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fix FSDP model resume optimizer & scheduler #25852

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merged 2 commits into from
Sep 1, 2023
Merged

fix FSDP model resume optimizer & scheduler #25852

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58f6438
fix FSDP resume optimizer & scheduler
Aug 30, 2023
8cb3015
improve trainer code quality
Sep 1, 2023
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12 changes: 5 additions & 7 deletions src/transformers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -222,6 +222,7 @@
TRAINING_ARGS_NAME = "training_args.bin"
TRAINER_STATE_NAME = "trainer_state.json"
OPTIMIZER_NAME = "optimizer.pt"
OPTIMIZER_NAME_BIN = "optimizer.bin"
SCHEDULER_NAME = "scheduler.pt"
SCALER_NAME = "scaler.pt"

Expand Down Expand Up @@ -2359,16 +2360,12 @@ def _save_checkpoint(self, model, trial, metrics=None):
partial=True,
v3=smp.state.cfg.shard_optimizer_state,
)
if self.args.should_save:
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
if self.do_grad_scaling:
torch.save(self.scaler.state_dict(), os.path.join(output_dir, SCALER_NAME))
elif self.args.should_save and not self.is_deepspeed_enabled and not (self.fsdp or self.is_fsdp_enabled):
# deepspeed.save_checkpoint above saves model/optim/sched
torch.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))

# Save SCHEDULER & SCALER
if self.args.should_save and not self.is_deepspeed_enabled and not is_torch_tpu_available():
with warnings.catch_warnings(record=True) as caught_warnings:
torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, SCHEDULER_NAME))
reissue_pt_warnings(caught_warnings)
Expand Down Expand Up @@ -2439,7 +2436,8 @@ def _load_optimizer_and_scheduler(self, checkpoint):
checkpoint_file_exists = (
glob.glob(os.path.join(checkpoint, OPTIMIZER_NAME) + "_*")
if is_sagemaker_mp_enabled()
else os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))
else (os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME))
or os.path.isfile(os.path.join(checkpoint, OPTIMIZER_NAME_BIN)))
)
if checkpoint_file_exists and os.path.isfile(os.path.join(checkpoint, SCHEDULER_NAME)):
# Load in optimizer and scheduler states
Expand Down