Skip to content

P
Paddle-Lite

PaddlePaddle / Paddle-Lite

通知 338
Star 4
Fork 1
P
Paddle-Lite
提交 36004a9a 编写于 作者: B baolei.an
浏览文件
操作

update the newest version

上级 aa64ce33
展开全部 隐藏空白更改
内联 并排
Showing with 5457 addition and 20291 deletion
README.md
浏览文件 @ 36004a9a
编译方法： ./lite/tools/build_bm.sh --target_name=bm --bm_sdk_root=/Paddle-Lite/third-party/bmnnsdk2-bm1684_v2.0.1 bm
[中文版](./README_cn.md)
# Paddle Lite
<!--[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle-Lite.svg?branch=develop&longCache=true&style=flat-square)](https://travis-ci.org/PaddlePaddle/Paddle-Lite)-->
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://paddlepaddle.github.io/Paddle-Lite/)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
<!-- [![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle-Mobile.svg)](https://github.com/PaddlePaddle/Paddle-Mobile/releases) -->
Paddle Lite is an updated version of Paddle-Mobile, an open-open source deep learning framework designed to make it easy to perform inference on mobile, embeded, and IoT devices. It is compatible with PaddlePaddle and pre-trained models from other sources.
For tutorials, please see [PaddleLite Document](https://paddlepaddle.github.io/Paddle-Lite/).
## Key Features
### Light Weight
On mobile devices, execution module can be deployed without third-party libraries, because our excecution module and analysis module are decoupled.
On ARM V7, only 800KB are taken up, while on ARM V8, 1.3MB are taken up with the 80 operators and 85 kernels in the dynamic libraries provided by Paddle Lite.
Paddle Lite enables immediate inference without extra optimization.
### High Performance
Paddle Lite enables device-optimized kernels, maximizing ARM CPU performance.
It also supports INT8 quantizations with [PaddleSlim model compression tools](https://github.com/PaddlePaddle/models/tree/v1.5/PaddleSlim), reducing the size of models and increasing the performance of models.
On Huawei NPU and FPGA, the performance is also boosted.
The latest benchmark is located at [benchmark](https://paddlepaddle.github.io/Paddle-Lite/develop/benchmark/)
### High Compatibility
Hardware compatibility: Paddle Lite supports a diversity of hardwares — ARM CPU, Mali GPU, Adreno GPU, Huawei NPU and FPGA. In the near future, we will also support AI microchips from Cambricon and Bitmain.
Model compatibility: The Op of Paddle Lite is fully compatible to that of PaddlePaddle. The accuracy and performance of 18 models (mostly CV models and OCR models) and 85 operators have been validated. In the future, we will also support other models.
Framework compatibility: In addition to models trained on PaddlePaddle, those trained on Caffe and TensorFlow can also be converted to be used on Paddle Lite, via [X2Paddle](https://github.com/PaddlePaddle/X2Paddle). In the future to come, we will also support models of ONNX format.
## Architecture
Paddle Lite is designed to support a wide range of hardwares and devices, and it enables mixed execution of a single model on multiple devices, optimization on various phases, and leight-weighted applications on devices.
![img](https://user-images.githubusercontent.com/45189361/70908123-6ce4fd00-2045-11ea-97e1-ad08446c5c86.png)
As is shown in the figure above, analysis phase includes Machine IR module, and it enables optimizations like Op fusion and redundant computation pruning. Besides, excecution phase only involves Kernal exevution, so it can be deployed on its own to ensure maximized light-weighted deployment.
## Key Info about the Update
The earlier Paddle-Mobile was designed to be compatible with PaddlePaddle and multiple hardwares, including ARM CPU, Mali GPU, Adreno GPU, FPGA, ARM-Linux and Apple's GPU Metal. Within Baidu, inc, many product lines have been using Paddle-Mobile. For more details, please see: [mobile/README](https://github.com/PaddlePaddle/Paddle-Lite/blob/develop/mobile/README.md).
As an update of Paddle-Mobile, Paddle Lite has incorporated many older capabilities into the [new architecture](https://github.com/PaddlePaddle/Paddle-Lite/tree/develop/lite). For the time being, the code of Paddle-mobile will be kept under the directory `mobile/`, before complete transfer to Paddle Lite.
For demands of Apple's GPU Metal and web front end inference, please see `./metal` and `./web` . These two modules will be further developed and maintained.
## Special Thanks
Paddle Lite has referenced the following open-source projects:
- [ARM compute library](http://agroup.baidu.com/paddle-infer/md/article/%28https://github.com/ARM-software/ComputeLibrary%29)
- [Anakin](https://github.com/PaddlePaddle/Anakin). The optimizations under Anakin has been incorporated into Paddle Lite, and so there will not be any future updates of Anakin. As another high-performance inference project under PaddlePaddle, Anakin has been forward-looking and helpful to the making of Paddle Lite.
## Feedback and Community Support
- Questions, reports, and suggestions are welcome through Github Issues!
- Forum: Opinions and questions are welcome at our [PaddlePaddle Forum](https://ai.baidu.com/forum/topic/list/168)！
- WeChat Official Account: PaddlePaddle
- QQ Group Chat: 696965088
<p align="center"><img width="200" height="200" src="https://user-images.githubusercontent.com/45189361/64117959-1969de80-cdc9-11e9-84f7-e1c2849a004c.jpeg"/>&#8194;&#8194;&#8194;&#8194;&#8194;<img width="200" height="200" margin="500" src="https://user-images.githubusercontent.com/45189361/64117844-cb54db00-cdc8-11e9-8c08-24bbe594608e.jpeg"/></p>
<p align="center">&#8194; WeChat Official Account&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;&#8194;QQ Group Chat&#8194;&#8194;&#8194;&#8194;&#8194;</p>
cmake/cross_compiling/ios.cmake
浏览文件 @ 36004a9a
......@@ -120,6 +120,7 @@
#
## Lite settings
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -flto")
if (ARM_TARGET_OS STREQUAL "ios")
set(PLATFORM "OS")
elseif(ARM_TARGET_OS STREQUAL "ios64")
......
cmake/cross_compiling/npu.cmake
浏览文件 @ 36004a9a
......@@ -30,7 +30,7 @@ if(NOT NPU_DDK_INC)
message(FATAL_ERROR "Can not find HiAiModelManagerService.h in ${NPU_DDK_ROOT}/include")
endif()
include_directories("${NPU_DDK_ROOT}")
include_directories("${NPU_DDK_ROOT}/include")
set(NPU_SUB_LIB_PATH "lib64")
if(ARM_TARGET_ARCH_ABI STREQUAL "armv8")
......
lite/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -224,10 +224,14 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND LITE_WITH_ARM)
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_full/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_full/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_light" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_light/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_light/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/ssd_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/ssd_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/ssd_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/yolov3_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/yolov3_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/yolov3_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_classify" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_classify/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_classify/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/test_cv" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/test_cv/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/test_cv/Makefile"
)
add_dependencies(publish_inference_android_cxx_demos logging gflags)
add_dependencies(publish_inference_cxx_lib publish_inference_android_cxx_demos)
......@@ -239,10 +243,14 @@ if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK AND LITE_WITH_ARM)
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/README.md" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_light" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_light/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_light/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/ssd_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/ssd_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/ssd_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/yolov3_detection" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/yolov3_detection/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/yolov3_detection/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/mobile_classify" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/mobile_classify/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/mobile_classify/Makefile"
COMMAND cp -r "${CMAKE_SOURCE_DIR}/lite/demo/cxx/test_cv" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx"
COMMAND cp "${CMAKE_SOURCE_DIR}/lite/demo/cxx/makefiles/test_cv/Makefile.${ARM_TARGET_OS}.${ARM_TARGET_ARCH_ABI}" "${INFER_LITE_PUBLISH_ROOT}/demo/cxx/test_cv/Makefile"
)
add_dependencies(tiny_publish_cxx_lib publish_inference_android_cxx_demos)
endif()
......
lite/api/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -35,6 +35,7 @@ if ((NOT LITE_ON_TINY_PUBLISH) AND (LITE_WITH_CUDA OR LITE_WITH_X86 OR ARM_TARGE
NPU_DEPS ${npu_kernels})
target_link_libraries(paddle_light_api_shared ${light_lib_DEPS} ${arm_kernels} ${npu_kernels})
if (LITE_WITH_NPU)
# Strips the symbols of our protobuf functions to fix the conflicts during
# loading HIAI builder libs (libhiai_ir.so and libhiai_ir_build.so)
......@@ -45,8 +46,8 @@ else()
if ((ARM_TARGET_OS STREQUAL "android") OR (ARM_TARGET_OS STREQUAL "armlinux"))
add_library(paddle_light_api_shared SHARED "")
target_sources(paddle_light_api_shared PUBLIC ${__lite_cc_files} paddle_api.cc light_api.cc light_api_impl.cc)
set_target_properties(paddle_light_api_shared PROPERTIES COMPILE_FLAGS "-flto -fdata-sections")
add_dependencies(paddle_light_api_shared op_list_h kernel_list_h)
set_target_properties(paddle_light_api_shared PROPERTIES COMPILE_FLAGS "-flto -fdata-sections")
add_dependencies(paddle_light_api_shared op_list_h kernel_list_h)
if (LITE_WITH_NPU)
# Need to add HIAI runtime libs (libhiai.so) dependency
target_link_libraries(paddle_light_api_shared ${npu_builder_libs} ${npu_runtime_libs})
......@@ -91,6 +92,7 @@ if (NOT LITE_ON_TINY_PUBLISH)
SRCS cxx_api.cc
DEPS ${cxx_api_deps} ${ops} ${host_kernels} program
X86_DEPS ${x86_kernels}
CUDA_DEPS ${cuda_kernels}
ARM_DEPS ${arm_kernels}
CV_DEPS paddle_cv_arm
NPU_DEPS ${npu_kernels}
......@@ -129,7 +131,9 @@ if(WITH_TESTING)
DEPS cxx_api mir_passes lite_api_test_helper
${ops} ${host_kernels}
X86_DEPS ${x86_kernels}
CUDA_DEPS ${cuda_kernels}
ARM_DEPS ${arm_kernels}
CV_DEPS paddle_cv_arm
NPU_DEPS ${npu_kernels}
XPU_DEPS ${xpu_kernels}
CL_DEPS ${opencl_kernels}
......@@ -293,12 +297,13 @@ if (LITE_ON_MODEL_OPTIMIZE_TOOL)
message(STATUS "Compiling model_optimize_tool")
lite_cc_binary(model_optimize_tool SRCS model_optimize_tool.cc cxx_api_impl.cc paddle_api.cc cxx_api.cc
DEPS gflags kernel op optimizer mir_passes utils)
add_dependencies(model_optimize_tool op_list_h kernel_list_h all_kernel_faked_cc)
add_dependencies(model_optimize_tool op_list_h kernel_list_h all_kernel_faked_cc supported_kernel_op_info_h)
endif(LITE_ON_MODEL_OPTIMIZE_TOOL)
lite_cc_test(test_paddle_api SRCS paddle_api_test.cc DEPS paddle_api_full paddle_api_light
${ops}
ARM_DEPS ${arm_kernels}
CV_DEPS paddle_cv_arm
NPU_DEPS ${npu_kernels}
XPU_DEPS ${xpu_kernels}
CL_DEPS ${opencl_kernels}
......@@ -327,13 +332,14 @@ if(NOT IOS)
lite_cc_binary(benchmark_bin SRCS benchmark.cc DEPS paddle_api_full paddle_api_light gflags utils
${ops} ${host_kernels}
ARM_DEPS ${arm_kernels}
CV_DEPS paddle_cv_arm
NPU_DEPS ${npu_kernels}
XPU_DEPS ${xpu_kernels}
CL_DEPS ${opencl_kernels}
FPGA_DEPS ${fpga_kernels}
X86_DEPS ${x86_kernels}
CUDA_DEPS ${cuda_kernels})
lite_cc_binary(multithread_test SRCS lite_multithread_test.cc DEPS paddle_api_full paddle_api_light gflags utils
lite_cc_binary(multithread_test SRCS lite_multithread_test.cc DEPS paddle_api_full paddle_api_light gflags utils
${ops} ${host_kernels}
ARM_DEPS ${arm_kernels}
CV_DEPS paddle_cv_arm
......
lite/api/cxx_api.cc
浏览文件 @ 36004a9a
......@@ -201,7 +201,11 @@ void Predictor::Build(const lite_api::CxxConfig &config,
const std::string &model_file = config.model_file();
const std::string &param_file = config.param_file();
const bool model_from_memory = config.model_from_memory();
LOG(INFO) << "load from memory " << model_from_memory;
if (model_from_memory) {
LOG(INFO) << "Load model from memory.";
} else {
LOG(INFO) << "Load model from file.";
}
Build(model_path,
model_file,
......
lite/api/cxx_api_impl.cc
浏览文件 @ 36004a9a
......@@ -42,11 +42,11 @@ void CxxPaddleApiImpl::Init(const lite_api::CxxConfig &config) {
#if (defined LITE_WITH_X86) && (defined PADDLE_WITH_MKLML) && \
!(defined LITE_ON_MODEL_OPTIMIZE_TOOL)
int num_threads = config.cpu_math_library_num_threads();
int num_threads = config.x86_math_library_num_threads();
int real_num_threads = num_threads > 1 ? num_threads : 1;
paddle::lite::x86::MKL_Set_Num_Threads(real_num_threads);
omp_set_num_threads(real_num_threads);
VLOG(3) << "set_cpu_math_library_math_threads() is set successfully and the "
VLOG(3) << "set_x86_math_library_math_threads() is set successfully and the "
"number of threads is:"
<< num_threads;
#endif
......
lite/api/lite_multithread_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/api/model_optimize_tool.cc
浏览文件 @ 36004a9a
......@@ -16,8 +16,9 @@
#ifdef PADDLE_WITH_TESTING
#include <gtest/gtest.h>
#endif
// "all_kernel_faked.cc" and "kernel_src_map.h" are created automatically during
// model_optimize_tool's compiling period
// "supported_kernel_op_info.h", "all_kernel_faked.cc" and "kernel_src_map.h"
// are created automatically during model_optimize_tool's compiling period
#include <iomanip>
#include "all_kernel_faked.cc" // NOLINT
#include "kernel_src_map.h" // NOLINT
#include "lite/api/cxx_api.h"
......@@ -25,8 +26,11 @@
#include "lite/api/paddle_use_ops.h"
#include "lite/api/paddle_use_passes.h"
#include "lite/core/op_registry.h"
#include "lite/model_parser/compatible_pb.h"
#include "lite/model_parser/pb/program_desc.h"
#include "lite/utils/cp_logging.h"
#include "lite/utils/string.h"
#include "supported_kernel_op_info.h" // NOLINT
DEFINE_string(model_dir,
"",
......@@ -62,10 +66,16 @@ DEFINE_string(valid_targets,
"The targets this model optimized for, should be one of (arm, "
"opencl, x86), splitted by space");
DEFINE_bool(prefer_int8_kernel, false, "Prefer to run model with int8 kernels");
DEFINE_bool(print_supported_ops,
false,
"Print supported operators on the inputed target");
DEFINE_bool(print_all_ops,
false,
"Print all the valid operators of Paddle-Lite");
DEFINE_bool(print_model_ops, false, "Print operators in the input model");
namespace paddle {
namespace lite_api {
//! Display the kernel information.
void DisplayKernels() {
LOG(INFO) << ::paddle::lite::KernelRegistry::Global().DebugString();
......@@ -130,9 +140,7 @@ void RunOptimize(const std::string& model_dir,
config.set_model_dir(model_dir);
config.set_model_file(model_file);
config.set_param_file(param_file);
config.set_valid_places(valid_places);
auto predictor = lite_api::CreatePaddlePredictor(config);
LiteModelType model_type;
......@@ -168,6 +176,202 @@ void CollectModelMetaInfo(const std::string& output_dir,
lite::WriteLines(std::vector<std::string>(total.begin(), total.end()),
output_path);
}
void PrintOpsInfo(std::set<std::string> valid_ops = {}) {
std::vector<std::string> targets = {"kHost",
"kX86",
"kCUDA",
"kARM",
"kOpenCL",
"kFPGA",
"kNPU",
"kXPU",
"kAny",
"kUnk"};
int maximum_optype_length = 0;
for (auto it = supported_ops.begin(); it != supported_ops.end(); it++) {
maximum_optype_length = it->first.size() > maximum_optype_length
? it->first.size()
: maximum_optype_length;
}
std::cout << std::setiosflags(std::ios::internal);
std::cout << std::setw(maximum_optype_length) << "OP_name";
for (int i = 0; i < targets.size(); i++) {
std::cout << std::setw(10) << targets[i].substr(1);
}
std::cout << std::endl;
if (valid_ops.empty()) {
for (auto it = supported_ops.begin(); it != supported_ops.end(); it++) {
std::cout << std::setw(maximum_optype_length) << it->first;
auto ops_valid_places = it->second;
for (int i = 0; i < targets.size(); i++) {
if (std::find(ops_valid_places.begin(),
ops_valid_places.end(),
targets[i]) != ops_valid_places.end()) {
std::cout << std::setw(10) << "Y";
} else {
std::cout << std::setw(10) << " ";
}
}
std::cout << std::endl;
}
} else {
for (auto op = valid_ops.begin(); op != valid_ops.end(); op++) {
std::cout << std::setw(maximum_optype_length) << *op;
// Check: If this kernel doesn't match any operator, we will skip it.
if (supported_ops.find(*op) == supported_ops.end()) {
continue;
}
// Print OP info.
auto ops_valid_places = supported_ops.at(*op);
for (int i = 0; i < targets.size(); i++) {
if (std::find(ops_valid_places.begin(),
ops_valid_places.end(),
targets[i]) != ops_valid_places.end()) {
std::cout << std::setw(10) << "Y";
} else {
std::cout << std::setw(10) << " ";
}
}
std::cout << std::endl;
}
}
}
/// Print help information
void PrintHelpInfo() {
// at least one argument should be inputed
const char help_info[] =
"At least one argument should be inputed. Valid arguments are listed "
"below:\n"
" Arguments of model optimization:\n"
" `--model_dir=<model_param_dir>`\n"
" `--model_file=<model_path>`\n"
" `--param_file=<param_path>`\n"
" `--optimize_out_type=(protobuf|naive_buffer)`\n"
" `--optimize_out=<output_optimize_model_dir>`\n"
" `--valid_targets=(arm|opencl|x86|npu|xpu)`\n"
" `--prefer_int8_kernel=(true|false)`\n"
" `--record_tailoring_info=(true|false)`\n"
" Arguments of model checking and ops information:\n"
" `--print_all_ops=true` Display all the valid operators of "
"Paddle-Lite\n"
" `--print_supported_ops=true "
"--valid_targets=(arm|opencl|x86|npu|xpu)`"
" Display valid operators of input targets\n"
" `--print_model_ops=true --model_dir=<model_param_dir> "
"--valid_targets=(arm|opencl|x86|npu|xpu)`"
" Display operators in the input model\n";
std::cout << help_info << std::endl;
exit(1);
}
// Parse Input command
void ParseInputCommand() {
if (FLAGS_print_all_ops) {
std::cout << "All OPs supported by Paddle-Lite: " << supported_ops.size()
<< " ops in total." << std::endl;
PrintOpsInfo();
exit(1);
} else if (FLAGS_print_supported_ops) {
auto valid_places = paddle::lite_api::ParserValidPlaces();
// get valid_targets string
std::vector<TargetType> target_types = {};
for (int i = 0; i < valid_places.size(); i++) {
target_types.push_back(valid_places[i].target);
}
std::string targets_str = TargetToStr(target_types[0]);
for (int i = 1; i < target_types.size(); i++) {
targets_str = targets_str + TargetToStr(target_types[i]);
}
std::cout << "Supported OPs on '" << targets_str << "': " << std::endl;
target_types.push_back(TARGET(kHost));
target_types.push_back(TARGET(kUnk));
std::set<std::string> valid_ops;
for (int i = 0; i < target_types.size(); i++) {
auto ops = supported_ops_target[static_cast<int>(target_types[i])];
valid_ops.insert(ops.begin(), ops.end());
}
PrintOpsInfo(valid_ops);
exit(1);
}
}
// test whether this model is supported
void CheckIfModelSupported() {
// 1. parse valid places and valid targets
auto valid_places = paddle::lite_api::ParserValidPlaces();
// set valid_ops
auto valid_ops = supported_ops_target[static_cast<int>(TARGET(kHost))];
auto valid_unktype_ops = supported_ops_target[static_cast<int>(TARGET(kUnk))];
valid_ops.insert(
valid_ops.end(), valid_unktype_ops.begin(), valid_unktype_ops.end());
for (int i = 0; i < valid_places.size(); i++) {
auto target = valid_places[i].target;
auto ops = supported_ops_target[static_cast<int>(target)];
valid_ops.insert(valid_ops.end(), ops.begin(), ops.end());
}
// get valid ops
std::set<std::string> valid_ops_set(valid_ops.begin(), valid_ops.end());
// 2.Load model into program to get ops in model
std::string prog_path = FLAGS_model_dir + "/__model__";
if (!FLAGS_model_file.empty() && !FLAGS_param_file.empty()) {
prog_path = FLAGS_model_file;
}
lite::cpp::ProgramDesc cpp_prog;
framework::proto::ProgramDesc pb_proto_prog =
*lite::LoadProgram(prog_path, false);
lite::pb::ProgramDesc pb_prog(&pb_proto_prog);
// Transform to cpp::ProgramDesc
lite::TransformProgramDescAnyToCpp(pb_prog, &cpp_prog);
std::set<std::string> unsupported_ops;
std::set<std::string> input_model_ops;
for (int index = 0; index < cpp_prog.BlocksSize(); index++) {
auto current_block = cpp_prog.GetBlock<lite::cpp::BlockDesc>(index);
for (size_t i = 0; i < current_block->OpsSize(); ++i) {
auto& op_desc = *current_block->GetOp<lite::cpp::OpDesc>(i);
auto op_type = op_desc.Type();
input_model_ops.insert(op_type);
if (valid_ops_set.count(op_type) == 0) {
unsupported_ops.insert(op_type);
}
}
}
// 3. Print ops_info of input model and check if this model is supported
if (FLAGS_print_model_ops) {
std::cout << "OPs in the input model include:\n";
PrintOpsInfo(input_model_ops);
}
if (!unsupported_ops.empty()) {
std::string unsupported_ops_str = *unsupported_ops.begin();
for (auto op_str = ++unsupported_ops.begin();
op_str != unsupported_ops.end();
op_str++) {
unsupported_ops_str = unsupported_ops_str + ", " + *op_str;
}
std::vector<TargetType> targets = {};
for (int i = 0; i < valid_places.size(); i++) {
targets.push_back(valid_places[i].target);
}
std::sort(targets.begin(), targets.end());
targets.erase(unique(targets.begin(), targets.end()), targets.end());
std::string targets_str = TargetToStr(targets[0]);
for (int i = 1; i < targets.size(); i++) {
targets_str = targets_str + "," + TargetToStr(targets[i]);
}
LOG(ERROR) << "Error: This model is not supported, because "
<< unsupported_ops.size() << " ops are not supported on '"
<< targets_str << "'. These unsupported ops are: '"
<< unsupported_ops_str << "'.";
exit(1);
}
if (FLAGS_print_model_ops) {
std::cout << "Paddle-Lite supports this model!" << std::endl;
exit(1);
}
}
void Main() {
if (FLAGS_display_kernels) {
......@@ -241,7 +445,13 @@ void Main() {
} // namespace paddle
int main(int argc, char** argv) {
// If there is none input argument, print help info.
if (argc < 2) {
paddle::lite_api::PrintHelpInfo();
}
google::ParseCommandLineFlags(&argc, &argv, false);
paddle::lite_api::ParseInputCommand();
paddle::lite_api::CheckIfModelSupported();
paddle::lite_api::Main();
return 0;
}
lite/api/model_test.cc
浏览文件 @ 36004a9a
......@@ -86,6 +86,7 @@ void Run(const std::vector<std::vector<int64_t>>& input_shapes,
for (int i = 0; i < input_shapes[j].size(); ++i) {
input_num *= input_shapes[j][i];
}
for (int i = 0; i < input_num; ++i) {
input_data[i] = 1.f;
}
......
lite/api/paddle_api.h
浏览文件 @ 36004a9a
......@@ -133,7 +133,9 @@ class LITE_API CxxConfig : public ConfigBase {
std::string model_file_;
std::string param_file_;
bool model_from_memory_{false};
int cpu_math_library_math_threads_ = 1;
#ifdef LITE_WITH_X86
int x86_math_library_math_threads_ = 1;
#endif
public:
void set_valid_places(const std::vector<Place>& x) { valid_places_ = x; }
......@@ -153,12 +155,14 @@ class LITE_API CxxConfig : public ConfigBase {
std::string param_file() const { return param_file_; }
bool model_from_memory() const { return model_from_memory_; }
void set_cpu_math_library_num_threads(int threads) {
cpu_math_library_math_threads_ = threads;
#ifdef LITE_WITH_X86
void set_x86_math_library_num_threads(int threads) {
x86_math_library_math_threads_ = threads;
}
int cpu_math_library_num_threads() const {
return cpu_math_library_math_threads_;
int x86_math_library_num_threads() const {
return x86_math_library_math_threads_;
}
#endif
};
/// MobileConfig is the config for the light weight predictor, it will skip
......
lite/api/test_step_rnn_lite_x86.cc
浏览文件 @ 36004a9a
......@@ -30,7 +30,9 @@ TEST(Step_rnn, test_step_rnn_lite_x86) {
std::string model_dir = FLAGS_model_dir;
lite_api::CxxConfig config;
config.set_model_dir(model_dir);
config.set_cpu_math_library_num_threads(1);
#ifdef LITE_WITH_X86
config.set_x86_math_library_num_threads(1);
#endif
config.set_valid_places({lite_api::Place{TARGET(kX86), PRECISION(kInt64)},
lite_api::Place{TARGET(kX86), PRECISION(kFloat)},
lite_api::Place{TARGET(kHost), PRECISION(kFloat)}});
......
lite/backends/arm/math/conv3x3s2_depthwise_fp32.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
// 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 <arm_neon.h>
#include "lite/backends/arm/math/conv_block_utils.h"
#include "lite/backends/arm/math/conv_impl.h"
#include "lite/core/context.h"
#include "lite/operators/op_params.h"
#ifdef ARM_WITH_OMP
#include <omp.h>
#endif
namespace paddle {
namespace lite {
namespace arm {
namespace math {
void conv_3x3s2_depthwise_fp32(const float* i_data,
float* o_data,
int bs,
int oc,
int oh,
int ow,
int ic,
int ih,
int win,
const float* weights,
const float* bias,
const operators::ConvParam& param,
ARMContext* ctx) {
int threads = ctx->threads();
const int pad_h = param.paddings[0];
const int pad_w = param.paddings[1];
const int out_c_block = 4;
const int out_h_kernel = 1;
const int out_w_kernel = 4;
const int win_ext = ow * 2 + 1;
const int ow_round = ROUNDUP(ow, 4);
const int win_round = ROUNDUP(win_ext, 4);
const int hin_round = oh * 2 + 1;
const int prein_size = win_round * hin_round * out_c_block;
auto workspace_size =
threads * prein_size + win_round /*tmp zero*/ + ow_round /*tmp writer*/;
ctx->ExtendWorkspace(sizeof(float) * workspace_size);
bool flag_relu = param.fuse_relu;
bool flag_bias = param.bias != nullptr;
/// get workspace
auto ptr_zero = ctx->workspace_data<float>();
memset(ptr_zero, 0, sizeof(float) * win_round);
float* ptr_write = ptr_zero + win_round;
int size_in_channel = win * ih;
int size_out_channel = ow * oh;
int ws = -pad_w;
int we = ws + win_round;
int hs = -pad_h;
int he = hs + hin_round;
int w_loop = ow_round / 4;
auto remain = w_loop * 4 - ow;
bool flag_remain = remain > 0;
remain = 4 - remain;
remain = remain > 0 ? remain : 0;
int row_len = win_round * out_c_block;
for (int n = 0; n < bs; ++n) {
const float* din_batch = i_data + n * ic * size_in_channel;
float* dout_batch = o_data + n * oc * size_out_channel;
#pragma omp parallel for num_threads(threads)
for (int c = 0; c < oc; c += out_c_block) {
#ifdef ARM_WITH_OMP
float* pre_din = ptr_write + ow_round + omp_get_thread_num() * prein_size;
#else
float* pre_din = ptr_write + ow_round;
#endif
/// const array size
prepack_input_nxwc4_dw(
din_batch, pre_din, c, hs, he, ws, we, ic, win, ih, ptr_zero);
const float* weight_c = weights + c * 9; // kernel_w * kernel_h
float* dout_c00 = dout_batch + c * size_out_channel;
float bias_local[4] = {0, 0, 0, 0};
if (flag_bias) {
bias_local[0] = bias[c];
bias_local[1] = bias[c + 1];
bias_local[2] = bias[c + 2];
bias_local[3] = bias[c + 3];
}
#ifdef __aarch64__
float32x4_t w0 = vld1q_f32(weight_c); // w0, v23
float32x4_t w1 = vld1q_f32(weight_c + 4); // w1, v24
float32x4_t w2 = vld1q_f32(weight_c + 8); // w2, v25
float32x4_t w3 = vld1q_f32(weight_c + 12); // w3, v26
float32x4_t w4 = vld1q_f32(weight_c + 16); // w4, v27
float32x4_t w5 = vld1q_f32(weight_c + 20); // w5, v28
float32x4_t w6 = vld1q_f32(weight_c + 24); // w6, v29
float32x4_t w7 = vld1q_f32(weight_c + 28); // w7, v30
float32x4_t w8 = vld1q_f32(weight_c + 32); // w8, v31
#endif
for (int h = 0; h < oh; h += out_h_kernel) {
float* outc0 = dout_c00 + h * ow;
float* outc1 = outc0 + size_out_channel;
float* outc2 = outc1 + size_out_channel;
float* outc3 = outc2 + size_out_channel;
const float* inr0 = pre_din + h * 2 * row_len;
const float* inr1 = inr0 + row_len;
const float* inr2 = inr1 + row_len;
if (c + out_c_block > oc) {
switch (c + out_c_block - oc) {
case 3:
outc1 = ptr_write;
case 2:
outc2 = ptr_write;
case 1:
outc3 = ptr_write;
default:
break;
}
}
auto c0 = outc0;
auto c1 = outc1;
auto c2 = outc2;
auto c3 = outc3;
float pre_out[16];
for (int w = 0; w < w_loop; ++w) {
bool flag_mask = (w == w_loop - 1) && flag_remain;
if (flag_mask) {
c0 = outc0;
c1 = outc1;
c2 = outc2;
c3 = outc3;
outc0 = pre_out;
outc1 = pre_out + 4;
outc2 = pre_out + 8;
outc3 = pre_out + 12;
}
// clang-format off
#ifdef __aarch64__
asm volatile(
"ldr q8, [%[bias]]\n" /* load bias */
"ldp q0, q1, [%[inr0]], #32\n" /* load input r0*/
"and v19.16b, v8.16b, v8.16b\n"
"ldp q2, q3, [%[inr0]], #32\n" /* load input r0*/
"and v20.16b, v8.16b, v8.16b\n"
"ldp q4, q5, [%[inr0]], #32\n" /* load input r0*/
"and v21.16b, v8.16b, v8.16b\n"
"ldp q6, q7, [%[inr0]], #32\n" /* load input r0*/
"and v22.16b, v8.16b, v8.16b\n"
"ldr q8, [%[inr0]]\n" /* load input r0*/
/* r0 mul w0-w2, get out */
"fmla v19.4s , %[w0].4s, v0.4s\n" /* outr0 = w0 * r0, 0*/
"fmla v20.4s , %[w0].4s, v2.4s\n" /* outr1 = w0 * r0, 2*/
"fmla v21.4s , %[w0].4s, v4.4s\n" /* outr2 = w0 * r0, 4*/
"fmla v22.4s , %[w0].4s, v6.4s\n" /* outr3 = w0 * r0, 6*/
"fmla v19.4s , %[w1].4s, v1.4s\n" /* outr0 = w1 * r0, 1*/
"ldp q0, q1, [%[inr1]], #32\n" /* load input r1*/
"fmla v20.4s , %[w1].4s, v3.4s\n" /* outr1 = w1 * r0, 3*/
"fmla v21.4s , %[w1].4s, v5.4s\n" /* outr2 = w1 * r0, 5*/
"fmla v22.4s , %[w1].4s, v7.4s\n" /* outr3 = w1 * r0, 7*/
"fmla v19.4s , %[w2].4s, v2.4s\n" /* outr0 = w0 * r0, 2*/
"ldp q2, q3, [%[inr1]], #32\n" /* load input r1*/
"fmla v20.4s , %[w2].4s, v4.4s\n" /* outr1 = w0 * r0, 4*/
"ldp q4, q5, [%[inr1]], #32\n" /* load input r1*/
"fmla v21.4s , %[w2].4s, v6.4s\n" /* outr2 = w0 * r0, 6*/
"ldp q6, q7, [%[inr1]], #32\n" /* load input r1*/
"fmla v22.4s , %[w2].4s, v8.4s\n" /* outr3 = w0 * r0, 8*/
"ldr q8, [%[inr1]]\n" /* load input r1*/
/* r1, mul w3-w5, get out */
"fmla v19.4s , %[w3].4s, v0.4s\n" /* outr0 = w3 * r1, 0*/
"fmla v20.4s , %[w3].4s, v2.4s\n" /* outr1 = w3 * r1, 2*/
"fmla v21.4s , %[w3].4s, v4.4s\n" /* outr2 = w3 * r1, 4*/
"fmla v22.4s , %[w3].4s, v6.4s\n" /* outr3 = w3 * r1, 6*/
"fmla v19.4s , %[w4].4s, v1.4s\n" /* outr0 = w4 * r1, 1*/
"ldp q0, q1, [%[inr2]], #32\n" /* load input r2*/
"fmla v20.4s , %[w4].4s, v3.4s\n" /* outr1 = w4 * r1, 3*/
"fmla v21.4s , %[w4].4s, v5.4s\n" /* outr2 = w4 * r1, 5*/
"fmla v22.4s , %[w4].4s, v7.4s\n" /* outr3 = w4 * r1, 7*/
"fmla v19.4s , %[w5].4s, v2.4s\n" /* outr0 = w5 * r1, 2*/
"ldp q2, q3, [%[inr2]], #32\n" /* load input r2*/
"fmla v20.4s , %[w5].4s, v4.4s\n" /* outr1 = w5 * r1, 4*/
"ldp q4, q5, [%[inr2]], #32\n" /* load input r2*/
"fmla v21.4s , %[w5].4s, v6.4s\n" /* outr2 = w5 * r1, 6*/
"ldp q6, q7, [%[inr2]], #32\n" /* load input r2*/
"fmla v22.4s , %[w5].4s, v8.4s\n" /* outr3 = w5 * r1, 8*/
"ldr q8, [%[inr2]]\n" /* load input r2*/
/* r2, mul w6-w8, get out r0, r1 */
"fmla v19.4s , %[w6].4s, v0.4s\n" /* outr0 = w6 * r2, 0*/
"fmla v20.4s , %[w6].4s, v2.4s\n" /* outr1 = w6 * r2, 2*/
"fmla v21.4s , %[w6].4s, v4.4s\n" /* outr2 = w6 * r2, 4*/
"fmla v22.4s , %[w6].4s, v6.4s\n" /* outr3 = w6 * r2, 6*/
"fmla v19.4s , %[w7].4s, v1.4s\n" /* outr0 = w7 * r2, 1*/
"fmla v20.4s , %[w7].4s, v3.4s\n" /* outr1 = w7 * r2, 3*/
"fmla v21.4s , %[w7].4s, v5.4s\n" /* outr2 = w7 * r2, 5*/
"fmla v22.4s , %[w7].4s, v7.4s\n" /* outr3 = w7 * r2, 7*/
"fmla v19.4s , %[w8].4s, v2.4s\n" /* outr0 = w8 * r2, 2*/
"fmla v20.4s , %[w8].4s, v4.4s\n" /* outr1 = w8 * r2, 4*/
"fmla v21.4s , %[w8].4s, v6.4s\n" /* outr2 = w8 * r2, 6*/
"fmla v22.4s , %[w8].4s, v8.4s\n" /* outr3 = w8 * r2, 8*/
/* transpose */
"trn1 v0.4s, v19.4s, v20.4s\n" /* r0: a0a1c0c1*/
"trn2 v1.4s, v19.4s, v20.4s\n" /* r0: b0b1d0d1*/
"trn1 v2.4s, v21.4s, v22.4s\n" /* r0: a2a3c2c3*/
"trn2 v3.4s, v21.4s, v22.4s\n" /* r0: b2b3d2d3*/
"trn1 v19.2d, v0.2d, v2.2d\n" /* r0: a0a1a2a3*/
"trn2 v21.2d, v0.2d, v2.2d\n" /* r0: c0c1c2c3*/
"trn1 v20.2d, v1.2d, v3.2d\n" /* r0: b0b1b2b3*/
"trn2 v22.2d, v1.2d, v3.2d\n" /* r0: d0d1d2d3*/
/* relu */
"cbz %w[flag_relu], 0f\n" /* skip relu*/
"movi v0.4s, #0\n" /* for relu */
"fmax v19.4s, v19.4s, v0.4s\n"
"fmax v20.4s, v20.4s, v0.4s\n"
"fmax v21.4s, v21.4s, v0.4s\n"
"fmax v22.4s, v22.4s, v0.4s\n"
/* save result */
"0:\n"
"str q19, [%[outc0]], #16\n"
"str q20, [%[outc1]], #16\n"
"str q21, [%[outc2]], #16\n"
"str q22, [%[outc3]], #16\n"
:[inr0] "+r"(inr0), [inr1] "+r"(inr1),
[inr2] "+r"(inr2),
[outc0]"+r"(outc0), [outc1]"+r"(outc1),
[outc2]"+r"(outc2), [outc3]"+r"(outc3)
:[w0] "w"(w0), [w1] "w"(w1), [w2] "w"(w2),
[w3] "w"(w3), [w4] "w"(w4), [w5] "w"(w5),
[w6] "w"(w6), [w7] "w"(w7), [w8] "w"(w8),
[bias] "r" (bias_local), [flag_relu]"r"(flag_relu)
: "cc", "memory",
"v0","v1","v2","v3","v4","v5","v6","v7",
"v8", "v19","v20","v21","v22"
);
#else
asm volatile(
/* fill with bias */
"vld1.32 {d16-d17}, [%[bias]]\n" /* load bias */
/* load weights */
"vld1.32 {d18-d21}, [%[wc0]]!\n" /* load w0-2, to q9-11 */
"vld1.32 {d0-d3}, [%[r0]]!\n" /* load input r0, 0,1*/
"vand.i32 q12, q8, q8\n"
"vld1.32 {d4-d7}, [%[r0]]!\n" /* load input r0, 2,3*/
"vand.i32 q13, q8, q8\n"
"vld1.32 {d8-d11}, [%[r0]]!\n" /* load input r0, 4,5*/
"vand.i32 q14, q8, q8\n"
"vld1.32 {d12-d15}, [%[r0]]!\n" /* load input r0, 6,7*/
"vand.i32 q15, q8, q8\n"
"vld1.32 {d16-d17}, [%[r0]]\n" /* load input r0, 8*/
/* mul r0 with w0, w1, w2 */
"vmla.f32 q12, q9, q0 @ w0 * inr0\n"
"vmla.f32 q13, q9, q2 @ w0 * inr2\n"
"vld1.32 {d22-d23}, [%[wc0]]!\n" /* load w2, to q11 */
"vmla.f32 q14, q9, q4 @ w0 * inr4\n"
"vmla.f32 q15, q9, q6 @ w0 * inr6\n"
"vmla.f32 q12, q10, q1 @ w1 * inr1\n"
"vld1.32 {d0-d3}, [%[r1]]! @ load r1, 0, 1\n"
"vmla.f32 q13, q10, q3 @ w1 * inr3\n"
"vmla.f32 q14, q10, q5 @ w1 * inr5\n"
"vmla.f32 q15, q10, q7 @ w1 * inr7\n"
"vld1.32 {d18-d21}, [%[wc0]]!\n" /* load w3-4, to q9-10 */
"vmla.f32 q12, q11, q2 @ w2 * inr2\n"
"vld1.32 {d4-d7}, [%[r1]]! @ load r1, 2, 3\n"
"vmla.f32 q13, q11, q4 @ w2 * inr4\n"
"vld1.32 {d8-d11}, [%[r1]]! @ load r1, 4, 5\n"
"vmla.f32 q14, q11, q6 @ w2 * inr6\n"
"vld1.32 {d12-d15}, [%[r1]]! @ load r1, 6, 7\n"
"vmla.f32 q15, q11, q8 @ w2 * inr8\n"
/* mul r1 with w3, w4, w5 */
"vmla.f32 q12, q9, q0 @ w3 * inr0\n"
"vmla.f32 q13, q9, q2 @ w3 * inr2\n"
"vld1.32 {d22-d23}, [%[wc0]]!\n" /* load w5, to q11 */
"vmla.f32 q14, q9, q4 @ w3 * inr4\n"
"vmla.f32 q15, q9, q6 @ w3 * inr6\n"
"vld1.32 {d16-d17}, [%[r1]]\n" /* load input r1, 8*/
"vmla.f32 q12, q10, q1 @ w4 * inr1\n"
"vld1.32 {d0-d3}, [%[r2]]! @ load r2, 0, 1\n"
"vmla.f32 q13, q10, q3 @ w4 * inr3\n"
"vmla.f32 q14, q10, q5 @ w4 * inr5\n"
"vmla.f32 q15, q10, q7 @ w4 * inr7\n"
"vld1.32 {d18-d21}, [%[wc0]]!\n" /* load w6-7, to q9-10 */
"vmla.f32 q12, q11, q2 @ w5 * inr2\n"
"vld1.32 {d4-d7}, [%[r2]]! @ load r2, 2, 3\n"
"vmla.f32 q13, q11, q4 @ w5 * inr4\n"
"vld1.32 {d8-d11}, [%[r2]]! @ load r2, 4, 5\n"
"vmla.f32 q14, q11, q6 @ w5 * inr6\n"
"vld1.32 {d12-d15}, [%[r2]]! @ load r2, 6, 7\n"
"vmla.f32 q15, q11, q8 @ w5 * inr8\n"
/* mul r2 with w6, w7, w8 */
"vmla.f32 q12, q9, q0 @ w6 * inr0\n"
"vmla.f32 q13, q9, q2 @ w6 * inr2\n"
"vld1.32 {d22-d23}, [%[wc0]]!\n" /* load w8, to q11 */
"vmla.f32 q14, q9, q4 @ w6 * inr4\n"
"vmla.f32 q15, q9, q6 @ w6 * inr6\n"
"vld1.32 {d16-d17}, [%[r2]]\n" /* load input r2, 8*/
"vmla.f32 q12, q10, q1 @ w7 * inr1\n"
"vmla.f32 q13, q10, q3 @ w7 * inr3\n"
"vmla.f32 q14, q10, q5 @ w7 * inr5\n"
"vmla.f32 q15, q10, q7 @ w7 * inr7\n"
"sub %[wc0], %[wc0], #144 @ wc0 - 144 to start address\n"
"vmla.f32 q12, q11, q2 @ w8 * inr2\n"
"vmla.f32 q13, q11, q4 @ w8 * inr4\n"
"vmla.f32 q14, q11, q6 @ w8 * inr6\n"
"vmla.f32 q15, q11, q8 @ w8 * inr8\n"
/* transpose */
"vtrn.32 q12, q13\n" /* a0a1c0c1, b0b1d0d1*/
"vtrn.32 q14, q15\n" /* a2a3c2c3, b2b3d2d3*/
"vswp d25, d28\n" /* a0a1a2a3, c0c1c2c3*/
"vswp d27, d30\n" /* b0b1b2b3, d0d1d2d3*/
"cmp %[flag_relu], #0\n"
"beq 0f\n" /* skip relu*/
"vmov.u32 q0, #0\n"
"vmax.f32 q12, q12, q0\n"
"vmax.f32 q13, q13, q0\n"
"vmax.f32 q14, q14, q0\n"
"vmax.f32 q15, q15, q0\n"
"0:\n"
"vst1.32 {d24-d25}, [%[outc0]]!\n" /* save outc0*/
"vst1.32 {d26-d27}, [%[outc1]]!\n" /* save outc1*/
"vst1.32 {d28-d29}, [%[outc2]]!\n" /* save outc2*/
"vst1.32 {d30-d31}, [%[outc3]]!\n" /* save outc3*/
:[r0] "+r"(inr0), [r1] "+r"(inr1),
[r2] "+r"(inr2), [wc0] "+r" (weight_c),
[outc0]"+r"(outc0), [outc1]"+r"(outc1),
[outc2]"+r"(outc2), [outc3]"+r"(outc3)
:[bias] "r" (bias_local),
[flag_relu]"r"(flag_relu)
:"cc", "memory",
"q0","q1","q2","q3","q4","q5","q6","q7",
"q8", "q9","q10","q11","q12","q13","q14","q15"
);
#endif // __arch64__
// clang-format off
if (flag_mask) {
for (int i = 0; i < remain; ++i) {
c0[i] = pre_out[i];
c1[i] = pre_out[i + 4];
c2[i] = pre_out[i + 8];
c3[i] = pre_out[i + 12];
}
}
}
}
}
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
lite/backends/arm/math/reduce_prod.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/arm/math/reduce_prod.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/arm/math/split_merge_lod_tenosr.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/arm/math/split_merge_lod_tenosr.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/fpga/KD/debugger.hpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/fpga/KD/dl_engine.cpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/dl_engine.hpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/llapi/zynqmp_api.cpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/llapi/zynqmp_api.h 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/conv_process.hpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/crop_pe.cpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/depthwise_conv_pe.hpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/elementwise_mul_pe.hpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/fpga/KD/pes/fully_connected_pe.hpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/gru_pe.hpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/fpga/KD/pes/gru_util.hpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/fpga/KD/pes/output_pe.hpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/pooling_pe.hpp 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/fpga/KD/pes/scale_pe.hpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/fpga/lite_tensor.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/backends/npu/builder.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
// 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/backends/npu/builder.h"
#include <mutex> // NOLINT
#include <utility>
#include "lite/backends/npu/runtime.h"
namespace paddle {
namespace lite {
namespace npu {
// Build HIAI IR graph to om model, and store om model data into lite tensor
bool BuildModel(std::vector<ge::Operator>& inputs, // NOLINT
std::vector<ge::Operator>& outputs, // NOLINT
lite::Tensor* model_data) {
LOG(INFO) << "[NPU] Build model.";
CHECK_GT(inputs.size(), 0);
CHECK_GT(outputs.size(), 0);
CHECK_NE(model_data, 0);
// build IR graph to om model
ge::Graph ir_graph("graph");
ir_graph.SetInputs(inputs).SetOutputs(outputs);
ge::Model om_model("model", "model");
om_model.SetGraph(ir_graph);
domi::HiaiIrBuild ir_build;
domi::ModelBufferData om_model_buf;
if (!ir_build.CreateModelBuff(om_model, om_model_buf)) {
LOG(WARNING) << "[NPU] CreateModelBuff failed!";
return false;
}
if (!ir_build.BuildIRModel(om_model, om_model_buf)) {
LOG(WARNING) << "[NPU] BuildIRModel failed!";
return false;
}
// store om model into tensor
model_data->Resize({om_model_buf.length});
memcpy(model_data->mutable_data<int8_t>(),
om_model_buf.data,
om_model_buf.length);
ir_build.ReleaseModelBuff(om_model_buf);
return true;
}
std::string UniqueName(const std::string& prefix) {
static std::mutex counter_mtx;
static std::unordered_map<std::string, int> counter_map;
std::unique_lock<std::mutex> counter_lck(counter_mtx);
int counter = 1;
auto it = counter_map.find(prefix);
if (it == counter_map.end()) {
counter_map[prefix] = counter;
} else {
counter = ++(it->second);
}
return prefix + "_" + std::to_string(counter);
}
ge::DataType CvtPrecisionType(PrecisionType itype) {
ge::DataType otype = ge::DT_FLOAT;
switch (itype) {
case PRECISION(kFloat):
otype = ge::DT_FLOAT;
break;
case PRECISION(kInt8):
otype = ge::DT_INT8;
break;
case PRECISION(kInt32):
otype = ge::DT_INT32;
break;
default:
LOG(FATAL) << "[NPU] Can not convert precision type("
<< PrecisionToStr(itype) << ") from Lite to NPU";
break;
}
return otype;
}
ge::Format CvtDataLayoutType(DataLayoutType itype) {
ge::Format otype = ge::FORMAT_NCHW;
switch (itype) {
case DATALAYOUT(kNCHW):
otype = ge::FORMAT_NCHW;
break;
// TODO(hong19860320) support more data layout type
default:
LOG(FATAL) << "[NPU] Can not convert data layout type("
<< DataLayoutToStr(itype) << ") from Lite to NPU";
break;
}
return otype;
}
ge::TensorPtr CvtTensor(lite::Tensor* in_tensor,
std::vector<int64_t> out_shape,
PrecisionType in_ptype,
DataLayoutType in_ltype) {
uint8_t* in_data = nullptr;
auto in_size = in_tensor->dims().production();
auto in_shape = in_tensor->dims().Vectorize();
if (out_shape.empty()) {
out_shape = in_shape;
}
int in_bytes;
if (in_ptype == PRECISION(kFloat)) {
in_data = reinterpret_cast<uint8_t*>(in_tensor->mutable_data<float>());
in_bytes = in_size * sizeof(float);
} else if (in_ptype == PRECISION(kInt32)) {
in_data = reinterpret_cast<uint8_t*>(in_tensor->mutable_data<int32_t>());
in_bytes = in_size * sizeof(int32_t);
} else if (in_ptype == PRECISION(kInt8)) {
in_data = reinterpret_cast<uint8_t*>(in_tensor->mutable_data<int8_t>());
in_bytes = in_size * sizeof(int8_t);
} else {
LOG(FATAL) << "[NPU] Unknow precision type " << PrecisionToStr(in_ptype);
}
ge::DataType out_ptype = CvtPrecisionType(in_ptype);
ge::Format out_ltype = CvtDataLayoutType(in_ltype);
ge::TensorDesc out_desc(ge::Shape(out_shape), out_ltype, out_ptype);
CHECK_EQ(out_ltype, ge::FORMAT_NCHW);
auto out_size = out_desc.GetShape().GetShapeSize();
CHECK_EQ(out_size, in_size);
ge::TensorPtr out_tensor = std::make_shared<ge::Tensor>();
out_tensor->SetTensorDesc(out_desc);
out_tensor->SetData(in_data, in_bytes);
return out_tensor;
}
int CvtActMode(std::string act_type) {
int act_mode = 1;
if (act_type == "sigmoid") {
act_mode = 0;
} else if (act_type == "relu") {
act_mode = 1;
} else if (act_type == "tanh") {
act_mode = 2;
} else if (act_type == "relu_clipped") {
act_mode = 3;
} else if (act_type == "elu") {
act_mode = 4;
} else if (act_type == "leaky_relu") {
act_mode = 5;
} else if (act_type == "abs") {
act_mode = 6;
} else if (act_type == "softsign") {
act_mode = 8;
} else if (act_type == "softplus") {
act_mode = 9;
} else if (act_type == "hard_sigmoid") {
act_mode = 10;
} else {
// TODO(hong19860320) support more activation mode
LOG(FATAL) << "[NPU] Unsupported activation type " << act_type;
}
return act_mode;
}
bool HasInputArg(const OpInfo* op_info,
const Scope* scope,
const std::string& argname) {
auto iarg_names = op_info->input_argnames();
if (std::find(iarg_names.begin(), iarg_names.end(), argname) !=
iarg_names.end()) {
auto inputs = op_info->Input(argname);
if (inputs.empty()) {
return false;
}
auto var_name = inputs.front();
auto var = scope->FindVar(var_name);
return var != nullptr;
} else {
return false;
}
}
} // namespace npu
} // namespace lite
} // namespace paddle
lite/backends/npu/builder.h 已删除 100644 → 0
浏览文件 @ aa64ce33
// 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 <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "ai_ddk_lib/include/graph/buffer.h"
#include "ai_ddk_lib/include/graph/graph.h"
#include "ai_ddk_lib/include/graph/model.h"
#include "ai_ddk_lib/include/graph/op/all_ops.h"
#include "ai_ddk_lib/include/graph/operator.h"
#include "ai_ddk_lib/include/graph/operator_reg.h"
#include "ai_ddk_lib/include/hiai_ir_build.h"
#include "lite/core/op_lite.h"
#include "lite/core/target_wrapper.h"
#include "lite/core/tensor.h"
// Extended Ops of HIAI DDK
namespace ge {
/**
* Pads a tensor.
* <Input>
* x : the input tensor
* padding : the input tensor must be 2-D
* constant_values : constant values must be a scalar
* <Output>
* output : the output tensor
* <Attr>
* t_paddings : Default DT_INT32 , t_paddings must be the same with
* datatype of the padding
* mode : 0: CONSTANT, 1: REFLECT, 2: SYMMETRIC
* T : datatype of constant_values DT_INT32:3 DT_FLOAT:0
*/
REG_OP(Pad)
.INPUT(x, TensorType({DT_FLOAT, DT_INT32}))
.INPUT(padding, TensorType({DT_INT32}))
.OPTIONAL_INPUT(constant_values, TensorType({DT_INT32, DT_FLOAT}))
.OUTPUT(output, TensorType({DT_FLOAT, DT_INT32}))
.ATTR(t_paddings, AttrValue::INT{3})
.ATTR(mode, AttrValue::INT{0})
.REQUIRED_ATTR(T, AttrValue::INT)
.OP_END();
} // namespace ge
namespace paddle {
namespace lite {
namespace npu {
class OpList {
public:
static OpList& Global() {
static thread_local OpList x;
return x;
}
void clear() { lists_.clear(); }
void add(std::shared_ptr<ge::Operator> p) { lists_.push_back(p); }
private:
std::vector<std::shared_ptr<ge::Operator>> lists_;
};
// Build HIAI IR graph to om model, and store om model data into lite tensor
bool BuildModel(std::vector<ge::Operator>& inputs, // NOLINT
std::vector<ge::Operator>& outputs, // NOLINT
lite::Tensor* model_data);
std::string UniqueName(const std::string& prefix);
ge::DataType CvtPrecisionType(PrecisionType itype);
ge::Format CvtDataLayoutType(DataLayoutType itype);
ge::TensorPtr CvtTensor(Tensor* in_tensor,
std::vector<int64_t> out_shape = {},
PrecisionType in_ptype = PRECISION(kFloat),
DataLayoutType in_ltype = DATALAYOUT(kNCHW));
template <typename T>
ge::TensorPtr CreateTensorAndFillData(std::vector<T> data,
std::vector<int64_t> shape = {},
ge::Format format = ge::FORMAT_NCHW) {
const std::type_info& info = typeid(T);
ge::DataType type = ge::DT_FLOAT;
if (info == typeid(float)) {
type = ge::DT_FLOAT;
} else if (info == typeid(int8_t)) {
type = ge::DT_INT8;
} else if (info == typeid(int32_t)) {
type = ge::DT_INT32;
} else {
LOG(FATAL) << "[NPU] Unknow value type " << info.name();
}
if (shape.empty()) {
shape = {static_cast<int64_t>(data.size())};
} else {
int size = 1;
for (auto i : shape) {
size *= i;
}
CHECK_EQ(data.size(), size);
}
ge::TensorDesc desc(ge::Shape(shape), format, type);
ge::TensorPtr tensor = std::make_shared<ge::Tensor>();
tensor->SetTensorDesc(desc);
tensor->SetData(reinterpret_cast<uint8_t*>(data.data()),
data.size() * sizeof(T));
return tensor;
}
template <typename T>
ge::TensorPtr CreateTensorAndFillData(T value,
std::vector<int64_t> shape = {1},
ge::Format format = ge::FORMAT_NCHW) {
int64_t size = 1;
for (auto i : shape) {
size *= i;
}
std::vector<T> data(size, value);
return CreateTensorAndFillData(data, shape, format);
}
int CvtActMode(std::string act_type);
bool HasInputArg(const OpInfo* op_info,
const Scope* scope,
const std::string& argname);
} // namespace npu
} // namespace lite
} // namespace paddle
lite/backends/npu/device.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/npu/device.h 100755 → 100644
浏览文件 @ 36004a9a
......@@ -18,8 +18,8 @@
#include <string>
#include <unordered_map>
#include <vector>
#include "ai_ddk_lib/include/HiAiModelManagerService.h"
#include "ai_ddk_lib/include/hiai_ir_build.h"
#include "HiAiModelManagerService.h" // NOLINT
#include "hiai_ir_build.h" // NOLINT
namespace paddle {
namespace lite {
......
lite/backends/npu/runtime.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
// 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/backends/npu/runtime.h"
#include <string>
#include <vector>
#include "lite/utils/cp_logging.h"
namespace paddle {
namespace lite {
namespace npu {
// Create hiai model manager to load om model from lite tensor, and return the
// manager and an unique model name
bool LoadModel(const lite::Tensor &model_data,
std::shared_ptr<hiai::AiModelMngerClient> *model_client,
std::string *model_name) {
LOG(INFO) << "[NPU] Load model.";
auto model_data_ptr = model_data.data<int8_t>();
auto model_data_size = model_data.numel() * sizeof(int8_t);
if (model_data_ptr == nullptr || model_data_size == 0) {
return false;
}
*model_client = std::make_shared<hiai::AiModelMngerClient>();
int ret = (*model_client)->Init(nullptr);
if (ret != hiai::AI_SUCCESS) {
LOG(WARNING) << "[NPU] AiModelMngerClient init failed(" << ret << ")!";
return false;
}
*model_name = "model.om";
auto model_desc = std::make_shared<hiai::AiModelDescription>(
*model_name,
DeviceInfo::Global().freq_level(),
DeviceInfo::Global().framework_type(),
DeviceInfo::Global().model_type(),
DeviceInfo::Global().device_type());
model_desc->SetModelBuffer(model_data_ptr, model_data_size);
std::vector<std::shared_ptr<hiai::AiModelDescription>> model_descs;
model_descs.push_back(model_desc);
if ((*model_client)->Load(model_descs) != hiai::AI_SUCCESS) {
LOG(WARNING) << "[NPU] AiModelMngerClient load model failed!";
return false;
}
return true;
}
} // namespace npu
} // namespace lite
} // namespace paddle
lite/backends/npu/runtime.h 已删除 100644 → 0
浏览文件 @ aa64ce33
// 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 <memory>
#include <string>
#include "ai_ddk_lib/include/HiAiModelManagerService.h"
#include "lite/core/tensor.h"
namespace paddle {
namespace lite {
namespace npu {
class DeviceInfo {
public:
static DeviceInfo &Global() {
static DeviceInfo x;
return x;
}
DeviceInfo() {}
int freq_level() { return freq_level_; }
int framework_type() { return framework_type_; }
int model_type() { return model_type_; }
int device_type() { return device_type_; }
private:
int freq_level_{3};
int framework_type_{0};
int model_type_{0};
int device_type_{0};
};
bool LoadModel(const lite::Tensor &model_data,
std::shared_ptr<hiai::AiModelMngerClient> *model_client,
std::string *model_name);
} // namespace npu
} // namespace lite
} // namespace paddle
lite/backends/opencl/cl_kernel/image/conv2d_1x1_kernel.cl 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/opencl/cl_kernel/image/reshape_kernel.cl 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/x86/jit/README.en.md
浏览文件 @ 36004a9a
......@@ -89,7 +89,7 @@ All kernels are inlcuded in `lite/backends/x86/jit/kernels.h`, which is automati
3. Add reference function of `your_key`.
Note:
- this should be run on CPU and do not depend on any third-party.
- Add `USE_JITKERNEL_REFER(your_key)` in `refer/CmakeLists.txt` to make sure this code can be used.
- Add `USE_JITKERNEL_REFER_LITE(your_key)` in `refer/CmakeLists.txt` to make sure this code can be used.
4. Add unit test in `test.cc`, and verfiy at least `float` and `double`.
Test more data type for some special functions if necessary, for example `int8`.
5. Add functions in `benchmark.cc` to test all function of same `KernelType`. Make sure `GetDefaultBestFunc` always get the best one.
......
lite/backends/x86/jit/README.md
浏览文件 @ 36004a9a
......@@ -79,7 +79,7 @@ PaddlePaddle/Paddle/paddle/fluid/
# 如何添加新的算子
1. 在`KernelType` 中添加 `your_key` 。
2. 实现Reference 的逻辑，这个是必须是在CPU上的实现，并且不能依赖任何第三方库。实现后在`refer/CmakeLists.txt`中添加`USE_JITKERNEL_REFER(your_key)`来使用该kernel。
2. 实现Reference 的逻辑，这个是必须是在CPU上的实现，并且不能依赖任何第三方库。实现后在`refer/CmakeLists.txt`中添加`USE_JITKERNEL_REFER_LITE(your_key)`来使用该kernel。
3. (optional) 实现更多的算法在`more`目录下，可以依赖mkl，intrinsic或者mkldnn等第三方库。
4. (optional) 实现基于Xbyak的生成code，在`gen`目下。 jitcode需要实现自己的`JitCodeCreator`，并注册在与refer相同的`KernelType`上。
5. 添加新的`KernelTuple`，需要与`KernelType`一一对应，是所有类型的一个打包，包括数据类型，属性的类型，以及返回的函数类型。可以参考`SeqPoolTuple`，新加的Attr类型需要特例化`JitCodeKey`方法。
......
lite/backends/x86/jit/gen/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -4,33 +4,33 @@ file(GLOB jitcode_cc_srcs RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*.cc")
cc_library(jit_kernel_jitcode SRCS ${jitcode_cc_srcs} DEPS jit_kernel_base xbyak)
set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} xbyak jit_kernel_jitcode PARENT_SCOPE)
function(USE_JITKERNEL_GEN TARGET)
file(APPEND ${jit_file} "USE_JITKERNEL_GEN(${TARGET});\n")
function(USE_JITKERNEL_GEN_LITE TARGET)
file(APPEND ${jit_file} "USE_JITKERNEL_GEN_LITE(${TARGET});\n")
endfunction()
# use gen jitcode kernel by name
USE_JITKERNEL_GEN(kMatMul)
USE_JITKERNEL_GEN(kVMul)
USE_JITKERNEL_GEN(kVAdd)
USE_JITKERNEL_GEN(kVSub)
USE_JITKERNEL_GEN(kVAddRelu)
USE_JITKERNEL_GEN(kVScal)
USE_JITKERNEL_GEN(kVAddBias)
USE_JITKERNEL_GEN(kVRelu)
USE_JITKERNEL_GEN(kVSquare)
USE_JITKERNEL_GEN(kVIdentity)
USE_JITKERNEL_GEN(kVExp)
USE_JITKERNEL_GEN(kVSigmoid)
USE_JITKERNEL_GEN(kVTanh)
USE_JITKERNEL_GEN(kLSTMCtHt)
USE_JITKERNEL_GEN(kLSTMC1H1)
USE_JITKERNEL_GEN(kGRUH1)
USE_JITKERNEL_GEN(kGRUHtPart1)
USE_JITKERNEL_GEN(kGRUHtPart2)
USE_JITKERNEL_GEN(kNCHW16CMulNC)
USE_JITKERNEL_GEN(kSeqPool)
USE_JITKERNEL_GEN(kHMax)
USE_JITKERNEL_GEN(kHSum)
USE_JITKERNEL_GEN(kEmbSeqPool)
USE_JITKERNEL_GEN(kSgd)
USE_JITKERNEL_GEN(kVBroadcast)
USE_JITKERNEL_GEN_LITE(kMatMul)
USE_JITKERNEL_GEN_LITE(kVMul)
USE_JITKERNEL_GEN_LITE(kVAdd)
USE_JITKERNEL_GEN_LITE(kVSub)
USE_JITKERNEL_GEN_LITE(kVAddRelu)
USE_JITKERNEL_GEN_LITE(kVScal)
USE_JITKERNEL_GEN_LITE(kVAddBias)
USE_JITKERNEL_GEN_LITE(kVRelu)
USE_JITKERNEL_GEN_LITE(kVSquare)
USE_JITKERNEL_GEN_LITE(kVIdentity)
USE_JITKERNEL_GEN_LITE(kVExp)
USE_JITKERNEL_GEN_LITE(kVSigmoid)
USE_JITKERNEL_GEN_LITE(kVTanh)
USE_JITKERNEL_GEN_LITE(kLSTMCtHt)
USE_JITKERNEL_GEN_LITE(kLSTMC1H1)
USE_JITKERNEL_GEN_LITE(kGRUH1)
USE_JITKERNEL_GEN_LITE(kGRUHtPart1)
USE_JITKERNEL_GEN_LITE(kGRUHtPart2)
USE_JITKERNEL_GEN_LITE(kNCHW16CMulNC)
USE_JITKERNEL_GEN_LITE(kSeqPool)
USE_JITKERNEL_GEN_LITE(kHMax)
USE_JITKERNEL_GEN_LITE(kHSum)
USE_JITKERNEL_GEN_LITE(kEmbSeqPool)
USE_JITKERNEL_GEN_LITE(kSgd)
USE_JITKERNEL_GEN_LITE(kVBroadcast)
lite/backends/x86/jit/gen/act.cc
浏览文件 @ 36004a9a
......@@ -156,9 +156,9 @@ size_t VTanhCreator::CodeSize(const int& d) const {
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kVRelu, gen::VReluCreator);
REGISTER_JITKERNEL_GEN(kVSquare, gen::VSquareCreator);
REGISTER_JITKERNEL_GEN(kVIdentity, gen::VIdentityCreator);
REGISTER_JITKERNEL_GEN(kVExp, gen::VExpCreator);
REGISTER_JITKERNEL_GEN(kVSigmoid, gen::VSigmoidCreator);
REGISTER_JITKERNEL_GEN(kVTanh, gen::VTanhCreator);
REGISTER_JITKERNEL_GEN_LITE(kVRelu, gen::VReluCreator);
REGISTER_JITKERNEL_GEN_LITE(kVSquare, gen::VSquareCreator);
REGISTER_JITKERNEL_GEN_LITE(kVIdentity, gen::VIdentityCreator);
REGISTER_JITKERNEL_GEN_LITE(kVExp, gen::VExpCreator);
REGISTER_JITKERNEL_GEN_LITE(kVSigmoid, gen::VSigmoidCreator);
REGISTER_JITKERNEL_GEN_LITE(kVTanh, gen::VTanhCreator);
lite/backends/x86/jit/gen/blas.cc
浏览文件 @ 36004a9a
......@@ -181,10 +181,10 @@ DECLARE_BLAS_CREATOR(VAddBias);
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kVMul, gen::VMulCreator);
REGISTER_JITKERNEL_GEN(kVAdd, gen::VAddCreator);
REGISTER_JITKERNEL_GEN(kVSub, gen::VSubCreator);
REGISTER_JITKERNEL_GEN(kVAddRelu, gen::VAddReluCreator);
REGISTER_JITKERNEL_GEN(kVScal, gen::VScalCreator);
REGISTER_JITKERNEL_GEN(kVAddBias, gen::VAddBiasCreator);
REGISTER_JITKERNEL_GEN(kNCHW16CMulNC, gen::NCHW16CMulNCCreator);
REGISTER_JITKERNEL_GEN_LITE(kVMul, gen::VMulCreator);
REGISTER_JITKERNEL_GEN_LITE(kVAdd, gen::VAddCreator);
REGISTER_JITKERNEL_GEN_LITE(kVSub, gen::VSubCreator);
REGISTER_JITKERNEL_GEN_LITE(kVAddRelu, gen::VAddReluCreator);
REGISTER_JITKERNEL_GEN_LITE(kVScal, gen::VScalCreator);
REGISTER_JITKERNEL_GEN_LITE(kVAddBias, gen::VAddBiasCreator);
REGISTER_JITKERNEL_GEN_LITE(kNCHW16CMulNC, gen::NCHW16CMulNCCreator);
lite/backends/x86/jit/gen/embseqpool.cc
浏览文件 @ 36004a9a
......@@ -145,4 +145,4 @@ class EmbSeqPoolCreator : public JitCodeCreator<emb_seq_pool_attr_t> {
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kEmbSeqPool, gen::EmbSeqPoolCreator);
REGISTER_JITKERNEL_GEN_LITE(kEmbSeqPool, gen::EmbSeqPoolCreator);
lite/backends/x86/jit/gen/gru.cc
浏览文件 @ 36004a9a
......@@ -111,6 +111,6 @@ DECLARE_GRU_CREATOR(GRUHtPart2);
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kGRUH1, gen::GRUH1Creator);
REGISTER_JITKERNEL_GEN(kGRUHtPart1, gen::GRUHtPart1Creator);
REGISTER_JITKERNEL_GEN(kGRUHtPart2, gen::GRUHtPart2Creator);
REGISTER_JITKERNEL_GEN_LITE(kGRUH1, gen::GRUH1Creator);
REGISTER_JITKERNEL_GEN_LITE(kGRUHtPart1, gen::GRUHtPart1Creator);
REGISTER_JITKERNEL_GEN_LITE(kGRUHtPart2, gen::GRUHtPart2Creator);
lite/backends/x86/jit/gen/hopv.cc
浏览文件 @ 36004a9a
......@@ -99,5 +99,5 @@ DECLARE_HOP_CREATOR(HSum);
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kHMax, gen::HMaxCreator);
REGISTER_JITKERNEL_GEN(kHSum, gen::HSumCreator);
REGISTER_JITKERNEL_GEN_LITE(kHMax, gen::HMaxCreator);
REGISTER_JITKERNEL_GEN_LITE(kHSum, gen::HSumCreator);
lite/backends/x86/jit/gen/lstm.cc
浏览文件 @ 36004a9a
......@@ -138,5 +138,5 @@ DECLARE_LSTM_CREATOR(LSTMC1H1);
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kLSTMCtHt, gen::LSTMCtHtCreator);
REGISTER_JITKERNEL_GEN(kLSTMC1H1, gen::LSTMC1H1Creator);
REGISTER_JITKERNEL_GEN_LITE(kLSTMCtHt, gen::LSTMCtHtCreator);
REGISTER_JITKERNEL_GEN_LITE(kLSTMC1H1, gen::LSTMC1H1Creator);
lite/backends/x86/jit/gen/matmul.cc
浏览文件 @ 36004a9a
......@@ -130,4 +130,4 @@ class MatMulCreator : public JitCodeCreator<matmul_attr_t> {
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kMatMul, gen::MatMulCreator);
REGISTER_JITKERNEL_GEN_LITE(kMatMul, gen::MatMulCreator);
lite/backends/x86/jit/gen/seqpool.cc
浏览文件 @ 36004a9a
......@@ -82,4 +82,4 @@ class SeqPoolCreator : public JitCodeCreator<seq_pool_attr_t> {
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kSeqPool, gen::SeqPoolCreator);
REGISTER_JITKERNEL_GEN_LITE(kSeqPool, gen::SeqPoolCreator);
lite/backends/x86/jit/gen/sgd.cc
浏览文件 @ 36004a9a
......@@ -127,4 +127,4 @@ class SgdCreator : public JitCodeCreator<sgd_attr_t> {
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kSgd, gen::SgdCreator);
REGISTER_JITKERNEL_GEN_LITE(kSgd, gen::SgdCreator);
lite/backends/x86/jit/gen/vbroadcast.cc
浏览文件 @ 36004a9a
......@@ -88,4 +88,4 @@ class VBroadcastCreator : public JitCodeCreator<int64_t> {
namespace gen = paddle::lite::jit::gen;
REGISTER_JITKERNEL_GEN(kVBroadcast, gen::VBroadcastCreator);
REGISTER_JITKERNEL_GEN_LITE(kVBroadcast, gen::VBroadcastCreator);
lite/backends/x86/jit/more/CMakeLists.txt
浏览文件 @ 36004a9a
function(USE_JITKERNEL_MORE TARGET TYPE)
file(APPEND ${jit_file} "USE_JITKERNEL_MORE(${TARGET} ${TYPE});\n")
function(USE_JITKERNEL_MORE_LITE TARGET TYPE)
file(APPEND ${jit_file} "USE_JITKERNEL_MORE_LITE(${TARGET} ${TYPE});\n")
endfunction()
# enable it latter
......
lite/backends/x86/jit/more/intrinsic/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -5,5 +5,5 @@ cc_library(jit_kernel_intrinsic SRCS ${jit_kernel_cc_intrinsic} DEPS jit_kernel_
set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_intrinsic PARENT_SCOPE)
# use mkl kernels by name and type
USE_JITKERNEL_MORE(kCRFDecoding, intrinsic)
USE_JITKERNEL_MORE(kLayerNorm, intrinsic)
USE_JITKERNEL_MORE_LITE(kCRFDecoding, intrinsic)
USE_JITKERNEL_MORE_LITE(kLayerNorm, intrinsic)
lite/backends/x86/jit/more/mix/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -5,11 +5,11 @@ cc_library(jit_kernel_mix SRCS ${jit_kernel_mix_cc} DEPS jit_kernel_base)
set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_mix PARENT_SCOPE)
USE_JITKERNEL_MORE(kVSigmoid, mix)
USE_JITKERNEL_MORE(kVTanh, mix)
USE_JITKERNEL_MORE(kLSTMCtHt, mix)
USE_JITKERNEL_MORE(kLSTMC1H1, mix)
USE_JITKERNEL_MORE(kGRUH1, mix)
USE_JITKERNEL_MORE(kGRUHtPart1, mix)
USE_JITKERNEL_MORE(kGRUHtPart2, mix)
USE_JITKERNEL_MORE(kSoftmax, mix)
USE_JITKERNEL_MORE_LITE(kVSigmoid, mix)
USE_JITKERNEL_MORE_LITE(kVTanh, mix)
USE_JITKERNEL_MORE_LITE(kLSTMCtHt, mix)
USE_JITKERNEL_MORE_LITE(kLSTMC1H1, mix)
USE_JITKERNEL_MORE_LITE(kGRUH1, mix)
USE_JITKERNEL_MORE_LITE(kGRUHtPart1, mix)
USE_JITKERNEL_MORE_LITE(kGRUHtPart2, mix)
USE_JITKERNEL_MORE_LITE(kSoftmax, mix)
lite/backends/x86/jit/more/mkl/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -3,18 +3,18 @@ cc_library(jit_kernel_mkl SRCS mkl.cc DEPS jit_kernel_base dynload_mklml)
set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} dynload_mklml jit_kernel_mkl PARENT_SCOPE)
# use mkl kernels by name and type
USE_JITKERNEL_MORE(kMatMul, mkl)
USE_JITKERNEL_MORE(kVMul, mkl)
USE_JITKERNEL_MORE(kVAdd, mkl)
USE_JITKERNEL_MORE(kVScal, mkl)
USE_JITKERNEL_MORE(kStrideScal, mkl)
USE_JITKERNEL_MORE(kVExp, mkl)
USE_JITKERNEL_MORE(kVSquare, mkl)
USE_JITKERNEL_MORE(kVCopy, mkl)
USE_JITKERNEL_MORE(kVSigmoid, mkl)
USE_JITKERNEL_MORE(kVTanh, mkl)
USE_JITKERNEL_MORE(kSeqPool, mkl)
USE_JITKERNEL_MORE(kSoftmax, mkl)
USE_JITKERNEL_MORE(kEmbSeqPool, mkl)
USE_JITKERNEL_MORE(kSgd, mkl)
USE_JITKERNEL_MORE(kVBroadcast, mkl)
USE_JITKERNEL_MORE_LITE(kMatMul, mkl)
USE_JITKERNEL_MORE_LITE(kVMul, mkl)
USE_JITKERNEL_MORE_LITE(kVAdd, mkl)
USE_JITKERNEL_MORE_LITE(kVScal, mkl)
USE_JITKERNEL_MORE_LITE(kStrideScal, mkl)
USE_JITKERNEL_MORE_LITE(kVExp, mkl)
USE_JITKERNEL_MORE_LITE(kVSquare, mkl)
USE_JITKERNEL_MORE_LITE(kVCopy, mkl)
USE_JITKERNEL_MORE_LITE(kVSigmoid, mkl)
USE_JITKERNEL_MORE_LITE(kVTanh, mkl)
USE_JITKERNEL_MORE_LITE(kSeqPool, mkl)
USE_JITKERNEL_MORE_LITE(kSoftmax, mkl)
USE_JITKERNEL_MORE_LITE(kEmbSeqPool, mkl)
USE_JITKERNEL_MORE_LITE(kSgd, mkl)
USE_JITKERNEL_MORE_LITE(kVBroadcast, mkl)
lite/backends/x86/jit/refer/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -2,39 +2,39 @@
cc_library(jit_kernel_refer SRCS refer.cc DEPS jit_kernel_base)
set(JIT_KERNEL_DEPS ${JIT_KERNEL_DEPS} jit_kernel_refer PARENT_SCOPE)
function(USE_JITKERNEL_REFER TARGET)
file(APPEND ${jit_file} "USE_JITKERNEL_REFER(${TARGET});\n")
function(USE_JITKERNEL_REFER_LITE TARGET)
file(APPEND ${jit_file} "USE_JITKERNEL_REFER_LITE(${TARGET});\n")
endfunction()
# use refer kernel by name
USE_JITKERNEL_REFER(kVMul)
USE_JITKERNEL_REFER(kVAdd)
USE_JITKERNEL_REFER(kVAddRelu)
USE_JITKERNEL_REFER(kVSub)
USE_JITKERNEL_REFER(kVScal)
USE_JITKERNEL_REFER(kStrideScal)
USE_JITKERNEL_REFER(kVAddBias)
USE_JITKERNEL_REFER(kVCopy)
USE_JITKERNEL_REFER(kVRelu)
USE_JITKERNEL_REFER(kVIdentity)
USE_JITKERNEL_REFER(kVExp)
USE_JITKERNEL_REFER(kVSigmoid)
USE_JITKERNEL_REFER(kVTanh)
USE_JITKERNEL_REFER(kLSTMCtHt)
USE_JITKERNEL_REFER(kLSTMC1H1)
USE_JITKERNEL_REFER(kGRUH1)
USE_JITKERNEL_REFER(kGRUHtPart1)
USE_JITKERNEL_REFER(kGRUHtPart2)
USE_JITKERNEL_REFER(kCRFDecoding)
USE_JITKERNEL_REFER(kLayerNorm)
USE_JITKERNEL_REFER(kNCHW16CMulNC)
USE_JITKERNEL_REFER(kSeqPool)
USE_JITKERNEL_REFER(kMatMul)
USE_JITKERNEL_REFER(kVSquare)
USE_JITKERNEL_REFER(kHSum)
USE_JITKERNEL_REFER(kHMax)
USE_JITKERNEL_REFER(kStrideASum)
USE_JITKERNEL_REFER(kSoftmax)
USE_JITKERNEL_REFER(kEmbSeqPool)
USE_JITKERNEL_REFER(kSgd)
USE_JITKERNEL_REFER(kVBroadcast)
USE_JITKERNEL_REFER_LITE(kVMul)
USE_JITKERNEL_REFER_LITE(kVAdd)
USE_JITKERNEL_REFER_LITE(kVAddRelu)
USE_JITKERNEL_REFER_LITE(kVSub)
USE_JITKERNEL_REFER_LITE(kVScal)
USE_JITKERNEL_REFER_LITE(kStrideScal)
USE_JITKERNEL_REFER_LITE(kVAddBias)
USE_JITKERNEL_REFER_LITE(kVCopy)
USE_JITKERNEL_REFER_LITE(kVRelu)
USE_JITKERNEL_REFER_LITE(kVIdentity)
USE_JITKERNEL_REFER_LITE(kVExp)
USE_JITKERNEL_REFER_LITE(kVSigmoid)
USE_JITKERNEL_REFER_LITE(kVTanh)
USE_JITKERNEL_REFER_LITE(kLSTMCtHt)
USE_JITKERNEL_REFER_LITE(kLSTMC1H1)
USE_JITKERNEL_REFER_LITE(kGRUH1)
USE_JITKERNEL_REFER_LITE(kGRUHtPart1)
USE_JITKERNEL_REFER_LITE(kGRUHtPart2)
USE_JITKERNEL_REFER_LITE(kCRFDecoding)
USE_JITKERNEL_REFER_LITE(kLayerNorm)
USE_JITKERNEL_REFER_LITE(kNCHW16CMulNC)
USE_JITKERNEL_REFER_LITE(kSeqPool)
USE_JITKERNEL_REFER_LITE(kMatMul)
USE_JITKERNEL_REFER_LITE(kVSquare)
USE_JITKERNEL_REFER_LITE(kHSum)
USE_JITKERNEL_REFER_LITE(kHMax)
USE_JITKERNEL_REFER_LITE(kStrideASum)
USE_JITKERNEL_REFER_LITE(kSoftmax)
USE_JITKERNEL_REFER_LITE(kEmbSeqPool)
USE_JITKERNEL_REFER_LITE(kSgd)
USE_JITKERNEL_REFER_LITE(kVBroadcast)
lite/backends/x86/jit/refer/refer.cc
浏览文件 @ 36004a9a
......@@ -18,7 +18,7 @@
namespace refer = paddle::lite::jit::refer;
#define REGISTER_REFER_KERNEL(func) \
REGISTER_JITKERNEL_REFER( \
REGISTER_JITKERNEL_REFER_LITE( \
k##func, refer::func##Kernel<float>, refer::func##Kernel<double>)
REGISTER_REFER_KERNEL(VMul);
......
lite/backends/x86/jit/registry.h
浏览文件 @ 36004a9a
......@@ -77,16 +77,16 @@ class JitKernelRegistrar {
void Touch() {}
};
#define STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE(uniq_name, msg) \
#define STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE(uniq_name, msg) \
struct __test_global_namespace_##uniq_name##__ {}; \
static_assert(std::is_same<::__test_global_namespace_##uniq_name##__, \
__test_global_namespace_##uniq_name##__>::value, \
msg)
// Refer always on CPUPlace
#define REGISTER_JITKERNEL_REFER(kernel_type, ...) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE( \
__reg_jitkernel_##kernel_type##_refer_CPUPlace, \
#define REGISTER_JITKERNEL_REFER_LITE(kernel_type, ...) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE( \
__reg_litejitkernel_##kernel_type##_refer_CPUPlace, \
"REGISTER_KERNEL_REFER must be called in global namespace"); \
static ::paddle::lite::jit::JitKernelRegistrar< \
::paddle::lite::jit::ReferKernelPool, \
......@@ -94,84 +94,84 @@ class JitKernelRegistrar {
__VA_ARGS__> \
__jit_kernel_registrar_##kernel_type##_refer_CPUPlace_( \
::paddle::lite::jit::KernelType::kernel_type); \
int TouchJitKernelReg_##kernel_type##_refer_CPUPlace_() { \
int LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_() { \
__jit_kernel_registrar_##kernel_type##_refer_CPUPlace_.Touch(); \
return 0; \
}
// kernel_type: should be in paddle::lite::jit::KernelType
// place_type: should be one of CPUPlace and GPUPlace in paddle::platform
#define REGISTER_KERNEL_MORE(kernel_type, impl_type, place_type, ...) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE( \
__reg_jitkernel_##kernel_type##_##impl_type##_##place_type, \
"REGISTER_KERNEL_MORE must be called in global namespace"); \
extern int TouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
#define REGISTER_KERNEL_MORE_LITE(kernel_type, impl_type, place_type, ...) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE( \
__reg_litejitkernel_##kernel_type##_##impl_type##_##place_type, \
"REGISTER_KERNEL_MORE_LITE must be called in global namespace"); \
extern int LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static int __assert_##kernel_type##_##impl_type##_##place_type##_has_refer_ \
UNUSED = TouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
UNUSED = LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static ::paddle::lite::jit::JitKernelRegistrar< \
::paddle::lite::jit::KernelPool, \
::paddle::lite::fluid::place_type, \
__VA_ARGS__> \
__jit_kernel_registrar_##kernel_type##_##impl_type##_##place_type##_( \
::paddle::lite::jit::KernelType::kernel_type); \
int TouchJitKernelReg_##kernel_type##_##impl_type##_##place_type##_() { \
int LiteTouchJitKernelReg_##kernel_type##_##impl_type##_##place_type##_() { \
__jit_kernel_registrar_##kernel_type##_##impl_type##_##place_type##_ \
.Touch(); \
return 0; \
}
#define REGISTER_JITKERNEL_MORE(kernel_type, impl_type, ...) \
REGISTER_KERNEL_MORE(kernel_type, impl_type, CPUPlace, __VA_ARGS__)
#define REGISTER_GPUKERNEL_MORE(kernel_type, impl_type, ...) \
REGISTER_KERNEL_MORE(kernel_type, impl_type, GPUPlace, __VA_ARGS__)
#define REGISTER_JITKERNEL_GEN(kernel_type, ...) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE( \
__reg_jitkernel_gen_##kernel_type##_CPUPlace_, \
"REGISTER_JITKERNEL_GEN must be called in global namespace"); \
extern int TouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static int __assert_gen_##kernel_type##_has_refer_ UNUSED = \
TouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static ::paddle::lite::jit::JitKernelRegistrar< \
::paddle::lite::jit::JitCodeCreatorPool, \
::paddle::lite::fluid::CPUPlace, \
__VA_ARGS__> \
__jit_kernel_registrar_gen_##kernel_type##_CPUPlace_( \
::paddle::lite::jit::KernelType::kernel_type); \
int TouchJitKernelReg_gen_##kernel_type##_CPUPlace_() { \
__jit_kernel_registrar_gen_##kernel_type##_CPUPlace_.Touch(); \
return 0; \
REGISTER_KERNEL_MORE_LITE(kernel_type, impl_type, CPUPlace, __VA_ARGS__)
#define REGISTER_GPUKERNEL_MORE_LITE(kernel_type, impl_type, ...) \
REGISTER_KERNEL_MORE_LITE(kernel_type, impl_type, GPUPlace, __VA_ARGS__)
#define REGISTER_JITKERNEL_GEN_LITE(kernel_type, ...) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE( \
__reg_litejitkernel_gen_##kernel_type##_CPUPlace_, \
"REGISTER_JITKERNEL_GEN_LITE must be called in global namespace"); \
extern int LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static int __assert_gen_##kernel_type##_has_refer_ UNUSED = \
LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static ::paddle::lite::jit::JitKernelRegistrar< \
::paddle::lite::jit::JitCodeCreatorPool, \
::paddle::lite::fluid::CPUPlace, \
__VA_ARGS__> \
__jit_kernel_registrar_gen_##kernel_type##_CPUPlace_( \
::paddle::lite::jit::KernelType::kernel_type); \
int LiteTouchJitKernelReg_gen_##kernel_type##_CPUPlace_() { \
__jit_kernel_registrar_gen_##kernel_type##_CPUPlace_.Touch(); \
return 0; \
}
#define USE_JITKERNEL_GEN(kernel_type) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE( \
__reg_jitkernel_gen_##kernel_type##_CPUPlace_, \
"USE_JITKERNEL_GEN must be called in global namespace"); \
extern int TouchJitKernelReg_gen_##kernel_type##_CPUPlace_(); \
static int use_jitkernel_gen_##kernel_type##_CPUPlace_ UNUSED = \
TouchJitKernelReg_gen_##kernel_type##_CPUPlace_()
#define USE_JITKERNEL_REFER(kernel_type) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE( \
__reg_jitkernel_##kernel_type##_refer_CPUPlace_, \
"USE_JITKERNEL_REFER must be called in global namespace"); \
extern int TouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static int use_jitkernel_##kernel_type##_refer_CPUPlace_ UNUSED = \
TouchJitKernelReg_##kernel_type##_refer_CPUPlace_()
#define USE_KERNEL_MORE(kernel_type, impl_type, place_type) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE( \
__reg_jitkernel_##kernel_type##_##impl_type##_##place_type##_, \
"USE_JITKERNEL_MORE must be called in global namespace"); \
extern int \
TouchJitKernelReg_##kernel_type##_##impl_type##_##place_type##_(); \
static int use_jitkernel_##kernel_type##_##impl_type##_##place_type##_ \
UNUSED = \
TouchJitKernelReg_##kernel_type##_##impl_type##_##place_type##_()
#define USE_JITKERNEL_MORE(kernel_type, impl_type) \
USE_KERNEL_MORE(kernel_type, impl_type, CPUPlace)
#define USE_JITKERNEL_GEN_LITE(kernel_type) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE( \
__reg_litejitkernel_gen_##kernel_type##_CPUPlace_, \
"USE_JITKERNEL_GEN_LITE must be called in global namespace"); \
extern int LiteTouchJitKernelReg_gen_##kernel_type##_CPUPlace_(); \
static int use_litejitkernel_gen_##kernel_type##_CPUPlace_ UNUSED = \
LiteTouchJitKernelReg_gen_##kernel_type##_CPUPlace_()
#define USE_JITKERNEL_REFER_LITE(kernel_type) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE( \
__reg_litejitkernel_##kernel_type##_refer_CPUPlace_, \
"USE_JITKERNEL_REFER_LITE must be called in global namespace"); \
extern int LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_(); \
static int use_litejitkernel_##kernel_type##_refer_CPUPlace_ UNUSED = \
LiteTouchJitKernelReg_##kernel_type##_refer_CPUPlace_()
#define USE_KERNEL_MORE_LITE(kernel_type, impl_type, place_type) \
STATIC_ASSERT_JITKERNEL_GLOBAL_NAMESPACE_LITE( \
__reg_litejitkernel_##kernel_type##_##impl_type##_##place_type##_, \
"USE_JITKERNEL_MORE_LITE must be called in global namespace"); \
extern int \
LiteTouchJitKernelReg_##kernel_type##_##impl_type##_##place_type##_(); \
static int use_litejitkernel_##kernel_type##_##impl_type##_##place_type##_ \
UNUSED = \
LiteTouchJitKernelReg_##kernel_type##_##impl_type##_##place_type##_()
#define USE_JITKERNEL_MORE_LITE(kernel_type, impl_type) \
USE_KERNEL_MORE_LITE(kernel_type, impl_type, CPUPlace)
} // namespace jit
} // namespace lite
......
lite/backends/x86/parallel.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/backends/xpu/builder.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/backends/xpu/builder.h 已删除 100644 → 0
浏览文件 @ aa64ce33
// 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 <xtcl/xtcl.h>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "lite/core/op_lite.h"
#include "lite/core/target_wrapper.h"
#include "lite/core/tensor.h"
namespace paddle {
namespace lite {
namespace xpu {
bool HasInputArg(const OpInfo* op_info,
const Scope* scope,
const std::string& argname);
std::string UniqueName(const std::string& prefix);
xtcl::DataType CvtPrecisionType(PrecisionType in_type);
DLDataType CvtDataType(PrecisionType in_type);
xtcl::Array<xtcl::xIndexExpr> CvtShape(const std::vector<int>& in_shape);
xtcl::Array<xtcl::xIndexExpr> CvtShape(const std::vector<int64_t>& in_shape);
xtcl::Array<xtcl::xIndexExpr> CvtShape(const DDim& in_dims);
std::shared_ptr<xtcl::xNDArray> CvtTensor(
Tensor* in_tensor,
std::vector<int64_t> out_shape = {},
PrecisionType in_ptype = PRECISION(kFloat),
DataLayoutType in_ltype = DATALAYOUT(kNCHW));
bool BuildModel(
std::shared_ptr<xtcl::network::xNetworkBuilder> builder,
std::shared_ptr<xtcl::network::xTensorCompiler::ParamNDArrayMap> params,
std::vector<std::shared_ptr<xtcl::xExpr>>* outputs,
lite::Tensor* model);
} // namespace xpu
} // namespace lite
} // namespace paddle
lite/backends/xpu/device.cc 100755 → 100644
浏览文件 @ 36004a9a
......@@ -36,8 +36,11 @@ std::unique_ptr<xtcl::network::xRuntimeInstance> Device::Build(
}
xtcl::xNetwork network =
builder->FinalizeNetwork(xtcl::relay::TupleNode::make(all_outs));
auto target = xtcl::Target::Create(device_name_);
auto compiler = xtcl::network::xTensorCompiler(network, target);
auto target = xtcl::NullValue<xtcl::Target>();
if (!target_.empty()) {
target = xtcl::Target::Create(target_);
}
xtcl::network::xTensorCompiler compiler(network, target);
compiler.SetParams(*params); // Set the data of constant tensors
compiler.Build();
VLOG(3) << "[XPU] Build done";
......
lite/backends/xpu/device.h 100755 → 100644
浏览文件 @ 36004a9a
......@@ -15,6 +15,7 @@
#pragma once
#include <xtcl/xtcl.h>
#include <cstdlib>
#include <memory>
#include <string>
#include <utility>
......@@ -30,7 +31,18 @@ class Device {
static Device x;
return x;
}
Device() {}
Device() {
char* name = std::getenv("XPU_DEVICE_NAME");
if (name) {
name_ = std::string(name);
}
// XPU_DEVICE_TARGET for XPU model building, which supports 'llvm' and 'xpu
// -libs=xdnn'
char* target = std::getenv("XPU_DEVICE_TARGET");
if (target) {
target_ = std::string(target);
}
}
// Build the XPU graph to the XPU runtime, return the XPU runtime which can be
// used to run inference.
......@@ -39,10 +51,12 @@ class Device {
xtcl::network::xTensorCompiler::ParamNDArrayMap* params,
std::vector<xtcl::xExpr*>* outputs);
const std::string name() const { return name_; }
const std::string target() const { return target_; }
private:
// Keep reserved fields
int device_id_{0};
std::string device_name_{"llvm"};
std::string name_{""};
std::string target_{""};
};
} // namespace xpu
......
lite/backends/xpu/runtime.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/backends/xpu/runtime.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/core/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/arena/CMakeLists.txt
浏览文件 @ 36004a9a
......@@ -6,5 +6,5 @@ endif()
lite_cc_library(arena_framework SRCS framework.cc DEPS program gtest)
if((NOT LITE_WITH_OPENCL) AND (LITE_WITH_X86 OR LITE_WITH_ARM))
lite_cc_test(test_arena_framework SRCS framework_test.cc DEPS arena_framework ${npu_kernels} ${bm_kernels} ${xpu_kernels} ${x86_kernels} ${fpga_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
lite_cc_test(test_arena_framework SRCS framework_test.cc DEPS arena_framework ${npu_kernels} ${xpu_kernels} ${x86_kernels} ${cuda_kernels} ${fpga_kernels} ${arm_kernels} ${lite_ops} ${host_kernels})
endif()
lite/core/framework.proto
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/kernel.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/mir/fusion/fc_fuser.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/mir/fusion/fc_fuser.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/mir/fusion/sequence_pool_concat_fuse_pass.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/sequence_pool_concat_fuse_pass.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/sequence_pool_concat_fuser.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/sequence_pool_concat_fuser.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/var_conv_2d_activation_fuse_pass.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/var_conv_2d_activation_fuse_pass.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/var_conv_2d_activation_fuser.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/fusion/var_conv_2d_activation_fuser.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/subgraph/subgraph_detector.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/mir/subgraph/subgraph_detector.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/subgraph/subgraph_detector_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/subgraph/subgraph_pass.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/mir/subgraph/subgraph_pass.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/core/mir/subgraph/subgraph_pass_test.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/profile/profiler.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/profile/profiler.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/profile/test_timer.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/program.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/program.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/core/tensor.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/README.md
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/makefiles/test_cv/Makefile.android.armv7 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/makefiles/test_cv/Makefile.android.armv8 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/mobile_classify/mobile_classify.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/mobile_detection/test.jpg 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/demo/cxx/test_cv/README.md 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/test_cv/test_img_prepross.cc 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/test_cv/test_model_cv.cc 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/demo/cxx/yolov3_detection/yolov3_detection.cc 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/arm/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/arm/collect_fpn_proposals_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/collect_fpn_proposals_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/conditional_block_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/conditional_block_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/conv_compute.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/arm/distribute_fpn_proposals_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/distribute_fpn_proposals_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/grid_sampler_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/grid_sampler_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/instance_norm_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/instance_norm_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/merge_lod_tensor_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/merge_lod_tensor_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/merge_lod_tensor_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/reduce_prod_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/reduce_prod_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/split_lod_tensor_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/split_lod_tensor_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/arm/split_lod_tensor_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/cuda/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/cuda/elementwise_add_compute.cu 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/cuda/elementwise_add_compute.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/cuda/mul_compute.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/cuda/sequence_pool_concat_compute.cu 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/cuda/sequence_pool_concat_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/CMakeLists.txt 100644 → 100755
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/fpga/calib_compute.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/conv_compute.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/conv_compute.h 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/dropout_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/elementwise_compute.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/fc_compute.h 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/feed_compute.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/feed_compute.h 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/fetch_compute.h 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/gru_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/im2sequence_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/im2sequence_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/mul_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/multiclass_nms_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/norm_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/norm_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/pooling_compute_test.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/prior_box_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/prior_box_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/reshape_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/fpga/scale_compute.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/scale_compute.h 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/softmax_compute.cc 100644 → 100755
浏览文件 @ 36004a9a
文件模式从 100644 更改为 100755
lite/kernels/fpga/transpose_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/npu/bridges/act_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/argmax_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/argmax_op_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/npu/bridges/conv_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/engine.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/engine.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/npu/bridges/fc_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/graph.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/graph.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/mul_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/paddle_use_bridges.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/pool_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/pool_op_test.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/npu/bridges/registry.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/sqrt_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/transpose_op_test.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/npu/bridges/unsqueeze_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/unsqueeze_op_test.cc 已删除 100755 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/npu/bridges/utility.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/bridges/utility.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/graph_compute.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/npu/graph_compute.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/npu/subgraph_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/npu/subgraph_compute.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/opencl/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/opencl/conv2d_1x1_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/opencl/conv2d_1x1_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/opencl/reshape_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/opencl/reshape_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/kernels/x86/fc_compute_test.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/x86/relu_compute.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/x86/relu_compute.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/xpu/bridges/act_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/act_op_test.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/xpu/bridges/conv_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/dropout_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/gather_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/graph.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/graph.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/layer_norm_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/lookup_table_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/matmul_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/mul_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/paddle_use_bridges.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/pool_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/registry.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/xpu/bridges/registry.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/xpu/bridges/reshape_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/scale_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/slice_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/stack_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/transpose_op.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/utility.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/bridges/utility.h 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/graph_compute.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/xpu/graph_compute.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/kernels/xpu/subgraph_compute.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/kernels/xpu/subgraph_compute.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/operators/collect_fpn_proposals_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/collect_fpn_proposals_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/compare_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/operators/conditional_block_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/conditional_block_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/distribute_fpn_proposals_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/distribute_fpn_proposals_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/dropout_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/operators/fc_op.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/operators/graph_op.cc 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/operators/graph_op.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/operators/grid_sampler_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/grid_sampler_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/instance_norm_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/instance_norm_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/merge_lod_tensor_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/merge_lod_tensor_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/reduce_prod_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/reduce_prod_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/sequence_pool_concat_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/sequence_pool_concat_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/split_lod_tensor_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/split_lod_tensor_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/subgraph_op.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/operators/subgraph_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/cv/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/cv/cv_basic.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/cv/image_convert_test.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/kernels/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/kernels/batch_norm_compute_test.cc 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/kernels/dropout_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/gather_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/grid_sampler_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/instance_norm_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/layer_norm_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/lookup_table_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/mul_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/pool_compute_test.cc 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/kernels/reduce_prod_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
lite/tests/kernels/reshape_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/kernels/softmax_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/kernels/transpose_compute_test.cc 100755 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/tests/utils/timer.h 已删除 100644 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/tools/build_bm.sh 已删除 100755 → 0
浏览文件 @ aa64ce33
此差异已折叠。
lite/tools/build_xpu.sh
浏览文件 @ 36004a9a
此差异已折叠。
lite/tools/ci_build.sh
浏览文件 @ 36004a9a
此差异已折叠。
lite/tools/cmake_tools/record_supported_kernel_op.py 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image2tensor.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_convert.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_flip.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_flip.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_resize.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_resize.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_rotate.cc
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/cv/image_rotate.h
浏览文件 @ 36004a9a
此差异已折叠。
lite/utils/env.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/common/log.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/fpga/V2/image.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/fpga/V2/pe.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/framework/cl/cl_engine.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/cl/cl_helper.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/cl/cl_image.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/cl/cl_scope.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/context.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/executor.cpp
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/loader.cpp
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/framework/operator.cpp
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/io/opencl_interface.cpp
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/io/opencl_interface.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/io/paddle_mobile.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/src/operators/expand_op.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/expand_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/grid_sampler_op.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/grid_sampler_op.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/cl/cl_kernel/conv_kernel.inc.cl 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/cl/cl_kernel/expend.cl 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/cl/elementwise_sub_kernel.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/cl/expand_kernel.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/cl/grid_sampler_kernel.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/expand_kernel.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/fpga/V2/reshape2_kernel.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/fpga/V2/slice_kernel.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/kernel/grid_sampler_kernel.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/src/operators/op_param.h
浏览文件 @ 36004a9a
此差异已折叠。
mobile/test/CMakeLists.txt
浏览文件 @ 36004a9a
此差异已折叠。
mobile/test/executor_for_test_opencl.h 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/test/net/test_inference_api_v2.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/test/net/test_mobilenet_male2fe.cpp 0 → 100644
浏览文件 @ 36004a9a
此差异已折叠。
mobile/test/net/test_net_multi_feed.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/test/operators/test_expend_op.cpp 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
mobile/tools/python/fluidtools/run_multi_feed.py 100755 → 100644
浏览文件 @ 36004a9a
文件模式从 100755 更改为 100644
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或