[Serving][backend]serving support multi stream and backend support external stream (#431)

* serving support multi stream

* pybind add external stream

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
heliqi
2022-10-26 14:46:13 +08:00
committed by GitHub
parent 718698a32a
commit b064ddf7ed
10 changed files with 32 additions and 5 deletions

View File

@@ -63,6 +63,10 @@ void OrtBackend::BuildOption(const OrtBackendOption& option) {
} else {
OrtCUDAProviderOptions cuda_options;
cuda_options.device_id = option.gpu_id;
if(option.external_stream_) {
cuda_options.has_user_compute_stream = 1;
cuda_options.user_compute_stream = option.external_stream_;
}
session_options_.AppendExecutionProvider_CUDA(cuda_options);
}
}

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@@ -44,6 +44,7 @@ struct OrtBackendOption {
int execution_mode = -1;
bool use_gpu = false;
int gpu_id = 0;
void* external_stream_ = nullptr;
// inside parameter, maybe remove next version
bool remove_multiclass_nms_ = false;
@@ -66,7 +67,8 @@ class OrtBackend : public BaseBackend {
const OrtBackendOption& option = OrtBackendOption(),
bool from_memory_buffer = false);
bool Infer(std::vector<FDTensor>& inputs, std::vector<FDTensor>* outputs) override;
bool Infer(std::vector<FDTensor>& inputs,
std::vector<FDTensor>* outputs) override;
int NumInputs() const override { return inputs_desc_.size(); }

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@@ -22,6 +22,9 @@ void PaddleBackend::BuildOption(const PaddleBackendOption& option) {
option_ = option;
if (option.use_gpu) {
config_.EnableUseGpu(option.gpu_mem_init_size, option.gpu_id);
if(option_.external_stream_) {
config_.SetExecStream(option_.external_stream_);
}
if (option.enable_trt) {
#ifdef ENABLE_TRT_BACKEND
auto precision = paddle_infer::PrecisionType::kFloat32;

View File

@@ -54,6 +54,7 @@ struct PaddleBackendOption {
// gpu device id
int gpu_id = 0;
bool enable_pinned_memory = false;
void* external_stream_ = nullptr;
std::vector<std::string> delete_pass_names = {};
};

View File

@@ -258,8 +258,12 @@ bool TrtBackend::InitFromOnnx(const std::string& model_file,
ReaderDtypeToTrtDtype(onnx_reader.outputs[i].dtype);
}
FDASSERT(cudaStreamCreate(&stream_) == 0,
if (option_.external_stream_) {
stream_ = reinterpret_cast<cudaStream_t>(option_.external_stream_);
} else {
FDASSERT(cudaStreamCreate(&stream_) == 0,
"[ERROR] Error occurs while calling cudaStreamCreate().");
}
if (!CreateTrtEngineFromOnnx(onnx_content)) {
FDERROR << "Failed to create tensorrt engine." << std::endl;

View File

@@ -71,6 +71,7 @@ struct TrtBackendOption {
std::map<std::string, std::vector<int32_t>> opt_shape;
std::string serialize_file = "";
bool enable_pinned_memory = false;
void* external_stream_ = nullptr;
// inside parameter, maybe remove next version
bool remove_multiclass_nms_ = false;

View File

@@ -22,6 +22,7 @@ void BindRuntime(pybind11::module& m) {
.def("set_model_path", &RuntimeOption::SetModelPath)
.def("use_gpu", &RuntimeOption::UseGpu)
.def("use_cpu", &RuntimeOption::UseCpu)
.def("set_external_stream", &RuntimeOption::SetExternalStream)
.def("set_cpu_thread_num", &RuntimeOption::SetCpuThreadNum)
.def("use_paddle_backend", &RuntimeOption::UsePaddleBackend)
.def("use_poros_backend", &RuntimeOption::UsePorosBackend)
@@ -52,6 +53,7 @@ void BindRuntime(pybind11::module& m) {
.def_readwrite("params_file", &RuntimeOption::params_file)
.def_readwrite("model_format", &RuntimeOption::model_format)
.def_readwrite("backend", &RuntimeOption::backend)
.def_readwrite("backend", &RuntimeOption::external_stream_)
.def_readwrite("cpu_thread_num", &RuntimeOption::cpu_thread_num)
.def_readwrite("device_id", &RuntimeOption::device_id)
.def_readwrite("device", &RuntimeOption::device)

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@@ -223,6 +223,10 @@ void RuntimeOption::UseGpu(int gpu_id) {
void RuntimeOption::UseCpu() { device = Device::CPU; }
void RuntimeOption::SetExternalStream(void* external_stream) {
external_stream_ = external_stream;
}
void RuntimeOption::SetCpuThreadNum(int thread_num) {
FDASSERT(thread_num > 0, "The thread_num must be greater than 0.");
cpu_thread_num = thread_num;
@@ -508,6 +512,7 @@ void Runtime::CreatePaddleBackend() {
pd_option.delete_pass_names = option.pd_delete_pass_names;
pd_option.cpu_thread_num = option.cpu_thread_num;
pd_option.enable_pinned_memory = option.enable_pinned_memory;
pd_option.external_stream_ = option.external_stream_;
#ifdef ENABLE_TRT_BACKEND
if (pd_option.use_gpu && option.pd_enable_trt) {
pd_option.enable_trt = true;
@@ -574,6 +579,7 @@ void Runtime::CreateOrtBackend() {
ort_option.execution_mode = option.ort_execution_mode;
ort_option.use_gpu = (option.device == Device::GPU) ? true : false;
ort_option.gpu_id = option.device_id;
ort_option.external_stream_ = option.external_stream_;
// TODO(jiangjiajun): inside usage, maybe remove this later
ort_option.remove_multiclass_nms_ = option.remove_multiclass_nms_;
@@ -613,6 +619,7 @@ void Runtime::CreateTrtBackend() {
trt_option.opt_shape = option.trt_opt_shape;
trt_option.serialize_file = option.trt_serialize_file;
trt_option.enable_pinned_memory = option.enable_pinned_memory;
trt_option.external_stream_ = option.external_stream_;
// TODO(jiangjiajun): inside usage, maybe remove this later
trt_option.remove_multiclass_nms_ = option.remove_multiclass_nms_;

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@@ -102,6 +102,8 @@ struct FASTDEPLOY_DECL RuntimeOption {
/// Use Nvidia GPU to inference
void UseGpu(int gpu_id = 0);
void SetExternalStream(void* external_stream);
/*
* @brief Set number of cpu threads while inference on CPU, by default it will decided by the different backends
*/
@@ -232,6 +234,8 @@ struct FASTDEPLOY_DECL RuntimeOption {
Device device = Device::CPU;
void* external_stream_ = nullptr;
bool enable_pinned_memory = false;
// ======Only for ORT Backend========

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@@ -379,6 +379,7 @@ TRITONSERVER_Error* ModelState::LoadModel(
if ((instance_group_kind == TRITONSERVER_INSTANCEGROUPKIND_GPU) ||
(instance_group_kind == TRITONSERVER_INSTANCEGROUPKIND_AUTO)) {
runtime_options_->UseGpu(instance_group_device_id);
runtime_options_->SetExternalStream((void*)stream);
} else {
runtime_options_->UseCpu();
}
@@ -1001,9 +1002,7 @@ TRITONSERVER_Error* ModelInstanceState::Run(
runtime_->Infer(input_tensors_, &output_tensors_);
#ifdef TRITON_ENABLE_GPU
if (Kind() == TRITONSERVER_INSTANCEGROUPKIND_GPU) {
// TODO: stream controll
cudaDeviceSynchronize();
// cudaStreamSynchronize(CudaStream());
cudaStreamSynchronize(CudaStream());
}
#endif
return nullptr;