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@ -11,9 +11,11 @@
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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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import glob
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import json
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import os
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import re
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from pathlib import Path
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from typing import Union
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import paddle
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@ -25,128 +27,260 @@ from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ["load_parameters", "save_parameters"]
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def _load_latest_checkpoint(checkpoint_dir: str) -> int:
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"""Get the iteration number corresponding to the latest saved checkpoint.
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Args:
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checkpoint_dir (str): the directory where checkpoint is saved.
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Returns:
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int: the latest iteration number. -1 for no checkpoint to load.
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"""
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checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
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if not os.path.isfile(checkpoint_record):
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return -1
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# Fetch the latest checkpoint index.
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with open(checkpoint_record, "rt") as handle:
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latest_checkpoint = handle.readlines()[-1].strip()
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iteration = int(latest_checkpoint.split(":")[-1])
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return iteration
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def _save_record(checkpoint_dir: str, iteration: int):
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"""Save the iteration number of the latest model to be checkpoint record.
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Args:
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checkpoint_dir (str): the directory where checkpoint is saved.
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iteration (int): the latest iteration number.
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Returns:
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None
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"""
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checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
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# Update the latest checkpoint index.
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with open(checkpoint_record, "a+") as handle:
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handle.write("model_checkpoint_path:{}\n".format(iteration))
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def load_parameters(model,
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optimizer=None,
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checkpoint_dir=None,
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checkpoint_path=None):
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"""Load a specific model checkpoint from disk.
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Args:
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model (Layer): model to load parameters.
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optimizer (Optimizer, optional): optimizer to load states if needed.
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Defaults to None.
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checkpoint_dir (str, optional): the directory where checkpoint is saved.
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checkpoint_path (str, optional): if specified, load the checkpoint
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stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
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be ignored. Defaults to None.
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Returns:
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configs (dict): epoch or step, lr and other meta info should be saved.
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"""
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configs = {}
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if checkpoint_path is not None:
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tag = os.path.basename(checkpoint_path).split(":")[-1]
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elif checkpoint_dir is not None:
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iteration = _load_latest_checkpoint(checkpoint_dir)
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if iteration == -1:
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return configs
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checkpoint_path = os.path.join(checkpoint_dir, "{}".format(iteration))
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else:
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raise ValueError(
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"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
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)
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rank = dist.get_rank()
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params_path = checkpoint_path + ".pdparams"
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model_dict = paddle.load(params_path)
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model.set_state_dict(model_dict)
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logger.info("Rank {}: loaded model from {}".format(rank, params_path))
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optimizer_path = checkpoint_path + ".pdopt"
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if optimizer and os.path.isfile(optimizer_path):
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optimizer_dict = paddle.load(optimizer_path)
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optimizer.set_state_dict(optimizer_dict)
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logger.info("Rank {}: loaded optimizer state from {}".format(
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rank, optimizer_path))
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info_path = re.sub('.pdparams$', '.json', params_path)
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if os.path.exists(info_path):
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with open(info_path, 'r') as fin:
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configs = json.load(fin)
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return configs
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@mp_tools.rank_zero_only
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def save_parameters(checkpoint_dir: str,
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tag_or_iteration: Union[int, str],
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model: paddle.nn.Layer,
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optimizer: Optimizer=None,
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infos: dict=None):
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"""Checkpoint the latest trained model parameters.
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Args:
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checkpoint_dir (str): the directory where checkpoint is saved.
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tag_or_iteration (int or str): the latest iteration(step or epoch) number.
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model (Layer): model to be checkpointed.
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optimizer (Optimizer, optional): optimizer to be checkpointed.
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Defaults to None.
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infos (dict or None): any info you want to save.
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Returns:
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None
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"""
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checkpoint_path = os.path.join(checkpoint_dir,
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"{}".format(tag_or_iteration))
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model_dict = model.state_dict()
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params_path = checkpoint_path + ".pdparams"
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paddle.save(model_dict, params_path)
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logger.info("Saved model to {}".format(params_path))
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if optimizer:
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opt_dict = optimizer.state_dict()
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__all__ = ["Checkpoint"]
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class Checkpoint(object):
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def __init__(self, kbest_n: int=5, latest_n: int=1):
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self.best_records: Mapping[Path, float] = {}
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self.latest_records = []
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self.kbest_n = kbest_n
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self.latest_n = latest_n
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self._save_all = (kbest_n == -1)
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def add_checkpoint(self,
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checkpoint_dir,
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tag_or_iteration,
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model,
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optimizer,
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infos,
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metric_type="val_loss"):
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if (metric_type not in infos.keys()):
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self._save_parameters(checkpoint_dir, tag_or_iteration, model,
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optimizer, infos)
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return
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#save best
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if self._should_save_best(infos[metric_type]):
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self._save_best_checkpoint_and_update(
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infos[metric_type], checkpoint_dir, tag_or_iteration, model,
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optimizer, infos)
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#save latest
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self._save_latest_checkpoint_and_update(
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checkpoint_dir, tag_or_iteration, model, optimizer, infos)
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if isinstance(tag_or_iteration, int):
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self._save_checkpoint_record(checkpoint_dir, tag_or_iteration)
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def load_latest_parameters(self,
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model,
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optimizer=None,
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checkpoint_dir=None,
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checkpoint_path=None):
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"""Load a last model checkpoint from disk.
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Args:
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model (Layer): model to load parameters.
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optimizer (Optimizer, optional): optimizer to load states if needed.
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Defaults to None.
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checkpoint_dir (str, optional): the directory where checkpoint is saved.
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checkpoint_path (str, optional): if specified, load the checkpoint
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stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
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be ignored. Defaults to None.
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Returns:
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configs (dict): epoch or step, lr and other meta info should be saved.
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"""
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return self._load_parameters(model, optimizer, checkpoint_dir,
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checkpoint_path, "checkpoint_latest")
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def load_best_parameters(self,
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model,
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optimizer=None,
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checkpoint_dir=None,
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checkpoint_path=None):
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"""Load a last model checkpoint from disk.
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Args:
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model (Layer): model to load parameters.
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optimizer (Optimizer, optional): optimizer to load states if needed.
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Defaults to None.
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checkpoint_dir (str, optional): the directory where checkpoint is saved.
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checkpoint_path (str, optional): if specified, load the checkpoint
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stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
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be ignored. Defaults to None.
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Returns:
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configs (dict): epoch or step, lr and other meta info should be saved.
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"""
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return self._load_parameters(model, optimizer, checkpoint_dir,
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checkpoint_path, "checkpoint_best")
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def _should_save_best(self, metric: float) -> bool:
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if not self._best_full():
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return True
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# already full
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worst_record_path = max(self.best_records, key=self.best_records.get)
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# worst_record_path = max(self.best_records.iteritems(), key=operator.itemgetter(1))[0]
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worst_metric = self.best_records[worst_record_path]
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return metric < worst_metric
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def _best_full(self):
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return (not self._save_all) and len(self.best_records) == self.kbest_n
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def _latest_full(self):
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return len(self.latest_records) == self.latest_n
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def _save_best_checkpoint_and_update(self, metric, checkpoint_dir,
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tag_or_iteration, model, optimizer,
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infos):
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# remove the worst
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if self._best_full():
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worst_record_path = max(self.best_records,
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key=self.best_records.get)
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self.best_records.pop(worst_record_path)
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if (worst_record_path not in self.latest_records):
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logger.info(
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"remove the worst checkpoint: {}".format(worst_record_path))
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self._del_checkpoint(checkpoint_dir, worst_record_path)
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# add the new one
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self._save_parameters(checkpoint_dir, tag_or_iteration, model,
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optimizer, infos)
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self.best_records[tag_or_iteration] = metric
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def _save_latest_checkpoint_and_update(
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self, checkpoint_dir, tag_or_iteration, model, optimizer, infos):
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# remove the old
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if self._latest_full():
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to_del_fn = self.latest_records.pop(0)
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if (to_del_fn not in self.best_records.keys()):
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logger.info(
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"remove the latest checkpoint: {}".format(to_del_fn))
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self._del_checkpoint(checkpoint_dir, to_del_fn)
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self.latest_records.append(tag_or_iteration)
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self._save_parameters(checkpoint_dir, tag_or_iteration, model,
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optimizer, infos)
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def _del_checkpoint(self, checkpoint_dir, tag_or_iteration):
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checkpoint_path = os.path.join(checkpoint_dir,
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"{}".format(tag_or_iteration))
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for filename in glob.glob(checkpoint_path + ".*"):
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os.remove(filename)
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logger.info("delete file: {}".format(filename))
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def _load_checkpoint_idx(self, checkpoint_record: str) -> int:
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"""Get the iteration number corresponding to the latest saved checkpoint.
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Args:
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checkpoint_path (str): the saved path of checkpoint.
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Returns:
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int: the latest iteration number. -1 for no checkpoint to load.
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"""
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if not os.path.isfile(checkpoint_record):
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return -1
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# Fetch the latest checkpoint index.
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with open(checkpoint_record, "rt") as handle:
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latest_checkpoint = handle.readlines()[-1].strip()
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iteration = int(latest_checkpoint.split(":")[-1])
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return iteration
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def _save_checkpoint_record(self, checkpoint_dir: str, iteration: int):
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"""Save the iteration number of the latest model to be checkpoint record.
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Args:
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checkpoint_dir (str): the directory where checkpoint is saved.
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iteration (int): the latest iteration number.
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Returns:
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None
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"""
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checkpoint_record_latest = os.path.join(checkpoint_dir,
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"checkpoint_latest")
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checkpoint_record_best = os.path.join(checkpoint_dir, "checkpoint_best")
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with open(checkpoint_record_best, "w") as handle:
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for i in self.best_records.keys():
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handle.write("model_checkpoint_path:{}\n".format(i))
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with open(checkpoint_record_latest, "w") as handle:
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for i in self.latest_records:
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handle.write("model_checkpoint_path:{}\n".format(i))
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def _load_parameters(self,
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model,
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optimizer=None,
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checkpoint_dir=None,
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checkpoint_path=None,
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checkpoint_file=None):
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"""Load a last model checkpoint from disk.
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Args:
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model (Layer): model to load parameters.
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optimizer (Optimizer, optional): optimizer to load states if needed.
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Defaults to None.
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checkpoint_dir (str, optional): the directory where checkpoint is saved.
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checkpoint_path (str, optional): if specified, load the checkpoint
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stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
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be ignored. Defaults to None.
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checkpoint_file "checkpoint_latest" or "checkpoint_best"
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Returns:
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configs (dict): epoch or step, lr and other meta info should be saved.
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"""
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configs = {}
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if checkpoint_path is not None:
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tag = os.path.basename(checkpoint_path).split(":")[-1]
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elif checkpoint_dir is not None and checkpoint_file is not None:
|
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checkpoint_record = os.path.join(checkpoint_dir, checkpoint_file)
|
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iteration = self._load_checkpoint_idx(checkpoint_record)
|
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|
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if iteration == -1:
|
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|
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|
return configs
|
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|
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|
checkpoint_path = os.path.join(checkpoint_dir,
|
|
|
|
|
"{}".format(iteration))
|
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|
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else:
|
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|
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|
raise ValueError(
|
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|
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|
"At least one of 'checkpoint_dir' and 'checkpoint_file' and 'checkpoint_path' should be specified!"
|
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|
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|
)
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rank = dist.get_rank()
|
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|
|
|
|
|
|
|
|
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"
|
|
|
|
|
paddle.save(opt_dict, optimizer_path)
|
|
|
|
|
logger.info("Saved optimzier state to {}".format(optimizer_path))
|
|
|
|
|
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))
|
|
|
|
|
|
|
|
|
|
info_path = re.sub('.pdparams$', '.json', params_path)
|
|
|
|
|
infos = {} if infos is None else infos
|
|
|
|
|
with open(info_path, 'w') as fout:
|
|
|
|
|
data = json.dumps(infos)
|
|
|
|
|
fout.write(data)
|
|
|
|
|
if optimizer:
|
|
|
|
|
opt_dict = optimizer.state_dict()
|
|
|
|
|
optimizer_path = checkpoint_path + ".pdopt"
|
|
|
|
|
paddle.save(opt_dict, optimizer_path)
|
|
|
|
|
logger.info("Saved optimzier state to {}".format(optimizer_path))
|
|
|
|
|
|
|
|
|
|
if isinstance(tag_or_iteration, int):
|
|
|
|
|
_save_record(checkpoint_dir, tag_or_iteration)
|
|
|
|
|
info_path = re.sub('.pdparams$', '.json', params_path)
|
|
|
|
|
infos = {} if infos is None else infos
|
|
|
|
|
with open(info_path, 'w') as fout:
|
|
|
|
|
data = json.dumps(infos)
|
|
|
|
|
fout.write(data)
|
|
|
|
|
|
Hui Zhang
3 years ago
committed by
GitHub