# 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 os
import paddle
from paddle import distributed as dist
from paddlespeech.t2s.utils import mp_tools
__all__ = ["load_parameters", "save_parameters"]
def _load_latest_checkpoint(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.
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
if (not os.path.isfile(checkpoint_record)):
return 0
# Fetch the latest checkpoint index.
with open(checkpoint_record, "rt") as handle:
latest_checkpoint = handle.readline().split()[-1]
iteration = int(latest_checkpoint.split("-")[-1])
return iteration
def _save_checkpoint(checkpoint_dir: str, iteration: int):
"""Save the iteration number of the latest model to be checkpointed.
Args:
checkpoint_dir (str):
the directory where checkpoint is saved.
iteration (int):
the latest iteration number.
Returns:
None
"""
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint")
# Update the latest checkpoint index.
with open(checkpoint_record, "wt") as handle:
handle.write("model_checkpoint_path: step-{}".format(iteration))
[docs]def load_parameters(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 and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
Returns:
iteration (int): number of iterations that the loaded checkpoint has
been trained.
"""
if checkpoint_path is not None:
iteration = int(os.path.basename(checkpoint_path).split("-")[-1])
elif checkpoint_dir is not None:
iteration = _load_latest_checkpoint(checkpoint_dir)
if iteration == 0:
return iteration
checkpoint_path = os.path.join(checkpoint_dir,
"step-{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
)
local_rank = dist.get_rank()
params_path = checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
model.set_state_dict(model_dict)
print("[checkpoint] Rank {}: loaded model from {}".format(local_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)
print("[checkpoint] Rank {}: loaded optimizer state from {}".format(
local_rank, optimizer_path))
return iteration
[docs]@mp_tools.rank_zero_only
def save_parameters(checkpoint_dir, iteration, model, optimizer=None):
"""Checkpoint the latest trained model parameters.
Args:
checkpoint_dir (str):
the directory where checkpoint is saved.
iteration (int):
the latest iteration number.
model (Layer):
model to be checkpointed.
optimizer (Optimizer, optional):
optimizer to be checkpointed. Defaults to None.
Returns:
None
"""
checkpoint_path = os.path.join(checkpoint_dir, "step-{}".format(iteration))
model_dict = model.state_dict()
params_path = checkpoint_path + ".pdparams"
paddle.save(model_dict, params_path)
print("[checkpoint] Saved model to {}".format(params_path))
if optimizer:
opt_dict = optimizer.state_dict()
optimizer_path = checkpoint_path + ".pdopt"
paddle.save(opt_dict, optimizer_path)
print("[checkpoint] Saved optimzier state to {}".format(optimizer_path))
_save_checkpoint(checkpoint_dir, iteration)