuildVar("ys", {"y0", "y1"}, op_desc.add_outputs());
auto attr = op_desc.mutable_attrs()->Add();
attr->set_name("scale");
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(3.14);
auto attr0 = op_desc.mutable_attrs()->Add();
attr0->set_name("input_format");
attr0->set_type(paddle::framework::AttrType::INTS);
auto input_format = attr0->mutable_ints();
input_format->Add(0); // x0
input_format->Add(3); // k
input_format->Add(4); // end
auto attr1 = op_desc.mutable_attrs()->Add();
attr1->set_name("output_format");
attr1->set_type(paddle::framework::AttrType::INTS);
auto output_format = attr1->mutable_ints();
output_format->Add(0); // y0
output_format->Add(2); // y1
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
scope.NewVar("x0")->GetMutable<Tensor>();
......
paddle/framework/pybind.cc
浏览文件 @ 43ba24e0
......@@ -22,6 +22,7 @@ limitations under the License. */
#include "paddle/operators/net_op.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/string/to_string.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
......@@ -29,17 +30,18 @@ limitations under the License. */
namespace py = pybind11;
USE_OP(add_two);
USE_OP_CPU(onehot_cross_entropy);
USE_OP_WITHOUT_KERNEL(fc);
USE_OP(sgd);
USE_CPU_OP(onehot_cross_entropy);
USE_NO_GRAD_OP(sgd);
USE_OP(mul);
USE_OP(mean);
USE_OP(sigmoid);
USE_OP(softmax);
USE_OP(rowwise_add);
USE_OP(fill_zeros_like);
USE_OP_WITHOUT_KERNEL(recurrent_op);
USE_OP_ITSELF(recurrent_op);
USE_OP(gaussian_random);
USE_OP(uniform_random);
namespace paddle {
namespace framework {
......@@ -54,30 +56,18 @@ void ExposeOperator(ClassType &m) {
return op.type_;
})
.def("outputs",
[](const typename ClassType::type &op) -> std::vector<std::string> {
return op.outputs_;
})
[](const typename ClassType::type &op)
-> std::map<std::string, std::vector<std::string>> {
return op.outputs_;
})
.def("inputs",
[](const typename ClassType::type &op) -> std::vector<std::string> {
return op.inputs_;
[](const typename ClassType::type &op) { return op.inputs_; })
.def("__str__", &ClassType::type::DebugString)
.def("no_intermediate_outputs",
[](const typename ClassType::type &op) {
return op.OutputVars(false);
})
.def("support_gpu", &ClassType::type::SupportGPU)
.def("temp_outputs",
[](const typename ClassType::type &op) -> std::vector<std::string> {
auto iter = op.attrs_.find("temporary_index");
std::vector<std::string> ret;
if (iter == op.attrs_.end()) {
return ret;
} else {
auto tmp_idx = boost::get<std::vector<int>>(iter->second);
ret.reserve(tmp_idx.size());
for (auto &index : tmp_idx) {
ret.push_back(op.outputs_.at(index));
}
return ret;
}
})
.def("__str__", &ClassType::type::DebugString);
.def("support_gpu", &ClassType::type::SupportGPU);
}
static size_t UniqueIntegerGenerator() {
......@@ -170,7 +160,7 @@ All parameter, weight, gradient are variables in Paddle.
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
auto &protos = OpRegistry::protos();
auto &protos = OpProtos();
std::vector<py::bytes> ret_values;
for (auto it = protos.begin(); it != protos.end(); ++it) {
PADDLE_ENFORCE(it->second.IsInitialized(),
......@@ -205,9 +195,13 @@ All parameter, weight, gradient are variables in Paddle.
});
// clang-format on
py::class_<paddle::platform::GPUPlace>(m, "GPUPlace").def(py::init<int>());
py::class_<platform::GPUPlace>(m, "GPUPlace")
.def(py::init<int>())
.def("__str__", string::to_string<const platform::GPUPlace &>);
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace").def(py::init<>());
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace")
.def(py::init<>())
.def("__str__", string::to_string<const platform::CPUPlace &>);
py::class_<OperatorBase, std::shared_ptr<OperatorBase>> operator_base(
m, "Operator");
......
paddle/framework/tensor.h
浏览文件 @ 43ba24e0
......@@ -79,11 +79,11 @@ class Tensor {
inline const DDim& dims() const;
/*! Resize the dimensions of the memory block. */
inline void Resize(const DDim& dims);
inline Tensor& Resize(const DDim& dims);
/*! The internal of two tensors share the same memory block. */
template <typename T>
inline void ShareDataWith(const Tensor& src);
inline Tensor& ShareDataWith(const Tensor& src);
/**
* @brief Copy the content of external tensor to a new place.
......@@ -105,6 +105,8 @@ class Tensor {
template <typename T>
inline Tensor Slice(const int& begin_idx, const int& end_idx) const;
platform::Place place() const { return holder_->place(); }
private:
template <typename T>
inline void check_memory_size() const;
......
paddle/framework/tensor_impl.h
浏览文件 @ 43ba24e0
......@@ -23,9 +23,11 @@ template <typename T>
inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(holder_->size(), product(dims_) * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.");
PADDLE_ENFORCE_GE(
holder_->size(), product(dims_) * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
}
template <typename T>
......@@ -78,9 +80,10 @@ inline T* Tensor::mutable_data(platform::Place place) {
}
template <typename T>
inline void Tensor::ShareDataWith(const Tensor& src) {
inline Tensor& Tensor::ShareDataWith(const Tensor& src) {
src.check_memory_size<T>();
*this = src;
return *this;
}
template <typename T>
......@@ -136,7 +139,10 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
return dst;
}
inline void Tensor::Resize(const DDim& dims) { dims_ = dims; }
inline Tensor& Tensor::Resize(const DDim& dims) {
dims_ = dims;
return *this;
}
inline const DDim& Tensor::dims() const { return dims_; }
......
paddle/function/CMakeLists.txt
浏览文件 @ 43ba24e0
......@@ -38,10 +38,11 @@ if(WITH_GPU)
add_simple_unittest(RowConvOpTest)
add_simple_unittest(BlockExpandOpTest)
add_simple_unittest(CropOpTest)
add_simple_unittest(DepthwiseConvOpTest)
endif()
add_simple_unittest(ConvOpTest)
add_simple_unittest(Im2ColTest)
add_simple_unittest(GemmConvOpTest)
endif()
add_style_check_target(paddle_function ${h_files})
......
paddle/function/ConvOpTest.cpp 已删除 100644 → 0
浏览文件 @ 7c0cb0c7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <memory>
#include "Function.h"
#include "FunctionTest.h"
namespace paddle {
enum TestType {
kForwardTest = 0,
kBackwardInputTest = 1,
kBackwardFilterTest = 2,
};
template <DeviceType DType1, DeviceType DType2>
class ConvolutionTest {
public:
ConvolutionTest(const std::string& conv1,
const std::string& conv2,
TestType type,
bool useGroups = true,
std::string algo = "auto") {
for (size_t batchSize : {1, 32}) {
for (size_t inputSize : {7, 14, 54}) {
for (size_t filterSize : {1, 3, 5}) {
for (size_t inputChannels : {3, 64}) {
for (size_t outputChannels : {3, 64}) {
if (inputChannels > outputChannels) break;
size_t groups;
if (!useGroups) {
groups = 1;
} else {
if (outputChannels % inputChannels != 0) continue;
groups = inputChannels;
}
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize
<< " stride=" << stride << " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", groups)
.set("algo", algo));
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter;
if (groups > 1)
filter = TensorShape({groups,
outputChannels / groups,
inputChannels / groups,
filterSize,
filterSize});
else
filter = TensorShape({outputChannels,
inputChannels,
filterSize,
filterSize});
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
if (type == kForwardTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.run();
} else if (type == kBackwardInputTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
test.run();
} else if (type == kBackwardFilterTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
ADD_TO);
test.run();
}
}
}
}
}
}
}
}
}
};
// Mainly used to test cases where the height and width (input, filter)
// are not equal.
template <DeviceType DType1, DeviceType DType2>
class ConvolutionTest2 {
public:
ConvolutionTest2(const std::string& conv1,
const std::string& conv2,
TestType type,
bool useGroups = true,
std::string algo = "auto") {
for (size_t batchSize : {16}) {
for (size_t inputHeight : {7, 31}) {
for (size_t inputWidth : {10, 54}) {
for (size_t filterHeight : {1, 5}) {
for (size_t filterWidth : {3, 7}) {
for (size_t inputChannels : {7}) {
for (size_t outputChannels : {7}) {
size_t groups;
if (!useGroups) {
groups = 1;
} else {
if (outputChannels % inputChannels != 0) continue;
groups = inputChannels;
}
size_t stride = 1;
size_t padding = 0;
size_t outputHeight =
(inputHeight - filterHeight + 2 * padding + stride) /
stride;
size_t outputWidth =
(inputWidth - filterWidth + 2 * padding + stride) /
stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputHeight
<< " inputWidth=" << inputWidth
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterHeight
<< " filterWidth=" << filterWidth
<< " outputHeight=" << outputHeight
<< " outputWidth=" << outputWidth
<< " stride=" << stride << " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", groups)
.set("algo", algo));
TensorShape input{
batchSize, inputChannels, inputHeight, inputWidth};
TensorShape filter;
if (groups > 1)
filter = TensorShape({groups,
outputChannels / groups,
inputChannels / groups,
filterHeight,
filterWidth});
else
filter = TensorShape({outputChannels,
inputChannels,
filterHeight,
filterWidth});
TensorShape output{
batchSize, outputChannels, outputHeight, outputWidth};
if (type == kForwardTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.run();
} else if (type == kBackwardInputTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
test.run();
} else if (type == kBackwardFilterTest) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter),
ADD_TO);
test.run();
}
}
}
}
}
}
}
}
}
};
// ======Start Convolution TEST======
TEST(Forward, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test2(
"NaiveConv-CPU", "GemmConv-CPU", kForwardTest, false);
}
#ifndef PADDLE_ONLY_CPU
TEST(Forward, GEMM2) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConv-CPU", "GemmConv-GPU", kForwardTest, false);
}
TEST(BackwardInput, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradInput-CPU",
"GemmConvGradInput-GPU",
kBackwardInputTest,
false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradInput-CPU",
"GemmConvGradInput-GPU",
kBackwardInputTest,
false);
}
TEST(BackwardFilter, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradFilter-CPU",
"GemmConvGradFilter-GPU",
kBackwardFilterTest,
false);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradFilter-CPU",
"GemmConvGradFilter-GPU",
kBackwardFilterTest,
false);
}
#endif
// ======End Convolution TEST======
// ======Start DepthwiseConvolution TEST======
// TODO(zhaolong) The depthwise convolution cpu test will be added when the cpu
// version of depthwiseConv is implemented.
#ifndef PADDLE_ONLY_CPU
TEST(DepthwiseConvForward, GEMM2) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConv-CPU", "DepthwiseConv-GPU", kForwardTest);
}
TEST(DepthwiseConvBackwardInput, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradInput-CPU",
"DepthwiseConvGradInput-GPU",
kBackwardInputTest);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradInput-CPU",
"DepthwiseConvGradInput-GPU",
kBackwardInputTest);
}
TEST(DepthwiseConvBackwardFilter, GEMM) {
ConvolutionTest<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test(
"GemmConvGradFilter-CPU",
"DepthwiseConvGradFilter-GPU",
kBackwardFilterTest);
ConvolutionTest2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU> test2(
"GemmConvGradFilter-CPU",
"DepthwiseConvGradFilter-GPU",
kBackwardFilterTest);
}
#endif
// ======End DepthwiseConvolution TEST======
} // namespace paddle
paddle/function/ConvOpTest.h 0 → 100644
浏览文件 @ 43ba24e0
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "FunctionTest.h"
namespace paddle {
template <DeviceType DType1, DeviceType DType2>
void forward(Compare2Function<DType1, DType2>& test,
const TensorShape& input,
const TensorShape& filter,
const TensorShape& output) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.run();
}
template <DeviceType DType1, DeviceType DType2>
void backward_input(Compare2Function<DType1, DType2>& test,
const TensorShape& input,
const TensorShape& filter,
const TensorShape& output) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, filter));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, input), ADD_TO);
test.run();
}
template <DeviceType DType1, DeviceType DType2>
void backward_filter(Compare2Function<DType1, DType2>& test,
const TensorShape& input,
const TensorShape& filter,
const TensorShape& output) {
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, output));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, input));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, filter), ADD_TO);
test.run();
}
template <DeviceType DType1, DeviceType DType2>
using Function = void (*)(Compare2Function<DType1, DType2>& test,
const TensorShape& input,
const TensorShape& filter,
const TensorShape& output);
/**
* \brief A basic convolution function test interface.
*
* \param conv1 type name of convolution function 1.
* \param conv2 type name of convolution function 2.
* \param function test function, can be one of the forward, backward_input
* backward_filter function.
* Example:
* 1. Compare GemmConv's CPU and GPU implementation:
* Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
* "GemmConv-CPU", "GemmConv-GPU", forward);
*/
template <DeviceType DType1, DeviceType DType2>
void Convolution(const std::string& conv1,
const std::string& conv2,
Function<DType1, DType2> function) {
for (size_t batchSize : {1, 5}) {
for (size_t inputSize : {7, 14, 31}) {
for (size_t filterSize : {1, 3, 5}) {
for (size_t inputChannels : {3, 16}) {
for (size_t outputChannels : {3, 16}) {
if (outputChannels < inputChannels) continue;
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
// NNPACK only supports stride = 1 if batchSize > 1
if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
batchSize > 1 && stride > 1)
break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize << " stride=" << stride
<< " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("algo", (std::string) "auto"));
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
function(test, input, filter, output);
}
}
}
}
}
}
}
}
/**
* \brief A convolution function test interface for
* image height is not equal image width.
*/
template <DeviceType DType1, DeviceType DType2>
void Convolution2(const std::string& conv1,
const std::string& conv2,
Function<DType1, DType2> function) {
for (size_t batchSize : {4}) {
for (size_t inputHeight : {7, 31}) {
for (size_t inputWidth : {10, 54}) {
for (size_t filterHeight : {1, 5}) {
for (size_t filterWidth : {3, 7}) {
for (size_t inputChannels : {7}) {
for (size_t outputChannels : {7}) {
size_t stride = 1;
size_t padding = 0;
size_t outputHeight =
(inputHeight - filterHeight + 2 * padding + stride) /
stride;
size_t outputWidth =
(inputWidth - filterWidth + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputHeight
<< " inputWidth=" << inputWidth
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterHeight
<< " filterWidth=" << filterWidth
<< " outputHeight=" << outputHeight
<< " outputWidth=" << outputWidth
<< " stride=" << stride << " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("algo", (std::string) "auto"));
TensorShape input{
batchSize, inputChannels, inputHeight, inputWidth};
TensorShape filter{
outputChannels, inputChannels, filterHeight, filterWidth};
TensorShape output{
batchSize, outputChannels, outputHeight, outputWidth};
function(test, input, filter, output);
}
}
}
}
}
}
}
}
/**
* \brief A convolution function test interface for depthwise convolution.
*/
template <DeviceType DType1, DeviceType DType2>
void DepthwiseConvolution(const std::string& conv1,
const std::string& conv2,
Function<DType1, DType2> function) {
for (size_t batchSize : {1, 32}) {
for (size_t inputSize : {7, 14, 54}) {
for (size_t filterSize : {3, 4}) {
for (size_t inputChannels : {32}) {
for (size_t outputChannels : {32, 64}) {
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
// NNPACK only supports stride = 1 if batchSize > 1,
// and there has some bug when batchSize > 1 and groups != 1
if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
batchSize > 1)
break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize << " stride=" << stride
<< " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
size_t groups = inputChannels;
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", groups)
.set("algo", (std::string) "auto"));
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{groups,
outputChannels / groups,
inputChannels / groups,
filterSize,
filterSize};
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
function(test, input, filter, output);
}
}
}
}
}
}
}
}
} // namespace paddle
paddle/operators/mean_op_test.cc → paddle/function/DepthwiseConvOpTest.cpp
浏览文件 @ 43ba24e0
......@@ -13,13 +13,25 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "ConvOpTest.h"
#include <paddle/framework/op_registry.h>
namespace paddle {
USE_OP(mean);
#ifndef PADDLE_ONLY_CPU
TEST(DepthwiseConv, Forward) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConv-CPU", "DepthwiseConv-GPU", forward);
}
TEST(DepthwiseConv, BackwardInput) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConvGradInput-CPU", "DepthwiseConvGradInput-GPU", backward_input);
}
TEST(MeanOp, GetOpProto) {
auto& protos = paddle::framework::OpRegistry::protos();
auto it = protos.find("mean");
ASSERT_NE(it, protos.end());
TEST(DepthwiseConv, BackwardFilter) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConvGradFilter-CPU", "DepthwiseConvGradFilter-GPU", backward_filter);
}
#endif
} // namespace paddle
paddle/operators/add_op_test.cc → paddle/function/GemmConvOpTest.cpp
浏览文件 @ 43ba24e0
......@@ -13,16 +13,38 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#define private public
#include "paddle/framework/op_registry.h"
USE_OP(add_two);
TEST(AddOp, GetOpProto) {
auto& protos = paddle::framework::OpRegistry::protos();
auto it = protos.find("add_two");
ASSERT_NE(it, protos.end());
auto& op_creators = paddle::framework::OpRegistry::op_creators();
auto it1 = op_creators.find("add_two_grad");
ASSERT_NE(it1, op_creators.end());
#include "ConvOpTest.h"
namespace paddle {
TEST(GemmConv, NaiveConv) {
Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"NaiveConv-CPU", "GemmConv-CPU", forward);
Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"NaiveConv-CPU", "GemmConv-CPU", forward);
}
#ifndef PADDLE_ONLY_CPU
TEST(GemmConv, Forward) {
Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConv-CPU", "GemmConv-GPU", forward);
Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConv-CPU", "GemmConv-GPU", forward);
}
TEST(GemmConv, BackwardInput) {
Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", backward_input);
Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConvGradInput-CPU", "GemmConvGradInput-GPU", backward_input);
}
TEST(GemmConv, BackwardFilter) {
Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", backward_filter);
Convolution2<DEVICE_TYPE_CPU, DEVICE_TYPE_GPU>(
"GemmConvGradFilter-CPU", "GemmConvGradFilter-GPU", backward_filter);
}
#endif
} // namespace paddle
paddle/function/nnpack/NNPACKConvOp.cpp
浏览文件 @ 43ba24e0
......@@ -196,30 +196,30 @@ public:
CHECK_EQ(status, nnp_status_success);
}
} else {
for (size_t g = 0; g < groups_; g++) {
// only supports stride = 1
CHECK_EQ(strideH(), 1);
CHECK_EQ(strideW(), 1);
nnp_status status =
nnp_convolution_output(algorithm_,
batchSize,
inputChannels / groups_,
outputChannels / groups_,
inputSize,
padding,
kernelSize,
inputData + inputOffset * g,
filterData + filterOffset * g,
nullptr, /* bias */
outputData + outputOffset * g,
bufferPtr,
sizePtr,
nnp_activation_identity,
nullptr,
threadpool_, /* threadpool */
nullptr);
CHECK_EQ(status, nnp_status_success);
}
// only supports stride = 1
CHECK_EQ(strideH(), 1);
CHECK_EQ(strideW(), 1);
// TODO(hedaoyuan): There has some bug when batchSize > 1 and groups_ > 1.
CHECK_EQ(groups_, static_cast<size_t>(1));
nnp_status status = nnp_convolution_output(algorithm_,
batchSize,
inputChannels,
outputChannels,
inputSize,
padding,
kernelSize,
inputData,
filterData,
nullptr, /* bias */
outputData,
bufferPtr,
sizePtr,
nnp_activation_identity,
nullptr,
threadpool_, /* threadpool */
nullptr);
CHECK_EQ(status, nnp_status_success);
}
}
......
paddle/function/nnpack/NNPACKConvOpTest.cpp
浏览文件 @ 43ba24e0
......@@ -13,87 +13,18 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "paddle/function/Function.h"
#include "paddle/function/FunctionTest.h"
DEFINE_string(algo,
"auto",
"The algorithm (auto, ft8x8, ft16x16, wt8x8, "
"implicit-gemm, or direct) for computing convolution of NNPACK.");
#include "paddle/function/ConvOpTest.h"
namespace paddle {
#define IS_NNPACK_SUPPORT(algo, filterSize, stride) \
if (algo == "direct" && filterSize != 1) continue; \
if (algo == "direct" && batchSize != 1) continue; \
if (algo == "wt8x8" && filterSize != 3) continue; \
if (algo == "implicit-gemm" && batchSize != 1) continue; \
if (algo != "auto" && algo != "implicit-gemm" && stride > 1) continue;
class ConvolutionTest {
public:
ConvolutionTest(const std::string& conv1,
const std::string& conv2,
std::string algo = "auto") {
for (size_t batchSize : {1, 32}) {
for (size_t inputSize : {7, 14, 54}) {
for (size_t filterSize : {1, 3, 5}) {
for (size_t inputChannels : {3, 64}) {
for (size_t outputChannels : {3, 64, 128}) {
if (inputChannels < outputChannels) break;
for (size_t stride : {1, 2}) {
// if batchSize > 1 NNPACKConv only supports stride = 1
if (batchSize > 1 && stride > 1) break;
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
IS_NNPACK_SUPPORT(algo, filterSize, stride);
LOG(INFO) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize
<< " stride=" << stride << " padding=" << padding;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("algo", algo));
TensorShape shape0{
batchSize, inputChannels, inputSize, inputSize};
TensorShape shape1{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape shape2{
batchSize, outputChannels, outputSize, outputSize};
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape0));
test.addInputs(BufferArg(VALUE_TYPE_FLOAT, shape1));
test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, shape2));
test.run();
}
}
}
}
}
}
}
}
};
TEST(NNPACK, Forward) {
Convolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"GemmConv-CPU", "NNPACKConv-CPU", forward);
}
TEST(Convolution, NNPACK) {
// NNPACK only supports stride = 1
ConvolutionTest test("GemmConv-CPU", "NNPACKConv-CPU", FLAGS_algo);
TEST(NNPACK, Depthwise) {
DepthwiseConvolution<DEVICE_TYPE_CPU, DEVICE_TYPE_CPU>(
"GemmConv-CPU", "NNPACKConv-CPU", forward);
}
} // namespace paddle
paddle/gserver/CMakeLists.txt
浏览文件 @ 43ba24e0
......@@ -23,6 +23,17 @@ endmacro()
filter_test(GSERVER_HEADER)
filter_test(GSERVER_SOURCES)
if(NOT WITH_MKLDNN)
file(GLOB_RECURSE DNN_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.h")
file(GLOB_RECURSE DNN_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.cpp")
list(REMOVE_ITEM GSERVER_HEADER ${DNN_HEADER})
list(REMOVE_ITEM GSERVER_SOURCES ${DNN_SOURCES})
message(STATUS "Skip compiling with MKLDNNLayers and MKLDNNActivations")
else()
message(STATUS "Compile with MKLDNNLayers and MKLDNNActivations")
endif()
if(NOT WITH_GPU)
list(REMOVE_ITEM GSERVER_HEADER
layers/CudnnConvBaseLayer.h
......
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ 43ba24e0
......@@ -112,7 +112,6 @@ BEGIN_DEFINE_ACTIVATION(softmax)
private:
MatrixPtr sftMaxSum_;
MatrixPtr sftMaxDot_;
MatrixPtr one_;
public:
Error __must_check forward(Argument& act) {
......@@ -138,14 +137,6 @@ Error __must_check backward(Argument& act) {
1,
/* trans */ false,
useGpu(act.deviceId));
if (!one_ || one_->getWidth() != outputG->getWidth()) {
Matrix::resizeOrCreate(one_,
1,
outputG->getWidth(),
/* trans */ false,
useGpu(act.deviceId));
one_->one();
}
sftMaxDot_->dotMul(*outputG, *outputV);
sftMaxSum_->colMerge(*sftMaxDot_);
......
paddle/gserver/layers/MKLDNNBase.h 0 → 100644
浏览文件 @ 43ba24e0
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "mkldnn.hpp"
namespace paddle {
typedef enum {
MKLDNN_BASE = 1, // basical info of MKLDNN
MKLDNN_TESTS = 1, // gtest info of MKLDNN
MKLDNN_SIZES = 2, // size info of MKLDNN
MKLDNN_FMTS = 3, // format info of MKLDNN
MKLDNN_ALL = 4, // show all info of MKLDNN
} MKLDNN_LOG_LEVEL;
/**
* @brief MKLDNN CPU engine.
*
*/
class CPUEngine {
public:
static CPUEngine& Instance() {
// Thread-safe in C++11.
static CPUEngine myInstance;
return myInstance;
}
// Disallow copy or move
CPUEngine(const CPUEngine&) = delete; // Copy constructor
CPUEngine(CPUEngine&&) = delete; // Move constructor
CPUEngine& operator=(const CPUEngine&) = delete; // Copy assignment
CPUEngine& operator=(CPUEngine&&) = delete; // Move assignment
mkldnn::engine& getEngine() { return cpuEngine_; }
protected:
CPUEngine() : cpuEngine_(mkldnn::engine::cpu, 0) {}
// CPUEngine() : cpuEngine_(mkldnn::engine::cpu_lazy, 0) {}
~CPUEngine() {}
private:
mkldnn::engine cpuEngine_;
};
/**
* @brief MKLDNN Stream.
*
*/
class MKLDNNStream {
public:
MKLDNNStream() : ready_(false) { resetState(); }
virtual ~MKLDNNStream() {}
/**
* @brief Submit stream
* @param prims The primitives vector
* @param block Waiting for the stream to complete
*/
void submit(std::vector<mkldnn::primitive>& prims, bool block = true) {
resetState();
stream_->submit(prims).wait(block);
ready_ = false;
}
/**
* @brief Reset the mkldnn stream
*/
void resetState() {
if (ready_) {
return;
}
// TODO(TJ): change me when mkldnn have method to reset this state
// stream_.reset(new mkldnn::stream(mkldnn::stream::kind::lazy));
stream_.reset(new mkldnn::stream(mkldnn::stream::kind::eager));
ready_ = true;
}
private:
bool ready_;
std::shared_ptr<mkldnn::stream> stream_;
};
} // namespace paddle
paddle/gserver/layers/MKLDNNFcLayer.cpp 0 → 100644
浏览文件 @ 43ba24e0
/* Copyright (c) 2017 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 "MKLDNNFcLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
typedef inner_product_forward fc_fwd;
typedef inner_product_backward_weights fc_bwdWgt;
typedef inner_product_backward_data fc_bwdData;
namespace paddle {
REGISTER_LAYER(mkldnn_fc, MKLDNNFcLayer);
bool MKLDNNFcLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
if (!MKLDNNLayer::init(layerMap, parameterMap)) {
return false;
}
CHECK_EQ(inputLayers_.size(), 1) << "Only support one input layer yet";
CHECK_EQ(inputLayers_.size(), parameters_.size());
CHECK(!parameters_[0]->isSparse()) << "Do not support sparse yet";
// output size, cat not be changed
oc_ = getSize();
oh_ = 1;
ow_ = 1;
// input size can not change in FC
iLayerSize_ = inputLayers_[0]->getSize();
CHECK_EQ(parameters_[0]->getSize(), iLayerSize_ * oc_);
// create weight
weight_ =
std::unique_ptr<Weight>(new Weight(oc_, iLayerSize_, parameters_[0], 0));
// create biases
if (biasParameter_.get() != NULL) {
biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_));
}
return true;
}
void MKLDNNFcLayer::convertWeightsFromPaddle() {
if (FLAGS_use_mkldnn_wgt) {
return;
}
if (hasInitedWgt_) {
return;
}
// The weight_ is transposed from initial paddle weight
MatrixPtr paddleWgt = Matrix::create(
weight_->getW()->getData(), iLayerSize_, oc_, false, false);
// TODO(TJ): remove this print when do not need differ weights
std::ostringstream ostr;
paddleWgt->print(ostr);
VLOG(MKLDNN_ALL) << "Initial Weight from paddle: " << std::endl << ostr.str();
// The mkldnn weight is transposed from initial paddle matrix
MatrixPtr paddleWgtT;
paddleWgt->transpose(paddleWgtT, true);
weight_->getW()->copyFrom(*paddleWgtT);
hasInitedWgt_ = true;
}
void MKLDNNFcLayer::convertWeightsToPaddle() {
MatrixPtr dnnWgt = weight_->getW();
MatrixPtr paddleWgt;
dnnWgt->transpose(paddleWgt, true);
// copy paddle weight and override on weight_
MatrixPtr dnnWgtT = Matrix::create(
dnnWgt->getData(), dnnWgt->getWidth(), dnnWgt->getHeight(), false, false);
dnnWgtT->copyFrom(*paddleWgt);
}
void MKLDNNFcLayer::reshape() {
const Argument& input = getInput(0);
int batchSize = input.getBatchSize();
if (bs_ == batchSize) {
return;
}
bs_ = batchSize;
ih_ = input.getFrameHeight();
iw_ = input.getFrameWidth();
if (ih_ == 0) {
ih_ = 1;
}
if (iw_ == 0) {
iw_ = 1;
}
hasSpatial_ = true;
if (ih_ == 1 && iw_ == 1) {
hasSpatial_ = false;
}
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
ic_ = iLayerSize_ / (ih_ * iw_);
CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
CHECK_EQ(size_t(oc_), getSize());
printSizeInfo();
// reset output
output_.setFrameHeight(oh_);
output_.setFrameWidth(ow_);
resetOutput(bs_, oc_);
// reset mkldnn forward
resetFwd();
needResetBwd_ = true;
convertWeightsFromPaddle();
}
void MKLDNNFcLayer::resetFwd() {
bool hasBias = biases_ && biases_->getW();
real* iData = getInputValue(0)->getData();
real* oData = getOutputValue()->getData();
real* wData = weight_->getW()->getData();
real* bData = hasBias ? biases_->getW()->getData() : NULL;
// TODO(TJ): below create should be covered in MkldnnMatrix
// create memory desc
memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
: createMD({bs_, ic_}, format::nc);
memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
: createMD({oc_, ic_}, format::oi);
memory::desc bMD = bData != NULL ? createMD({oc_}, format::x)
: createMD({}, format::format_undef);
memory::desc oMD = createMD({bs_, oc_}, format::nc);
// create memory primitive desc and memory self
inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
wgtVal_.reset(new memory(memory::primitive_desc(wMD, engine_), wData));
outVal_.reset(new memory(memory::primitive_desc(oMD, engine_), oData));
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = bData != NULL ? fc_fwd::desc(pk, iMD, wMD, bMD, oMD)
: fc_fwd::desc(pk, iMD, wMD, oMD);
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (bData != NULL) {
biasVal_.reset(new memory(memory::primitive_desc(bMD, engine_), bData));
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
} else {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
}
pipelineFwd_.clear();
pipelineFwd_.push_back(*fwd_);
}
void MKLDNNFcLayer::resetBwd() {
if (!needResetBwd_) {
return;
}
needResetBwd_ = false;
bool hasBias = biases_ && biases_->getWGrad();
real* iData = getInputValue(0)->getData();
real* iDiff = getInputGrad(0) != nullptr ? getInputGrad(0)->getData() : NULL;
real* oDiff = getOutputGrad()->getData();
real* wDiff = weight_->getWGrad()->getData();
real* bDiff = hasBias ? biases_->getWGrad()->getData() : NULL;
/// backward weight
// create memory desc for backward memory
memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw)
: createMD({bs_, ic_}, format::nc);
memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw)
: createMD({oc_, ic_}, format::oi);
memory::desc oMD = createMD({bs_, oc_}, format::nc);
memory::desc bMD = bDiff != NULL ? createMD({oc_}, format::x)
: createMD({}, format::format_undef);
if (inVal_) {
// update data
inVal_->set_data_handle(iData);
} else {
inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData));
}
// create memory primitive desc and memory self
wgtGrad_.reset(new memory(memory::primitive_desc(wMD, engine_), wDiff));
outGrad_.reset(new memory(memory::primitive_desc(oMD, engine_), oDiff));
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, iMD, wMD, oMD);
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = bDiff != NULL
? fc_bwdWgt::desc(iMD, wMD, bMD, oMD)
: fc_bwdWgt::desc(iMD, wMD, oMD);
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (bDiff != NULL) {
biasGrad_.reset(new memory(memory::primitive_desc(bMD, engine_), bDiff));
bwdWgt_.reset(
new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
} else {
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_));
}
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwdWgt_);
/// backward data
if (iDiff == NULL) {
return;
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(iMD, wMD, oMD);
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
inGrad_.reset(new memory(memory::primitive_desc(iMD, engine_), iDiff));
CHECK(wgtVal_) << "Should have weight memory";
bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
pipelineBwd_.push_back(*bwdData_);
}
void MKLDNNFcLayer::forward(PassType passType) {
Layer::forward(passType);
reshape();
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
// update input data
// since it might be changed if this is after data layer
real* iData = getInputValue(0)->getData();
inVal_->set_data_handle(iData);
// just submit forward pipeline
stream_->submit(pipelineFwd_);
}
/* activation */ {
REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
forwardActivation();
}
}
void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
/* Do derivation */ {
REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
backwardActivation();
}
{
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
resetBwd();
// update diff
real* oDiff = getOutputGrad()->getData();
outGrad_->set_data_handle(oDiff);
// just sumbmit backward pipeline
stream_->submit(pipelineBwd_);
}
{
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
}
}
}
} // namespace paddle
paddle/gserver/layers/MKLDNNFcLayer.h 0 → 100644
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/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "MKLDNNLayer.h"
#include "mkldnn.hpp"
namespace paddle {
/**
* @brief A subclass of MKLDNNLayer fc layer.
*
* The config file api is mkldnn_fc
*/
class MKLDNNFcLayer : public MKLDNNLayer {
protected:
// input layer size, can not be change after init
size_t iLayerSize_; // == ic * ih * iw
// if has already init the weight
bool hasInitedWgt_;
// if input layer has image size info (ih>1 && iw>1)
bool hasSpatial_;
// fc weight and bias
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
public:
explicit MKLDNNFcLayer(const LayerConfig& config)
: MKLDNNLayer(config), hasInitedWgt_(false), hasSpatial_(true) {}
~MKLDNNFcLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void convertWeightsFromPaddle() override;
void convertWeightsToPaddle() override;
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
protected:
/**
* reshape the input image sizes
* and reset output buffer size
* and reset mkldnn forward
*/
void reshape();
/**
* reset the forward primitve and memory
* only would be called when input size changes
*/
void resetFwd();
/**
* reset the backward primitve and memory for mkldnn fc
* only would be called when needed
*/
void resetBwd();
};
} // namespace paddle
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paddle/gserver/tests/LayerGradUtil.cpp
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......@@ -388,14 +388,23 @@ void initDataLayer(TestConfig testConf,
data.grad->zeroMem();
break;
case INPUT_SELF_DEFINE_DATA: {
size_t height = testConf.inputDefs[i].selfDefinedData->getHeight();
size_t width = testConf.inputDefs[i].selfDefinedData->getWidth();
CHECK_GT(static_cast<int>(height), 0);
CHECK_GT(static_cast<int>(width), 0);
data.value = Matrix::create(height, width, false, useGpu);
data.grad = Matrix::create(height, width, false, useGpu);
data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData);
data.grad->zeroMem();
if (testConf.inputDefs[i].ids.size()) {
data.ids = IVector::create(testConf.inputDefs[i].ids.size(), useGpu);
data.ids->copyFrom(testConf.inputDefs[i].ids.data(),
testConf.inputDefs[i].ids.size());
} else if (testConf.inputDefs[i].selfDefinedData) {
size_t height = testConf.inputDefs[i].selfDefinedData->getHeight();
size_t width = testConf.inputDefs[i].selfDefinedData->getWidth();
CHECK_GT(static_cast<int>(height), 0);
CHECK_GT(static_cast<int>(width), 0);
data.value = Matrix::create(height, width, false, useGpu);
data.grad = Matrix::create(height, width, false, useGpu);
data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData);
data.grad->zeroMem();
} else {
LOG(FATAL) << "No self-defined data are given.";
return;
}
const std::vector<int>& labelSeqStartPositions =
testConf.inputDefs[i].labelSeqStartPositions;
......
paddle/gserver/tests/LayerGradUtil.h
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......@@ -68,6 +68,7 @@ struct InputDef {
std::vector<int> labelInitValue;
std::vector<int> labelSeqStartPositions;
std::vector<int> labelSubSeqStartPositions;
std::vector<int> ids;
MatrixPtr selfDefinedData;
InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) {
......@@ -95,6 +96,23 @@ struct InputDef {
isStatic = false;
}
InputDef(InputType type,
string nameIn,
const std::vector<int>& ids,
const std::vector<int>& selfDefinedSeqStartPos = {},
const std::vector<int>& selfDefinedSubSeqStartPos = {})
: labelSeqStartPositions(selfDefinedSeqStartPos),
labelSubSeqStartPositions(selfDefinedSubSeqStartPos),
ids(ids) {
selfDefinedData = nullptr;
inputType = type;
name = nameIn;
dim = 0;
sparse = {""};
paraSize = 0;
isStatic = false;
}
InputDef(InputType type,
string nameIn,
size_t dimIn,
......
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#include "paddle/operators/mul_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel<paddle::platform::GPUPlace, float>);
paddle/operators/mul_op.h
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