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提交 7b7a4afa 编写于 作者: Z Zhen Wang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-default-value 

Change the default value of the parameter 'drop_last' in 'paddle.batch' to False.
上级 18691901 16403342
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Showing with 6364 addition and 1992 deletion
CMakeLists.txt
浏览文件 @ 7b7a4afa
......@@ -66,6 +66,12 @@ option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
# PY_VERSION
if(NOT PY_VERSION)
set(PY_VERSION 2.7)
endif()
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
......@@ -146,6 +152,7 @@ endif()
########################################################################################
include(external/mklml) # download mklml package
include(external/libxsmm) # download, build, install libxsmm
include(external/zlib) # download, build, install zlib
include(external/gflags) # download, build, install gflags
include(external/glog) # download, build, install glog
......@@ -232,6 +239,10 @@ if(WITH_MKLML)
list(APPEND EXTERNAL_LIBS ${MKLML_IOMP_LIB})
endif()
if(WITH_LIBXSMM)
list(APPEND EXTERNAL_LIBS ${LIBXSMM_LIBS})
endif()
if(WITH_MKLDNN)
list(APPEND EXTERNAL_LIBS ${MKLDNN_LIB})
endif()
......@@ -271,7 +282,3 @@ if(WITH_DOC)
find_python_module(recommonmark REQUIRED)
add_subdirectory(doc)
endif()
if (WITH_CONTRIB)
add_subdirectory(paddle/contrib)
endif()
Dockerfile
浏览文件 @ 7b7a4afa
......@@ -80,7 +80,7 @@ RUN pip install pre-commit 'ipython==5.3.0' && \
pip install opencv-python
#For docstring checker
RUN pip install pylint pytest astroid isort
RUN pip install pylint pytest astroid isort LinkChecker
COPY ./python/requirements.txt /root/
RUN pip install -r /root/requirements.txt
......
benchmark/fluid/fluid_benchmark.py
浏览文件 @ 7b7a4afa
......@@ -210,7 +210,7 @@ def train_parallel(avg_loss, infer_prog, optimizer, train_reader, test_reader,
# generate fake:
if args.use_fake_data:
for var in feed_var_list:
v = startup_prog.global_block().clone_variable(var)
v = startup_prog.global_block()._clone_variable(var)
var.persistable = True
v.persistable = True
......
cmake/external/libxsmm.cmake 0 → 100644
浏览文件 @ 7b7a4afa
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
OPTION(WITH_LIBXSMM "Compile with libxsmm" OFF)
IF(NOT WITH_LIBXSMM)
return()
ENDIF()
IF(WIN32 OR APPLE OR ANDROID OR IOS)
MESSAGE(WARNING "Windows, Mac or Mobile are not supported with libxsmm in Paddle yet.")
SET(WITH_LIBXSMM OFF CACHE STRING "Disable LIBXSMM" FORCE)
return()
ENDIF()
INCLUDE (ExternalProject)
SET(LIBXSMM_SOURCES_DIR ${THIRD_PARTY_PATH}/libxsmm)
SET(LIBXSMM_INSTALL_DIR ${THIRD_PARTY_PATH}/install/libxsmm)
SET(LIBXSMM_INCLUDE_DIR "${LIBXSMM_INSTALL_DIR}/include" CACHE PATH "LIBXSMM include directory." FORCE)
SET(LIBXSMM_LIBRARY_DIR "${LIBXSMM_INSTALL_DIR}/lib" CACHE PATH "LIBXSMM library directory." FORCE)
SET(LIBXSMM_LIBS "${LIBXSMM_LIBRARY_DIR}/libxsmm.a"
"${LIBXSMM_LIBRARY_DIR}/libxsmmnoblas.a")
ExternalProject_Add(
extern_libxsmm
GIT_REPOSITORY "https://github.com/hfp/libxsmm.git"
GIT_TAG "7cc03b5b342fdbc6b6d990b190671c5dbb8489a2"
PREFIX ${LIBXSMM_SOURCES_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_IN_SOURCE 1
BUILD_COMMAND $(MAKE) --silent PREFIX=${LIBXSMM_INSTALL_DIR} CXX=g++ CC=gcc WARP=0 install
INSTALL_COMMAND ""
)
ADD_LIBRARY(libxsmm STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET libxsmm PROPERTY IMPORTED_LOCATION "${LIBXSMM_LIBRARY_DIR}/libxsmm.a")
SET_PROPERTY(TARGET libxsmm PROPERTY IMPORTED_LOCATION "${LIBXSMM_LIBRARY_DIR}/libxsmmnoblas.a")
MESSAGE(STATUS "Libxsmm library: ${LIBXSMM_LIBS}")
include_directories(${LIBXSMM_INCLUDE_DIR})
ADD_DEFINITIONS(-DPADDLE_WITH_LIBXSMM)
ADD_DEPENDENCIES(libxsmm extern_libxsmm)
LIST(APPEND external_project_dependencies libxsmm)
cmake/external/openblas.cmake
浏览文件 @ 7b7a4afa
......@@ -121,6 +121,11 @@ ELSE()
TARGET_LINK_LIBRARIES(cblas ${CBLAS_LIBRARIES})
ENDIF("${CBLAS_PROVIDER}" STREQUAL "MKLML")
IF(WITH_LIBXSMM)
TARGET_LINK_LIBRARIES(cblas ${LIBXSMM_LIBS})
ADD_DEPENDENCIES(cblas extern_libxsmm)
ENDIF()
IF(NOT ${CBLAS_FOUND})
ADD_DEPENDENCIES(cblas extern_openblas)
LIST(APPEND external_project_dependencies cblas)
......
cmake/external/python.cmake
浏览文件 @ 7b7a4afa
......@@ -18,8 +18,9 @@ ENDIF()
INCLUDE(python_module)
FIND_PACKAGE(PythonInterp 2.7)
FIND_PACKAGE(PythonLibs 2.7)
FIND_PACKAGE(PythonInterp ${PY_VERSION})
FIND_PACKAGE(PythonLibs ${PY_VERSION})
# Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE.
ADD_LIBRARY(python SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES})
......
cmake/inference_lib.cmake
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......@@ -138,25 +138,24 @@ copy(memory_lib
set(inference_deps paddle_fluid_shared paddle_fluid)
if(WITH_CONTRIB)
message(STATUS "installing contrib")
set(contrib_dst_dir "${FLUID_INSTALL_DIR}/contrib/inference")
if (WITH_ANAKIN AND WITH_GPU)
copy(contrib_anakin_inference_lib DEPS paddle_inference_api inference_anakin_api
SRCS
${PADDLE_BINARY_DIR}/paddle/contrib/inference/libinference_anakin_api* # compiled anakin api
${PADDLE_BINARY_DIR}/third_party/install/anakin/*.tar.gz # anakin release
DSTS ${contrib_dst_dir}/anakin ${contrib_dst_dir}/anakin)
list(APPEND inference_deps contrib_anakin_inference_lib)
endif()
copy(contrib_inference_lib DEPS paddle_inference_api paddle_inference_api_shared
SRCS ${PADDLE_SOURCE_DIR}/paddle/contrib/inference/paddle_inference_api.h
${PADDLE_BINARY_DIR}/paddle/contrib/inference/libpaddle_inference_api*
DSTS ${contrib_dst_dir} ${contrib_dst_dir})
list(APPEND inference_deps contrib_inference_lib)
set(module "inference/api")
if (WITH_ANAKIN AND WITH_GPU)
copy(anakin_inference_lib DEPS paddle_inference_api inference_anakin_api
SRCS
${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api
${PADDLE_BINARY_DIR}/third_party/install/anakin/*.tar.gz # anakin release
DSTS ${dst_dir}/inference/anakin ${dst_dir}/inference/anakin)
list(APPEND inference_deps anakin_inference_lib)
endif()
copy(inference_api_lib DEPS paddle_inference_api paddle_inference_api_shared
SRCS ${src_dir}/${module}/paddle_inference_api.h
${src_dir}/${module}/demo_ci
${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libpaddle_inference_api*
DSTS ${dst_dir}/inference ${dst_dir}/inference ${dst_dir}/inference
)
list(APPEND inference_deps inference_api_lib)
set(module "inference")
copy(inference_lib DEPS ${inference_deps}
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
......
doc/fluid/design/modules/python_api.md
浏览文件 @ 7b7a4afa
......@@ -98,13 +98,13 @@ class Block(objects):
def append_operator(self, ...):
self.ops.append(Operator(self, ...))
def prepend_operator(self, ...): # Parameter's ctor prepands initialize operators.
def _prepend_operator(self, ...): # Parameter's ctor prepands initialize operators.
self.ops.prepend(Operator(self, ...))
```
`create_parameter` is necessary because parameters are global variables, defined in the global block, but can be created in some sub-blocks. For example, an FC layer in the step block of an RNN operator.
`prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block.
`_prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block.
### Operator
......
doc/fluid/howto/performance/error_clip.md
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......@@ -78,7 +78,7 @@ def error_clip_callback(block, context):
op_desc = block.desc.op(block.desc.op_size() - 1)
for grad_n in filter(lambda n: grad_to_var.has_key(n),
op_desc.output_arg_names()):
fwd_var = block.var_recursive(grad_to_var[grad_n])
fwd_var = block.__var_recursive(grad_to_var[grad_n])
error_clip = getattr(fwd_var, "error_clip", None)
if not (error_clip is None or isinstance(error_clip,
BaseErrorClipAttr)):
......
doc/v2/api/index_en.rst
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......@@ -4,7 +4,6 @@ API
.. toctree::
:maxdepth: 1
overview.rst
model_configs.rst
data.rst
run_logic.rst
doc/v2/build_and_install/build_from_source_cn.rst
浏览文件 @ 7b7a4afa
......@@ -35,11 +35,16 @@ PaddlePaddle需要使用Docker环境完成编译，这样可以免去单独安
# 2. 可选步骤：源码中构建用于编译PaddlePaddle的Docker镜像
docker build -t paddle:dev .
# 3. 执行下面的命令编译CPU-Only的二进制
docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 ./paddle/scripts/paddle_build.sh build
docker run -it -v $PWD:/paddle -w /paddle -e "PYTHON_ABI=cp27-cp27mu" -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 ./paddle/scripts/paddle_build.sh build
# 4. 或者也可以使用为上述可选步骤构建的镜像（必须先执行第2步）
docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev ./paddle/scripts/paddle_build.sh build
注：上述命令把当前目录（源码树根目录）映射为 container 里的 :code:`/paddle` 目录。
注：
- 上述命令把当前目录（源码树根目录）映射为 container 里的 :code:`/paddle` 目录。
- 如果您使用的是 manylinux 的镜像进行编译, 那么您需要通过环境变量 :code:`PYTHON_ABI` 来指定一个 `Python ABI <https://www.python.org/dev/peps/pep-0425/#id8>`__.
PaddlePaddle目前支持的 Python ABI 有 :code:`cp27-cp27m` 和 :code:`cp27-cp27mu`.
编译完成后会在build/python/dist目录下生成输出的whl包，可以选在在当前机器安装也可以拷贝到目标机器安装：
......
doc/v2/build_and_install/build_from_source_en.rst
浏览文件 @ 7b7a4afa
......@@ -36,13 +36,18 @@ If you don't wish to use docker，you need to install several compile dependenci
# 2. Optional: build development docker image from source
docker build -t paddle:dev .
# 3. Run the following command to build a CPU-Only binaries
docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 ./paddle/scripts/paddle_build.sh build
docker run -it -v $PWD:/paddle -w /paddle -e "PYTHON_ABI=cp27-cp27mu" -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 ./paddle/scripts/paddle_build.sh build
# 4. Or, use your built Docker image to build PaddlePaddle (must run step 2)
docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev ./paddle/scripts/paddle_build.sh build
NOTE: The above command try to mount the current working directory (root directory of source code)
NOTE:
- The above command try to mount the current working directory (root directory of source code)
into :code:`/paddle` directory inside docker container.
- You need to pass in the required environment variable :code:`PYTHON_ABI` to specify a `Python ABI <https://www.python.org/dev/peps/pep-0425/#id8>`__.
Currently PaddlePaddle supported Python ABIs include :code:`cp27-cp27m` and :code:`cp27-cp27mu` .
When the compile finishes, you can get the output whl package under
build/python/dist, then you can choose to install the whl on local
machine or copy it to the target machine.
......
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ 7b7a4afa
......@@ -118,7 +118,7 @@ class Float16Transpiler:
for var in self.block.vars.keys():
if var not in args:
self.block.remove_var(var)
self.block._remove_var(var)
def _modify_feed_fetch(self):
'''
......@@ -165,7 +165,7 @@ class Float16Transpiler:
dtype=core.VarDesc.VarType.FP16,
shape=var.shape,
persistable=var.persistable)
self.block.insert_op(
self.block._insert_op(
i + 1,
type="cast",
inputs={"X": var},
......@@ -188,7 +188,7 @@ class Float16Transpiler:
persistable=var.persistable)
find_op(var)
var.op.rename_output(var_name, tmp_var_name)
self.block.insert_op(
self.block._insert_op(
i,
type="cast",
inputs={"X": tmp_var},
......@@ -253,4 +253,4 @@ class Float16Transpiler:
# old var will be replaced by the fp16 var in program desc
self.input_map[var.name] = fp16_var_name
self.block.remove_var(var.name)
self.block._remove_var(var.name)
paddle/contrib/inference/demo/CMakeLists.txt 已删除 100644 → 0
浏览文件 @ 18691901
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
inference_api_test(simple_on_word2vec ARGS test_word2vec)
option(WITH_INFERENCE_DEMO "Compile with Inference demo" OFF)
if(NOT WITH_INFERENCE_DEMO)
return()
endif()
set(DEMO_INSTALL_DIR "${PADDLE_BINARY_DIR}/inference_demo")
set(URL_ROOT http://paddlemodels.bj.bcebos.com/inference-vis-demos%2F)
function(inference_download_test_demo TARGET)
if (NOT WITH_TESTING)
return()
endif()
set(options "")
set(oneValueArgs URL)
set(multiValueArgs SRCS)
cmake_parse_arguments(tests "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(test_dir "${DEMO_INSTALL_DIR}/${TARGET}")
message(STATUS "inference demo ${test_dir}")
if(NOT EXISTS "${test_dir}")
message(STATUS "Download ${TARGET} model from ${tests_URL}")
execute_process(COMMAND bash -c "mkdir -p ${test_dir}")
execute_process(COMMAND bash -c "cd ${test_dir}; wget -q ${tests_URL}")
execute_process(COMMAND bash -c "cd ${test_dir}; tar xzf *.tar.gz")
endif()
cc_test(${TARGET} SRCS "${tests_SRCS}"
DEPS paddle_inference_api paddle_fluid
ARGS --data=${test_dir}/data.txt
--modeldir=${test_dir}/model
--refer=${test_dir}/result.txt)
endfunction()
# disable mobilenet test
#inference_download_test_demo(mobilenet_inference_demo
# SRCS vis_demo.cc
# URL ${URL_ROOT}mobilenet.tar.gz)
inference_download_test_demo(se_resnext50_inference_demo
SRCS vis_demo.cc
URL ${URL_ROOT}se_resnext50.tar.gz)
inference_download_test_demo(ocr_inference_demo
SRCS vis_demo.cc
URL ${URL_ROOT}ocr.tar.gz)
paddle/contrib/inference/demo/README.md 已删除 100644 → 0
浏览文件 @ 18691901
# Infernce Demos
Input data format:
- Each line contains a single record
- Each record's format is
```
<space splitted floats as data>\t<space splitted ints as shape>
```
Follow the C++ codes in `vis_demo.cc`.
## MobileNet
To execute the demo, simply run
```sh
./mobilenet_inference_demo --modeldir <model> --data <datafile>
```
## SE-ResNeXt-50
To execute the demo, simply run
```sh
./se_resnext50_inference_demo --modeldir <model> --data <datafile>
```
## OCR
To execute the demo, simply run
```sh
./ocr_inference_demo --modeldir <model> --data <datafile>
```
paddle/fluid/API.spec 0 → 100644
浏览文件 @ 7b7a4afa
此差异已折叠。
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 7b7a4afa
......@@ -276,13 +276,22 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
}
}
// Insert BCast Ops
for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(&result, bcast_name, dev_id);
bool use_gpu = false;
#ifdef PADDLE_WITH_CUDA
use_gpu = nccl_ctxs_ != nullptr;
#endif
if (use_gpu ||
strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
// Insert BCast Ops
for (size_t dev_id = 0; dev_id < bcast_var_name_set.size(); ++dev_id) {
auto &to_bcast_set = bcast_var_name_set[dev_id];
for (auto &bcast_name : to_bcast_set) {
CreateBroadcastOp(&result, bcast_name, dev_id);
}
}
}
/*
Dependency graph has been constructed. However, there are still data
hazards need to be handled.
......@@ -412,14 +421,19 @@ int MultiDevSSAGraphBuilder::GetOpDeviceID(const OpDesc &op) const {
if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kReduce) {
return -1;
}
for (auto &varname : op.InputArgumentNames()) {
int dev_id = GetVarDeviceID(varname);
if (dev_id != -1) {
return dev_id;
}
int op_role = boost::get<int>(
op.GetAttr(framework::OpProtoAndCheckerMaker::OpRoleAttrName()));
if (op_role != static_cast<int>(framework::OpRole::kOptimize)) {
return -1;
}
return -1;
auto param_grad = boost::get<std::vector<std::string>>(
op.GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(param_grad.size(), 2U);
int dev_id = GetVarDeviceID(param_grad[1]);
PADDLE_ENFORCE_NE(dev_id, -1, "dev_id should not be -1.[%s, %s]", op.Type(),
param_grad[0]);
return dev_id;
}
int MultiDevSSAGraphBuilder::GetVarDeviceID(const std::string &varname) const {
......
paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc
浏览文件 @ 7b7a4afa
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include <stdexcept>
#include <string>
#include <vector>
#include "paddle/fluid/framework/executor.h"
......@@ -53,8 +54,14 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
}
}
}
std::vector<framework::LoDTensor> fetch_data;
std::exception_ptr eptr;
try {
fetch_data = underlying_executor_->Run(fetch_tensors);
} catch (...) {
eptr = std::current_exception();
}
auto fetch_data = underlying_executor_->Run(fetch_tensors);
drop_scope_counter_ += 1;
if (!fetch_tensors.empty() ||
drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
......@@ -69,7 +76,11 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
scope->DeleteScope(local_scope);
}
}
return fetch_data;
if (eptr) {
std::rethrow_exception(eptr);
} else {
return fetch_data;
}
}
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 7b7a4afa
......@@ -78,6 +78,10 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
set.clear();
};
// Clean run context
run_op_futures_.clear();
exception_.reset();
// Step 3. Execution
while (!pending_vars.empty()) {
// 1. Run All Ready ops
......@@ -96,16 +100,19 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
if (timeout) {
std::lock_guard<std::mutex> l(exception_mu_);
std::unique_lock<std::mutex> l(exception_mu_);
if (exception_) {
l.unlock();
for (auto &run_op_future : run_op_futures_) {
run_op_future.wait();
}
l.lock();
std::exception *exp = exception_.get();
if (dynamic_cast<platform::EOFException *>(exp)) {
auto e = *static_cast<platform::EOFException *>(exp);
exception_.reset();
throw e;
} else if (dynamic_cast<platform::EnforceNotMet *>(exp)) {
auto e = *static_cast<platform::EnforceNotMet *>(exp);
exception_.reset();
throw e;
} else {
LOG(FATAL) << "Unknown exception.";
......@@ -222,7 +229,7 @@ void ThreadedSSAGraphExecutor::RunOp(
}
};
if (pool_) {
pool_->enqueue(op_run);
run_op_futures_.emplace_back(pool_->enqueue(op_run));
} else {
op_run();
}
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ 7b7a4afa
......@@ -15,6 +15,7 @@
#pragma once
#include <deque>
#include <list>
#include <string>
#include <unordered_set>
#include <utility>
......@@ -77,6 +78,8 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
private:
ExecutionStrategy strategy_;
// use std::list because clear(), push_back, and for_each are O(1)
std::list<std::future<void>> run_op_futures_;
};
} // namespace details
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 7b7a4afa
......@@ -45,6 +45,7 @@ class ParallelExecutorPrivate {
#endif
bool own_local_scope_;
bool use_cuda_;
bool use_all_reduce_;
};
std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
......@@ -62,6 +63,14 @@ ParallelExecutor::ParallelExecutor(
: member_(new ParallelExecutorPrivate(places)) {
member_->global_scope_ = scope;
member_->use_cuda_ = exec_strategy.use_cuda_;
member_->use_all_reduce_ =
build_strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce;
if (!member_->use_all_reduce_) {
PADDLE_ENFORCE(places.size() > 1,
"If you set build_strategy.reduce with 'Reduce',"
"the number of places must be greater than 1.");
}
// Step 1. Bcast the params to devs.
// Create local scopes
......@@ -95,7 +104,7 @@ ParallelExecutor::ParallelExecutor(
}
if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
BCastParamsToGPUs(bcast_vars);
BCastParamsToDevices(bcast_vars);
}
// Startup Program has been run. All local scopes has correct parameters.
......@@ -117,7 +126,7 @@ ParallelExecutor::ParallelExecutor(
#ifdef PADDLE_WITH_CUDA
builder_factory.SetNCCLContextMap(member_->nccl_ctxs_.get());
#else
PADDLE_THROW("Not compiled with CUDA");
PADDLE_THROW("Not compiled with CUDA.");
#endif
}
......@@ -131,9 +140,9 @@ ParallelExecutor::ParallelExecutor(
member_->places_, std::move(member_->executor_)));
}
void ParallelExecutor::BCastParamsToGPUs(
void ParallelExecutor::BCastParamsToDevices(
const std::unordered_set<std::string> &vars) const {
// the the initializing bcast, all vars would be bcast from device(0),
// the initializing bcast, all vars would be bcast from device(0),
// otherwise
// bcast from the specified device.
bool initializing = builder_.get() == nullptr ? true : false;
......@@ -202,12 +211,23 @@ void ParallelExecutor::BCastParamsToGPUs(
#endif
} else {
platform::CPUPlace cpu;
for (size_t i = 1; i < member_->places_.size(); ++i) {
for (size_t i = 0; i < member_->places_.size(); ++i) {
if ((initializing && i == 0) ||
(!initializing && static_cast<int>(i) == var_dev_id))
continue;
auto local_scope = member_->local_scopes_[i];
auto *t = local_scope->Var(var)->GetMutable<LoDTensor>();
t->Resize(dims);
t->mutable_data(cpu, main_tensor.type());
paddle::framework::TensorCopy(main_tensor, cpu, t);
// FIXME(zcd): LR_DECAY_COUNTER should not be shared. This is a hot fix.
if (member_->use_all_reduce_ || member_->use_cuda_ ||
var == "@LR_DECAY_COUNTER@") {
t->Resize(dims);
t->mutable_data(cpu, main_tensor.type());
paddle::framework::TensorCopy(main_tensor, cpu, t);
} else {
t->ShareDataWith(main_tensor);
}
}
}
}
......
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 7b7a4afa
......@@ -66,7 +66,7 @@ class ParallelExecutor {
void Run(const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name);
void BCastParamsToGPUs(const std::unordered_set<std::string> &vars) const;
void BCastParamsToDevices(const std::unordered_set<std::string> &vars) const;
private:
ParallelExecutorPrivate *member_;
......
paddle/fluid/framework/reader.h
浏览文件 @ 7b7a4afa
......@@ -29,11 +29,11 @@ enum ReaderStatus { kRunning, kStopped };
class ReaderBase {
public:
void ReadNext(std::vector<LoDTensor>* out);
virtual void ReadNext(std::vector<LoDTensor>* out);
void Shutdown();
virtual void Shutdown();
void Start();
virtual void Start();
// Return the readers which are the end of decorating chain. Basically
// they are readers just before read op.
......@@ -42,7 +42,7 @@ class ReaderBase {
virtual ~ReaderBase();
protected:
virtual void ReadNextImpl(std::vector<LoDTensor>* out) = 0;
virtual void ReadNextImpl(std::vector<LoDTensor>* out) {}
virtual void ShutdownImpl() {}
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 7b7a4afa
set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor )
# analysis and tensorrt must be added before creating static library,
# otherwise, there would be undefined reference to them in static library.
add_subdirectory(analysis)
if (TENSORRT_FOUND)
add_subdirectory(tensorrt)
endif()
set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor)
# TODO(panyx0718): Should this be called paddle_fluid_inference_api_internal?
cc_library(paddle_fluid_api
......@@ -7,12 +14,14 @@ cc_library(paddle_fluid_api
get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
if(WITH_CONTRIB)
set(fluid_modules "${fluid_modules}" paddle_inference_api)
endif()
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
set_target_properties(paddle_fluid PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
endif()
# Create shared library
cc_library(paddle_fluid_shared SHARED
SRCS io.cc
......@@ -29,9 +38,4 @@ if(WITH_TESTING)
# both tests/book and analysis depends the models that generated by python/paddle/fluid/tests/book
add_subdirectory(tests/book)
endif()
add_subdirectory(analysis)
if (TENSORRT_FOUND)
add_subdirectory(tensorrt)
endif()
add_subdirectory(api)
paddle/fluid/inference/analysis/data_flow_graph.cc
浏览文件 @ 7b7a4afa
......@@ -90,6 +90,20 @@ std::string DataFlowGraph::DotString() const {
return dot.Build();
}
std::string DataFlowGraph::HumanReadableInfo(bool show_values,
bool show_functions) const {
std::stringstream values, functions;
for (auto &n : nodes.nodes()) {
if (show_values && n->IsValue()) {
values << n->repr() << "\n";
}
if (show_functions && n->IsFunction()) {
functions << n->repr() << "\n";
}
}
return "Values:\n" + values.str() + "\n\n" + "Functions:\n" + functions.str();
}
//
// NodesBFSIterator
//
......@@ -146,7 +160,7 @@ bool GraphTraits<DataFlowGraph>::NodesBFSIterator::operator==(
if ((!queue_.empty()) && (!other.queue_.empty())) {
return queue_.front() == other.queue_.front() &&
visited_.size() == other.visited_.size(); // here need to check the
// equality of queue and
// equality of queue and
// visited. Just a light but week implementation.
}
return false;
......@@ -208,6 +222,76 @@ Node *GraphTraits<DataFlowGraph>::NodesDFSIterator::operator->() {
return stack_.top();
}
GraphTraits<DataFlowGraph>::NodesTSIterator::NodesTSIterator(
const std::vector<Node *> &source) {
PADDLE_ENFORCE(!source.empty(),
"Start points of topological sorting should not be empty!");
std::unordered_set<Node *> visited;
std::unordered_set<Node *> to_visit{source.begin(), source.end()};
std::vector<Node *> inlink_visited;
while (!to_visit.empty()) {
std::vector<Node *> queue(to_visit.begin(), to_visit.end());
for (auto *p : queue) {
inlink_visited.clear();
std::copy_if(p->inlinks.begin(), p->inlinks.end(),
std::back_inserter(inlink_visited),
[&](Node *x) { return visited.count(x); });
if (inlink_visited.size() == p->inlinks.size()) {
sorted_.push_back(p);
for (auto *_ : p->outlinks) {
if (!visited.count(_)) {
to_visit.insert(_);
}
}
to_visit.erase(p);
visited.insert(p);
}
}
}
}
GraphTraits<DataFlowGraph>::NodesTSIterator::NodesTSIterator(
const paddle::inference::analysis::GraphTraits<
DataFlowGraph>::NodesTSIterator &other)
: sorted_(other.sorted_), cursor_(other.cursor_) {}
Node &GraphTraits<DataFlowGraph>::NodesTSIterator::operator*() {
PADDLE_ENFORCE_LT(cursor_, sorted_.size());
return *sorted_[cursor_];
}
paddle::inference::analysis::GraphTraits<DataFlowGraph>::NodesTSIterator
&GraphTraits<DataFlowGraph>::NodesTSIterator::operator++() {
if (++cursor_ >= sorted_.size()) {
sorted_.clear();
cursor_ = 0;
}
return *this;
}
paddle::inference::analysis::GraphTraits<DataFlowGraph>::NodesTSIterator &
GraphTraits<DataFlowGraph>::NodesTSIterator::operator=(
const paddle::inference::analysis::GraphTraits<
DataFlowGraph>::NodesTSIterator &other) {
cursor_ = other.cursor_;
sorted_ = other.sorted_;
return *this;
}
bool GraphTraits<DataFlowGraph>::NodesTSIterator::operator==(
const paddle::inference::analysis::GraphTraits<
DataFlowGraph>::NodesTSIterator &other) {
return sorted_ == other.sorted_ && cursor_ == other.cursor_;
}
Node *GraphTraits<DataFlowGraph>::NodesTSIterator::operator->() {
PADDLE_ENFORCE_LT(cursor_, sorted_.size());
return sorted_[cursor_];
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/data_flow_graph.h
浏览文件 @ 7b7a4afa
......@@ -48,6 +48,9 @@ struct DataFlowGraph {
// Output a DOT graph file for debug.
std::string DotString() const;
std::string HumanReadableInfo(bool show_values = true,
bool show_functions = true) const;
private:
// Remove duplicate edges and so on.
void Clean();
......@@ -107,6 +110,32 @@ struct GraphTraits<DataFlowGraph> {
std::unordered_set<Node *> visited_;
};
// Topological sorting iterator on nodes.
struct NodesTSIterator
: public std::iterator<std::forward_iterator_tag, Node *> {
NodesTSIterator() = default;
explicit NodesTSIterator(const std::vector<Node *> &source);
NodesTSIterator(NodesTSIterator &&other)
: sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
other.cursor_ = 0;
}
NodesTSIterator(const NodesTSIterator &other);
Node &operator*();
NodesTSIterator &operator++();
// TODO(Superjomn) current implementation just compare the first
// element, need to compare the graph and all the elements in the queue and
// set.
NodesTSIterator &operator=(const NodesTSIterator &other);
bool operator==(const NodesTSIterator &other);
bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
Node *operator->();
private:
std::vector<Node *> sorted_;
int cursor_{0};
};
explicit GraphTraits(DataFlowGraph *graph) : graph_(graph) {}
// default use BFS to visit the nodes.
......@@ -119,17 +148,24 @@ struct GraphTraits<DataFlowGraph> {
iterator_range<NodesDFSIterator> nodes_in_DFS() {
return iterator_range<NodesDFSIterator>(nodes_dfs_begin(), nodes_dfs_end());
}
iterator_range<NodesTSIterator> nodes_in_TS() {
return iterator_range<NodesTSIterator>(nodes_ts_begin(), nodes_ts_end());
}
private:
NodesBFSIterator nodes_bfs_begin() {
return NodesBFSIterator(graph_->inputs);
}
NodesBFSIterator nodes_bfs_end() { return NodesBFSIterator(); }
NodesDFSIterator nodes_dfs_begin() {
return NodesDFSIterator(graph_->inputs);
}
NodesDFSIterator nodes_dfs_end() { return NodesDFSIterator(); }
NodesTSIterator nodes_ts_begin() { return NodesTSIterator(graph_->inputs); }
NodesTSIterator nodes_ts_end() { return NodesTSIterator(); }
private:
DataFlowGraph *graph_;
};
......
paddle/fluid/inference/analysis/data_flow_graph_tester.cc
浏览文件 @ 7b7a4afa
......@@ -24,11 +24,11 @@ TEST(DataFlowGraph, BFS) {
auto dfg = ProgramDescToDFG(desc);
dfg.Build();
for (auto* in : dfg.inputs) {
for (auto *in : dfg.inputs) {
LOG(INFO) << "inputs: " << in->name() << " "
<< static_cast<int>(in->type());
}
for (auto* out : dfg.outputs) {
for (auto *out : dfg.outputs) {
LOG(INFO) << "outputs: " << out->name() << " "
<< static_cast<int>(out->type());
}
......@@ -57,6 +57,71 @@ TEST(DataFlowGraph, DFS) {
ASSERT_EQ(count, dfg.nodes.size());
}
// Topological sorting.
/*
* Graph topology
* inputs: 0, 1, 2
* 0 -> 4
* 0 -> 5
* 1 -> 6
* 2 -> 7
* 4 -> 5
* 4 -> 7
* 4 -> 3
* 7 -> 3
*/
TEST(DataFlowGraph, TS) {
DataFlowGraph graph;
for (int i = 0; i < 8; i++) {
auto *node = graph.nodes.Create(Node::Type::kValue);
node->SetName("node-" + std::to_string(i));
}
auto add_link = [&](int i, int j) {
Node *source = graph.nodes.GetMutable(i);
Node *target = graph.nodes.GetMutable(j);
target->inlinks.push_back(source);
source->outlinks.push_back(target);
};
graph.inputs.push_back(graph.nodes.GetMutable(0));
graph.inputs.push_back(graph.nodes.GetMutable(1));
graph.inputs.push_back(graph.nodes.GetMutable(2));
add_link(0, 4);
add_link(0, 5);
add_link(1, 6);
add_link(2, 7);
add_link(4, 5);
add_link(4, 7);
add_link(4, 3);
add_link(7, 3);
auto its = GraphTraits<DataFlowGraph>(&graph).nodes_in_TS();
std::vector<int> sorted_ids;
for (auto it = its.begin(); it != its.end(); ++it) {
LOG(INFO) << it->name();
sorted_ids.push_back(it->id());
}
// Assert a occurs prior to b in the sorted_ids.
auto assert_positive_sequence_pair = [&](int a, int b) {
auto a_offset = std::find(sorted_ids.begin(), sorted_ids.end(), a);
auto b_offset = std::find(sorted_ids.begin(), sorted_ids.end(), b);
ASSERT_LT(a_offset, b_offset);
};
assert_positive_sequence_pair(2, 7);
assert_positive_sequence_pair(7, 3);
assert_positive_sequence_pair(4, 3);
assert_positive_sequence_pair(0, 4);
assert_positive_sequence_pair(0, 5);
assert_positive_sequence_pair(1, 6);
assert_positive_sequence_pair(4, 5);
assert_positive_sequence_pair(4, 7);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/contrib/inference/CMakeLists.txt → paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ 7b7a4afa
......@@ -43,53 +43,64 @@ function(inference_api_test TARGET_NAME)
endfunction(inference_api_test)
cc_library(paddle_inference_api
SRCS paddle_inference_api.cc paddle_inference_api_impl.cc
SRCS api.cc api_impl.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
if(NOT APPLE)
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/api.sym")
set_target_properties(paddle_inference_api PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
endif()
# Here the shared library doesn't depend on other fluid libraries, or double free will occur.
cc_library(paddle_inference_api_shared SHARED
SRCS paddle_inference_api.cc paddle_inference_api_impl.cc)
SRCS api.cc api_impl.cc)
add_dependencies(paddle_inference_api_shared ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
set_target_properties(paddle_inference_api_shared PROPERTIES OUTPUT_NAME paddle_inference_api)
if(NOT APPLE)
set(LINK_FLAGS "-fPIC -fvisibility=hidden")
set(LINK_FLAGS "-Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/api.map")
set_target_properties(paddle_inference_api_shared PROPERTIES LINK_FLAGS "${LINK_FLAGS}")
FILE(WRITE ${CMAKE_CURRENT_BINARY_DIR}/check_symbol.cmake
"execute_process(COMMAND bash -c \"${CMAKE_CURRENT_SOURCE_DIR}/check_symbol.sh"
" ${CMAKE_CURRENT_BINARY_DIR}/libpaddle_inference_api.so\" RESULT_VARIABLE symbol_res)\n"
"if(NOT \"\${symbol_res}\" STREQUAL \"0\")\n"
" message(FATAL_ERROR \"Check symbol failed.\")\n"
"endif()\n")
add_custom_command(
OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/.check_symbol"
COMMAND ${CMAKE_COMMAND} -P "${CMAKE_CURRENT_BINARY_DIR}/check_symbol.cmake"
DEPENDS paddle_inference_api_shared)
add_custom_target(check_symbol ALL DEPENDS "${CMAKE_CURRENT_BINARY_DIR}/.check_symbol")
endif()
cc_test(test_paddle_inference_api
SRCS test_paddle_inference_api.cc
SRCS test_api.cc
DEPS paddle_inference_api)
inference_api_test(test_paddle_inference_api_impl
inference_api_test(test_api_impl
ARGS test_word2vec test_image_classification)
if(WITH_GPU AND TENSORRT_FOUND)
cc_library(paddle_inference_tensorrt_subgraph_engine
SRCS paddle_inference_api_tensorrt_subgraph_engine.cc
DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api)
SRCS api_tensorrt_subgraph_engine.cc
DEPS paddle_inference_api analysis tensorrt_engine paddle_fluid_api)
inference_api_test(test_paddle_inference_api_tensorrt_subgraph_engine ARGS test_word2vec)
inference_api_test(test_api_tensorrt_subgraph_engine ARGS test_word2vec)
endif()
if (WITH_ANAKIN) # only needed in CI
# Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
# so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
# compile the libinference_anakin_api.a and compile with anakin.so.
nv_library(inference_anakin_api SRCS paddle_inference_api.cc paddle_inference_api_anakin_engine.cc)
nv_library(inference_anakin_api_shared SHARED SRCS paddle_inference_api.cc paddle_inference_api_anakin_engine.cc)
nv_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc)
nv_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc)
target_compile_options(inference_anakin_api BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
target_compile_options(inference_anakin_api_shared BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
target_link_libraries(inference_anakin_api anakin anakin_saber_common)
target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
if (WITH_TESTING)
cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
cc_test(inference_anakin_test SRCS api_anakin_engine_tester.cc
ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
DEPS inference_anakin_api)
target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
endif(WITH_TESTING)
endif()
if(WITH_TESTING)
add_subdirectory(demo)
endif()
paddle/contrib/inference/paddle_inference_api.cc → paddle/fluid/inference/api/api.cc
浏览文件 @ 7b7a4afa
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
......
paddle/fluid/inference/api/api.map 0 → 100644
浏览文件 @ 7b7a4afa
{
global:
*paddle*;
local:
*;
};
paddle/contrib/inference/paddle_inference_api_anakin_engine.cc → paddle/fluid/inference/api/api_anakin_engine.cc
浏览文件 @ 7b7a4afa
......@@ -12,8 +12,9 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/contrib/inference/paddle_inference_api_anakin_engine.h"
#include "paddle/fluid/inference/api/api_anakin_engine.h"
#include <cuda.h>
#include <vector>
namespace paddle {
......@@ -47,13 +48,13 @@ bool PaddleInferenceAnakinPredictor::Run(
}
auto d_tensor_in_p = executor_.get_in(input.name);
float *d_data_p = d_tensor_in_p->mutable_data();
if (cudaMemcpy(d_data_p,
static_cast<float *>(input.data.data()),
if (cudaMemcpy(d_data_p, static_cast<float *>(input.data.data()),
d_tensor_in_p->valid_size() * sizeof(float),
cudaMemcpyHostToDevice) != 0) {
LOG(ERROR) << "copy data from CPU to GPU error";
return false;
}
cudaStreamSynchronize(NULL);
}
executor_.prediction();
......@@ -69,13 +70,13 @@ bool PaddleInferenceAnakinPredictor::Run(
output.data.Resize(tensor->valid_size() * sizeof(float));
}
// Copy data from GPU -> CPU
if (cudaMemcpy(output.data.data(),
tensor->mutable_data(),
if (cudaMemcpy(output.data.data(), tensor->mutable_data(),
tensor->valid_size() * sizeof(float),
cudaMemcpyDeviceToHost) != 0) {
LOG(ERROR) << "copy data from GPU to CPU error";
return false;
}
cudaStreamSynchronize(NULL);
}
return true;
}
......@@ -104,13 +105,12 @@ std::unique_ptr<PaddlePredictor> PaddleInferenceAnakinPredictor::Clone() {
// A factory to help create difference predictor.
template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<AnakinConfig, PaddleEngineKind::kAnakin>(
const AnakinConfig &config) {
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
AnakinConfig, PaddleEngineKind::kAnakin>(const AnakinConfig &config) {
VLOG(3) << "Anakin Predictor create.";
std::unique_ptr<PaddlePredictor> x(
new PaddleInferenceAnakinPredictor(config));
return x;
};
}
} // namespace paddle
paddle/contrib/inference/paddle_inference_api_anakin_engine.h → paddle/fluid/inference/api/api_anakin_engine.h
浏览文件 @ 7b7a4afa
......@@ -19,7 +19,8 @@ limitations under the License. */
#pragma once
#include "paddle/contrib/inference/paddle_inference_api.h"
#include <vector>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
// from anakin
#include "framework/core/net/net.h"
......@@ -31,7 +32,7 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
public:
PaddleInferenceAnakinPredictor() {}
PaddleInferenceAnakinPredictor(const AnakinConfig& config);
explicit PaddleInferenceAnakinPredictor(const AnakinConfig& config);
// NOTE Unlike the native engine, the buffers of anakin engine's output_data
// should be allocated first.
......@@ -48,8 +49,7 @@ class PaddleInferenceAnakinPredictor : public PaddlePredictor {
private:
bool Init(const AnakinConfig& config);
anakin::graph::Graph<anakin::NV,
anakin::saber::AK_FLOAT,
anakin::graph::Graph<anakin::NV, anakin::saber::AK_FLOAT,
anakin::Precision::FP32>
graph_;
anakin::Net<anakin::NV, anakin::saber::AK_FLOAT, anakin::Precision::FP32>
......
paddle/contrib/inference/paddle_inference_api_anakin_engine_tester.cc → paddle/fluid/inference/api/api_anakin_engine_tester.cc
浏览文件 @ 7b7a4afa
......@@ -16,7 +16,7 @@ limitations under the License. */
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
DEFINE_string(model, "", "Directory of the inference model.");
......
paddle/contrib/inference/paddle_inference_api_impl.cc → paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 7b7a4afa
......@@ -21,7 +21,7 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/contrib/inference/paddle_inference_api_impl.h"
#include "paddle/fluid/inference/api/api_impl.h"
namespace paddle {
namespace {
......@@ -77,8 +77,8 @@ bool NativePaddlePredictor::Init(
if (!config_.model_dir.empty()) {
// Parameters are saved in separate files sited in
// the specified `dirname`.
inference_program_ = paddle::inference::Load(
executor_.get(), scope_.get(), config_.model_dir);
inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
config_.model_dir);
} else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
// All parameters are saved in a single file.
// The file names should be consistent with that used
......@@ -91,8 +91,8 @@ bool NativePaddlePredictor::Init(
}
ctx_ = executor_->Prepare(*inference_program_, 0);
executor_->CreateVariables(
*inference_program_, sub_scope_ ? sub_scope_ : scope_.get(), 0);
executor_->CreateVariables(*inference_program_,
sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
feed_target_names_ = inference_program_->GetFeedTargetNames();
......@@ -105,7 +105,7 @@ NativePaddlePredictor::~NativePaddlePredictor() {
PADDLE_ENFORCE_NOT_NULL(scope_, "Should have parent scope!");
scope_->DeleteScope(sub_scope_);
}
};
}
bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
std::vector<PaddleTensor> *output_data) {
......@@ -134,10 +134,8 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
// if share variables, we need not create variables
VLOG(4) << "Run prepared context";
executor_->RunPreparedContext(
ctx_.get(),
sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
&feed_targets,
&fetch_targets,
ctx_.get(), sub_scope_ != nullptr ? sub_scope_ : scope_.get(),
&feed_targets, &fetch_targets,
false /* don't create variable eatch time */);
VLOG(4) << "Finish prepared context";
if (!GetFetch(fetchs, output_data)) {
......@@ -181,8 +179,7 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
}
// TODO(panyx0718): Init LoDTensor from existing memcpy to save a copy.
std::memcpy(static_cast<void *>(input_ptr),
inputs[i].data.data(),
std::memcpy(static_cast<void *>(input_ptr), inputs[i].data.data(),
inputs[i].data.length());
feeds->push_back(input);
}
......@@ -232,8 +229,7 @@ bool NativePaddlePredictor::GetFetch(
size_t start = lod[0][j - 1] * common_dim;
size_t end = lod[0][j] * common_dim;
if (end > start) {
std::copy(output_ptr + start,
output_ptr + end,
std::copy(output_ptr + start, output_ptr + end,
data.begin() + (j - 1) * max_dim * common_dim);
}
}
......@@ -257,15 +253,13 @@ bool NativePaddlePredictor::GetFetch(
}
template <>
std::unique_ptr<PaddlePredictor>
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
const NativeConfig &config) {
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
NativeConfig, PaddleEngineKind::kNative>(const NativeConfig &config) {
VLOG(3) << "create NativePaddlePredictor";
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
config.fraction_of_gpu_memory,
0.f,
config.fraction_of_gpu_memory, 0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
std::vector<std::string> flags;
......
paddle/contrib/inference/paddle_inference_api_impl.h → paddle/fluid/inference/api/api_impl.h
浏览文件 @ 7b7a4afa
......@@ -19,7 +19,7 @@
#include <string>
#include <vector>
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/framework/ddim.h"
#include "paddle/fluid/framework/lod_tensor.h"
......
paddle/contrib/inference/paddle_inference_api_tensorrt_subgraph_engine.cc → paddle/fluid/inference/api/api_tensorrt_subgraph_engine.cc
浏览文件 @ 7b7a4afa
......@@ -12,9 +12,9 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/contrib/inference/paddle_inference_api_impl.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/utils/singleton.h"
namespace paddle {
......@@ -77,8 +77,8 @@ class TensorRTSubgraphPredictor : public NativePaddlePredictor {
ctx_ = executor_->Prepare(*inference_program_, 0);
VLOG(5) << "to create variables";
executor_->CreateVariables(
*inference_program_, sub_scope_ ? sub_scope_ : scope_.get(), 0);
executor_->CreateVariables(*inference_program_,
sub_scope_ ? sub_scope_ : scope_.get(), 0);
// Get the feed_target_names and fetch_target_names
feed_target_names_ = inference_program_->GetFeedTargetNames();
......@@ -98,8 +98,7 @@ CreatePaddlePredictor<TensorRTConfig, PaddleEngineKind::kAutoMixedTensorRT>(
if (config.use_gpu) {
// 1. GPU memeroy
PADDLE_ENFORCE_GT(
config.fraction_of_gpu_memory,
0.f,
config.fraction_of_gpu_memory, 0.f,
"fraction_of_gpu_memory in the config should be set to range (0., 1.]");
PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
std::vector<std::string> flags;
......
paddle/fluid/inference/api/check_symbol.sh 0 → 100755
浏览文件 @ 7b7a4afa
#!/bin/bash
lib=$1
if [ $# -ne 1 ]; then echo "No input library"; exit -1 ; fi
num_paddle_syms=$(nm -D --defined-only ${lib} | grep paddle | wc -l)
num_google_syms=$(nm -D --defined-only ${lib} | grep google | wc -l)
if [ $num_paddle_syms -le 0 ]; then echo "Have no paddle symbols"; exit -1 ; fi
if [ $num_google_syms -ge 1 ]; then echo "Have some google symbols"; exit -1 ; fi
exit 0
paddle/fluid/inference/api/demo_ci/CMakeLists.txt 0 → 100644
浏览文件 @ 7b7a4afa
cmake_minimum_required(VERSION 3.0)
project(cpp_inference_demo CXX C)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
if(NOT DEFINED PADDLE_LIB)
message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib")
endif()
if(NOT DEFINED DEMO_NAME)
message(FATAL_ERROR "please set DEMO_NAME with -DDEMO_NAME=demo_name")
endif()
option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON)
option(WITH_GPU "Compile demo with GPU/CPU, default use CPU." OFF)
option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static." ON)
if(WITH_GPU)
set(CUDA_LIB "/usr/local/cuda/lib64/" CACHE STRING "CUDA Library")
endif()
include_directories("${PADDLE_LIB}")
include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
include_directories("${PADDLE_LIB}/third_party/install/glog/include")
include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
include_directories("${PADDLE_LIB}/third_party/boost")
include_directories("${PADDLE_LIB}/third_party/eigen3")
link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
if(WITH_MKL)
include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel.so
${PADDLE_LIB}/third_party/install/mklml/lib/libiomp5.so)
set(MKLDNN_PATH "${PADDLE_LIB}/third_party/install/mkldnn")
if(EXISTS ${MKLDNN_PATH})
include_directories("${MKLDNN_PATH}/include")
set(MKLDNN_LIB ${MKLDNN_PATH}/lib/libmkldnn.so.0)
endif()
else()
set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.a)
endif()
# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
if(WITH_STATIC_LIB)
set(DEPS
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_inference_api.a
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.a)
else()
set(DEPS
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_inference_api.so
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.so)
endif()
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
set(DEPS ${DEPS}
${MATH_LIB} ${MKLDNN_LIB}
glog gflags protobuf snappystream snappy z
${EXTERNAL_LIB})
if(WITH_GPU)
set(DEPS ${DEPS} ${CUDA_LIB}/libcudart.so)
endif()
target_link_libraries(${DEMO_NAME} ${DEPS})
paddle/fluid/inference/api/demo_ci/README.md 0 → 100644
浏览文件 @ 7b7a4afa
# Inference Demos
There are several demos:
- simple_on_word2vec:
- Follow the C++ codes is in `simple_on_word2vec.cc`.
- It is suitable for word2vec model.
- vis_demo:
- Follow the C++ codes is in `vis_demo.cc`.
- It is suitable for mobilenet, se_resnext50 and ocr three models.
- Input data format:
- Each line contains a single record
- Each record's format is
```
<space splitted floats as data>\t<space splitted ints as shape>
```
To build and execute the demos, simply run
```
./run.sh $PADDLE_ROOT $TURN_ON_MKL $TEST_GPU_CPU
```
- It will build and execute the demos in both static and shared library.
- `$PADDLE_ROOT`: paddle library path
- `$TURN_ON_MKL`: use MKL or Openblas
- `$TEST_GPU_CPU`: test both GPU/CPU mode or only CPU mode
- NOTE: for simple_on_word2vec, must run `ctest -R test_word2vec -R` to obtain word2vec model at first.
paddle/fluid/inference/api/demo_ci/run.sh 0 → 100755
浏览文件 @ 7b7a4afa
set -x
PADDLE_ROOT=$1
TURN_ON_MKL=$2 # use MKL or Openblas
TEST_GPU_CPU=$3 # test both GPU/CPU mode or only CPU mode
if [ $2 == ON ]; then
# You can export yourself if move the install path
MKL_LIB=${PADDLE_ROOT}/build/fluid_install_dir/third_party/install/mklml/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${MKL_LIB}
fi
if [ $3 == ON ]; then
use_gpu_list='true false'
else
use_gpu_list='false'
fi
# download vis_demo data
function download() {
dir_name=$1
mkdir -p $dir_name
cd $dir_name
wget -q ${URL_ROOT}$dir_name.tar.gz
tar xzf *.tar.gz
cd ..
}
URL_ROOT=http://paddlemodels.bj.bcebos.com/inference-vis-demos%2F
mkdir -p data
cd data
vis_demo_list='se_resnext50 ocr mobilenet'
for vis_demo_name in $vis_demo_list; do
download $vis_demo_name
done
cd ..
# compile and test the demo
mkdir -p build
cd build
for WITH_STATIC_LIB in ON OFF; do
# -----simple_on_word2vec-----
rm -rf *
cmake .. -DPADDLE_LIB=${PADDLE_ROOT}/build/fluid_install_dir/ \
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=simple_on_word2vec \
-DWITH_GPU=$TEST_GPU_CPU \
-DWITH_STATIC_LIB=$WITH_STATIC_LIB
make -j
word2vec_model=${PADDLE_ROOT}'/build/python/paddle/fluid/tests/book/word2vec.inference.model'
if [ -d $word2vec_model ]; then
for use_gpu in $use_gpu_list; do
./simple_on_word2vec \
--dirname=$word2vec_model \
--use_gpu=$use_gpu
if [ $? -ne 0 ]; then
echo "simple_on_word2vec demo runs fail."
exit 1
fi
done
fi
# ---------vis_demo---------
rm -rf *
cmake .. -DPADDLE_LIB=${PADDLE_ROOT}/build/fluid_install_dir/ \
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=vis_demo \
-DWITH_GPU=$TEST_GPU_CPU \
-DWITH_STATIC_LIB=$WITH_STATIC_LIB
make -j
for use_gpu in $use_gpu_list; do
for vis_demo_name in $vis_demo_list; do
./vis_demo \
--modeldir=../data/$vis_demo_name/model \
--data=../data/$vis_demo_name/data.txt \
--refer=../data/$vis_demo_name/result.txt \
--use_gpu=$use_gpu
if [ $? -ne 0 ]; then
echo "vis demo $vis_demo_name runs fail."
exit 1
fi
done
done
done
set +x
paddle/contrib/inference/demo/simple_on_word2vec.cc → paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc
浏览文件 @ 7b7a4afa
......@@ -16,21 +16,27 @@ limitations under the License. */
* This file contains a simple demo for how to take a model for inference.
*/
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <memory>
#include <thread>
#include "paddle/contrib/inference/paddle_inference_api.h"
#include <thread> //NOLINT
#include "paddle/fluid/inference/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_bool(use_gpu, false, "Whether use gpu.");
namespace paddle {
namespace demo {
DEFINE_string(dirname, "", "Directory of the inference model.");
void Main(bool use_gpu) {
//# 1. Create PaddlePredictor with a config.
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
if (FLAGS_dirname.empty()) {
LOG(INFO) << "Usage: ./simple_on_word2vec --dirname=path/to/your/model";
exit(1);
}
config.model_dir = FLAGS_dirname;
config.use_gpu = use_gpu;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
......@@ -54,12 +60,16 @@ void Main(bool use_gpu) {
CHECK(predictor->Run(slots, &outputs));
//# 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "output buffer size: " << outputs.front().data.length();
PADDLE_ENFORCE(outputs.size(), 1UL);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
0.000932706};
const size_t num_elements = outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
}
}
}
......@@ -68,7 +78,7 @@ void MainThreads(int num_threads, bool use_gpu) {
// Multi-threads only support on CPU
// 0. Create PaddlePredictor with a config.
NativeConfig config;
config.model_dir = FLAGS_dirname + "word2vec.inference.model";
config.model_dir = FLAGS_dirname;
config.use_gpu = use_gpu;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
......@@ -94,14 +104,17 @@ void MainThreads(int num_threads, bool use_gpu) {
CHECK(predictor->Run(inputs, &outputs));
// 4. Get output.
ASSERT_EQ(outputs.size(), 1UL);
LOG(INFO) << "TID: " << tid << ", "
<< "output buffer size: " << outputs.front().data.length();
PADDLE_ENFORCE(outputs.size(), 1UL);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
0.000932706};
const size_t num_elements =
outputs.front().data.length() / sizeof(float);
// The outputs' buffers are in CPU memory.
for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
LOG(INFO) << static_cast<float*>(outputs.front().data.data())[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
}
}
});
......@@ -111,15 +124,18 @@ void MainThreads(int num_threads, bool use_gpu) {
}
}
TEST(demo, word2vec_cpu) { Main(false /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_cpu_1) { MainThreads(1, false /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_cpu_4) { MainThreads(4, false /*use_gpu*/); }
#ifdef PADDLE_WITH_CUDA
TEST(demo, word2vec_gpu) { Main(true /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_gpu_1) { MainThreads(1, true /*use_gpu*/); }
TEST(demo_multi_threads, word2vec_gpu_4) { MainThreads(4, true /*use_gpu*/); }
#endif
} // namespace demo
} // namespace paddle
int main(int argc, char** argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
paddle::demo::Main(false /* use_gpu*/);
paddle::demo::MainThreads(1, false /* use_gpu*/);
paddle::demo::MainThreads(4, false /* use_gpu*/);
if (FLAGS_use_gpu) {
paddle::demo::Main(true /*use_gpu*/);
paddle::demo::MainThreads(1, true /*use_gpu*/);
paddle::demo::MainThreads(4, true /*use_gpu*/);
}
return 0;
}
paddle/contrib/inference/demo/utils.h → paddle/fluid/inference/api/demo_ci/utils.h
浏览文件 @ 7b7a4afa
......@@ -13,16 +13,15 @@
// limitations under the License.
#pragma once
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/inference/paddle_inference_api.h"
namespace paddle {
namespace demo {
static void split(const std::string& str,
char sep,
static void split(const std::string& str, char sep,
std::vector<std::string>* pieces) {
pieces->clear();
if (str.empty()) {
......
paddle/contrib/inference/demo/vis_demo.cc → paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ 7b7a4afa
......@@ -18,26 +18,24 @@ limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <gtest/gtest.h>
#include <fstream>
#include <iostream>
#include "paddle/contrib/inference/demo/utils.h"
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
#include "utils.h"
#ifdef PADDLE_WITH_CUDA
DECLARE_double(fraction_of_gpu_memory_to_use);
#endif
namespace paddle {
namespace demo {
DEFINE_string(modeldir, "", "Directory of the inference model.");
DEFINE_string(refer, "", "path to reference result for comparison.");
DEFINE_string(
data,
"",
data, "",
"path of data; each line is a record, format is "
"'<space splitted floats as data>\t<space splitted ints as shape'");
DEFINE_bool(use_gpu, false, "Whether use gpu.");
namespace paddle {
namespace demo {
struct Record {
std::vector<float> data;
......@@ -47,7 +45,7 @@ struct Record {
void split(const std::string& str, char sep, std::vector<std::string>* pieces);
Record ProcessALine(const std::string& line) {
LOG(INFO) << "process a line";
VLOG(3) << "process a line";
std::vector<std::string> columns;
split(line, '\t', &columns);
CHECK_EQ(columns.size(), 2UL)
......@@ -65,8 +63,8 @@ Record ProcessALine(const std::string& line) {
for (auto& s : shape_strs) {
record.shape.push_back(std::stoi(s));
}
LOG(INFO) << "data size " << record.data.size();
LOG(INFO) << "data shape size " << record.shape.size();
VLOG(3) << "data size " << record.data.size();
VLOG(3) << "data shape size " << record.shape.size();
return record;
}
......@@ -78,20 +76,22 @@ void CheckOutput(const std::string& referfile, const PaddleTensor& output) {
file.close();
size_t numel = output.data.length() / PaddleDtypeSize(output.dtype);
LOG(INFO) << "predictor output numel " << numel;
LOG(INFO) << "reference output numel " << refer.data.size();
EXPECT_EQ(numel, refer.data.size());
VLOG(3) << "predictor output numel " << numel;
VLOG(3) << "reference output numel " << refer.data.size();
PADDLE_ENFORCE_EQ(numel, refer.data.size());
switch (output.dtype) {
case PaddleDType::INT64: {
for (size_t i = 0; i < numel; ++i) {
EXPECT_EQ(static_cast<int64_t*>(output.data.data())[i], refer.data[i]);
PADDLE_ENFORCE_EQ(static_cast<int64_t*>(output.data.data())[i],
refer.data[i]);
}
break;
}
case PaddleDType::FLOAT32:
for (size_t i = 0; i < numel; ++i) {
EXPECT_NEAR(
static_cast<float*>(output.data.data())[i], refer.data[i], 1e-5);
PADDLE_ENFORCE_LT(
fabs(static_cast<float*>(output.data.data())[i] - refer.data[i]),
1e-5);
}
break;
}
......@@ -106,15 +106,15 @@ void Main(bool use_gpu) {
config.prog_file = FLAGS_modeldir + "/__model__";
config.use_gpu = use_gpu;
config.device = 0;
#ifdef PADDLE_WITH_CUDA
config.fraction_of_gpu_memory = FLAGS_fraction_of_gpu_memory_to_use;
#endif
if (FLAGS_use_gpu) {
config.fraction_of_gpu_memory = 0.1; // set by yourself
}
LOG(INFO) << "init predictor";
VLOG(3) << "init predictor";
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
LOG(INFO) << "begin to process data";
VLOG(3) << "begin to process data";
// Just a single batch of data.
std::string line;
std::ifstream file(FLAGS_data);
......@@ -129,21 +129,26 @@ void Main(bool use_gpu) {
.data = PaddleBuf(record.data.data(), record.data.size() * sizeof(float)),
.dtype = PaddleDType::FLOAT32};
LOG(INFO) << "run executor";
VLOG(3) << "run executor";
std::vector<PaddleTensor> output;
predictor->Run({input}, &output);
LOG(INFO) << "output.size " << output.size();
VLOG(3) << "output.size " << output.size();
auto& tensor = output.front();
LOG(INFO) << "output: " << SummaryTensor(tensor);
VLOG(3) << "output: " << SummaryTensor(tensor);
// compare with reference result
CheckOutput(FLAGS_refer, tensor);
}
TEST(demo, vis_demo_cpu) { Main(false /*use_gpu*/); }
#ifdef PADDLE_WITH_CUDA
TEST(demo, vis_demo_gpu) { Main(true /*use_gpu*/); }
#endif
} // namespace demo
} // namespace paddle
int main(int argc, char** argv) {
google::ParseCommandLineFlags(&argc, &argv, true);
paddle::demo::Main(false /* use_gpu*/);
if (FLAGS_use_gpu) {
paddle::demo::Main(true /*use_gpu*/);
}
return 0;
}
paddle/contrib/inference/test_paddle_inference_api.cc → paddle/fluid/inference/api/test_api.cc
浏览文件 @ 7b7a4afa
......@@ -12,10 +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 "paddle/contrib/inference/paddle_inference_api.h"
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
......
paddle/contrib/inference/test_paddle_inference_api_impl.cc → paddle/fluid/inference/api/test_api_impl.cc
浏览文件 @ 7b7a4afa
......@@ -15,10 +15,10 @@ limitations under the License. */
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <thread>
#include <thread> // NOLINT
#include "gflags/gflags.h"
#include "paddle/contrib/inference/paddle_inference_api_impl.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -121,8 +121,8 @@ void MainImageClassification(bool use_gpu) {
// which should be in the range [0.0, 1.0].
feed_target_shapes[0][0] = batch_size;
framework::DDim input_dims = framework::make_ddim(feed_target_shapes[0]);
SetupTensor<float>(
&input, input_dims, static_cast<float>(0), static_cast<float>(1));
SetupTensor<float>(&input, input_dims, static_cast<float>(0),
static_cast<float>(1));
std::vector<framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
......
paddle/contrib/inference/test_paddle_inference_api_tensorrt_subgraph_engine.cc → paddle/fluid/inference/api/test_api_tensorrt_subgraph_engine.cc
浏览文件 @ 7b7a4afa
......@@ -15,7 +15,7 @@
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
namespace paddle {
......@@ -61,4 +61,4 @@ void Main(bool use_gpu) {
TEST(paddle_inference_api_tensorrt_subgraph_engine, main) { Main(true); }
} // namespace paddle
\ No newline at end of file
} // namespace paddle
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 7b7a4afa
......@@ -259,12 +259,15 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
op_library(sequence_conv_op DEPS context_project)
op_library(sequence_pool_op DEPS sequence_pooling)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
op_library(cos_sim_op DEPS cos_sim_functor)
op_library(parallel_do_op DEPS executor)
op_library(unsqueeze_op DEPS reshape_op)
op_library(squeeze_op DEPS reshape_op)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
......
paddle/fluid/operators/auc_op.cc
浏览文件 @ 7b7a4afa
......@@ -35,7 +35,14 @@ class AucOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(inference_height, label_height,
"Out and Label should have same height.");
int num_thres = ctx->Attrs().Get<int>("num_thresholds");
ctx->SetOutputDim("AUC", {1});
ctx->SetOutputDim("TPOut", {num_thres});
ctx->SetOutputDim("TNOut", {num_thres});
ctx->SetOutputDim("FPOut", {num_thres});
ctx->SetOutputDim("FNOut", {num_thres});
ctx->ShareLoD("Out", /*->*/ "AUC");
}
......@@ -63,10 +70,18 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Label",
"A 2D int tensor indicating the label of the training data."
"The height is batch size and width is always 1.");
AddInput("TP", "True-Positive value.");
AddInput("FP", "False-Positive value.");
AddInput("TN", "True-Negative value.");
AddInput("FN", "False-Negative value.");
// TODO(typhoonzero): support weight input
AddOutput("AUC",
"A scalar representing the "
"current area-under-the-curve.");
AddOutput("TPOut", "True-Positive value.");
AddOutput("FPOut", "False-Positive value.");
AddOutput("TNOut", "True-Negative value.");
AddOutput("FNOut", "False-Negative value.");
AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
.SetDefault("ROC");
......
paddle/fluid/operators/auc_op.h
浏览文件 @ 7b7a4afa
......@@ -34,6 +34,12 @@ class AucKernel : public framework::OpKernel<T> {
auto* inference = ctx.Input<Tensor>("Out");
auto* label = ctx.Input<Tensor>("Label");
auto* auc = ctx.Output<Tensor>("AUC");
// Only use output var for now, make sure it's persistable and
// not cleaned up for each batch.
auto* true_positive = ctx.Output<Tensor>("TPOut");
auto* false_positive = ctx.Output<Tensor>("FPOut");
auto* true_negative = ctx.Output<Tensor>("TNOut");
auto* false_negative = ctx.Output<Tensor>("FNOut");
float* auc_data = auc->mutable_data<float>(ctx.GetPlace());
......@@ -54,19 +60,10 @@ class AucKernel : public framework::OpKernel<T> {
const T* inference_data = inference->data<T>();
const int64_t* label_data = label->data<int64_t>();
// Create local tensor for storing the curve: TP, FN, TN, FP
// TODO(typhoonzero): use eigen op to caculate these values.
Tensor true_positive, false_positive, true_negative, false_negative;
true_positive.Resize({num_thresholds});
false_negative.Resize({num_thresholds});
true_negative.Resize({num_thresholds});
false_positive.Resize({num_thresholds});
int64_t* tp_data = true_positive.mutable_data<int64_t>(ctx.GetPlace());
int64_t* fn_data = false_negative.mutable_data<int64_t>(ctx.GetPlace());
int64_t* tn_data = true_negative.mutable_data<int64_t>(ctx.GetPlace());
int64_t* fp_data = false_positive.mutable_data<int64_t>(ctx.GetPlace());
auto* tp_data = true_positive->mutable_data<int64_t>(ctx.GetPlace());
auto* fn_data = false_negative->mutable_data<int64_t>(ctx.GetPlace());
auto* tn_data = true_negative->mutable_data<int64_t>(ctx.GetPlace());
auto* fp_data = false_positive->mutable_data<int64_t>(ctx.GetPlace());
for (int idx_thresh = 0; idx_thresh < num_thresholds; idx_thresh++) {
// caculate TP, FN, TN, FP for current thresh
......@@ -91,10 +88,10 @@ class AucKernel : public framework::OpKernel<T> {
}
}
// store rates
tp_data[idx_thresh] = tp;
fn_data[idx_thresh] = fn;
tn_data[idx_thresh] = tn;
fp_data[idx_thresh] = fp;
tp_data[idx_thresh] += tp;
fn_data[idx_thresh] += fn;
tn_data[idx_thresh] += tn;
fp_data[idx_thresh] += fp;
}
// epsilon to avoid divide by zero.
float epsilon = 1e-6;
......
paddle/fluid/operators/checkpoint_notify_op.cc
浏览文件 @ 7b7a4afa
......@@ -48,7 +48,7 @@ class CheckpointNotifyOp : public framework::OperatorBase {
VLOG(3) << "checkpoint notify sending lookup table: " << lookup_table_name
<< " and dir:" << dir << " to " << epmap[i];
}
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
};
......
paddle/fluid/operators/detection/prior_box_op.cc
浏览文件 @ 7b7a4afa
......@@ -149,6 +149,13 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"(float) "
"Prior boxes center offset.")
.SetDefault(0.5);
AddAttr<bool>(
"min_max_aspect_ratios_order",
"(bool) If set True, the output prior box is in order of"
"[min, max, aspect_ratios], which is consistent with Caffe."
"Please note, this order affects the weights order of convolution layer"
"followed by and does not affect the final detection results.")
.SetDefault(false);
AddComment(R"DOC(
Prior box operator
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
......
paddle/fluid/operators/detection/prior_box_op.cu
浏览文件 @ 7b7a4afa
......@@ -28,8 +28,8 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
const int im_width, const int as_num,
const T offset, const T step_width,
const T step_height, const T* min_sizes,
const T* max_sizes, const int min_num,
bool is_clip) {
const T* max_sizes, const int min_num, bool is_clip,
bool min_max_aspect_ratios_order) {
int num_priors = max_sizes ? as_num * min_num + min_num : as_num * min_num;
int box_num = height * width * num_priors;
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < box_num;
......@@ -44,14 +44,28 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
T min_size = min_sizes[m];
if (max_sizes) {
int s = p % (as_num + 1);
if (s < as_num) {
T ar = aspect_ratios[s];
bw = min_size * sqrt(ar) / 2.;
bh = min_size / sqrt(ar) / 2.;
if (!min_max_aspect_ratios_order) {
if (s < as_num) {
T ar = aspect_ratios[s];
bw = min_size * sqrt(ar) / 2.;
bh = min_size / sqrt(ar) / 2.;
} else {
T max_size = max_sizes[m];
bw = sqrt(min_size * max_size) / 2.;
bh = bw;
}
} else {
T max_size = max_sizes[m];
bw = sqrt(min_size * max_size) / 2.;
bh = bw;
if (s == 0) {
bw = bh = min_size / 2.;
} else if (s == 1) {
T max_size = max_sizes[m];
bw = sqrt(min_size * max_size) / 2.;
bh = bw;
} else {
T ar = aspect_ratios[s - 1];
bw = min_size * sqrt(ar) / 2.;
bh = min_size / sqrt(ar) / 2.;
}
}
} else {
int s = p % as_num;
......@@ -94,6 +108,8 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
auto clip = ctx.Attr<bool>("clip");
auto min_max_aspect_ratios_order =
ctx.Attr<bool>("min_max_aspect_ratios_order");
std::vector<float> aspect_ratios;
ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);
......@@ -149,7 +165,7 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
GenPriorBox<T><<<grid, block, 0, stream>>>(
boxes->data<T>(), r.data<T>(), height, width, im_height, im_width,
aspect_ratios.size(), offset, step_width, step_height, min.data<T>(),
max_data, min_num, clip);
max_data, min_num, clip, min_max_aspect_ratios_order);
framework::Tensor v;
framework::TensorFromVector(variances, ctx.device_context(), &v);
......
paddle/fluid/operators/detection/prior_box_op.h
浏览文件 @ 7b7a4afa
......@@ -68,6 +68,8 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
auto clip = ctx.Attr<bool>("clip");
auto min_max_aspect_ratios_order =
ctx.Attr<bool>("min_max_aspect_ratios_order");
std::vector<float> aspect_ratios;
ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);
......@@ -108,26 +110,59 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
auto min_size = min_sizes[s];
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
if (min_max_aspect_ratios_order) {
box_width = box_height = min_size / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
if (fabs(ar - 1.) < 1e-6) {
continue;
}
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
} else {
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
}
}
}
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ 7b7a4afa
......@@ -86,8 +86,9 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
std::minstd_rand engine,
std::vector<int>* inds) const {
std::uniform_real_distribution<float> uniform(0, 1);
if (inds->size() > num) {
for (int i = num; i < inds->size(); ++i) {
const int64_t size = static_cast<int64_t>(inds->size());
if (size > num) {
for (int64_t i = num; i < size; ++i) {
int rng_ind = std::floor(uniform(engine) * i);
if (rng_ind < num)
std::iter_swap(inds->begin() + rng_ind + offset,
......
paddle/fluid/operators/distributed/grpc_client.cc
浏览文件 @ 7b7a4afa
......@@ -281,9 +281,10 @@ void GRPCClient::AsyncCheckpointNotify(const std::string& ep,
req_count_++;
}
void GRPCClient::Wait() {
bool GRPCClient::Wait() {
std::unique_lock<std::mutex> lk(sync_mutex_);
sync_cond_.wait(lk, [this] { return req_count_ == 0; });
sync_cond_.wait(lk, [this] { return (req_count_ == 0 || ok_ == false); });
return ok_;
}
void GRPCClient::Proceed() {
......@@ -297,6 +298,14 @@ void GRPCClient::Proceed() {
if (c->status_.ok()) {
VLOG(3) << c->var_h_.String() << " process";
c->Process();
} else if (c->status_.error_code() == grpc::StatusCode::DEADLINE_EXCEEDED) {
LOG(ERROR) << c->var_h_.String()
<< " meets grpc error:" << c->status_.error_message();
{
std::lock_guard<std::mutex> lk(sync_mutex_);
ok_ = false;
}
sync_cond_.notify_all();
} else {
LOG(FATAL) << c->var_h_.String()
<< " meets grpc error:" << c->status_.error_message();
......
paddle/fluid/operators/distributed/grpc_client.h
浏览文件 @ 7b7a4afa
......@@ -188,7 +188,7 @@ class CheckpointNotifyProcessor : public BaseProcessor {
class GRPCClient : public RPCClient {
public:
GRPCClient() {}
GRPCClient() : ok_(true) {}
virtual ~GRPCClient();
bool AsyncSendVar(const std::string& ep, const platform::DeviceContext& ctx,
......@@ -221,7 +221,7 @@ class GRPCClient : public RPCClient {
void AsyncSendEndPass(const std::string& ep,
int64_t time_out = FLAGS_rpc_deadline) override;
void Wait() override;
bool Wait() override;
void SendBeginPass() override;
......@@ -247,6 +247,7 @@ class GRPCClient : public RPCClient {
std::mutex sync_mutex_;
std::condition_variable sync_cond_;
std::atomic<int64_t> req_count_{0};
bool ok_;
// mutex for GetChannel thread safety
std::mutex chan_mutex_;
......
paddle/fluid/operators/distributed/rpc_client.h
浏览文件 @ 7b7a4afa
......@@ -72,7 +72,7 @@ class RPCClient {
virtual void SendBeginPass() = 0;
virtual void SendEndPass() = 0;
virtual void Wait() = 0;
virtual bool Wait() = 0;
template <typename T>
static RPCClient* GetInstance() {
......
paddle/fluid/operators/fake_quantize_op.cc 0 → 100644
浏览文件 @ 7b7a4afa
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/fake_quantize_op.h"
#include <string>
namespace paddle {
namespace operators {
class FakeQuantizeOp : public framework::OperatorWithKernel {
public:
FakeQuantizeOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of FakeQuantizeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of FakeQuantizeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("OutMovingScale"),
"OutMovingScale(Out) of FakeQuantizeOp should not be null");
// if (ctx->HasInput("InMovingScale")) {
ctx->SetOutputDim("OutMovingScale", ctx->GetInputDim("InMovingScale"));
//}
// if (ctx->HasInput("InScales")) {
PADDLE_ENFORCE(ctx->HasOutput("OutScales"),
"OutScales(Out) of FakeQuantizeOp should not be null");
ctx->SetOutputDim("OutScales", ctx->GetInputDim("InScales"));
// PADDLE_ENFORCE_EQ(ctx->Inputs("InScales")[0],
// ctx->Outputs("OutScales")[0],
// "Mean and MeanOut should share the same memory");
//}
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class FakeQuantizeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) Input tensor of scale operator.");
AddInput("InScales", "(Tensor) scale buffer, used in static quantization.")
.AsDispensable();
AddInput("InMovingScale", "Last scale, used in static quantization.")
.AsDispensable();
AddInput("InCurrentIter",
"Last iteration number, used in static quantization.")
.AsDispensable();
AddOutput("Out", "(Tensor) Output of quantized low level tensor.");
AddOutput("OutScales",
"(Tensor) scale buffer, used in static quantization.")
.AsDispensable();
AddOutput("OutMovingScale", " Current scale");
AddOutput("OutCurrentIter", "Current iteration number.").AsDispensable();
AddAttr<std::string>("quantize_type",
"(string, default abs_max)"
"The scaling tpe of the quantize operator.")
.SetDefault("abs_max");
AddAttr<int>("window_size", "(int, default 10000)").SetDefault(10000);
AddAttr<int>("bit_length", "(int, default 8)")
.SetDefault(8)
.AddCustomChecker([](const int &bit_length) {
PADDLE_ENFORCE(bit_length >= 1 && bit_length <= 16,
"'bit_length' should be between 1 and 16.");
});
AddAttr<bool>("is_test", "").SetDefault(false);
AddComment(R"DOC(
FakeQuantize operator
quantize_type = abs_max:
$$scale = max(abs(x))$$
quantize_type = range_abs_max:
$$scale = max(max(abs(x)), history_abs_max)$$
quantize_type = moving_average_abs_max:
$$scale = 0.1*scale+0.9*new_abs_max)$$
$$Out = scale*X$$
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(fake_quantize, ops::FakeQuantizeOp, ops::FakeQuantizeOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
fake_quantize,
ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, float>,
ops::FakeQuantizeKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/fake_quantize_op.cu 0 → 100644
浏览文件 @ 7b7a4afa
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <string>
#include "paddle/fluid/operators/fake_quantize_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void FindAbsMaxKernel(const int n, const T* in, T* out) {
int bid = threadIdx.x + blockIdx.x * blockDim.x;
int tid = threadIdx.x;
extern __shared__ T shared_max_data[];
if (gridDim.x > 1) {
shared_max_data[tid] = T(0);
for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
T tmp = fabs(in[i]);
if (tmp > shared_max_data[tid]) {
shared_max_data[tid] = tmp;
}
}
} else {
if (bid < n) {
shared_max_data[tid] = fabs(in[bid]);
} else {
shared_max_data[tid] = T(0);
}
}
__syncthreads();
for (int i = blockDim.x / 2; i > 0; i >>= 1) {
if (tid < i && shared_max_data[tid] < shared_max_data[tid + i]) {
shared_max_data[tid] = shared_max_data[tid + i];
}
__syncthreads();
}
if (tid == 0) {
out[blockIdx.x] = shared_max_data[0];
}
}
float FindAbsMaxGpu(const platform::CUDADeviceContext& ctx, const float* array,
int length) {
float host_max;
int kNumTheads = 1024;
int gridDimx = (kNumTheads - 1 + length) / kNumTheads;
gridDimx = (gridDimx > kNumTheads) ? kNumTheads : gridDimx;
framework::Tensor t;
float* device_max = t.mutable_data<float>(framework::make_ddim({gridDimx}),
platform::CUDAPlace());
FindAbsMaxKernel<float><<<gridDimx, kNumTheads, kNumTheads * sizeof(float),
ctx.stream()>>>(length, array, device_max);
FindAbsMaxKernel<
float><<<1, kNumTheads, kNumTheads * sizeof(float), ctx.stream()>>>(
gridDimx, device_max, device_max);
PADDLE_ENFORCE_EQ(
cudaMemcpy(&host_max, device_max, sizeof(float), cudaMemcpyDeviceToHost),
cudaSuccess, "cudaMemcpy failed");
return host_max;
}
template <typename T>
__global__ void ApplySaturateKernel(const int n, const T* in, T* out,
int* num_saturate, const T min,
const T max) {
int bid = threadIdx.x + blockIdx.x * blockDim.x;
int tid = threadIdx.x;
extern __shared__ int shared_count[];
shared_count[tid] = 0;
for (int i = bid; i < n; i += blockDim.x * gridDim.x) {
if (in[i] > max) {
out[i] = max;
shared_count[tid] += 1;
} else if (in[i] < min) {
out[i] = min;
shared_count[tid] += 1;
} else {
out[i] = in[i];
}
}
__syncthreads();
for (int i = blockDim.x / 2; i > 0; i >>= 1) {
if (tid < i) {
shared_count[tid] += shared_count[tid + i];
}
__syncthreads();
}
if (tid == 0) {
num_saturate[blockIdx.x] = shared_count[0];
}
}
template <typename T>
__global__ void ReduceKernel(const int n, const T* in, T* out) {
int tid = threadIdx.x;
extern __shared__ T shared_sum[];
if (tid < n) {
shared_sum[tid] = in[tid];
} else {
shared_sum[tid] = T(0);
}
__syncthreads();
// blockDim.x must >= n
for (int i = (n + 1) / 2; i > 0; i >>= 1) {
if (tid < i) {
shared_sum[tid] += shared_sum[tid + i];
}
__syncthreads();
}
if (tid == 0) {
out[0] = shared_sum[0];
}
}
template <typename T>
int ApplySaturateGpu(const platform::CUDADeviceContext& ctx, const int n,
const T* in, T* out, const T min, const T max) {
int host_num_saturate;
int kNumTheads = 1024;
int gridDimx = (n + kNumTheads - 1) / kNumTheads;
gridDimx = (gridDimx > kNumTheads) ? kNumTheads : gridDimx;
framework::Tensor t;
int* device_num_saturate = t.mutable_data<int>(
framework::make_ddim({gridDimx}), platform::CUDAPlace());
ApplySaturateKernel<
T><<<gridDimx, kNumTheads, kNumTheads * sizeof(T), ctx.stream()>>>(
n, in, out, device_num_saturate, min, max);
ReduceKernel<int><<<1, kNumTheads, kNumTheads * sizeof(T), ctx.stream()>>>(
gridDimx, device_num_saturate, device_num_saturate);
PADDLE_ENFORCE_EQ(cudaSuccess,
cudaMemcpy(&host_num_saturate, device_num_saturate,
sizeof(int), cudaMemcpyDeviceToHost),
"cudaMemcpy failed");
return host_num_saturate;
}
template <typename DeviceContext, typename T>
class FakeQuantizeCUDAKernel : public framework::OpKernel<T> {
public:
T FindRangeAbsMax(const platform::CUDADeviceContext& ctx,
framework::Tensor* scale_list, framework::Tensor* out_scale,
const T& cur_scale, int window_size,
int current_iter) const {
T* sl = scale_list->mutable_data<T>(platform::CPUPlace());
T remove_tmp = sl[current_iter];
sl[current_iter] = cur_scale;
T& max_scale = out_scale->mutable_data<T>(platform::CPUPlace())[0];
if (max_scale < cur_scale) {
max_scale = cur_scale;
} else if (fabs(remove_tmp - max_scale) < 1e-6) {
int size = (current_iter > window_size) ? window_size : current_iter;
max_scale = T(FindAbsMaxGpu(ctx, scale_list->data<float>(), size));
}
return max_scale;
}
T FindMovingAverageAbsMmax(framework::Tensor* in_scale,
framework::Tensor* out_scale,
const T& cur_scale) const {
T* ins = in_scale->mutable_data<T>(platform::CPUPlace());
T* outs = out_scale->mutable_data<T>(platform::CPUPlace());
outs[0] = 0.9 * cur_scale + 0.1 * ins[0];
return T(outs[0]);
}
virtual void Compute(const framework::ExecutionContext& context) const {
PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
"This kernel only runs on GPU device.");
auto& device_ctx = context.cuda_device_context();
auto* tensor = context.Output<framework::Tensor>("Out");
auto* in = context.Input<framework::Tensor>("X");
const bool is_test = context.Attr<bool>("is_test");
tensor->mutable_data<T>(in->place());
context.Output<framework::Tensor>("OutMovingScale")
->mutable_data<T>(
context.Input<framework::Tensor>("InMovingScale")->place());
auto quantize_type =
static_cast<std::string>(context.Attr<std::string>("quantize_type"));
if (quantize_type == std::string("range_abs_max")) {
context.Output<framework::Tensor>("OutScales")
->mutable_data<T>(
context.Input<framework::Tensor>("InScales")->place());
context.Output<framework::Tensor>("OutCurrentIter")
->mutable_data<T>(
context.Input<framework::Tensor>("InCurrentIter")->place());
}
T scale = T(1);
int window_size = context.Attr<int>("window_size");
T bin_cnt = (T)((1 << (context.Attr<int>("bit_length") - 1)) - 1);
if (quantize_type == std::string("abs_max")) {
auto* saving_scale = context.Output<framework::Tensor>("OutMovingScale");
scale = (T)FindAbsMaxGpu(device_ctx, in->data<float>(), in->numel());
saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
auto& device_ctx = context.template device_context<DeviceContext>();
auto* scale_list = context.Output<framework::Tensor>("OutScales");
math::SetConstant<DeviceContext, T> scalar;
scale_list->mutable_data<T>(context.GetPlace());
scalar(device_ctx, scale_list, static_cast<T>(0));
auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
iter->mutable_data<T>(context.GetPlace());
scalar(device_ctx, iter, static_cast<T>(0));
} else if (quantize_type == std::string("range_abs_max")) {
auto* moving_scale = const_cast<framework::Tensor*>(
context.Input<framework::Tensor>("InMovingScale"));
if (is_test) {
scale = moving_scale->mutable_data<T>(platform::CPUPlace())[0];
} else {
auto* it = const_cast<framework::Tensor*>(
context.Input<framework::Tensor>("InCurrentIter"));
auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
int* last_iter = it->mutable_data<int>(platform::CPUPlace());
int* current_iter = iter->mutable_data<int>(platform::CPUPlace());
auto* scale_list = context.Output<framework::Tensor>("OutScales");
auto* saving_scale =
context.Output<framework::Tensor>("OutMovingScale");
scale = (T)FindAbsMaxGpu(device_ctx, in->data<float>(), in->numel());
scale = FindRangeAbsMax(device_ctx, scale_list, saving_scale, scale,
window_size, current_iter[0]);
(*current_iter) = (*last_iter) + 1;
}
} else if (quantize_type == std::string("moving_average_abs_max")) {
auto* moving_scale = const_cast<framework::Tensor*>(
context.Input<framework::Tensor>("InMovingScale"));
if (is_test) {
scale = moving_scale->mutable_data<T>(platform::CPUPlace())[0];
} else {
scale = (T)FindAbsMaxGpu(device_ctx, in->data<float>(), in->numel());
auto* saving_scale =
context.Output<framework::Tensor>("OutMovingScale");
scale = FindMovingAverageAbsMmax(
const_cast<framework::Tensor*>(moving_scale), saving_scale, scale);
}
}
ApplySaturateGpu<T>(device_ctx, in->numel(), in->data<T>(),
tensor->mutable_data<T>(in->place()), -scale, scale);
scale = bin_cnt / scale;
auto& dev =
*context.template device_context<DeviceContext>().eigen_device();
auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
auto eigen_in = framework::EigenVector<T>::Flatten(*tensor);
eigen_out.device(dev) = (scale * eigen_in).round();
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(fake_quantize,
paddle::operators::FakeQuantizeCUDAKernel<
paddle::platform::CUDADeviceContext, float>,
paddle::operators::FakeQuantizeCUDAKernel<
paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/fake_quantize_op.h 0 → 100644
浏览文件 @ 7b7a4afa
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace operators {
using platform::Transform;
template <typename DeviceContext, typename T>
class FakeQuantizeKernel : public framework::OpKernel<T> {
public:
T FindAbsMax(framework::Tensor* in, int n) const {
T* p = in->mutable_data<T>(platform::CPUPlace());
T abs_max = (T)0.00000001;
for (int i = 0; i < n; i++) {
T tmp = fabs(p[i]);
if (tmp > abs_max) abs_max = tmp;
}
return T(abs_max);
}
T FindRangeAbsMax(framework::Tensor* scale_list, framework::Tensor* out_scale,
const T& cur_scale, int window_size,
int current_iter) const {
T* sl = scale_list->mutable_data<T>(platform::CPUPlace());
T remove_tmp = sl[current_iter];
sl[current_iter] = cur_scale;
T& max_scale = out_scale->mutable_data<T>(platform::CPUPlace())[0];
if (max_scale < cur_scale) {
max_scale = cur_scale;
} else if (fabs(remove_tmp - max_scale) < 1e-6) {
int size = (current_iter > window_size) ? window_size : current_iter;
max_scale = T(FindAbsMax(scale_list, size));
}
return max_scale;
}
T FindMovingAverageAbsMmax(framework::Tensor* in_scale,
framework::Tensor* out_scale,
const T& cur_scale) const {
T* ins = in_scale->mutable_data<T>(platform::CPUPlace());
T* outs = out_scale->mutable_data<T>(platform::CPUPlace());
outs[0] = 0.9 * cur_scale + 0.1 * ins[0];
return T(outs[0]);
}
virtual void Compute(const framework::ExecutionContext& context) const {
auto* tensor = context.Output<framework::Tensor>("Out");
auto* in = context.Input<framework::Tensor>("X");
const bool is_test = context.Attr<bool>("is_test");
tensor->mutable_data<T>(in->place());
auto* oms_tensor = context.Output<framework::Tensor>("OutMovingScale");
oms_tensor->mutable_data<T>(in->place());
auto quantize_type =
static_cast<std::string>(context.Attr<std::string>("quantize_type"));
if (quantize_type == std::string("range_abs_max")) {
auto* oss_tensor = context.Output<framework::Tensor>("OutScales");
oss_tensor->mutable_data<T>(
context.Input<framework::Tensor>("InScales")->place());
auto* oci_tensor = context.Output<framework::Tensor>("OutCurrentIter");
oci_tensor->mutable_data<T>(
context.Input<framework::Tensor>("InCurrentIter")->place());
}
T scale = static_cast<T>(1);
int window_size = context.Attr<int>("window_size");
int bit_length = context.Attr<int>("bit_length");
int bin_cnt = std::pow(2, bit_length - 1) - 1;
auto& dev =
*context.template device_context<DeviceContext>().eigen_device();
auto raw_in = framework::EigenVector<T>::Flatten(*in);
if (quantize_type == std::string("abs_max")) {
auto* saving_scale = context.Output<framework::Tensor>("OutMovingScale");
auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
scale_out.device(dev) = raw_in.abs().maximum();
scale = scale_out(0);
auto& device_ctx = context.template device_context<DeviceContext>();
auto* scale_list = context.Output<framework::Tensor>("OutScales");
math::SetConstant<DeviceContext, T> scalar;
scale_list->mutable_data<T>(context.GetPlace());
scalar(device_ctx, scale_list, static_cast<T>(0));
auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
iter->mutable_data<T>(context.GetPlace());
scalar(device_ctx, iter, static_cast<T>(0));
} else if (quantize_type == std::string("range_abs_max")) {
auto* moving_scale = context.Input<framework::Tensor>("InMovingScale");
if (is_test) {
scale = moving_scale->data<T>()[0];
} else {
auto* it = context.Input<framework::Tensor>("InCurrentIter");
auto* iter = context.Output<framework::Tensor>("OutCurrentIter");
const int* last_iter = it->data<int>();
int* current_iter = iter->mutable_data<int>(platform::CPUPlace());
auto* scale_list = context.Output<framework::Tensor>("OutScales");
auto* saving_scale =
context.Output<framework::Tensor>("OutMovingScale");
auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
scale_out.device(dev) = raw_in.abs().maximum();
scale = saving_scale->mutable_data<T>(platform::CPUPlace())[0];
scale = FindRangeAbsMax(scale_list, saving_scale, scale, window_size,
current_iter[0]);
saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
(*current_iter) = (*last_iter) + 1;
}
} else if (quantize_type == std::string("moving_average_abs_max")) {
auto* moving_scale = context.Input<framework::Tensor>("InMovingScale");
if (is_test) {
scale = moving_scale->data<T>()[0];
} else {
auto* saving_scale =
context.Output<framework::Tensor>("OutMovingScale");
auto scale_out = framework::EigenVector<T>::Flatten(*saving_scale);
scale_out.device(dev) = raw_in.abs().maximum();
scale = saving_scale->mutable_data<T>(platform::CPUPlace())[0];
scale = FindMovingAverageAbsMmax(
const_cast<framework::Tensor*>(moving_scale), saving_scale, scale);
saving_scale->mutable_data<T>(platform::CPUPlace())[0] = scale;
}
}
Transform<DeviceContext> trans;
trans(context.template device_context<DeviceContext>(), in->data<T>(),
in->data<T>() + in->numel(), tensor->mutable_data<T>(in->place()),
ClipFunctor<T>(-scale, scale));
auto eigen_out = framework::EigenVector<T>::Flatten(*tensor);
auto eigen_in = framework::EigenVector<T>::Flatten(*tensor);
eigen_out.device(dev) = (bin_cnt / scale * eigen_in).round();
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/fetch_barrier_op.cc
浏览文件 @ 7b7a4afa
......@@ -45,13 +45,13 @@ class FetchBarrierOp : public framework::OperatorBase {
distributed::RPCClient* rpc_client =
distributed::RPCClient::GetInstance<RPCCLIENT_T>();
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
for (auto& ep : eps) {
VLOG(3) << "fetch barrier, ep: " << ep;
rpc_client->AsyncSendFetchBarrier(ep);
}
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
};
......
paddle/fluid/operators/hierarchical_sigmoid_op.cc 0 → 100644
浏览文件 @ 7b7a4afa
/* 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/fluid/operators/hierarchical_sigmoid_op.h"
#include <vector>
namespace paddle {
namespace operators {
/**
* Organize the classes into a binary tree. At each node, a sigmoid function
* is used to calculate the probability of belonging to the right branch.
* This idea is from "F. Morin, Y. Bengio (AISTATS 05):
* Hierarchical Probabilistic Neural Network Language Model."
*
* Here we uses a simple way of making the binary tree.
* Assuming the number of classes C = 6,
* The classes are organized as a binary tree in the following way:
*
* @code{.py}
* *-*-*- 2
* | | |- 3
* | |
* | |-*- 4
* | |- 5
* |
* |-*- 0
* |- 1
* @endcode
*
* where * indicates an internal node, and each leaf node represents a class.
* - Node 0 ... C-2 are internal nodes.
* - Node C-1 ... 2C-2 are leaf nodes.
* - Class c is represented by leaf node \f$c+C-1\f$.
*
* We assign an id for each node:
* - the id of root be 0.
* - the left child of a node i is 2*i+1.
* - the right child of a node i is 2*i+2.
*
* It's easy to see that:
* - the parent of node i is \f$\left\lfloor(i-1)/2\right\rfloor\f$.
* - the j-th level ancestor of node i is
* \f$\left\lfloor(i+1)/2^{j+1}\right\rfloor - 1\f$.
* - A node i is a left child of its parent if \f$(i-1)\%2==0\f$.
*
*/
class HierarchicalSigmoidOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("PreOut"),
"Output(PreOut) should not be null.");
const int64_t batch_size = ctx->GetInputDim("X")[0];
std::vector<int64_t> output_shape({batch_size, 1});
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.GetPlace());
}
};
template <typename AttrType>
class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor, required) The input tensor with shape [N, D], "
"where N is the size of mini-batch, and D is the feature size.");
AddInput("W",
"(Tensor, required), The parameters of hierarchical "
"sigmoid operator, each of them is a 2-D tensor, the shape is"
"[num_classes - 1, D].");
AddInput("Label",
"(Tensor, required), The labels of training data. It's a"
"tensor with shape [N, 1].");
AddInput("Bias",
"(Tensor, optional), The bias is a tensor with shape"
"[1, num_classes - 1].");
AddOutput("Out",
"(Tensor, required) The output of hierarchical sigmoid operator."
"The shape is [N, 1].");
AddOutput("PreOut",
"(Tensor, required) A intermedia 2-D tensor with shape "
"[batch_size, code_length], where code_length represents the "
"maximum path length from root to leaf nodes.")
.AsIntermediate();
AddAttr<AttrType>("num_classes", "(int, required), The number of classes")
.SetDefault(2);
AddComment(R"DOC(
The hierarchical sigmoid operator organize the classes into a binary tree.
At each node, a sigmoid function is used to calculate the probability of
belonging to the right branch. This idea is from
"F. Morin, Y. Bengio (AISTATS 05):
Hierarchical Probabilistic Neural Network Language Model."
)DOC");
}
};
class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("PreOut"),
"Input(Preout) should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("W")),
"Output(W@Grad should not be null.)");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")));
if (ctx->HasOutput(framework::GradVarName("Bias"))) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
}
ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.GetPlace());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(hierarchical_sigmoid, ops::HierarchicalSigmoidOp,
ops::HierarchicalSigmoidOpMaker<int>,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp);
REGISTER_OP_CPU_KERNEL(
hierarchical_sigmoid,
ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext,
double>);
REGISTER_OP_CPU_KERNEL(
hierarchical_sigmoid_grad,
ops::HierarchicalSigmoidGradOpKernel<paddle::platform::CPUDeviceContext,
float>,
ops::HierarchicalSigmoidGradOpKernel<paddle::platform::CPUDeviceContext,
double>);
paddle/fluid/operators/hierarchical_sigmoid_op.h 0 → 100644
浏览文件 @ 7b7a4afa
/* 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 <iostream>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/clip_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/matrix_bit_code.h"
#include "paddle/fluid/platform/transform.h"
namespace paddle {
namespace operators {
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
using platform::Transform;
template <typename DeviceContext, typename T>
class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* w = ctx.Input<framework::Tensor>("W");
auto* label = ctx.Input<framework::Tensor>("Label");
auto* bias = ctx.Input<framework::Tensor>("Bias");
auto* out = ctx.Output<framework::Tensor>("Out");
auto* pre_out = ctx.Output<framework::Tensor>("PreOut");
size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
int64_t code_length = math::FindLastSet(num_classes - 1);
int64_t batch_size = in->dims()[0];
framework::Tensor sum;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto* pre_out_data = pre_out->mutable_data<T>(
framework::make_ddim({batch_size, code_length}), ctx.GetPlace());
auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
// Not all class(leaf) nodes' path lengths equal code_length, thus init as
// 0s can avoid out of path's loss.
math::SetConstant<DeviceContext, T> zero;
zero(dev_ctx, pre_out, static_cast<T>(0.0));
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
math::RowwiseSum<DeviceContext, T> row_sum;
math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
std::vector<int64_t> sum_dims({batch_size, 1UL});
sum.mutable_data<T>(framework::make_ddim(sum_dims), ctx.GetPlace());
auto sum_mat = EigenMatrix<T>::From(sum);
out->mutable_data<T>(ctx.GetPlace());
auto out_mat = framework::EigenVector<T>::Flatten(*out);
if (bias) {
bit_code.Add(pre_out, *bias);
}
bit_code.Mul(pre_out, *w, *in);
// clip to [-40, 40]
Transform<DeviceContext> trans;
trans(ctx.template device_context<DeviceContext>(), pre_out_data,
pre_out_data + pre_out->numel(), pre_out_data,
ClipFunctor<T>(static_cast<T>(-40.0), static_cast<T>(40.0)));
bit_code.Sum(*pre_out, out, static_cast<T>(-1));
// use softrelu to calculate cross entropy
pre_out_mat.device(place) = (static_cast<T>(1.0) + pre_out_mat.exp()).log();
row_sum(dev_ctx, *pre_out, &sum);
// TODO(guosheng): Subtract the out of path's loss, since not all
// class(leaf) nodes' path lengths equal code_length. But it won't break the
// gradient check since both have the out of path's loss and will cancel out
// each other.
out_mat.device(place) = sum_mat + out_mat;
}
};
template <typename DeviceContext, typename T>
class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* w = ctx.Input<framework::Tensor>("W");
auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* w_grad = ctx.Output<framework::Tensor>(framework::GradVarName("W"));
auto* bias_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
auto* label = ctx.Input<framework::Tensor>("Label");
auto* pre_out = ctx.Input<framework::Tensor>("PreOut");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor pre_out_grad;
pre_out_grad.mutable_data<T>(pre_out->dims(), ctx.GetPlace());
in_grad->mutable_data<T>(ctx.GetPlace());
w_grad->mutable_data<T>(ctx.GetPlace());
auto& dev_ctx = ctx.template device_context<DeviceContext>();
math::SetConstant<DeviceContext, T> zero;
zero(dev_ctx, in_grad, static_cast<T>(0.0));
zero(dev_ctx, w_grad, static_cast<T>(0.0));
size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
Eigen::array<int, 2> bcast({{1, static_cast<int>(pre_out_grad.dims()[1])}});
// softrelu derivative
pre_out_grad_mat.device(place) =
static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
bit_code.Sub(&pre_out_grad); // the gradient of clip(w * x + b)
pre_out_grad_mat.device(place) =
pre_out_grad_mat * out_grad_mat.broadcast(bcast);
// TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
// be consistent with the clipping in forward.
if (bias_grad) {
bias_grad->mutable_data<T>(ctx.GetPlace());
zero(dev_ctx, bias_grad, static_cast<T>(0.0));
bit_code.AddGrad(pre_out_grad, bias_grad);
}
bit_code.MulGradWeight(pre_out_grad, w_grad, *in);
bit_code.MulGradError(pre_out_grad, *w, in_grad);
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/im2sequence_op.cc
浏览文件 @ 7b7a4afa
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/im2sequence_op.h"
#include <string>
#include <vector>
namespace paddle {
......@@ -28,27 +29,18 @@ class Im2SequenceOp : public framework::OperatorWithKernel {
"Input(X) of Im2SequenceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of Im2SequenceOp op should not be null.");
auto in_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(in_dim.size(), 4,
"Input(X) format must be 4D tensor, eg., NCHW.");
int img_channels = in_dim[1];
auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
paddings[2], strides[0]);
int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
paddings[3], strides[1]);
ctx->SetOutputDim("Out", {batch_size * output_height * output_width,
img_channels * kernels[0] * kernels[1]});
ctx->SetOutputDim("Out",
{in_dim[0], img_channels * kernels[0] * kernels[1]});
}
};
......@@ -61,6 +53,10 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
"C: channels"
"H: height"
"W: width");
AddInput("Y",
"(Tensor) The input tensor of image real size(H, W)."
"2-D with shape [batchsize, 2]")
.AsDispensable();
AddOutput("Out", "(LodTensor) The output data of im2sequence op,");
AddAttr<std::vector<int>>("kernels",
"(vector<int>), the "
......@@ -73,6 +69,13 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
"(vector<int> default:{0, 0, 0, 0}), the "
"paddings(up_pad, left_pad, down_pad, right_pad)")
.SetDefault({0, 0, 0, 0});
AddAttr<std::vector<int>>("out_stride",
"the attribute is valid only when input(Y)"
"is not NULL.this attribute represents the"
"scaling of the pic through the CNN"
"(vector<int> dedault:{1,1}),the out_stride"
" (out_stride_height, out_stride_width)")
.SetDefault({1, 1});
AddComment(R"DOC(
This op uses kernels to scan images and converts these images to sequences.
After expanding, The number of time steps are output_height * output_width
......@@ -123,7 +126,7 @@ output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.dims = {8, 8}
output.lod = [[0, 4, 8]]
)DOC");
......
paddle/fluid/operators/im2sequence_op.h
浏览文件 @ 7b7a4afa
......@@ -13,6 +13,7 @@
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/eigen.h"
......@@ -39,50 +40,107 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
const Tensor* in = ctx.Input<Tensor>("X");
LoDTensor* out = ctx.Output<LoDTensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
// TODO(wanghaoshuang): Add layout checker after 'set_layout'
// being available for python API
// PADDLE_ENFORCE_EQ(in->layout(), framework::DataLayout::kNCHW,
// "Input(X) layout must be NCHW");
auto in_dim = in->dims();
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
auto kernels = ctx.Attr<std::vector<int>>("kernels");
auto strides = ctx.Attr<std::vector<int>>("strides");
auto paddings = ctx.Attr<std::vector<int>>("paddings");
int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
paddings[2], strides[0]);
int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
paddings[3], strides[1]);
const std::vector<int> dilations({1, 1});
auto out_dims = out->dims();
out->Resize({batch_size, out->numel() / batch_size});
for (int i = 0; i < batch_size; i++) {
const Tensor src =
in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
Tensor dst = out->Slice(i, i + 1).Resize(
{output_height, output_width, img_channels, kernels[0], kernels[1]});
math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
f(dev_ctx, src, dilations, strides, paddings, &dst);
}
out->Resize(out_dims);
// set lod information
// TODO(wanghaoshuang): Move this to InferShape
framework::LoD lod(1);
lod[0].reserve(batch_size + 1);
for (int i = 0, offset = 0; i < batch_size + 1; ++i) {
if (ctx.HasInput("Y") && batch_size > 1) {
const Tensor* imgrealsize = ctx.Input<Tensor>("Y");
auto out_stride = ctx.Attr<std::vector<int>>("out_stride");
Tensor cpu_shape_tensor;
TensorCopySync(*imgrealsize, platform::CPUPlace(), &cpu_shape_tensor);
std::vector<int> imgreal_h;
std::vector<int> imgreal_w;
std::vector<int> output_height;
std::vector<int> output_width;
int result = 0;
for (int i = 0; i < batch_size; i++) {
int tmp_real_h = static_cast<int>((cpu_shape_tensor.data<T>())[2 * i]);
int tmp_real_w =
static_cast<int>((cpu_shape_tensor.data<T>())[2 * i + 1]);
if (tmp_real_h % out_stride[0] == 0) {
tmp_real_h = tmp_real_h / out_stride[0];
} else {
tmp_real_h = tmp_real_h / out_stride[0] + 1;
}
if (tmp_real_w % out_stride[1] == 0) {
tmp_real_w = tmp_real_w / out_stride[1];
} else {
tmp_real_w = tmp_real_w / out_stride[1] + 1;
}
imgreal_h.push_back(tmp_real_h);
imgreal_w.push_back(tmp_real_w);
output_height.push_back(Im2SeqOutputSize(
imgreal_h[i], kernels[0], paddings[0], paddings[2], strides[0]));
output_width.push_back(Im2SeqOutputSize(
imgreal_w[i], kernels[1], paddings[1], paddings[3], strides[1]));
result += output_height[i] * output_width[i];
}
out->mutable_data<T>({result, img_channels * kernels[0] * kernels[1]},
ctx.GetPlace());
const std::vector<int> dilations({1, 1});
int offset_out = 0;
for (int i = 0; i < batch_size; i++) {
const Tensor src =
in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
Tensor dst = out->Slice(offset_out,
offset_out + output_height[i] * output_width[i])
.Resize({output_height[i], output_width[i],
img_channels, kernels[0], kernels[1]});
offset_out += output_height[i] * output_width[i];
math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
f(dev_ctx, src, dilations, strides, paddings, &dst);
}
framework::LoD lod(1);
lod[0].reserve(batch_size + 1);
int offset = 0;
lod[0].push_back(offset);
for (int i = 0; i < batch_size; ++i) {
offset += output_height[i] * output_width[i];
lod[0].push_back(offset);
}
out->set_lod(lod);
} else {
int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
paddings[2], strides[0]);
int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
paddings[3], strides[1]);
out->mutable_data<T>({batch_size * output_height * output_width,
img_channels * kernels[0] * kernels[1]},
ctx.GetPlace());
const std::vector<int> dilations({1, 1});
auto out_dims = out->dims();
out->Resize({batch_size, out->numel() / batch_size});
for (int i = 0; i < batch_size; i++) {
const Tensor src =
in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
Tensor dst =
out->Slice(i, i + 1).Resize({output_height, output_width,
img_channels, kernels[0], kernels[1]});
math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
f(dev_ctx, src, dilations, strides, paddings, &dst);
}
out->Resize(out_dims);
framework::LoD lod(1);
lod[0].reserve(batch_size + 1);
int offset = 0;
lod[0].push_back(offset);
offset += output_height * output_width;
for (int i = 0; i < batch_size; ++i) {
offset += output_height * output_width;
lod[0].push_back(offset);
}
out->set_lod(lod);
}
out->set_lod(lod);
}
};
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 7b7a4afa
......@@ -61,6 +61,8 @@ static void ParallelExecuteBlocks(
framework::Async([&executor, &prepared, &program, &scope, idx]() {
int run_block = idx; // thread local
try {
VLOG(3) << "running server block: " << run_block
<< "pointer: " << prepared[run_block].get();
executor->RunPreparedContext(prepared[run_block].get(), scope);
} catch (const std::exception &e) {
LOG(ERROR) << "run sub program error " << e.what();
......@@ -107,12 +109,14 @@ void ListenAndServOp::RunSyncLoop(
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
std::vector<int> optimize_blocks_idx;
for (auto blk : optimize_blocks) {
optimize_blocks_idx.push_back(blk->ID());
// Prepare all the server block
std::vector<int> optimize_blocks_list;
for (size_t i = 1; i < program->Size(); ++i) {
optimize_blocks_list.push_back(i);
}
auto optimize_prepared = executor->Prepare(*program, optimize_blocks_idx);
// Insert placeholder for block0 which holds current op itself.
auto optimize_prepared = executor->Prepare(*program, optimize_blocks_list);
// Insert placeholder for block0 which holds current op itself,
// NOTE the first block in `optimize_prepared` should never be ran.
optimize_prepared.insert(
optimize_prepared.begin(),
std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 7b7a4afa
......@@ -51,6 +51,7 @@ math_library(sequence_padding)
math_library(sequence_pooling DEPS math_function)
math_library(sequence_scale)
math_library(softmax DEPS math_function)
math_library(matrix_bit_code)
math_library(unpooling)
math_library(vol2col)
......
paddle/fluid/operators/math/blas.h
浏览文件 @ 7b7a4afa
......@@ -21,6 +21,10 @@
#include "paddle/fluid/platform/dynload/mklml.h"
#endif
#ifdef PADDLE_WITH_LIBXSMM
#include <libxsmm.h>
#endif
#ifdef PADDLE_USE_OPENBLAS
#include <cblas.h>
#endif
......
paddle/fluid/operators/math/blas_impl.h
浏览文件 @ 7b7a4afa
......@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <limits>
#include <vector>
#include "paddle/fluid/operators/math/math_function.h"
......@@ -30,6 +31,12 @@ struct CBlas<float> {
platform::dynload::cblas_sgemm(args...);
}
#ifdef PADDLE_WITH_LIBXSMM
template <typename... ARGS>
static void SMM_GEMM(ARGS... args) {
libxsmm_sgemm(args...);
}
#endif
template <typename... ARGS>
static void AXPY(ARGS... args) {
platform::dynload::cblas_saxpy(args...);
......@@ -63,6 +70,12 @@ struct CBlas<double> {
platform::dynload::cblas_dgemm(args...);
}
#ifdef PADDLE_WITH_LIBXSMM
template <typename... ARGS>
static void SMM_GEMM(ARGS... args) {
libxsmm_dgemm(args...);
}
#endif
template <typename... ARGS>
static void AXPY(ARGS... args) {
platform::dynload::cblas_daxpy(args...);
......@@ -140,6 +153,9 @@ struct CBlas<double> {
template <>
struct CBlas<platform::float16> {
static void GEMM(...) { PADDLE_THROW("float16 GEMM not supported on CPU"); }
static void SMM_GEMM(...) {
PADDLE_THROW("float16 SMM_GEMM not supported on CPU");
}
#ifdef PADDLE_WITH_MKLML
static void GEMM_BATCH(...) {
PADDLE_THROW("float16 GEMM_BATCH not supported on CPU");
......@@ -147,6 +163,33 @@ struct CBlas<platform::float16> {
#endif
};
template <typename T>
inline bool UseXSMM(const int &m, const int &n, const int &k, bool transa,
bool transb, const T &alpha, const T &beta) {
#ifdef PADDLE_WITH_LIBXSMM
// Refer to https://github.com/hfp/libxsmm/blob/master/README.md
// But the threshold is custom
constexpr int LIBXSMM_THRESHOLD = 20 * 20 * 20;
if (m * n * k > LIBXSMM_THRESHOLD || transa || transb ||
std::abs<T>(alpha - static_cast<T>(1) >
std::numeric_limits<T>::epsilon()) ||
std::abs<T>(beta) > std::numeric_limits<T>::epsilon()) {
return false;
} else {
return true;
}
#endif
return false;
}
template <>
inline bool UseXSMM<platform::float16>(const int &m, const int &n, const int &k,
bool transa, bool transb,
const platform::float16 &alpha,
const platform::float16 &beta) {
return false;
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
......@@ -156,8 +199,21 @@ void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
#ifdef PADDLE_WITH_LIBXSMM
if (UseXSMM(M, N, K, transA != CblasNoTrans, transB != CblasNoTrans, alpha,
beta)) {
// Note: SMM use ColMajor
const char transa = 'N';
const char transb = 'N';
CBlas<T>::SMM_GEMM(&transa, &transb, &N, &M, &K, &alpha, B, &ldb, A, &lda,
&beta, C, &ldc);
} else {
#endif
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B,
ldb, beta, C, ldc);
#ifdef PADDLE_WITH_LIBXSMM
}
#endif
}
template <>
......
paddle/fluid/operators/math/im2col.cc
浏览文件 @ 7b7a4afa
......@@ -43,21 +43,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int col_height = col->dims()[3];
int col_width = col->dims()[4];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
((dilation[0] * (filter_height - 1) + 1))) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
((dilation[1] * (filter_width - 1) + 1))) /
stride[1] +
1,
col_width,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
......@@ -178,17 +163,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int col_height = col->dims()[0];
int col_width = col->dims()[1];
PADDLE_ENFORCE_EQ(
(im_height + padding[0] + padding[2] - filter_height) / stride[0] + 1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(im_width + padding[1] + padding[3] - filter_width) / stride[1] + 1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
......
paddle/fluid/operators/math/im2col.cu
浏览文件 @ 7b7a4afa
......@@ -77,21 +77,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int col_height = col->dims()[3];
int col_width = col->dims()[4];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int num_outputs = im_channels * col_height * col_width;
int blocks = (num_outputs + 1024 - 1) / 1024;
int block_x = 512;
......@@ -274,21 +259,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int col_height = col->dims()[0];
int col_width = col->dims()[1];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int block_dim_x = 0;
int block_dim_y = 0;
if (filter_height <= 4 && filter_width <= 4) {
......
paddle/fluid/operators/math/math_function_impl.h
浏览文件 @ 7b7a4afa
......@@ -155,7 +155,7 @@ class RowwiseSum<platform::CPUDeviceContext, T> {
PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
auto height = in_dims[0];
auto size = in_dims[1];
PADDLE_ENFORCE_EQ(out->numel(), size);
PADDLE_ENFORCE_EQ(out->numel(), height);
T* out_buf = out->mutable_data<T>(out->place());
const T* in_buf = input.data<T>();
......
paddle/fluid/operators/math/math_function_test.cc
浏览文件 @ 7b7a4afa
......@@ -54,8 +54,64 @@ TEST(math_function, gemm_notrans_cblas) {
EXPECT_EQ(input3_ptr[6], 86);
EXPECT_EQ(input3_ptr[7], 99);
}
#ifdef PADDLE_WITH_LIBXSMM
template <typename T>
void MklSmmCompare(int m, int n, int k) {
paddle::framework::Tensor mat_a;
paddle::framework::Tensor mat_b;
paddle::framework::Tensor mat_c_smm;
paddle::framework::Tensor mat_c_mkl;
auto* cpu_place = new paddle::platform::CPUPlace();
T* A = mat_a.mutable_data<T>({m, k}, *cpu_place);
T* B = mat_b.mutable_data<T>({k, n}, *cpu_place);
T* CSMM = mat_c_smm.mutable_data<T>({m, n}, *cpu_place);
T* CMKL = mat_c_mkl.mutable_data<T>({m, n}, *cpu_place);
T alpha = static_cast<T>(1);
T beta = static_cast<T>(0);
for (int i = 0; i < mat_a.numel(); ++i) {
A[i] = static_cast<T>(i);
}
for (int i = 0; i < mat_b.numel(); ++i) {
B[i] = static_cast<T>(i);
}
// lda,ldb,ldc follow RowMajor
int lda = k;
int ldb = n;
int ldc = n;
auto smm = [&, m, n, k, lda, ldb, ldc, alpha, beta]() {
const char transa = 'N';
const char transb = 'N';
paddle::operators::math::CBlas<T>::SMM_GEMM(&transa, &transb, &n, &m, &k,
&alpha, B, &ldb, A, &lda, &beta,
CSMM, &ldc);
};
auto mkl = [&, m, n, k, lda, ldb, ldc, alpha, beta]() {
paddle::operators::math::CBlas<T>::GEMM(CblasRowMajor, CblasNoTrans,
CblasNoTrans, m, n, k, alpha, A,
lda, B, ldb, beta, CMKL, ldc);
};
smm();
mkl();
ASSERT_EQ(mat_c_mkl.numel(), mat_c_smm.numel());
for (int i = 0; i < mat_c_mkl.numel(); ++i) {
EXPECT_FLOAT_EQ(CSMM[i], CMKL[i]);
}
}
TEST(math_function, gemm_mkl_vs_smm) {
MklSmmCompare<float>(1, 2, 3);
MklSmmCompare<double>(1, 2, 3);
MklSmmCompare<float>(3, 2, 1);
MklSmmCompare<double>(3, 2, 1);
MklSmmCompare<float>(3, 8, 5);
MklSmmCompare<double>(3, 8, 5);
}
#endif
TEST(math_function, gemm_trans_clbas) {
TEST(math_function, gemm_trans_cblas) {
paddle::framework::Tensor input1;
paddle::framework::Tensor input2;
paddle::framework::Tensor input3;
......
paddle/fluid/operators/math/matrix_bit_code.cc 0 → 100644
浏览文件 @ 7b7a4afa
/* 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 "paddle/fluid/operators/math/matrix_bit_code.h"
#include <iostream>
namespace paddle {
namespace operators {
namespace math {
template <typename T>
void MatrixBitCodeFunctor<T>::Add(framework::Tensor* tmat,
const framework::Tensor& vec) {
SimpleCodeTable code_table(num_classes_);
size_t batch_size = tmat->dims()[0];
size_t width = tmat->dims()[1];
for (size_t i = 0; i < batch_size; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
tmat->data<T>()[i * width + j] += vec.data<T>()[index];
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
framework::Tensor* vec) {
SimpleCodeTable code_table(num_classes_);
size_t batch_size = tmat.dims()[0];
size_t width = tmat.dims()[1];
for (size_t i = 0; i < batch_size; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
vec->data<T>()[index] += tmat.data<T>()[i * width + j];
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::Sum(const framework::Tensor& tmat,
framework::Tensor* sum, T scale_sum) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat.dims()[0];
size_t o_width = tmat.dims()[1];
for (size_t i = 0; i < num_samples; ++i) {
T sm = static_cast<T>(0.0);
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
if (code.calc_bit(j)) {
// calc_bit starts from right most bit, while data in tmat[i] is in the
// reverse order.
sm += tmat.data<T>()[i * o_width + j];
}
}
sum->data<T>()[i] = scale_sum * sm;
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
const framework::Tensor& weight,
const framework::Tensor& input) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat->dims()[0];
size_t tmat_width = tmat->dims()[1];
size_t input_width = input.dims()[1];
size_t weight_width = weight.dims()[1];
auto tmat_value = tmat->data<T>();
auto weight_value = weight.data<T>();
auto input_value = input.data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
T sum = static_cast<T>(0.0);
for (size_t k = 0; k < input_width; ++k) {
sum += weight_value[weight_width * index + k] *
input_value[input_width * i + k];
}
tmat_value[i * tmat_width + j] += sum;
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
framework::Tensor* weight,
const framework::Tensor& input) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat.dims()[0];
size_t input_width = input.dims()[1];
size_t tmat_width = tmat.dims()[1];
size_t weight_width = weight->dims()[1];
auto tmat_value = tmat.data<T>();
auto weight_value = weight->data<T>();
auto input_value = input.data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
weight_value[weight_width * index + k] +=
tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
}
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
const framework::Tensor& weight,
framework::Tensor* input) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat.dims()[0];
size_t tmat_width = tmat.dims()[1];
size_t input_width = input->dims()[1];
size_t weight_width = weight.dims()[1];
auto tmat_value = tmat.data<T>();
auto weight_value = weight.data<T>();
auto input_value = input->data<T>();
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
size_t index = code.calc_index(j);
for (size_t k = 0; k < input_width; ++k) {
input_value[input_width * i + k] +=
tmat_value[i * tmat_width + j] *
weight_value[weight_width * index + k];
}
}
}
}
template <typename T>
void MatrixBitCodeFunctor<T>::Sub(framework::Tensor* tmat) {
SimpleCodeTable code_table(num_classes_);
size_t num_samples = tmat->dims()[0];
size_t o_width = tmat->dims()[1];
for (size_t i = 0; i < num_samples; ++i) {
auto code = code_table(static_cast<size_t>(ids_[i]));
int code_length = code.get_length();
for (int j = 0; j < code_length; ++j) {
if (code.calc_bit(j)) {
tmat->data<T>()[i * o_width + j] -= 1;
}
}
}
}
template class MatrixBitCodeFunctor<float>;
template class MatrixBitCodeFunctor<double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/math/matrix_bit_code.h 0 → 100644
浏览文件 @ 7b7a4afa
/* 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 "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
namespace paddle {
namespace operators {
namespace math {
/**
* SimpleCodeTable class should support 3 functions:
*
* size_t size()
* return the number of ids
*
* int get_max_code_length()
* return the maximal code length
*
* SimpleCode operator()(size_t i)
* return the i-th code. Code class is descriebed below.
*
* SimpleCode class should support 3 functions:
*
* int get_length()
* return the length of the code
*
* size_t cal_index(int bit)
* bit ranges from 0 to get_length() - 1
* return the index for the (1+bit) level parent
*
* bool calc_bit(int bit)
* return true if the bit level parent is the right child of (1+bit) level
* parent
*
*/
/**
* return the 1-based index of the highest bit set
*
* for x > 0:
* \f[
* FindLastSet(x) = 1 + \floor*{\log_{2}x}
* \f]
*/
inline constexpr size_t FindLastSet(size_t x) {
return std::is_same<size_t, unsigned int>::value
? (x ? 8 * sizeof(x) - __builtin_clz(x) : 0)
: (std::is_same<size_t, unsigned long>::value // NOLINT
? (x ? 8 * sizeof(x) - __builtin_clzl(x) : 0)
: (x ? 8 * sizeof(x) - __builtin_clzll(x) : 0));
}
struct SimpleCode {
SimpleCode(size_t code, size_t num_classes) : c_(code + num_classes) {}
/**
* Here the id of root shoud be 1 rather than 0, thus the encoding of class c
* is `c + num_classes` and all siblings can get the same weight indice using
* prefixes.
* Weight index is the prefixes of encoding, thus leave out the right most
* bit in calc_index.
* Binary classification path is the suffixes of encoding, thus leave out the
* left most bit in calc_bit.
*/
inline size_t calc_index(int bit) const { return (c_ >> (bit + 1)) - 1; }
inline bool calc_bit(int bit) const { return c_ & (1 << bit); }
inline int get_length() const { return FindLastSet(c_) - 1; }
private:
size_t c_;
};
struct SimpleCodeTable {
explicit SimpleCodeTable(size_t num_classes) : num_classes_(num_classes) {}
SimpleCode operator()(size_t code) const {
return SimpleCode(code, num_classes_);
}
size_t size() const { return num_classes_; }
int get_max_code_length() const { return FindLastSet(num_classes_ - 1); }
private:
size_t num_classes_;
};
template <typename T>
class MatrixBitCodeFunctor {
public:
explicit MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
: num_classes_(num_classes), ids_(ids) {}
/* For j < code_length
tmat(i, j) += vec(0, index(i, j))
*/
void Add(framework::Tensor* tmat, const framework::Tensor& vec);
/* For j < code_length
vec(0, index(i, j)) += tmat(i, j)
*/
void AddGrad(const framework::Tensor& tmat, framework::Tensor* vec);
/* For j < code_length
sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
*/
void Sum(const framework::Tensor& tmat, framework::Tensor* sum, T scale_sum);
/* For j < code_length
tmat(i, j) -= bit(i, j)
*/
void Sub(framework::Tensor* tmat);
/* For j < code_length
input.row(i) += tmat(i, j) * weight.row(index(i, j))
*/
void Mul(framework::Tensor* tmat, const framework::Tensor& weight,
const framework::Tensor& input);
/* For index(i, j) >= 0:
weight.row(index(i, j)) += tmat(i, j) * input.row(i)
*/
void MulGradWeight(const framework::Tensor& tmat, framework::Tensor* weight,
const framework::Tensor& input);
/* For j < code_length
input.row(i) += tmat(i, j) * weight.row(index(i, j))
*/
void MulGradError(const framework::Tensor& tmat,
const framework::Tensor& weight, framework::Tensor* input);
size_t num_classes_;
const int64_t* ids_;
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/fluid/operators/prefetch_op.cc
浏览文件 @ 7b7a4afa
......@@ -53,7 +53,7 @@ class PrefetchOp : public framework::OperatorBase {
VLOG(3) << "don't send no-initialied variable: " << ins[i];
}
}
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
};
......
paddle/fluid/operators/reader/blocking_queue.h
浏览文件 @ 7b7a4afa
......@@ -81,6 +81,15 @@ class BlockingQueue {
}
}
void ReOpen() {
std::lock_guard<std::mutex> lock(mutex_);
closed_ = false;
std::deque<T> new_deque;
queue_.swap(new_deque);
send_cv_.notify_all();
receive_cv_.notify_all();
}
void Close() {
std::lock_guard<std::mutex> lock(mutex_);
closed_ = true;
......
paddle/fluid/operators/reader/create_batch_reader_op.cc
浏览文件 @ 7b7a4afa
......@@ -23,7 +23,7 @@ class BatchReader : public framework::DecoratedReader {
BatchReader(const std::shared_ptr<ReaderBase>& reader, int batch_size,
bool discard_leftover)
: DecoratedReader(reader),
batch_size_(batch_size),
batch_size_(static_cast<size_t>(batch_size)),
discard_leftover_(discard_leftover) {
buffer_.reserve(batch_size_);
}
......@@ -31,7 +31,7 @@ class BatchReader : public framework::DecoratedReader {
void ReadNextImpl(std::vector<framework::LoDTensor>* out) override;
private:
int batch_size_;
size_t batch_size_;
bool discard_leftover_;
std::vector<std::vector<framework::LoDTensor>> buffer_;
};
......@@ -78,7 +78,7 @@ class CreateBatchReaderOpMaker : public DecoratedReaderMakerBase {
void BatchReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
buffer_.clear();
buffer_.reserve(batch_size_);
for (int i = 0; i < batch_size_; ++i) {
for (size_t i = 0; i < batch_size_; ++i) {
buffer_.push_back(std::vector<framework::LoDTensor>());
reader_->ReadNext(&buffer_.back());
if (buffer_.back().empty()) {
......@@ -95,9 +95,9 @@ void BatchReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
// if buffer_ is empty, the 'out' will return as an empty vector.
return;
}
int out_num = buffer_[0].size();
size_t out_num = buffer_[0].size();
out->reserve(out_num);
for (int j = 0; j < out_num; ++j) {
for (size_t j = 0; j < out_num; ++j) {
// Merge shape and check date type
std::type_index batch_type = buffer_[0][j].type();
framework::DDim batch_shape = buffer_[0][j].dims();
......
paddle/fluid/operators/reader/create_py_reader_op.cc
浏览文件 @ 7b7a4afa
......@@ -27,19 +27,17 @@ class PyReader : public framework::FileReader {
queue_ = queue;
}
void ReadNextImpl(std::vector<framework::LoDTensor>* out) override {
void ReadNext(std::vector<framework::LoDTensor>* out) override {
bool success;
*out = queue_->Pop(&success);
if (!success) out->clear();
}
private:
void ShutdownImpl() override { /* TODO */
}
void Shutdown() override { queue_->Close(); }
void StartImpl() override { /* TODO */
}
void Start() override { queue_->ReOpen(); }
private:
std::shared_ptr<LoDTensorBlockingQueue> queue_;
};
......
paddle/fluid/operators/reader/lod_tensor_blocking_queue.h
浏览文件 @ 7b7a4afa
......@@ -58,12 +58,15 @@ class LoDTensorBlockingQueue {
inline size_t Size() const { return queue_.Size(); }
inline void Close() { return queue_.Close(); }
inline void ReOpen() { queue_.ReOpen(); }
inline void Close() { queue_.Close(); }
inline bool IsClosed() const { return queue_.IsClosed(); }
private:
void CheckDims(const std::vector<framework::LoDTensor>& lod_tensor_vec) {
void CheckDims(
const std::vector<framework::LoDTensor>& lod_tensor_vec) const {
PADDLE_ENFORCE(dims_.size() == lod_tensor_vec.size(),
"Expect input size is %d but found %s", dims_.size(),
lod_tensor_vec.size());
......
paddle/fluid/operators/recv_op.cc
浏览文件 @ 7b7a4afa
......@@ -51,7 +51,7 @@ class RecvOp : public framework::OperatorBase {
rpc_client->AsyncGetVar(epmap[i], ctx, scope, outs[i]);
}
if (sync_mode) {
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
}
};
......
paddle/fluid/operators/send_barrier_op.cc
浏览文件 @ 7b7a4afa
......@@ -50,13 +50,13 @@ class SendBarrierOp : public framework::OperatorBase {
VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
// need to wait before sending send_barrier message
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
if (sync_mode) {
for (auto& ep : eps) {
VLOG(3) << "send barrier, ep: " << ep;
rpc_client->AsyncSendBatchBarrier(ep);
}
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
}
};
......
paddle/fluid/operators/send_op.cc
浏览文件 @ 7b7a4afa
......@@ -59,7 +59,7 @@ class SendOp : public framework::OperatorBase {
}
}
if (sync_send) {
rpc_client->Wait();
PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
}
}
};
......
paddle/fluid/operators/squeeze_op.cc 0 → 100644
浏览文件 @ 7b7a4afa
此差异已折叠。
paddle/fluid/operators/sum_mkldnn_op.cc
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......@@ -88,7 +88,7 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
input_format = memory::format::nc;
}
for (int i = in_place ? 1 : 0; i < N; i++) {
for (int i = 0; i < N; i++) {
PADDLE_ENFORCE(in_vars[i]->IsType<LoDTensor>(),
"all inputs must be all LoDTensors");
auto& input = in_vars[i]->Get<LoDTensor>();
......
paddle/fluid/operators/top_k_op.h
浏览文件 @ 7b7a4afa
......@@ -60,6 +60,7 @@ class TopkKernel : public framework::OpKernel<T> {
#endif
for (size_t i = 0; i < row; i++) {
std::vector<std::pair<T, size_t>> vec;
vec.reserve(col);
for (size_t j = 0; j < col; j++) {
vec.push_back(std::pair<T, size_t>(eg_input(i, j), j));
}
......
paddle/fluid/operators/unsqueeze_op.cc 0 → 100644
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paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 7b7a4afa
......@@ -46,7 +46,7 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library(device_context SRCS device_context.cc init.cc DEPS malloc
place eigen3 stringpiece cpu_helper ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
place eigen3 stringpiece cpu_helper framework_proto ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_info)
cc_test(init_test SRCS init_test.cc DEPS device_context)
......
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @ 7b7a4afa
......@@ -222,15 +222,16 @@ class MKLDNNHandler {
static std::string GetHash(mkldnn::memory::dims& operand_dims, // NOLINT
const std::string& suffix) {
auto dims2str = [](const mkldnn::memory::dims& operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
dstr += std::to_string(operand_dims[i]) + "-";
}
return dstr;
};
return dims2str(operand_dims) + suffix;
};
protected:
static std::string dims2str(const mkldnn::memory::dims& operand_dims) {
std::string dstr = "";
for (size_t i = 0; i < operand_dims.size(); ++i) {
dstr += std::to_string(operand_dims[i]) + "-";
}
return dstr;
}
protected:
......
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 7b7a4afa
......@@ -145,14 +145,14 @@ void BindBlockDesc(pybind11::module *m) {
.def_property_readonly("id", &pd::BlockDesc::ID)
.def_property_readonly("parent", &pd::BlockDesc::Parent)
.def("get_forward_block_idx", &pd::BlockDesc::ForwardBlockID)
.def("set_forward_block_idx", &pd::BlockDesc::SetForwardBlockID)
.def("_set_forward_block_idx", &pd::BlockDesc::SetForwardBlockID)
.def("append_op", &pd::BlockDesc::AppendOp,
pybind11::return_value_policy::reference)
.def("prepend_op", &pd::BlockDesc::PrependOp,
.def("_prepend_op", &pd::BlockDesc::PrependOp,
pybind11::return_value_policy::reference)
.def("insert_op", &pd::BlockDesc::InsertOp,
.def("_insert_op", &pd::BlockDesc::InsertOp,
pybind11::return_value_policy::reference)
.def("remove_op", &pd::BlockDesc::RemoveOp)
.def("_remove_op", &pd::BlockDesc::RemoveOp)
.def("var",
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
......@@ -165,7 +165,7 @@ void BindBlockDesc(pybind11::module *m) {
return self.HasVar(name);
},
pybind11::return_value_policy::reference)
.def("rename_var",
.def("_rename_var",
[](pd::BlockDesc &self, const pybind11::bytes &byte_name,
const pybind11::bytes &byte_name_new) {
std::string name = byte_name;
......@@ -189,7 +189,7 @@ void BindBlockDesc(pybind11::module *m) {
return self.FindVarRecursive(name);
},
pybind11::return_value_policy::reference)
.def("remove_var",
.def("_remove_var",
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.RemoveVar(name);
......
paddle/fluid/pybind/pybind.cc
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paddle/legacy/capi/Arguments.cpp
浏览文件 @ 7b7a4afa
......@@ -66,6 +66,17 @@ paddle_error paddle_arguments_get_value(paddle_arguments args,
return kPD_NO_ERROR;
}
PD_API paddle_error paddle_arguments_get_prob(paddle_arguments args,
uint64_t ID,
paddle_matrix mat) {
if (args == nullptr || mat == nullptr) return kPD_NULLPTR;
auto m = paddle::capi::cast<paddle::capi::CMatrix>(mat);
auto a = castArg(args);
if (ID >= a->args.size()) return kPD_OUT_OF_RANGE;
m->mat = a->args[ID].in;
return kPD_NO_ERROR;
}
paddle_error paddle_arguments_get_ids(paddle_arguments args,
uint64_t ID,
paddle_ivector ids) {
......
paddle/legacy/capi/arguments.h
浏览文件 @ 7b7a4afa
此差异已折叠。
paddle/legacy/utils/PythonUtil.cpp
浏览文件 @ 7b7a4afa
此差异已折叠。
paddle/legacy/utils/PythonUtil.h
浏览文件 @ 7b7a4afa
此差异已折叠。
paddle/scripts/paddle_build.sh
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此差异已折叠。
python/CMakeLists.txt
浏览文件 @ 7b7a4afa
此差异已折叠。
python/paddle/__init__.py
浏览文件 @ 7b7a4afa
此差异已折叠。
python/paddle/dataset/__init__.py
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此差异已折叠。
python/paddle/fluid/__init__.py
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此差异已折叠。
python/paddle/fluid/backward.py
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此差异已折叠。
python/paddle/fluid/clip.py
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此差异已折叠。
python/paddle/fluid/concurrency.py
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此差异已折叠。
python/paddle/fluid/executor.py
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此差异已折叠。
python/paddle/fluid/framework.py
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python/paddle/fluid/initializer.py
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python/paddle/fluid/io.py
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python/paddle/fluid/layer_helper.py
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python/paddle/fluid/layers/io.py
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python/paddle/fluid/layers/nn.py
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python/paddle/fluid/optimizer.py
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python/paddle/fluid/param_attr.py
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python/paddle/fluid/trainer.py
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python/paddle/reader/__init__.py
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python/paddle/reader/decorator.py
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python/paddle/v2/image.py
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python/requirements.txt
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python/setup.py.in
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