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[Other] Optimize runtime module (#1356)

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* Optimize runtime

* fix error

* [Backend] Add option to print tensorrt conversion log (#1386)

Add option to print tensorrt conversion log

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

---------

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
This commit is contained in:
Jason
2023-02-21 17:01:32 +08:00
committed by GitHub
parent 42817ddc18
commit 18e33bae5c
7 changed files with 70 additions and 67 deletions
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27
fastdeploy/runtime/backends/paddle/paddle_backend.cc
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@@ -98,6 +98,33 @@ void PaddleBackend::BuildOption(const PaddleBackendOption& option) {
}
}
bool PaddleBackend::Init(const RuntimeOption& runtime_option) {
if (!(Supported(runtime_option.model_format, Backend::PDINFER) && Supported(runtime_option.device, Backend::PDINFER))) {
return false;
}
auto option = runtime_option;
option.paddle_infer_option.model_file = runtime_option.model_file;
option.paddle_infer_option.params_file = runtime_option.params_file;
option.paddle_infer_option.model_from_memory_ = runtime_option.model_from_memory_;
option.paddle_infer_option.device = runtime_option.device;
option.paddle_infer_option.device_id = runtime_option.device_id;
option.paddle_infer_option.enable_pinned_memory = runtime_option.enable_pinned_memory;
option.paddle_infer_option.external_stream_ = runtime_option.external_stream_;
option.paddle_infer_option.trt_option = runtime_option.trt_option;
option.paddle_infer_option.trt_option.gpu_id = runtime_option.device_id;
if (option.model_from_memory_) {
return InitFromPaddle(option.model_file, option.params_file, option.paddle_infer_option);
} else {
std::string model_buffer = "";
std::string params_buffer = "";
FDASSERT(ReadBinaryFromFile(option.model_file, &model_buffer), "Failed to read model file from %s.", option.model_file.c_str());
FDASSERT(ReadBinaryFromFile(option.params_file, &params_buffer), "Failed to read parameters file from %s.", option.params_file.c_str());
return InitFromPaddle(model_buffer, params_buffer, option.paddle_infer_option);
}
return false;
}
bool PaddleBackend::InitFromPaddle(const std::string& model_buffer,
const std::string& params_buffer,
const PaddleBackendOption& option) {

13
fastdeploy/runtime/backends/paddle/paddle_backend.h
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@@ -54,12 +54,7 @@ class PaddleBackend : public BaseBackend {
public:
PaddleBackend() {}
virtual ~PaddleBackend() = default;
void BuildOption(const PaddleBackendOption& option);
bool InitFromPaddle(const std::string& model_buffer,
const std::string& params_buffer,
const PaddleBackendOption& option = PaddleBackendOption());
bool Init(const RuntimeOption& option);
bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs,
bool copy_to_fd = true) override;
@@ -77,6 +72,12 @@ class PaddleBackend : public BaseBackend {
std::vector<TensorInfo> GetOutputInfos() override;
private:
void BuildOption(const PaddleBackendOption& option);
bool InitFromPaddle(const std::string& model_buffer,
const std::string& params_buffer,
const PaddleBackendOption& option = PaddleBackendOption());
void
CollectShapeRun(paddle_infer::Predictor* predictor,
const std::map<std::string, std::vector<int>>& shape) const;

3
fastdeploy/runtime/backends/tensorrt/option.h
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@@ -30,6 +30,9 @@ struct TrtBackendOption {
/// `max_workspace_size` for TensorRT
size_t max_workspace_size = 1 << 30;
/// Enable log while converting onnx model to tensorrt
bool enable_log_info = false;
/*
* @brief Enable half precison inference, on some device not support half precision, it will fallback to float32 mode
*/

1
fastdeploy/runtime/backends/tensorrt/option_pybind.cc
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@@ -21,6 +21,7 @@ void BindTrtOption(pybind11::module& m) {
pybind11::class_<TrtBackendOption>(m, "TrtBackendOption")
.def(pybind11::init())
.def_readwrite("enable_fp16", &TrtBackendOption::enable_fp16)
.def_readwrite("enable_log_info", &TrtBackendOption::enable_log_info)
.def_readwrite("max_batch_size", &TrtBackendOption::max_batch_size)
.def_readwrite("max_workspace_size",
&TrtBackendOption::max_workspace_size)

18
fastdeploy/runtime/backends/tensorrt/trt_backend.cc
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@@ -114,6 +114,13 @@ bool TrtBackend::LoadTrtCache(const std::string& trt_engine_file) {
}
bool TrtBackend::Init(const RuntimeOption& runtime_option) {
auto trt_option = runtime_option.trt_option;
trt_option.model_file = runtime_option.model_file;
trt_option.params_file = runtime_option.params_file;
trt_option.model_format = runtime_option.model_format;
trt_option.gpu_id = runtime_option.device_id;
trt_option.enable_pinned_memory = runtime_option.enable_pinned_memory;
trt_option.external_stream_ = runtime_option.external_stream_;
if (runtime_option.device != Device::GPU) {
FDERROR << "TrtBackend only supports Device::GPU, but now it's "
<< runtime_option.device << "." << std::endl;
@@ -130,7 +137,7 @@ bool TrtBackend::Init(const RuntimeOption& runtime_option) {
if (runtime_option.model_from_memory_) {
return InitFromPaddle(runtime_option.model_file,
runtime_option.params_file,
runtime_option.trt_option);
trt_option);
} else {
std::string model_buffer;
std::string params_buffer;
@@ -141,17 +148,17 @@ bool TrtBackend::Init(const RuntimeOption& runtime_option) {
"Failed to read parameters file %s.",
runtime_option.params_file.c_str());
return InitFromPaddle(model_buffer, params_buffer,
runtime_option.trt_option);
trt_option);
}
} else {
if (runtime_option.model_from_memory_) {
return InitFromOnnx(runtime_option.model_file, runtime_option.trt_option);
return InitFromOnnx(runtime_option.model_file, trt_option);
} else {
std::string model_buffer;
FDASSERT(ReadBinaryFromFile(runtime_option.model_file, &model_buffer),
"Failed to read model file %s.",
runtime_option.model_file.c_str());
return InitFromOnnx(model_buffer, runtime_option.trt_option);
return InitFromOnnx(model_buffer, trt_option);
}
}
return true;
@@ -525,6 +532,9 @@ void TrtBackend::AllocateOutputsBuffer(std::vector<FDTensor>* outputs,
}
bool TrtBackend::BuildTrtEngine() {
if (option_.enable_log_info) {
FDTrtLogger::Get()->SetLog(true, true);
}
auto config =
FDUniquePtr<nvinfer1::IBuilderConfig>(builder_->createBuilderConfig());
if (!config) {

18
fastdeploy/runtime/backends/tensorrt/utils.h
View File

@@ -220,20 +220,30 @@ class FDTrtLogger : public nvinfer1::ILogger {
logger = new FDTrtLogger();
return logger;
}
void SetLog(bool enable_info = false, bool enable_warning = false) {
enable_info_ = enable_info;
enable_warning_ = enable_warning;
}
void log(nvinfer1::ILogger::Severity severity,
const char* msg) noexcept override {
if (severity == nvinfer1::ILogger::Severity::kINFO) {
// Disable this log
// FDINFO << msg << std::endl;
if (enable_info_) {
FDINFO << msg << std::endl;
}
} else if (severity == nvinfer1::ILogger::Severity::kWARNING) {
// Disable this log
// FDWARNING << msg << std::endl;
if (enable_warning_) {
FDWARNING << msg << std::endl;
}
} else if (severity == nvinfer1::ILogger::Severity::kERROR) {
FDERROR << msg << std::endl;
} else if (severity == nvinfer1::ILogger::Severity::kINTERNAL_ERROR) {
FDASSERT(false, "%s", msg);
}
}
private:
bool enable_info_ = false;
bool enable_warning_ = false;
};
struct ShapeRangeInfo {

57
fastdeploy/runtime/runtime.cc
View File

@@ -154,19 +154,10 @@ bool Runtime::Init(const RuntimeOption& _option) {
} else if (option.backend == Backend::SOPHGOTPU) {
CreateSophgoNPUBackend();
} else if (option.backend == Backend::POROS) {
FDASSERT(option.device == Device::CPU || option.device == Device::GPU,
"Backend::POROS only supports Device::CPU/Device::GPU.");
FDASSERT(option.model_format == ModelFormat::TORCHSCRIPT,
"Backend::POROS only supports model format of "
"ModelFormat::TORCHSCRIPT.");
FDINFO << "Runtime initialized with Backend::POROS in " << option.device
<< "." << std::endl;
return true;
CreatePorosBackend();
} else {
FDERROR << "Runtime only support "
"Backend::ORT/Backend::TRT/Backend::PDINFER/Backend::POROS as "
"backend now."
<< std::endl;
std::string msg = Str(GetAvailableBackends());
FDERROR << "The compiled FastDeploy only supports " << msg << ", " << option.backend << " is not supported now." << std::endl;
return false;
}
backend_->benchmark_option_ = option.benchmark_option;
@@ -264,43 +255,9 @@ void Runtime::ReleaseModelMemoryBuffer() {
}
void Runtime::CreatePaddleBackend() {
FDASSERT(
option.device == Device::CPU || option.device == Device::GPU ||
option.device == Device::IPU,
"Backend::PDINFER only supports Device::CPU/Device::GPU/Device::IPU.");
FDASSERT(
option.model_format == ModelFormat::PADDLE,
"Backend::PDINFER only supports model format of ModelFormat::PADDLE.");
#ifdef ENABLE_PADDLE_BACKEND
option.paddle_infer_option.model_file = option.model_file;
option.paddle_infer_option.params_file = option.params_file;
option.paddle_infer_option.model_from_memory_ = option.model_from_memory_;
option.paddle_infer_option.device = option.device;
option.paddle_infer_option.device_id = option.device_id;
option.paddle_infer_option.enable_pinned_memory = option.enable_pinned_memory;
option.paddle_infer_option.external_stream_ = option.external_stream_;
option.paddle_infer_option.trt_option = option.trt_option;
option.paddle_infer_option.trt_option.gpu_id = option.device_id;
backend_ = utils::make_unique<PaddleBackend>();
auto casted_backend = dynamic_cast<PaddleBackend*>(backend_.get());
if (option.model_from_memory_) {
FDASSERT(
casted_backend->InitFromPaddle(option.model_file, option.params_file,
option.paddle_infer_option),
"Load model from Paddle failed while initliazing PaddleBackend.");
ReleaseModelMemoryBuffer();
} else {
std::string model_buffer = "";
std::string params_buffer = "";
FDASSERT(ReadBinaryFromFile(option.model_file, &model_buffer),
"Fail to read binary from model file");
FDASSERT(ReadBinaryFromFile(option.params_file, &params_buffer),
"Fail to read binary from parameter file");
FDASSERT(casted_backend->InitFromPaddle(model_buffer, params_buffer,
option.paddle_infer_option),
"Load model from Paddle failed while initliazing PaddleBackend.");
}
FDASSERT(backend_->Init(option), "Failed to initialized Paddle Inference backend.");
#else
FDASSERT(false,
"PaddleBackend is not available, please compiled with "
@@ -339,12 +296,6 @@ void Runtime::CreateOrtBackend() {
void Runtime::CreateTrtBackend() {
#ifdef ENABLE_TRT_BACKEND
option.trt_option.model_file = option.model_file;
option.trt_option.params_file = option.params_file;
option.trt_option.model_format = option.model_format;
option.trt_option.gpu_id = option.device_id;
option.trt_option.enable_pinned_memory = option.enable_pinned_memory;
option.trt_option.external_stream_ = option.external_stream_;
backend_ = utils::make_unique<TrtBackend>();
FDASSERT(backend_->Init(option), "Failed to initialize TensorRT backend.");
#else