未验证 提交 be26b71b 编写于 作者: Q Qiao Longfei 提交者: GitHub

Add cpp trainer lib and demo (#10681)

add cpp trainer lib and demo
上级 f6543a11
cmake_minimum_required(VERSION 3.0)
project(cpp_train_demo CXX C)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
if(NOT DEFINED PADDLE_LIB)
message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/paddle/lib/dir")
endif()
option(WITH_MKLDNN "Compile PaddlePaddle with MKLDNN" OFF)
option(WITH_MKL "Compile PaddlePaddle with MKL support, default use openblas." OFF)
include_directories("${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_trainer demo_trainer.cc)
if(WITH_MKLDNN)
include_directories("${PADDLE_LIB}/third_party/install/mkldnn/include")
set(MKLDNN_LIB ${PADDLE_LIB}/third_party/install/mkldnn/lib/libmkldnn.so.0)
endif()
if(WITH_MKL)
include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel.so)
else()
if(APPLE)
set(MATH_LIB cblas)
else(APPLE)
set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.a)
endif(APPLE)
endif()
if(APPLE)
set(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load -framework CoreFoundation -framework Security")
else(APPLE)
set(ARCHIVE_START "-Wl,--whole-archive")
set(ARCHIVE_END "-Wl,--no-whole-archive")
set(EXTERNAL_LIB "-lrt -ldl -lpthread")
endif(APPLE)
target_link_libraries(demo_trainer
${MACOS_LD_FLAGS}
${ARCHIVE_START}
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.a
${ARCHIVE_END}
${MATH_LIB}
${MKLDNN_LIB}
glog gflags protobuf snappystream snappy z
${EXTERNAL_LIB})
### step 1. build paddle lib
```
# WITH_MKL=ON|OFF
# WITH_MKLDNN=ON|OFF
PADDLE_LIB=/paddle/lib/dir
cmake .. -DCMAKE_INSTALL_PREFIX=$PADDLE_LIB \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_FLUID_ONLY=ON \
-DWITH_GPU=OFF \
-DWITH_STYLE_CHECK=OFF \
-DWITH_MKL=OFF \
-DWITH_MKLDNN=OFF
make -j8
make -j8 inference_lib_dist
```
### step 2. generate program desc
```
# please install paddle before run this scripe
pip install --upgrade paddlepaddle-*.whl
python demo_network.py
```
This will generate two program desc files:
- startup_program: used to init all parameters
- main_program: main logic of the network
### step 3. build demo_trainer and run it.
```
# Make a build dir at the same dir of this README.md document.
# The demo dir can be put anywhere.
mkdir build
cd build
# WITH_MKL=ON|OFF
# WITH_MKLDNN=ON|OFF
PADDLE_LIB=/paddle/lib/dir
# PADDLE_LIB is the same with CMAKE_INSTALL_PREFIX when building the lib
cmake .. -DPADDLE_LIB=$PADDLE_LIB \
-DWITH_MKLDNN=OFF \
-DWITH_MKL=OFF
make
# copy startup_program and main_program to this dir
cp ../startup_program .
cp ../main_program .
# run demo cpp trainer
./demo_trainer
```
The output will be:
```
step: 0 loss: 1069.02
step: 1 loss: 1069.02
step: 2 loss: 1069.02
....
```
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid as fluid
import paddle.fluid.framework as framework
def train_network(with_optimize):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
if with_optimize:
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.00001)
sgd_optimizer.minimize(avg_cost)
else:
fluid.backward.append_backward(avg_cost)
def save_program_desc(network_func):
startup_program = framework.Program()
train_program = framework.Program()
with framework.program_guard(train_program, startup_program):
network_func(with_optimize=False)
with open("startup_program", "w") as f:
f.write(startup_program.desc.serialize_to_string())
with open("main_program", "w") as f:
f.write(train_program.desc.serialize_to_string())
save_program_desc(train_network)
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <fstream>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/init.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace train {
void ReadBinaryFile(const std::string& filename, std::string* contents) {
std::ifstream fin(filename, std::ios::in | std::ios::binary);
PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
fin.seekg(0, std::ios::end);
contents->clear();
contents->resize(fin.tellg());
fin.seekg(0, std::ios::beg);
fin.read(&(contents->at(0)), contents->size());
fin.close();
}
std::unique_ptr<paddle::framework::ProgramDesc> Load(
paddle::framework::Executor* executor, const std::string& model_filename) {
VLOG(3) << "loading model from " << model_filename;
std::string program_desc_str;
ReadBinaryFile(model_filename, &program_desc_str);
std::unique_ptr<paddle::framework::ProgramDesc> main_program(
new paddle::framework::ProgramDesc(program_desc_str));
return main_program;
}
} // namespace train
} // namespace paddle
int main() {
paddle::framework::InitDevices(false);
const auto cpu_place = paddle::platform::CPUPlace();
paddle::framework::Executor executor(cpu_place);
paddle::framework::Scope scope;
auto startup_program = paddle::train::Load(&executor, "startup_program");
auto train_program = paddle::train::Load(&executor, "main_program");
std::string loss_name = "";
for (auto op_desc : train_program->Block(0).AllOps()) {
if (op_desc->Type() == "mean") {
loss_name = op_desc->Output("Out")[0];
break;
}
}
PADDLE_ENFORCE_NE(loss_name, "", "loss not found");
// init all parameters
executor.Run(*startup_program.get(), &scope, 0);
// prepare data
auto x_var = scope.Var("x");
auto x_tensor = x_var->GetMutable<paddle::framework::LoDTensor>();
x_tensor->Resize({2, 13});
auto x_data = x_tensor->mutable_data<float>(cpu_place);
for (int i = 0; i < 2 * 13; ++i) {
x_data[i] = static_cast<float>(i);
}
auto y_var = scope.Var("y");
auto y_tensor = y_var->GetMutable<paddle::framework::LoDTensor>();
y_tensor->Resize({2, 1});
auto y_data = y_tensor->mutable_data<float>(cpu_place);
for (int i = 0; i < 2 * 1; ++i) {
y_data[i] = static_cast<float>(i);
}
auto loss_var = scope.Var(loss_name);
for (int i = 0; i < 10; ++i) {
executor.Run(*train_program.get(), &scope, 0, false, true);
std::cout << "step: " << i << " loss: "
<< loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]
<< std::endl;
}
return 0;
}
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