#include "common/inner_common.h" #include "framework/channel.h" #include "common/constant.h" #include "framework/service.h" #include // butil::Timer #include "framework/server.h" #include "framework/dag_view.h" #include "framework/manager.h" #include "framework/resource.h" #include "framework/predictor_metric.h" // PredictorMetric namespace baidu { namespace paddle_serving { namespace predictor { int InferService::init(const configure::InferService& conf) { _infer_service_format = conf.name(); std::string merger = conf.merger(); if (merger == "") { merger = "default"; } if (!MergerManager::instance().get(merger, _merger)) { LOG(ERROR) << "Failed get merger: " << merger; return ERR_INTERNAL_FAILURE; } else { LOG(WARNING) << "Succ get merger: " << merger << " for service: " << _infer_service_format; } ServerManager& svr_mgr = ServerManager::instance(); if (svr_mgr.add_service_by_format(_infer_service_format) != 0) { LOG(ERROR) << "Not found service by format name:" << _infer_service_format << "!"; return ERR_INTERNAL_FAILURE; } _enable_map_request_to_workflow = conf.enable_map_request_to_workflow(); LOG(INFO) << "service[" << _infer_service_format << "], enable_map_request_to_workflow[" << _enable_map_request_to_workflow << "]."; if (_enable_map_request_to_workflow) { if (_request_to_workflow_map.init( MAX_WORKFLOW_NUM_IN_ONE_SERVICE/*load_factor=80*/) != 0) { LOG(ERROR) << "init request to workflow map failed, bucket_count[" << MAX_WORKFLOW_NUM_IN_ONE_SERVICE << "]."; return ERR_INTERNAL_FAILURE; } int err = 0; _request_field_key = conf.request_field_key().c_str(); if (_request_field_key == "") { LOG(ERROR) << "read request_field_key failed, request_field_key[" << _request_field_key << "]."; return ERR_INTERNAL_FAILURE; } LOG(INFO) << "service[" << _infer_service_format << "], request_field_key[" << _request_field_key << "]."; uint32_t value_mapped_workflows_size = conf.value_mapped_workflows_size(); for (uint32_t fi = 0; fi < value_mapped_workflows_size; fi++) { std::vector tokens; std::vector workflows; std::string list = conf.value_mapped_workflows(fi).workflow(); boost::split(tokens, list, boost::is_any_of(",")); uint32_t tsize = tokens.size(); for (uint32_t ti = 0; ti < tsize; ++ti) { boost::trim_if(tokens[ti], boost::is_any_of(" ")); Workflow* workflow = WorkflowManager::instance().item(tokens[ti]); if (workflow == NULL) { LOG(ERROR) << "Failed get workflow by name:" << tokens[ti] << ", ti: " << ti; return ERR_INTERNAL_FAILURE; } workflow->regist_metric(full_name()); workflows.push_back(workflow); } const std::string& request_field_value = conf.value_mapped_workflows(fi).request_field_value(); if (_request_to_workflow_map.insert(request_field_value, workflows) == NULL) { LOG(ERROR) << "insert [" << request_field_value << "," << list << "] to _request_to_workflow_map failed."; return ERR_INTERNAL_FAILURE; } LOG(INFO) << "workflow[" << list << "], request_field_value[" << request_field_value << "]."; } } else { uint32_t flow_size = conf.workflows_size(); for (uint32_t fi = 0; fi < flow_size; fi++) { const std::string& workflow_name = conf.workflows(fi); Workflow* workflow = WorkflowManager::instance().item(workflow_name); if (workflow == NULL) { LOG(ERROR) << "Failed get workflow by name:" << workflow_name; return ERR_INTERNAL_FAILURE; } workflow->regist_metric(full_name()); _flows.push_back(workflow); } } LOG(INFO) << "Succ load infer_service: " << _infer_service_format << "!"; return ERR_OK; } int InferService::reload() { return ERR_OK; } const std::string& InferService::name() const { return _infer_service_format; } // ´®ÐÐÖ´ÐÐÿ¸öworkflow int InferService::inference( const google::protobuf::Message* request, google::protobuf::Message* response, butil::IOBufBuilder* debug_os) { TRACEPRINTF("start to inference"); // when funtion call begins, framework will reset // thread local variables&resources automatically. if (Resource::instance().thread_clear() != 0) { LOG(ERROR) << "Failed thread clear whole resource"; return ERR_INTERNAL_FAILURE; } TRACEPRINTF("finish to thread clear"); if (_enable_map_request_to_workflow) { std::vector* workflows = _map_request_to_workflow(request); if (!workflows || workflows->size() == 0) { LOG(ERROR) << "Failed to map request to workflow"; return ERR_INTERNAL_FAILURE; } size_t fsize = workflows->size(); for (size_t fi = 0; fi < fsize; ++fi) { Workflow* workflow = (*workflows)[fi]; if (workflow == NULL) { LOG(ERROR) << "Failed to get valid workflow at: " << fi; return ERR_INTERNAL_FAILURE; } TRACEPRINTF("start to execute workflow[%s]", workflow->name().c_str()); int errcode = _execute_workflow(workflow, request, response, debug_os); TRACEPRINTF("finish to execute workflow[%s]", workflow->name().c_str()); if (errcode < 0) { LOG(ERROR) << "Failed execute workflow[" << workflow->name() << "] in:" << name(); return errcode; } } } else { TRACEPRINTF("start to execute one workflow"); size_t fsize = _flows.size(); for (size_t fi = 0; fi < fsize; ++fi) { TRACEPRINTF("start to execute one workflow-%lu", fi); int errcode = execute_one_workflow(fi, request, response, debug_os); TRACEPRINTF("finish to execute one workflow-%lu", fi); if (errcode < 0) { LOG(ERROR) << "Failed execute 0-th workflow in:" << name(); return errcode; } } } return ERR_OK; } int InferService::debug( const google::protobuf::Message* request, google::protobuf::Message* response, butil::IOBufBuilder* debug_os) { return inference(request, response, debug_os); } int InferService::execute_one_workflow( uint32_t index, const google::protobuf::Message* request, google::protobuf::Message* response, butil::IOBufBuilder* debug_os) { if (index >= _flows.size()) { LOG(ERROR) << "Faield execute workflow, index: " << index << " >= max:" << _flows.size(); return ERR_OVERFLOW_FAILURE; } Workflow* workflow = _flows[index]; return _execute_workflow(workflow, request, response, debug_os); } int InferService::_execute_workflow( Workflow* workflow, const google::protobuf::Message* request, google::protobuf::Message* response, butil::IOBufBuilder* debug_os) { butil::Timer workflow_time(butil::Timer::STARTED); // create and submit beginer channel BuiltinChannel req_channel; req_channel.init(0, START_OP_NAME); req_channel = request; DagView* dv = workflow->fetch_dag_view(full_name()); dv->set_request_channel(req_channel); // call actual inference interface int errcode = dv->execute(debug_os); if (errcode < 0) { LOG(ERROR) << "Failed execute dag for workflow:" << workflow->name(); return errcode; } TRACEPRINTF("finish to dv execute"); // create ender channel and copy const Channel* res_channel = dv->get_response_channel(); if (!_merger || !_merger->merge(res_channel->message(), response)) { LOG(ERROR) << "Failed merge channel res to response"; return ERR_INTERNAL_FAILURE; } TRACEPRINTF("finish to copy from"); workflow_time.stop(); PredictorMetric::GetInstance()->update_latency_metric( WORKFLOW_METRIC_PREFIX + dv->full_name(), workflow_time.u_elapsed()); // return tls data to object pool workflow->return_dag_view(dv); TRACEPRINTF("finish to return dag view"); return ERR_OK; } std::vector* InferService::_map_request_to_workflow( const google::protobuf::Message* request) { const google::protobuf::Descriptor* desc = request->GetDescriptor(); const google::protobuf::FieldDescriptor* field = desc->FindFieldByName(_request_field_key); if (field == NULL) { LOG(ERROR) << "No field[" << _request_field_key << "] in [" << desc->full_name() << "]."; return NULL; } if (field->is_repeated()) { LOG(ERROR) << "field[" << desc->full_name() << "." << _request_field_key << "] is repeated."; return NULL; } if (field->cpp_type() != google::protobuf::FieldDescriptor::CPPTYPE_STRING) { LOG(ERROR) << "field[" << desc->full_name() << "." << _request_field_key << "] should be string"; return NULL; } const std::string& field_value = request->GetReflection()->GetString(*request, field); std::vector* p_workflow = _request_to_workflow_map.seek(field_value); if (p_workflow == NULL) { LOG(ERROR) << "cannot find key[" << field_value << "] in _request_to_workflow_map"; return NULL; } return p_workflow; } } // predictor } // paddle_serving } // baidu