Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Save checkpoint to temporary directory to handle partial saves during failures #35580

Open
wants to merge 3 commits into
base: main
Choose a base branch
from
+31 −20
Open

Save checkpoint to temporary directory to handle partial saves during failures #35580

Changes from 2 commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
f7a972a
Save checkpoint to temporary folder first
SilverSoldier Jan 22, 2025
aed2c9d
Merge branch 'main' into checkpoint
SilverSoldier Jan 24, 2025
7a36420
Merge branch 'main' into checkpoint
SilverSoldier Jan 27, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
51 changes: 31 additions & 20 deletions src/transformers/trainer.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@

import contextlib
import copy
import errno
import functools
import glob
import importlib.metadata
Expand Down Expand Up @@ -3218,31 +3219,41 @@ def _save_checkpoint(self, model, trial):
self.store_flos()

run_dir = self._get_output_dir(trial=trial)
output_dir = os.path.join(run_dir, checkpoint_folder)
self.save_model(output_dir, _internal_call=True)
checkpoint_dir = os.path.join(run_dir, checkpoint_folder)
with tempfile.TemporaryDirectory(prefix=f"tmp-{PREFIX_CHECKPOINT_DIR}-", dir=run_dir) as output_dir:
self.save_model(output_dir, _internal_call=True)

if not self.args.save_only_model:
# Save optimizer and scheduler
self._save_optimizer_and_scheduler(output_dir)
# Save RNG state
self._save_rng_state(output_dir)
if not self.args.save_only_model:
# Save optimizer and scheduler
self._save_optimizer_and_scheduler(output_dir)
# Save RNG state
self._save_rng_state(output_dir)

# Save the Trainer state
if self.args.should_save:
# Update `ExportableState` callbacks and `TrainerControl` state to where we are currently
for cb in [
cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
]:
cb_name = cb.__class__.__name__
cb_state = cb.state()
if isinstance(self.state.stateful_callbacks[cb_name], list):
self.state.stateful_callbacks[cb_name].append(cb_state)
# Save the Trainer state
if self.args.should_save:
# Update `ExportableState` callbacks and `TrainerControl` state to where we are currently
for cb in [
cb for cb in self.callback_handler.callbacks + [self.control] if isinstance(cb, ExportableState)
]:
cb_name = cb.__class__.__name__
cb_state = cb.state()
if isinstance(self.state.stateful_callbacks[cb_name], list):
self.state.stateful_callbacks[cb_name].append(cb_state)
else:
self.state.stateful_callbacks[cb_name] = cb_state
self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))

try:
os.renames(output_dir, checkpoint_dir)
except OSError as e:
if e.errno in [errno.ENOTEMPTY, errno.EEXIST]: # Directory/File already exists
shutil.rmtree(checkpoint_dir)
os.renames(output_dir, checkpoint_dir)
else:
self.state.stateful_callbacks[cb_name] = cb_state
self.state.save_to_json(os.path.join(output_dir, TRAINER_STATE_NAME))
raise

if self.args.push_to_hub:
self._push_from_checkpoint(output_dir)
self._push_from_checkpoint(checkpoint_dir)

# Maybe delete some older checkpoints.
if self.args.should_save:
Expand Down