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139 lines
4.8 KiB
139 lines
4.8 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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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 os
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import paddle
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from paddle import distributed as dist
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from parakeet.utils import mp_tools
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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.
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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 0
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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.readline().split()[-1]
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iteration = int(latest_checkpoint.split("-")[-1])
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return iteration
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def _save_checkpoint(checkpoint_dir: str, iteration: int):
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"""Save the iteration number of the latest model to be checkpointed.
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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, "wt") as handle:
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handle.write("model_checkpoint_path: step-{}".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 and the argument 'checkpoint_dir' will
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be ignored. Defaults to None.
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Returns:
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iteration (int): number of iterations that the loaded checkpoint has
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been trained.
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"""
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if checkpoint_path is not None:
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iteration = int(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 == 0:
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return iteration
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checkpoint_path = os.path.join(checkpoint_dir,
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"step-{}".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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local_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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print("[checkpoint] Rank {}: loaded model from {}".format(local_rank,
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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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print("[checkpoint] Rank {}: loaded optimizer state from {}".format(
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local_rank, optimizer_path))
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return iteration
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@mp_tools.rank_zero_only
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def save_parameters(checkpoint_dir, iteration, model, optimizer=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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iteration (int): the latest iteration 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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Returns:
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None
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"""
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checkpoint_path = os.path.join(checkpoint_dir, "step-{}".format(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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print("[checkpoint] 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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optimizer_path = checkpoint_path + ".pdopt"
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paddle.save(opt_dict, optimizer_path)
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print("[checkpoint] Saved optimzier state to {}".format(optimizer_path))
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_save_checkpoint(checkpoint_dir, iteration)
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