# Copyright (c) 2020 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. import abc import os import time import sys import yaml import traceback from paddle import fluid from paddlerec.core.utils import envs class EngineMode: """ There are various engine designed for different runing environment. """ SINGLE = 1 CLUSTER = 2 LOCAL_CLUSTER = 3 class FleetMode: """ Paddle Distributed train support: ParameterServer/Collective/PSlib """ PS = 1 COLLECTIVE = 2 PSLIB = 3 class Device: """ PaddleRec Support CPU/GPU, XPU will comming soon """ CPU = 1 GPU = 2 # XPU =3 class Trainer(object): """ Trainer Base """ __metaclass__ = abc.ABCMeta def __init__(self, config=None): self._status_processor = {} self.model = None self.inference_models = [] self.increment_models = [] self._exector_context = {} self._context = {'status': 'uninit', 'is_exit': False} self._config_yaml = config self._context["config_yaml"] = self._config_yaml self._config = envs.load_yaml(config) self._context["env"] = self._config self._model = {} self._dataset = {} envs.set_global_envs(self._config) envs.update_workspace() self._runner_name = envs.get_global_env("mode") self._context["runner_name"] = self._runner_name print("PaddleRec: Runner {} Begin".format(self._runner_name)) self.which_engine() self.which_device() self.which_fleet_mode() self.which_executor_mode() self.legality_check() def which_device(self): """R """ device = envs.get_global_env( "runner." + self._runner_name + ".device", default_value="CPU") if device.upper() == 'GPU': self.check_gpu() self.device = Device.GPU gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0)) self._place = fluid.CUDAPlace(gpu_id) self._exe = fluid.Executor(self._place) elif device.upper() == "CPU": self.device = Device.CPU self._place = fluid.CPUPlace() self._exe = fluid.Executor(self._place) else: raise ValueError("Not Support device {}".format(device)) self._context["device"] = device.upper() self._context["exe"] = self._exe self._context["place"] = self._place def check_gpu(self): """ Log error and exit when set use_gpu=true in paddlepaddle cpu version. """ err = "GPU cannot be set as true while you are " \ "using paddlepaddle cpu version ! \nPlease try: \n" \ "\t1. Install paddlepaddle-gpu to run model on GPU \n" \ "\t2. Set device as cpu in config file to run " \ "model on CPU" try: if not fluid.is_compiled_with_cuda(): raise RuntimeError(err) sys.exit(1) except Exception as e: pass def which_engine(self): engine = envs.get_runtime_environ("train.trainer.engine") if engine.upper() == "SINGLE": self.engine = EngineMode.SINGLE self.is_fleet = False elif engine.upper() == "LOCAL_CLUSTER": self.engine = EngineMode.LOCAL_CLUSTER self.is_fleet = True elif engine.upper() == "CLUSTER": self.engine = EngineMode.CLUSTER self.is_fleet = True else: raise ValueError("Not Support Engine {}".format(engine)) self._context["is_fleet"] = self.is_fleet self._context["engine"] = self.engine def which_fleet_mode(self): fleet_mode = envs.get_runtime_environ("fleet_mode") if fleet_mode.upper() == "PS": self.fleet_mode = FleetMode.PS elif fleet_mode.upper() == "COLLECTIVE": self.fleet_mode = FleetMode.COLLECTIVE elif fleet_mode.upper() == "PSLIB": self.fleet_mode = FleetMode.PSLIB else: raise ValueError("Not Support Fleet Mode {}".format(fleet_mode)) self._context["is_pslib"] = (fleet_mode.upper() == "PSLIB") self._context["fleet_mode"] = fleet_mode def which_executor_mode(self): executor_mode = envs.get_runtime_environ("train.trainer.executor_mode") if executor_mode.upper() not in ["TRAIN", "INFER"]: raise ValueError("Not Support Executor Mode {}".format( executor_mode)) if executor_mode.upper() == "TRAIN": self.is_infer = False else: self.is_infer = True print("Executor Mode: {}".format(executor_mode)) self._context["is_infer"] = self.is_infer def legality_check(self): if self.device == Device.CPU: assert self.fleet_mode != FleetMode.COLLECTIVE, "Not Support CPU with Collective Mode" if self.is_infer: assert self.engine == EngineMode.SINGLE, "Not Support Distributed Infer " @abc.abstractmethod def processor_register(self): pass def regist_context_processor(self, status_name, processor): """ regist a processor for specify status """ self._status_processor[status_name] = processor def context_process(self, context): """ select a processor to deal specify context Args: context : context with status Return: None : run a processor for this status """ status = context['status'] try: if status in self._status_processor: self._status_processor[context['status']](context) else: self.other_status_processor(context) except Exception as err: traceback.print_exc() print('Catch Exception:%s' % str(err)) sys.stdout.flush() self._context['is_exit'] = self.handle_processor_exception( status, context, err) def other_status_processor(self, context): """ if no processor match context.status, use defalut processor Return: None, just sleep in base """ print('unknow context_status:%s, do nothing' % context['status']) time.sleep(60) def handle_processor_exception(self, status, context, exception): """ when exception throwed from processor, will call this func to handle it Return: bool exit_app or not """ print('Exit app. catch exception in precoss status:%s, except:%s' % (context['status'], str(exception))) return True def reload_train_context(self): """ context maybe update timely, reload for update """ pass def run(self): """ keep running by statu context. """ while True: self.reload_train_context() self.context_process(self._context) if self._context['is_exit']: break def user_define_engine(engine_yaml): _config = envs.load_yaml(engine_yaml) envs.set_runtime_environs(_config) train_location = envs.get_global_env("engine.file") train_dirname = os.path.dirname(train_location) base_name = os.path.splitext(os.path.basename(train_location))[0] sys.path.append(train_dirname) trainer_class = envs.lazy_instance_by_fliename(base_name, "UserDefineTraining") return trainer_class