PaddleSpeech/deepspeech/utils/checkpoint.py

# 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 json
import os
import re
from typing import Union

import paddle
from paddle import distributed as dist
from paddle.optimizer import Optimizer

from deepspeech.utils import mp_tools
from deepspeech.utils.log import Log

import glob

logger = Log(__name__).getlog()

__all__ = ["load_parameters", "save_parameters"]

class KBestCheckpoint(object):
    def __init__(self,
                 max_size: int=5,
                 last_size: int=1):
        self.best_records: Mapping[Path, float] = {}
        self.last_records = []
        self.max_size = max_size
        self.last_size = last_size
        self._save_all = (max_size == -1)
    
    def should_save_best(self, metric: float) -> bool:
        if not self.best_full():
            return True

        # already full
        worst_record_path = max(self.best_records, key=self.best_records.get)
        worst_metric = self.best_records[worst_record_path]
        return metric < worst_metric

    def best_full(self):
        return (not self._save_all) and len(self.best_records) == self.max_size
    
    def last_full(self):
        return len(self.last_records) == self.last_size

    def add_checkpoint(self,
                        checkpoint_dir, tag_or_iteration,
                        model, optimizer, infos):
        if("val_loss" not in infos.keys()):
            self.save_parameters(checkpoint_dir, tag_or_iteration, 
                                model, optimizer, infos)
            return

        #save best
        if self.should_save_best(infos["val_loss"]):
            self.save_checkpoint_and_update(infos["val_loss"], 
                        checkpoint_dir, tag_or_iteration,
                        model, optimizer, infos)
        #save last
        self.save_last_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
                        model, optimizer, infos)
        
        if isinstance(tag_or_iteration, int):
            self._save_record(checkpoint_dir, tag_or_iteration)
    
    def save_checkpoint_and_update(self, metric, 
                        checkpoint_dir, tag_or_iteration,
                        model, optimizer, infos):
        # remove the worst
        if self.best_full():
            worst_record_path = max(self.best_records,
                                    key=self.best_records.get)
            self.best_records.pop(worst_record_path)
            if(worst_record_path not in self.last_records):
                print('----to remove (best)----')
                print(worst_record_path)
                self.del_checkpoint(checkpoint_dir, worst_record_path)

        # add the new one
        self.save_parameters(checkpoint_dir, tag_or_iteration,
                        model, optimizer, infos)
        self.best_records[tag_or_iteration] = metric
 
    def save_last_checkpoint_and_update(self, checkpoint_dir, tag_or_iteration,
                        model, optimizer, infos):
        # remove the old
        if self.last_full():
            to_del_fn = self.last_records.pop(0)
            if(to_del_fn not in self.best_records.keys()):
                print('----to remove (last)----')
                print(to_del_fn)
                self.del_checkpoint(checkpoint_dir, to_del_fn)
        self.last_records.append(tag_or_iteration)

        self.save_parameters(checkpoint_dir, tag_or_iteration,
                    model, optimizer, infos)
            # with open(os.path.join(checkpoint_dir, "checkpoint"), "w") as handle:
            #     for iteration in self.best_records
            #     handle.write("model_checkpoint_path:{}\n".format(iteration))


    def del_checkpoint(self, checkpoint_dir, tag_or_iteration):
        checkpoint_path = os.path.join(checkpoint_dir,
                                    "{}".format(tag_or_iteration))
        for filename in glob.glob(checkpoint_path+".*"):
            os.remove(filename)
            print("delete file: "+filename)
        


    def _load_latest_checkpoint(self, checkpoint_dir: str) -> int:
        """Get the iteration number corresponding to the latest saved checkpoint.
        Args:
            checkpoint_dir (str): the directory where checkpoint is saved.
        Returns:
            int: the latest iteration number. -1 for no checkpoint to load.
        """
        checkpoint_record = os.path.join(checkpoint_dir, "checkpoint_last")
        if not os.path.isfile(checkpoint_record):
            return -1

        # Fetch the latest checkpoint index.
        with open(checkpoint_record, "rt") as handle:
            latest_checkpoint = handle.readlines()[-1].strip()
            iteration = int(latest_checkpoint.split(":")[-1])
        return iteration


    def _save_record(self, checkpoint_dir: str, iteration: int):
        """Save the iteration number of the latest model to be checkpoint record.
        Args:
            checkpoint_dir (str): the directory where checkpoint is saved.
            iteration (int): the latest iteration number.
        Returns:
            None
        """
        checkpoint_record_last = os.path.join(checkpoint_dir, "checkpoint_last")
        checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
        # Update the latest checkpoint index.
        # with open(checkpoint_record, "a+") as handle:
        #     handle.write("model_checkpoint_path:{}\n".format(iteration))
        with open(checkpoint_record_best, "w") as handle:
            for i in self.best_records.keys():
                handle.write("model_checkpoint_path:{}\n".format(i))
        with open(checkpoint_record_last, "w") as handle:
            for i in self.last_records:
                handle.write("model_checkpoint_path:{}\n".format(i))


    def load_parameters(self, model,
                        optimizer=None,
                        checkpoint_dir=None,
                        checkpoint_path=None):
        """Load a specific model checkpoint from disk. 
        Args:
            model (Layer): model to load parameters.
            optimizer (Optimizer, optional): optimizer to load states if needed.
                Defaults to None.
            checkpoint_dir (str, optional): the directory where checkpoint is saved.
            checkpoint_path (str, optional): if specified, load the checkpoint
                stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will 
                be ignored. Defaults to None. 
        Returns:
            configs (dict): epoch or step, lr and other meta info should be saved.
        """
        configs = {}

        if checkpoint_path is not None:
            tag = os.path.basename(checkpoint_path).split(":")[-1]
        elif checkpoint_dir is not None:
            iteration = self._load_latest_checkpoint(checkpoint_dir)
            if iteration == -1:
                return configs
            checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
        else:
            raise ValueError(
                "At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
            )

        rank = dist.get_rank()

        params_path = checkpoint_path + ".pdparams"
        model_dict = paddle.load(params_path)
        model.set_state_dict(model_dict)
        logger.info("Rank {}: loaded model from {}".format(rank, params_path))

        optimizer_path = checkpoint_path + ".pdopt"
        if optimizer and os.path.isfile(optimizer_path):
            optimizer_dict = paddle.load(optimizer_path)
            optimizer.set_state_dict(optimizer_dict)
            logger.info("Rank {}: loaded optimizer state from {}".format(
                rank, optimizer_path))

        info_path = re.sub('.pdparams$', '.json', params_path)
        if os.path.exists(info_path):
            with open(info_path, 'r') as fin:
                configs = json.load(fin)
        return configs


    @mp_tools.rank_zero_only
    def save_parameters(self, checkpoint_dir: str,
                        tag_or_iteration: Union[int, str],
                        model: paddle.nn.Layer,
                        optimizer: Optimizer=None,
                        infos: dict=None):
        """Checkpoint the latest trained model parameters.
        Args:
            checkpoint_dir (str): the directory where checkpoint is saved.
            tag_or_iteration (int or str): the latest iteration(step or epoch) number.
            model (Layer): model to be checkpointed.
            optimizer (Optimizer, optional): optimizer to be checkpointed.
                Defaults to None.
            infos (dict or None): any info you want to save.
        Returns:
            None
        """
        checkpoint_path = os.path.join(checkpoint_dir,
                                    "{}".format(tag_or_iteration))

        model_dict = model.state_dict()
        params_path = checkpoint_path + ".pdparams"
        paddle.save(model_dict, params_path)
        logger.info("Saved model to {}".format(params_path))

        if optimizer:
            opt_dict = optimizer.state_dict()
            optimizer_path = checkpoint_path + ".pdopt"
            paddle.save(opt_dict, optimizer_path)
            logger.info("Saved optimzier state to {}".format(optimizer_path))

        info_path = re.sub('.pdparams$', '.json', params_path)
        infos = {} if infos is None else infos
        with open(info_path, 'w') as fout:
            data = json.dumps(infos)
            fout.write(data)