# Copyright (c) 2021 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 os from datetime import datetime from pathlib import Path from typing import Any from typing import Dict from typing import List import jsonlines from . import extension from ..reporter import get_observations from ..updaters.trainer import Trainer from deepspeech.utils.log import Log from deepspeech.utils.mp_tools import rank_zero_only logger = Log(__name__).getlog() def load_records(records_fp): """Load record files (json lines.)""" with jsonlines.open(records_fp, 'r') as reader: records = list(reader) return records class Snapshot(extension.Extension): """An extension to make snapshot of the updater object inside the trainer. It is done by calling the updater's `save` method. An Updater save its state_dict by default, which contains the updater state, (i.e. epoch and iteration) and all the model parameters and optimizer states. If the updater inside the trainer subclasses StandardUpdater, everything is good to go. Parameters ---------- checkpoint_dir : Union[str, Path] The directory to save checkpoints into. """ trigger = (1, 'epoch') priority = -100 default_name = "snapshot" def __init__(self, mode='latest', max_size: int=5, indicator=None, less_better=True, snapshot_on_error: bool=False): self.records: List[Dict[str, Any]] = [] assert mode in ('latest', 'kbest'), mode if mode == 'kbest': assert indicator is not None self.mode = mode self.indicator = indicator self.less_is_better = less_better self.max_size = max_size self._snapshot_on_error = snapshot_on_error self._save_all = (max_size == -1) self.checkpoint_dir = None def initialize(self, trainer: Trainer): """Setting up this extention.""" self.checkpoint_dir = trainer.out / "checkpoints" # load existing records record_path: Path = self.checkpoint_dir / "records.jsonl" if record_path.exists(): self.records = load_records(record_path) ckpt_path = self.records[-1]['path'] logger.info(f"Loading from an existing checkpoint {ckpt_path}") trainer.updater.load(ckpt_path) def on_error(self, trainer, exc, tb): if self._snapshot_on_error: self.save_checkpoint_and_update(trainer, 'latest') def __call__(self, trainer: Trainer): self.save_checkpoint_and_update(trainer, self.mode) def full(self): """Whether the number of snapshots it keeps track of is greater than the max_size.""" return (not self._save_all) and len(self.records) > self.max_size @rank_zero_only def save_checkpoint_and_update(self, trainer: Trainer, mode: str): """Saving new snapshot and remove the oldest snapshot if needed.""" iteration = trainer.updater.state.iteration epoch = trainer.updater.state.epoch num = epoch if self.trigger[1] == 'epoch' else iteration path = self.checkpoint_dir / f"{num}.pdz" # add the new one trainer.updater.save(path) record = { "time": str(datetime.now()), 'path': str(path.resolve()), # use absolute path 'iteration': iteration, 'epoch': epoch, 'indicator': get_observations()[self.indicator] } self.records.append(record) # remove the earist if self.full(): if mode == 'kbest': self.records = sorted( self.records, key=lambda record: record['indicator'], reverse=not self.less_is_better) eariest_record = self.records[0] os.remove(eariest_record["path"]) self.records.pop(0) # update the record file record_path = self.checkpoint_dir / "records.jsonl" with jsonlines.open(record_path, 'w') as writer: for record in self.records: # jsonlines.open may return a Writer or a Reader writer.write(record) # pylint: disable=no-member