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Paddle-Lite
未验证 提交 76e74ef1 编写于 作者: S sangoly 提交者： GitHub
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add python api (#2225) 

* [python api] init add python api test=develop
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CMakeLists.txt
浏览文件 @ 76e74ef1
......@@ -47,29 +47,14 @@ include(simd)
################################ Exposed Configurations #######################################
lite_option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
lite_option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ON IF ${AVX_FOUND})
lite_option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
lite_option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
lite_option(WITH_MKL "Compile PaddlePaddle with MKL support." ON IF ${AVX_FOUND})
lite_option(WITH_ARM_DOTPROD "Compile PaddlePaddle with ARM dot production" ON)
lite_option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
# TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
if(ANDROID OR IOS OR ARMLINUX)
set(WITH_GPU OFF CACHE STRING
"Disable GPU when cross-compiling for Android and iOS" FORCE)
set(WITH_DSO OFF CACHE STRING
"Disable DSO when cross-compiling for Android and iOS" FORCE)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when cross-compiling for Android and iOS" FORCE)
set(WITH_PYTHON OFF CACHE STRING
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
endif()
# for lite, both server and mobile framework.
lite_option(LITE_WITH_JAVA "Enable Java JNI lib in lite mode" OFF)
lite_option(LITE_WITH_PYTHON "Enable Python api lib in lite mode" OFF)
lite_option(LITE_WITH_CUDA "Enable CUDA in lite mode" OFF)
lite_option(LITE_WITH_X86 "Enable X86 in lite mode" ON)
lite_option(LITE_WITH_ARM "Enable ARM in lite mode" OFF)
......@@ -86,6 +71,22 @@ lite_option(LITE_ON_MODEL_OPTIMIZE_TOOL "Build the model optimize tool" OFF)
# publish options
lite_option(LITE_BUILD_EXTRA "Enable extra algorithm support in Lite, both kernels and operators" OFF)
# TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
if(ANDROID OR IOS OR ARMLINUX)
set(WITH_GPU OFF CACHE STRING
"Disable GPU when cross-compiling for Android and iOS" FORCE)
set(WITH_DSO OFF CACHE STRING
"Disable DSO when cross-compiling for Android and iOS" FORCE)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when cross-compiling for Android and iOS" FORCE)
set(LITE_WITH_PYTHON OFF CACHE STRING
"Disable PYTHON when cross-compiling for Android and iOS" FORCE)
set(WITH_RDMA OFF CACHE STRING
"Disable RDMA when cross-compiling for Android and iOS" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when cross-compiling for Android and iOS" FORCE)
endif()
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
......@@ -110,6 +111,12 @@ include_directories("${PADDLE_SOURCE_DIR}")
set(LITE_GENERATED_INCLUDE_DIR "${CMAKE_BINARY_DIR}")
include_directories("${LITE_GENERATED_INCLUDE_DIR}")
if (LITE_WITH_PYTHON)
include(external/python) # download, build, install python
include(external/pybind11) # download, build, install pybind11
endif()
# for mobile
if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
message(STATUS "Building the mobile framework")
......
cmake/cuda.cmake
浏览文件 @ 76e74ef1
......@@ -5,7 +5,7 @@ endif()
set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs7 "30 35 50 52")
set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
set(paddle_known_gpu_archs9 "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs9 "30 35 50 52 60 61 62 70")
set(paddle_known_gpu_archs10 "30 35 50 52 60 61 62 70 75")
######################################################################################
......
cmake/external/pybind11.cmake 0 → 100644
浏览文件 @ 76e74ef1
# 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.
if(NOT LITE_WITH_PYTHON)
return()
endif()
include(ExternalProject)
set(PYBIND_SOURCE_DIR ${THIRD_PARTY_PATH}/pybind)
include_directories(${PYBIND_SOURCE_DIR}/src/extern_pybind/include)
ExternalProject_Add(
extern_pybind
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/pybind/pybind11.git"
GIT_TAG "v2.2.4"
PREFIX ${PYBIND_SOURCE_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
TEST_COMMAND ""
)
if(${CMAKE_VERSION} VERSION_LESS "3.3.0")
set(dummyfile ${CMAKE_CURRENT_BINARY_DIR}/pybind_dummy.c)
file(WRITE ${dummyfile} "const char * dummy_pybind = \"${dummyfile}\";")
add_library(pybind STATIC ${dummyfile})
else()
add_library(pybind INTERFACE)
endif()
add_dependencies(pybind extern_pybind)
cmake/external/python.cmake 0 → 100644
浏览文件 @ 76e74ef1
# 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.
IF(NOT LITE_WITH_PYTHON)
return()
ENDIF()
INCLUDE(python_module)
FIND_PACKAGE(PythonInterp ${PY_VERSION} REQUIRED)
FIND_PACKAGE(PythonLibs ${PY_VERSION} REQUIRED)
if(WIN32)
execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
"from distutils import sysconfig as s;import sys;import struct;
print(sys.prefix);
print(s.get_config_var('LDVERSION') or s.get_config_var('VERSION'));
"
RESULT_VARIABLE _PYTHON_SUCCESS
OUTPUT_VARIABLE _PYTHON_VALUES
ERROR_VARIABLE _PYTHON_ERROR_VALUE)
if(NOT _PYTHON_SUCCESS MATCHES 0)
set(PYTHONLIBS_FOUND FALSE)
return()
endif()
# Convert the process output into a list
string(REGEX REPLACE ";" "\\\\;" _PYTHON_VALUES ${_PYTHON_VALUES})
string(REGEX REPLACE "\n" ";" _PYTHON_VALUES ${_PYTHON_VALUES})
list(GET _PYTHON_VALUES 0 PYTHON_PREFIX)
list(GET _PYTHON_VALUES 1 PYTHON_LIBRARY_SUFFIX)
# Make sure all directory separators are '/'
string(REGEX REPLACE "\\\\" "/" PYTHON_PREFIX ${PYTHON_PREFIX})
set(PYTHON_LIBRARY
"${PYTHON_PREFIX}/libs/Python${PYTHON_LIBRARY_SUFFIX}.lib")
# when run in a venv, PYTHON_PREFIX points to it. But the libraries remain in the
# original python installation. They may be found relative to PYTHON_INCLUDE_DIR.
if(NOT EXISTS "${PYTHON_LIBRARY}")
get_filename_component(_PYTHON_ROOT ${PYTHON_INCLUDE_DIR} DIRECTORY)
set(PYTHON_LIBRARY
"${_PYTHON_ROOT}/libs/Python${PYTHON_LIBRARY_SUFFIX}.lib")
endif()
# raise an error if the python libs are still not found.
if(NOT EXISTS "${PYTHON_LIBRARY}")
message(FATAL_ERROR "Python libraries not found")
endif()
SET(PYTHON_LIBRARIES "${PYTHON_LIBRARY}")
endif(WIN32)
# 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 ${PYTHON_LIBRARIES})
SET(py_env "")
IF(PYTHONINTERP_FOUND)
find_python_module(pip REQUIRED)
find_python_module(numpy REQUIRED)
#find_python_module(wheel REQUIRED)
#find_python_module(google.protobuf REQUIRED)
FIND_PACKAGE(NumPy REQUIRED)
#IF(${PY_GOOGLE.PROTOBUF_VERSION} AND ${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0")
# MESSAGE(FATAL_ERROR "Found Python Protobuf ${PY_GOOGLE.PROTOBUF_VERSION} < 3.0.0, "
# "please use pip to upgrade protobuf. pip install -U protobuf")
#ENDIF()
ENDIF(PYTHONINTERP_FOUND)
INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR})
cmake/python_module.cmake 0 → 100644
浏览文件 @ 76e74ef1
# Find if a Python module is installed
# Found at http://www.cmake.org/pipermail/cmake/2011-January/041666.html
# To use do: find_python_module(PyQt4 REQUIRED)
function(find_python_module module)
string(TOUPPER ${module} module_upper)
if(NOT PY_${module_upper})
if(ARGC GREATER 1 AND ARGV1 STREQUAL "REQUIRED")
set(${module}_FIND_REQUIRED TRUE)
else()
set(${module}_FIND_REQUIRED FALSE)
endif()
# A module's location is usually a directory, but for binary modules
# it's a .so file.
execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
"import re, ${module}; print(re.compile('/__init__.py.*').sub('',${module}.__file__))"
RESULT_VARIABLE _${module}_status
OUTPUT_VARIABLE _${module}_location
ERROR_QUIET
OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT _${module}_status)
set(PY_${module_upper} ${_${module}_location} CACHE STRING
"Location of Python module ${module}")
endif(NOT _${module}_status)
endif(NOT PY_${module_upper})
find_package_handle_standard_args(PY_${module} DEFAULT_MSG PY_${module_upper})
if(NOT PY_${module_upper}_FOUND AND ${module}_FIND_REQUIRED)
message(FATAL_ERROR "python module ${module} is not found")
endif()
execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
"import sys, ${module}; sys.stdout.write(${module}.__version__)"
OUTPUT_VARIABLE _${module}_version
RESULT_VARIABLE _${module}_status
ERROR_QUIET
OUTPUT_STRIP_TRAILING_WHITESPACE)
if(NOT _${module}_status)
set(PY_${module_upper}_VERSION ${_${module}_version} CACHE STRING
"Version of Python module ${module}")
endif(NOT _${module}_status)
set(PY_${module_upper}_FOUND ${PY_${module_upper}_FOUND} PARENT_SCOPE)
set(PY_${module_upper}_VERSION ${PY_${module_upper}_VERSION} PARENT_SCOPE)
endfunction(find_python_module)
lite/api/CMakeLists.txt
浏览文件 @ 76e74ef1
......@@ -222,6 +222,10 @@ if (LITE_WITH_JAVA AND LITE_WITH_ARM)
add_subdirectory(android)
endif()
if (LITE_WITH_PYTHON)
add_subdirectory(python)
endif()
if (LITE_ON_TINY_PUBLISH)
return()
endif()
......
lite/api/cxx_api.cc
浏览文件 @ 76e74ef1
......@@ -53,13 +53,9 @@ lite::Tensor *Predictor::GetInput(size_t offset) {
}
// get inputs names
const std::vector<std::string> &Predictor::GetInputNames() {
return input_names_;
}
std::vector<std::string> Predictor::GetInputNames() { return input_names_; }
// get outputnames
const std::vector<std::string> &Predictor::GetOutputNames() {
return output_names_;
}
std::vector<std::string> Predictor::GetOutputNames() { return output_names_; }
// append the names of inputs and outputs into input_names_ and output_names_
void Predictor::PrepareFeedFetch() {
auto current_block = program_desc_.GetBlock<cpp::BlockDesc>(0);
......
lite/api/cxx_api.h
浏览文件 @ 76e74ef1
......@@ -74,8 +74,8 @@ class LITE_API Predictor {
// get input by name.
lite::Tensor* GetInputByName(const std::string& name);
// get inputnames and get outputnames.
const std::vector<std::string>& GetInputNames();
const std::vector<std::string>& GetOutputNames();
std::vector<std::string> GetInputNames();
std::vector<std::string> GetOutputNames();
void PrepareFeedFetch();
// Get offset-th col of fetch results.
......@@ -111,6 +111,40 @@ class LITE_API Predictor {
std::vector<std::string> output_names_;
};
class CxxPaddleApiImpl : public lite_api::PaddlePredictor {
public:
CxxPaddleApiImpl() {}
/// Create a new predictor from a config.
void Init(const lite_api::CxxConfig& config);
std::unique_ptr<lite_api::Tensor> GetInput(int i) override;
std::unique_ptr<const lite_api::Tensor> GetOutput(int i) const override;
void Run() override;
std::string GetVersion() const override;
// get inputs names and get outputs names
std::vector<std::string> GetInputNames() override;
std::vector<std::string> GetOutputNames() override;
std::unique_ptr<const lite_api::Tensor> GetTensor(
const std::string& name) const override;
// Get InputTebsor by name
std::unique_ptr<lite_api::Tensor> GetInputByName(
const std::string& name) override;
void SaveOptimizedModel(const std::string& model_dir,
lite_api::LiteModelType model_type =
lite_api::LiteModelType::kProtobuf) override;
private:
Predictor raw_predictor_;
};
/*
* An executor for training.
*
......
lite/api/cxx_api_impl.cc
浏览文件 @ 76e74ef1
......@@ -21,42 +21,6 @@
namespace paddle {
namespace lite {
class CxxPaddleApiImpl : public lite_api::PaddlePredictor {
public:
CxxPaddleApiImpl();
/// Create a new predictor from a config.
void Init(const lite_api::CxxConfig &config);
std::unique_ptr<lite_api::Tensor> GetInput(int i) override;
std::unique_ptr<const lite_api::Tensor> GetOutput(int i) const override;
void Run() override;
std::string GetVersion() const override;
// get inputs names and get outputs names
const std::vector<std::string> &GetInputNames() override;
const std::vector<std::string> &GetOutputNames() override;
std::unique_ptr<const lite_api::Tensor> GetTensor(
const std::string &name) const override;
// Get InputTebsor by name
std::unique_ptr<lite_api::Tensor> GetInputByName(
const std::string &name) override;
void SaveOptimizedModel(const std::string &model_dir,
lite_api::LiteModelType model_type =
lite_api::LiteModelType::kProtobuf) override;
private:
Predictor raw_predictor_;
};
CxxPaddleApiImpl::CxxPaddleApiImpl() {}
void CxxPaddleApiImpl::Init(const lite_api::CxxConfig &config) {
#ifdef LITE_WITH_CUDA
Env<TARGET(kCUDA)>::Init();
......@@ -76,11 +40,11 @@ std::unique_ptr<const lite_api::Tensor> CxxPaddleApiImpl::GetOutput(
return std::unique_ptr<lite_api::Tensor>(new lite_api::Tensor(x));
}
const std::vector<std::string> &CxxPaddleApiImpl::GetInputNames() {
std::vector<std::string> CxxPaddleApiImpl::GetInputNames() {
return raw_predictor_.GetInputNames();
}
const std::vector<std::string> &CxxPaddleApiImpl::GetOutputNames() {
std::vector<std::string> CxxPaddleApiImpl::GetOutputNames() {
return raw_predictor_.GetOutputNames();
}
......
lite/api/light_api.cc
浏览文件 @ 76e74ef1
......@@ -81,11 +81,11 @@ const Tensor* LightPredictor::GetOutput(size_t offset) {
return out_var->GetMutable<lite::Tensor>();
}
// get inputs names
const std::vector<std::string>& LightPredictor::GetInputNames() {
std::vector<std::string> LightPredictor::GetInputNames() {
return input_names_;
}
// get outputnames
const std::vector<std::string>& LightPredictor::GetOutputNames() {
std::vector<std::string> LightPredictor::GetOutputNames() {
return output_names_;
}
// append the names of inputs and outputs into input_names_ and output_names_
......
lite/api/light_api.h
浏览文件 @ 76e74ef1
......@@ -64,8 +64,8 @@ class LITE_API LightPredictor {
}
// get inputnames and get outputnames.
const std::vector<std::string>& GetInputNames();
const std::vector<std::string>& GetOutputNames();
std::vector<std::string> GetInputNames();
std::vector<std::string> GetOutputNames();
void PrepareFeedFetch();
private:
......@@ -86,5 +86,31 @@ class LITE_API LightPredictor {
std::vector<std::string> output_names_;
};
class LightPredictorImpl : public lite_api::PaddlePredictor {
public:
LightPredictorImpl() = default;
std::unique_ptr<lite_api::Tensor> GetInput(int i) override;
std::unique_ptr<const lite_api::Tensor> GetOutput(int i) const override;
void Run() override;
std::string GetVersion() const override;
std::vector<std::string> GetInputNames() override;
std::vector<std::string> GetOutputNames() override;
std::unique_ptr<const lite_api::Tensor> GetTensor(
const std::string& name) const override;
// Get InputTebsor by name
std::unique_ptr<lite_api::Tensor> GetInputByName(
const std::string& name) override;
void Init(const lite_api::MobileConfig& config);
private:
std::unique_ptr<lite::LightPredictor> raw_predictor_;
};
} // namespace lite
} // namespace paddle
lite/api/light_api_impl.cc
浏览文件 @ 76e74ef1
......@@ -19,77 +19,60 @@
#include "lite/model_parser/model_parser.h"
namespace paddle {
namespace lite_api {
class LightPredictorImpl : public PaddlePredictor {
public:
LightPredictorImpl() = default;
std::unique_ptr<Tensor> GetInput(int i) override;
std::unique_ptr<const Tensor> GetOutput(int i) const override;
void Run() override;
std::string GetVersion() const override;
const std::vector<std::string>& GetInputNames() override;
const std::vector<std::string>& GetOutputNames() override;
namespace lite {
std::unique_ptr<const Tensor> GetTensor(
const std::string& name) const override;
// Get InputTebsor by name
std::unique_ptr<Tensor> GetInputByName(const std::string& name) override;
void Init(const MobileConfig& config);
private:
std::unique_ptr<lite::LightPredictor> raw_predictor_;
};
void LightPredictorImpl::Init(const MobileConfig& config) {
void LightPredictorImpl::Init(const lite_api::MobileConfig& config) {
// LightPredictor Only support NaiveBuffer backend in publish lib
raw_predictor_.reset(new lite::LightPredictor(config.model_dir(),
config.model_buffer(),
config.param_buffer(),
config.model_from_memory(),
LiteModelType::kNaiveBuffer));
raw_predictor_.reset(
new LightPredictor(config.model_dir(),
config.model_buffer(),
config.param_buffer(),
config.model_from_memory(),
lite_api::LiteModelType::kNaiveBuffer));
}
std::unique_ptr<Tensor> LightPredictorImpl::GetInput(int i) {
return std::unique_ptr<Tensor>(new Tensor(raw_predictor_->GetInput(i)));
std::unique_ptr<lite_api::Tensor> LightPredictorImpl::GetInput(int i) {
return std::unique_ptr<lite_api::Tensor>(
new lite_api::Tensor(raw_predictor_->GetInput(i)));
}
std::unique_ptr<const Tensor> LightPredictorImpl::GetOutput(int i) const {
return std::unique_ptr<Tensor>(new Tensor(raw_predictor_->GetOutput(i)));
std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetOutput(
int i) const {
return std::unique_ptr<lite_api::Tensor>(
new lite_api::Tensor(raw_predictor_->GetOutput(i)));
}
void LightPredictorImpl::Run() { raw_predictor_->Run(); }
std::string LightPredictorImpl::GetVersion() const { return lite::version(); }
std::unique_ptr<const Tensor> LightPredictorImpl::GetTensor(
std::unique_ptr<const lite_api::Tensor> LightPredictorImpl::GetTensor(
const std::string& name) const {
return std::unique_ptr<const Tensor>(
new Tensor(raw_predictor_->GetTensor(name)));
return std::unique_ptr<const lite_api::Tensor>(
new lite_api::Tensor(raw_predictor_->GetTensor(name)));
}
std::unique_ptr<Tensor> LightPredictorImpl::GetInputByName(
std::unique_ptr<lite_api::Tensor> LightPredictorImpl::GetInputByName(
const std::string& name) {
return std::unique_ptr<Tensor>(
new Tensor(raw_predictor_->GetInputByName(name)));
return std::unique_ptr<lite_api::Tensor>(
new lite_api::Tensor(raw_predictor_->GetInputByName(name)));
}
const std::vector<std::string>& LightPredictorImpl::GetInputNames() {
std::vector<std::string> LightPredictorImpl::GetInputNames() {
return raw_predictor_->GetInputNames();
}
const std::vector<std::string>& LightPredictorImpl::GetOutputNames() {
std::vector<std::string> LightPredictorImpl::GetOutputNames() {
return raw_predictor_->GetOutputNames();
}
} // namespace lite
namespace lite_api {
template <>
std::shared_ptr<PaddlePredictor> CreatePaddlePredictor(
const MobileConfig& config) {
auto x = std::make_shared<LightPredictorImpl>();
auto x = std::make_shared<lite::LightPredictorImpl>();
x->Init(config);
return x;
}
......
lite/api/light_api_test.cc
浏览文件 @ 76e74ef1
......@@ -37,13 +37,13 @@ TEST(LightAPI, load) {
}
predictor.PrepareFeedFetch();
const std::vector<std::string>& inputs = predictor.GetInputNames();
const std::vector<std::string> inputs = predictor.GetInputNames();
LOG(INFO) << "input size: " << inputs.size();
for (int i = 0; i < inputs.size(); i++) {
LOG(INFO) << "inputnames: " << inputs[i];
}
const std::vector<std::string>& outputs = predictor.GetOutputNames();
const std::vector<std::string> outputs = predictor.GetOutputNames();
for (int i = 0; i < outputs.size(); i++) {
LOG(INFO) << "outputnames: " << outputs[i];
}
......
lite/api/paddle_api.cc
浏览文件 @ 76e74ef1
......@@ -14,8 +14,13 @@
#include "lite/api/paddle_api.h"
#include "lite/core/device_info.h"
#include "lite/core/target_wrapper.h"
#include "lite/core/tensor.h"
#ifdef LITE_WITH_CUDA
#include "lite/backends/cuda/target_wrapper.h"
#endif
namespace paddle {
namespace lite_api {
......@@ -42,6 +47,11 @@ const int8_t *Tensor::data() const {
return ctensor(raw_tensor_)->data<int8_t>();
}
template <>
const int32_t *Tensor::data() const {
return ctensor(raw_tensor_)->data<int32_t>();
}
template <>
int *Tensor::mutable_data(TargetType type) const {
return tensor(raw_tensor_)->mutable_data<int>(type);
......@@ -55,10 +65,81 @@ int8_t *Tensor::mutable_data(TargetType type) const {
return tensor(raw_tensor_)->mutable_data<int8_t>(type);
}
template <typename T, TargetType type>
void Tensor::CopyFromCpu(const T *src_data) {
T *data = tensor(raw_tensor_)->mutable_data<T>(type);
int64_t num = tensor(raw_tensor_)->numel();
CHECK(num > 0) << "You should call Resize interface first";
if (type == TargetType::kHost || type == TargetType::kARM) {
lite::TargetWrapperHost::MemcpySync(
data, src_data, num * sizeof(T), lite::IoDirection::HtoH);
} else if (type == TargetType::kCUDA) {
#ifdef LITE_WITH_CUDA
lite::TargetWrapperCuda::MemcpySync(
data, src_data, num * sizeof(T), lite::IoDirection::HtoD);
#else
LOG(FATAL) << "Please compile the lib with CUDA.";
#endif
} else {
LOG(FATAL) << "The CopyFromCpu interface just support kHost, kARM, kCUDA";
}
}
template <typename T>
void Tensor::CopyToCpu(T *data) {
const T *src_data = tensor(raw_tensor_)->data<T>();
int64_t num = tensor(raw_tensor_)->numel();
CHECK(num > 0) << "You should call Resize interface first";
auto type = tensor(raw_tensor_)->target();
if (type == TargetType::kHost || type == TargetType::kARM) {
lite::TargetWrapperHost::MemcpySync(
data, src_data, num * sizeof(T), lite::IoDirection::HtoH);
} else if (type == TargetType::kCUDA) {
#ifdef LITE_WITH_CUDA
lite::TargetWrapperCuda::MemcpySync(
data, src_data, num * sizeof(T), lite::IoDirection::DtoH);
#else
LOG(FATAL) << "Please compile the lib with CUDA.";
#endif
} else {
LOG(FATAL) << "The CopyToCpu interface just support kHost, kARM, kCUDA";
}
}
template void Tensor::CopyFromCpu<int, TargetType::kHost>(const int *);
template void Tensor::CopyFromCpu<float, TargetType::kHost>(const float *);
template void Tensor::CopyFromCpu<int8_t, TargetType::kHost>(const int8_t *);
template void Tensor::CopyFromCpu<int, TargetType::kARM>(const int *);
template void Tensor::CopyFromCpu<float, TargetType::kARM>(const float *);
template void Tensor::CopyFromCpu<int8_t, TargetType::kARM>(const int8_t *);
template void Tensor::CopyFromCpu<int, TargetType::kCUDA>(const int *);
template void Tensor::CopyFromCpu<float, TargetType::kCUDA>(const float *);
template void Tensor::CopyFromCpu<int8_t, TargetType::kCUDA>(const int8_t *);
template void Tensor::CopyToCpu(int8_t *);
template void Tensor::CopyToCpu(float *);
template void Tensor::CopyToCpu(int *);
shape_t Tensor::shape() const {
return ctensor(raw_tensor_)->dims().Vectorize();
}
TargetType Tensor::target() const {
auto type = ctensor(raw_tensor_)->target();
if (type == TargetType::kUnk) {
CHECK(false) << "This tensor was not initialized.";
}
return type;
}
PrecisionType Tensor::precision() const {
auto precision = ctensor(raw_tensor_)->precision();
if (precision == PrecisionType::kUnk) {
CHECK(false) << "This tensor was not initialized.";
}
return precision;
}
lod_t Tensor::lod() const { return ctensor(raw_tensor_)->lod(); }
void Tensor::SetLoD(const lod_t &lod) { tensor(raw_tensor_)->set_lod(lod); }
......
lite/api/paddle_api.h
浏览文件 @ 76e74ef1
......@@ -45,8 +45,15 @@ struct LITE_API Tensor {
template <typename T>
T* mutable_data(TargetType type = TargetType::kHost) const;
template <typename T, TargetType type = TargetType::kHost>
void CopyFromCpu(const T* data);
template <typename T>
void CopyToCpu(T* data);
/// Shape of the tensor.
shape_t shape() const;
TargetType target() const;
PrecisionType precision() const;
// LoD of the tensor
lod_t lod() const;
......@@ -75,9 +82,9 @@ class LITE_API PaddlePredictor {
virtual std::string GetVersion() const = 0;
// Get input names
virtual const std::vector<std::string>& GetInputNames() = 0;
virtual std::vector<std::string> GetInputNames() = 0;
// Get output names
virtual const std::vector<std::string>& GetOutputNames() = 0;
virtual std::vector<std::string> GetOutputNames() = 0;
// Get Input by name
virtual std::unique_ptr<Tensor> GetInputByName(const std::string& name) = 0;
......
lite/api/paddle_api_test.cc
浏览文件 @ 76e74ef1
......@@ -37,12 +37,12 @@ TEST(CxxApi, run) {
LOG(INFO) << "Version: " << predictor->GetVersion();
auto& inputs = predictor->GetInputNames();
auto inputs = predictor->GetInputNames();
LOG(INFO) << "input size: " << inputs.size();
for (int i = 0; i < inputs.size(); i++) {
LOG(INFO) << "inputnames: " << inputs[i];
}
auto& outputs = predictor->GetOutputNames();
auto outputs = predictor->GetOutputNames();
for (int i = 0; i < outputs.size(); i++) {
LOG(INFO) << "outputnames: " << outputs[i];
}
......@@ -76,12 +76,12 @@ TEST(LightApi, run) {
auto predictor = lite_api::CreatePaddlePredictor(config);
auto& inputs = predictor->GetInputNames();
auto inputs = predictor->GetInputNames();
LOG(INFO) << "input size: " << inputs.size();
for (int i = 0; i < inputs.size(); i++) {
LOG(INFO) << "inputnames: " << inputs.at(i);
}
auto& outputs = predictor->GetOutputNames();
auto outputs = predictor->GetOutputNames();
for (int i = 0; i < outputs.size(); i++) {
LOG(INFO) << "outputnames: " << outputs.at(i);
}
......
lite/api/python/CMakeLists.txt 0 → 100644
浏览文件 @ 76e74ef1
if (NOT LITE_WITH_PYTHON)
return()
endif()
add_subdirectory(pybind)
#add_subdirectory(interface)
lite/api/python/pybind/CMakeLists.txt 0 → 100644
浏览文件 @ 76e74ef1
set(PYBIND_DEPS pybind python paddle_api_light paddle_api)
if (NOT LITE_ON_TINY_PUBLISH)
set(PYBIND_DEPS ${PYBIND_DEPS} paddle_api_full)
endif()
lite_cc_library(lite_pybind SHARED SRCS pybind.cc DEPS ${PYBIND_DEPS})
lite/api/python/pybind/pybind.cc 0 → 100644
浏览文件 @ 76e74ef1
// Copyright (c) 2019 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 "lite/api/python/pybind/pybind.h"
#include <pybind11/numpy.h>
#include <pybind11/stl.h>
#include <cstring>
#include <iostream>
#include <map>
#include <memory>
#include <string>
#include <unordered_set>
#include <utility>
#include <vector>
#ifndef LITE_ON_TINY_PUBLISH
#include "lite/api/cxx_api.h"
#include "lite/api/paddle_use_passes.h"
#endif
#include "lite/api/light_api.h"
#include "lite/api/paddle_api.h"
#include "lite/api/paddle_use_kernels.h"
#include "lite/api/paddle_use_ops.h"
#include "lite/core/tensor.h"
namespace py = pybind11;
namespace paddle {
namespace lite {
namespace pybind {
using lite_api::Tensor;
using lite_api::CxxConfig;
using lite_api::MobileConfig;
using lite_api::PowerMode;
using lite_api::TargetType;
using lite_api::PrecisionType;
using lite_api::DataLayoutType;
using lite_api::Place;
using lite::LightPredictorImpl;
#ifndef LITE_ON_TINY_PUBLISH
using lite::CxxPaddleApiImpl;
static void BindLiteCxxPredictor(py::module *m);
#endif
static void BindLiteLightPredictor(py::module *m);
static void BindLiteCxxConfig(py::module *m);
static void BindLiteMobileConfig(py::module *m);
static void BindLitePowerMode(py::module *m);
static void BindLitePlace(py::module *m);
static void BindLiteTensor(py::module *m);
void BindLiteApi(py::module *m) {
BindLiteCxxConfig(m);
BindLiteMobileConfig(m);
BindLitePowerMode(m);
BindLitePlace(m);
BindLiteTensor(m);
#ifndef LITE_ON_TINY_PUBLISH
BindLiteCxxPredictor(m);
#endif
BindLiteLightPredictor(m);
// Global helper methods
m->def("create_paddle_predictor",
[](const CxxConfig &config) -> std::unique_ptr<CxxPaddleApiImpl> {
auto x = std::unique_ptr<CxxPaddleApiImpl>(new CxxPaddleApiImpl());
x->Init(config);
return std::move(x);
});
m->def("create_paddle_predictor",
[](const MobileConfig &config) -> std::unique_ptr<LightPredictorImpl> {
auto x =
std::unique_ptr<LightPredictorImpl>(new LightPredictorImpl());
x->Init(config);
return std::move(x);
});
}
void BindLiteCxxConfig(py::module *m) {
py::class_<CxxConfig> cxx_config(*m, "CxxConfig");
cxx_config.def(py::init<>())
.def("set_model_dir", &CxxConfig::set_model_dir)
.def("model_dir", &CxxConfig::model_dir)
.def("set_model_file", &CxxConfig::set_model_file)
.def("model_file", &CxxConfig::model_file)
.def("set_param_file", &CxxConfig::set_param_file)
.def("param_file", &CxxConfig::param_file)
.def("set_valid_places", &CxxConfig::set_valid_places)
.def("set_model_buffer", &CxxConfig::set_model_buffer)
.def("model_from_memory", &CxxConfig::model_from_memory);
#ifdef LITE_WITH_ARM
cxx_config.def("set_threads", &CxxConfig::set_threads)
.def("threads", &CxxConfig::threads)
.def("set_power_mode", &CxxConfig::set_power_mode)
.def("power_mode", &CxxConfig::power_mode);
#endif
}
// TODO(sangoly): Should MobileConfig be renamed to LightConfig ??
void BindLiteMobileConfig(py::module *m) {
py::class_<MobileConfig> mobile_config(*m, "MobileConfig");
mobile_config.def(py::init<>())
.def("set_model_dir", &MobileConfig::set_model_dir)
.def("model_dir", &MobileConfig::model_dir)
.def("set_model_buffer", &MobileConfig::set_model_buffer)
.def("model_from_memory", &MobileConfig::model_from_memory);
#ifdef LITE_WITH_ARM
mobile_config.def("set_threads", &MobileConfig::set_threads)
.def("threads", &MobileConfig::threads)
.def("set_power_mode", &MobileConfig::set_power_mode)
.def("power_mode", &MobileConfig::power_mode);
#endif
}
void BindLitePowerMode(py::module *m) {
py::enum_<PowerMode>(*m, "PowerMode")
.value("LITE_POWER_HIGH", PowerMode::LITE_POWER_HIGH)
.value("LITE_POWER_LOW", PowerMode::LITE_POWER_LOW)
.value("LITE_POWER_FULL", PowerMode::LITE_POWER_FULL)
.value("LITE_POWER_NO_BIND", PowerMode::LITE_POWER_NO_BIND)
.value("LITE_POWER_RAND_HIGH", PowerMode::LITE_POWER_RAND_HIGH)
.value("LITE_POWER_RAND_LOW", PowerMode::LITE_POWER_RAND_LOW);
}
void BindLitePlace(py::module *m) {
// TargetType
py::enum_<TargetType>(*m, "TargetType")
.value("Host", TargetType::kHost)
.value("X86", TargetType::kX86)
.value("CUDA", TargetType::kCUDA)
.value("ARM", TargetType::kARM)
.value("OpenCL", TargetType::kOpenCL)
.value("FPGA", TargetType::kFPGA)
.value("NPU", TargetType::kNPU)
.value("Any", TargetType::kAny);
// PrecisionType
py::enum_<PrecisionType>(*m, "PrecisionType")
.value("FP16", PrecisionType::kFP16)
.value("FP32", PrecisionType::kFloat)
.value("INT8", PrecisionType::kInt8)
.value("INT16", PrecisionType::kInt16)
.value("INT32", PrecisionType::kInt32)
.value("INT64", PrecisionType::kInt64)
.value("BOOL", PrecisionType::kBool)
.value("Any", PrecisionType::kAny);
// DataLayoutType
py::enum_<DataLayoutType>(*m, "DataLayoutType")
.value("NCHW", DataLayoutType::kNCHW)
.value("NHWC", DataLayoutType::kNHWC)
.value("Any", DataLayoutType::kAny);
// Place
py::class_<Place>(*m, "Place")
.def(py::init<TargetType, PrecisionType, DataLayoutType, int16_t>(),
py::arg("target"),
py::arg("percision") = PrecisionType::kFloat,
py::arg("layout") = DataLayoutType::kNCHW,
py::arg("device") = 0)
.def("is_valid", &Place::is_valid);
}
void BindLiteTensor(py::module *m) {
auto data_size_func = [](const std::vector<int64_t> &shape) -> int64_t {
int64_t res = 1;
for (size_t i = 0; i < shape.size(); i++) {
res *= shape[i];
}
return res;
};
py::class_<Tensor> tensor(*m, "Tensor");
tensor.def("resize", &Tensor::Resize)
.def("shape", &Tensor::shape)
.def("target", &Tensor::target)
.def("precision", &Tensor::precision)
.def("lod", &Tensor::lod)
.def("set_lod", &Tensor::SetLoD);
#define DO_GETTER_ONCE(data_type__, name__) \
tensor.def(#name__, [=](Tensor &self) -> std::vector<data_type__> { \
std::vector<data_type__> data; \
auto shape = self.shape(); \
int64_t num = data_size_func(shape); \
data.resize(num); \
self.CopyToCpu<data_type__>(data.data()); \
return data; \
});
#define DO_SETTER_ONCE(data_type__, name__) \
tensor.def( \
#name__, \
[](Tensor &self, \
const std::vector<data_type__> &data, \
TargetType type = TargetType::kHost) { \
if (type == TargetType::kHost || type == TargetType::kARM) { \
self.CopyFromCpu<data_type__, TargetType::kHost>(data.data()); \
} else if (type == TargetType::kCUDA) { \
self.CopyFromCpu<data_type__, TargetType::kCUDA>(data.data()); \
} \
}, \
py::arg("data"), \
py::arg("type") = TargetType::kHost);
#define DATA_GETTER_SETTER_ONCE(data_type__, name__) \
DO_SETTER_ONCE(data_type__, set_##name__##_data) \
DO_GETTER_ONCE(data_type__, name__##_data)
DATA_GETTER_SETTER_ONCE(int8_t, int8);
DATA_GETTER_SETTER_ONCE(int32_t, int32);
DATA_GETTER_SETTER_ONCE(float, float);
#undef DO_GETTER_ONCE
#undef DO_SETTER_ONCE
#undef DATA_GETTER_SETTER_ONCE
}
#ifndef LITE_ON_TINY_PUBLISH
void BindLiteCxxPredictor(py::module *m) {
py::class_<CxxPaddleApiImpl>(*m, "CxxPredictor")
.def(py::init<>())
.def("get_input", &CxxPaddleApiImpl::GetInput)
.def("get_output", &CxxPaddleApiImpl::GetOutput)
.def("run", &CxxPaddleApiImpl::Run)
.def("get_version", &CxxPaddleApiImpl::GetVersion)
.def("save_optimized_model",
[](CxxPaddleApiImpl &self, const std::string &output_dir) {
self.SaveOptimizedModel(output_dir,
lite_api::LiteModelType::kNaiveBuffer);
});
}
#endif
void BindLiteLightPredictor(py::module *m) {
py::class_<LightPredictorImpl>(*m, "LightPredictor")
.def(py::init<>())
.def("get_input", &LightPredictorImpl::GetInput)
.def("get_output", &LightPredictorImpl::GetOutput)
.def("run", &LightPredictorImpl::Run)
.def("get_version", &LightPredictorImpl::GetVersion)
.def("save_optimized_model",
[](LightPredictorImpl &self, const std::string &output_dir) {
self.SaveOptimizedModel(output_dir,
lite_api::LiteModelType::kNaiveBuffer);
});
}
} // namespace pybind
} // namespace lite
} // namespace paddle
lite/api/python/pybind/pybind.h 0 → 100644
浏览文件 @ 76e74ef1
// Copyright (c) 2019 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 <Python.h>
#include <pybind11/pybind11.h>
namespace paddle {
namespace lite {
namespace pybind {
void BindLiteApi(pybind11::module *m);
PYBIND11_MODULE(lite_core, m) {
m.doc() = "C++ core of Paddle-Lite";
BindLiteApi(&m);
}
} // namespace pybind
} // namespace lite
} // namespace paddle
lite/backends/cuda/CMakeLists.txt
浏览文件 @ 76e74ef1
if(NOT LITE_WITH_CUDA)
return()
endif()
set(cuda_static_deps cudnn_static cublas_static curand_static
culibos_static cudart_static)
nv_library(target_wrapper_cuda SRCS target_wrapper.cc)
nv_library(cuda_blas SRCS blas.cc)
nv_library(target_wrapper_cuda SRCS target_wrapper.cc DEPS ${cuda_static_deps})
nv_library(cuda_blas SRCS blas.cc DEPS ${cuda_static_deps})
add_subdirectory(math)
lite/kernels/cuda/CMakeLists.txt
浏览文件 @ 76e74ef1
......@@ -37,5 +37,4 @@ nv_test(pool_compute_cuda_test SRCS pool_compute_test.cc DEPS pool_compute_cuda)
#nv_test(layout_cuda_test SRCS layout_compute_test.cc DEPS layout_compute_cuda)
nv_test(mul_compute_cuda_test SRCS mul_compute_test.cc DEPS mul_compute_cuda)
nv_test(dropout_compute_cuda_test SRCS dropout_compute_test.cc DEPS dropout_compute_cuda )
nv_test(pool_compute_cuda_test SRCS pool_compute_test.cc DEPS pool_compute_cuda )
nv_test(bilinear_interp_compute_cuda_test SRCS bilinear_interp_compute_test.cc DEPS bilinear_interp_compute_cuda)
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