forked from mindspore-Ecosystem/mindspore
137 lines
4.0 KiB
C++
137 lines
4.0 KiB
C++
/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "core/session.h"
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#include <grpcpp/grpcpp.h>
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#include <string>
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#include <map>
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#include <vector>
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#include <utility>
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#include <memory>
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#include <chrono>
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#include "include/infer_log.h"
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#include "serving/ms_service.grpc.pb.h"
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#include "core/util/option_parser.h"
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#include "core/version_control/version_controller.h"
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#include "core/util/file_system_operation.h"
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#include "core/serving_tensor.h"
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using ms_serving::MSService;
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using ms_serving::PredictReply;
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using ms_serving::PredictRequest;
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namespace mindspore {
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namespace serving {
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Status Session::CreatDeviceSession(const std::string &device, uint32_t device_id) {
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session_ = inference::InferSession::CreateSession(device, device_id);
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if (session_ == nullptr) {
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MSI_LOG(ERROR) << "Creat Session Failed";
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return FAILED;
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}
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device_type_ = device;
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return SUCCESS;
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}
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Session &Session::Instance() {
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static Session instance;
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return instance;
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}
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Status Session::Predict(const PredictRequest &request, PredictReply &reply) {
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if (!model_loaded_) {
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MSI_LOG(ERROR) << "the model has not loaded";
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return FAILED;
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}
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if (session_ == nullptr) {
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MSI_LOG(ERROR) << "the inference session has not be initialized";
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return FAILED;
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}
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std::lock_guard<std::mutex> lock(mutex_);
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MSI_LOG(INFO) << "run Predict";
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if (request.images_size() > 0) {
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ServingImagesRequest serving_images(request);
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ServingRequest serving_request(request);
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ServingReply serving_reply(reply);
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Status ret = session_->ExecuteModel(graph_id_, serving_images, serving_request, serving_reply);
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if (ret != SUCCESS) {
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MSI_LOG(ERROR) << "execute model with images return failed";
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return ret;
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}
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} else if (request.data_size() > 0) {
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ServingRequest serving_request(request);
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ServingReply serving_reply(reply);
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Status ret = session_->ExecuteModel(graph_id_, serving_request, serving_reply);
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if (ret != SUCCESS) {
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MSI_LOG(ERROR) << "execute model with datas return failed";
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return ret;
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}
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}
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MSI_LOG(INFO) << "run Predict finished";
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return SUCCESS;
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}
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Status Session::Warmup(const MindSporeModelPtr model) {
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if (session_ == nullptr) {
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MSI_LOG(ERROR) << "The CreatDeviceSession should be called, before warmup";
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return FAILED;
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}
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std::lock_guard<std::mutex> lock(mutex_);
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std::string file_name = model->GetModelPath() + '/' + model->GetModelName();
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model_loaded_ = false;
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MSI_TIME_STAMP_START(LoadModelFromFile)
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auto ret = session_->LoadModelFromFile(file_name, graph_id_);
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MSI_TIME_STAMP_END(LoadModelFromFile)
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if (ret != SUCCESS) {
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MSI_LOG(ERROR) << "Load graph model failed, file name is " << file_name.c_str();
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return ret;
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}
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model_loaded_ = true;
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MSI_LOG(INFO) << "Session Warmup finished";
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return SUCCESS;
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}
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Status Session::Clear() {
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if (session_ != nullptr) {
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session_->UnloadModel(graph_id_);
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session_->FinalizeEnv();
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session_ = nullptr;
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}
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return SUCCESS;
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}
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Status Session::GetModelInputsInfo(std::vector<inference::InferTensor> &tensor_list) {
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if (!model_loaded_) {
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MSI_LOG(ERROR) << "the model has not loaded";
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return FAILED;
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}
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if (session_ == nullptr) {
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MSI_LOG(ERROR) << "the inference session has not be initialized";
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return FAILED;
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}
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std::lock_guard<std::mutex> lock(mutex_);
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Status ret = session_->GetModelInputsInfo(graph_id_, &tensor_list);
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if (ret != SUCCESS) {
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MSI_LOG(ERROR) << "get model inputs info failed";
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}
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return ret;
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}
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} // namespace serving
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} // namespace mindspore
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