def save(
self,
config: PyTextConfig,
model: Model,
meta: Optional[CommonMetadata],
tensorizers: Dict[str, Tensorizer],
training_state: Optional[TrainingState] = None,
identifier: str = None,
) -> str:
"""
save a checkpoint to given path, config, model and training_state
together represent the checkpoint. When identifier is None, this
function is used to save post-training snapshot
"""
saved_path = ""
if identifier:
# saving during-training checkpoints
saved_path = self.generate_checkpoint_path(config, identifier)
print("Saving checkpoint to ", saved_path)
else:
# saving post-training snapshot if no identifer given
saved_path = config.save_snapshot_path
print(f"Saving pytorch model to: {saved_path}")
saved_folder = os.path.dirname(saved_path)
if not PathManager.exists(saved_folder):
PathManager.mkdirs(saved_folder)
print(f"created {saved_folder}")
with PathManager.open(saved_path, "wb") as checkpoint_f:
save_checkpoint(checkpoint_f, config, model, meta, tensorizers,
training_state)
if identifier:
self._saved_paths.append(saved_path)
else:
self._post_training_snapshot_path = saved_path
return saved_path
def save(
config: PyTextConfig,
model: Model,
meta: Optional[CommonMetadata],
tensorizers: Dict[str, Tensorizer],
training_state: Optional[TrainingState] = None,
identifier: Optional[str] = None,
) -> str:
"""
Save all stateful information of a training task to a specified file-like
object, will save the original config, model state, metadata,
training state if training is not completed
Args:
identifier (str): used to identify a checkpoint within a training job,
used as a suffix for save path
config (PytextConfig): contains all raw parameter/hyper-parameters
for training task
model (Model): actual model in training
training_state (TrainingState): stateful infomation during training
Returns:
identifier (str): if identifier is not specified, will save to
config.save_snapshot_path to be consistent to post-training snapshot;
if specified, will be used to save checkpoint during training,
identifier is used to identify checkpoints in the same training
"""
saved_path = ""
if identifier:
# saving during-training checkpoints
saved_path = generate_checkpoint_path(config, identifier)
else:
# saving post-training snapshot if no identifer given
saved_path = config.save_snapshot_path
print(f"Saving pytorch model to: {saved_path}")
saved_folder = os.path.dirname(saved_path)
if not PathManager.exists(saved_folder):
PathManager.mkdirs(saved_folder)
print(f"created {saved_folder}")
# Currently torch.save() has error pickling certain models when not saving
# by model.state_dict(), thus currently overriding the model in
# training_state with None, and put back saving
# https://github.com/pytorch/pytorch/issues/15116
model_in_training_state = None
if training_state:
model_in_training_state, training_state.model = training_state.model, None
try:
state = {
DATA_STATE: meta,
CONFIG_JSON: config_to_json(PyTextConfig, config),
MODEL_STATE: model.state_dict(),
SERIALIZE_VERSION_KEY: LATEST_SERIALIZE_VERSION,
TENSORIZERS: tensorizers,
TRAINING_STATE: training_state,
}
if identifier is not None:
_CHECKPOINT_MANAGER.save_checkpoint(state, saved_path)
else:
_CHECKPOINT_MANAGER.save_snapshot(state, saved_path)
finally:
if training_state:
training_state.model = model_in_training_state
return saved_path