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support acc fsdp optim_state_dict #6

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support acc fsdp optim_state_dict #6

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c60e8bd
support acc fsdp optim_state_dict
hanwen-sun Sep 24, 2024
1b2922b
change ACC_FSDP to FSDP
hanwen-sun Sep 24, 2024
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34 changes: 18 additions & 16 deletions src/transformers/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2959,16 +2959,14 @@ def _save_optimizer_and_scheduler(self, output_dir):
if is_torch_xla_available():
xm.rendezvous("saving_optimizer_states")
if self.is_fsdp_xla_enabled and not self.is_fsdp_xla_v2_enabled:
optm = {
"optimizer": self.optimizer.state_dict(),
"shard_metadata": self.model.get_shard_metadata(),
}
from torchacc.dist.fsdp import FullyShardedDataParallel as FSDP

optm = FSDP.full_optim_state_dict(self.model, self.optimizer)
xm.save(
optm,
os.path.join(
output_dir, f"rank{self.args.process_index}-of-{self.args.world_size}-{OPTIMIZER_NAME}"
),
master_only=False,
output_dir, f"{OPTIMIZER_NAME}"
)
)
else:
xm.save(self.optimizer.state_dict(), os.path.join(output_dir, OPTIMIZER_NAME))
Expand Down Expand Up @@ -3050,23 +3048,27 @@ def _load_optimizer_and_scheduler(self, checkpoint):
)
)
checkpoint_file_exists = (
glob.glob(os.path.join(checkpoint, f"rank*-of-{self.args.world_size}-{OPTIMIZER_NAME}"))
glob.glob(os.path.join(checkpoint, f"{OPTIMIZER_NAME}"))
if self.is_fsdp_xla_enabled and not self.is_fsdp_xla_v2_enabled
else checkpoint_file_exists
)

if checkpoint_file_exists and os.path.isfile(os.path.join(checkpoint, SCHEDULER_NAME)):
# Load in optimizer and scheduler states
if is_torch_xla_available():
from torchacc.dist.fsdp import FullyShardedDataParallel as FSDP
# On TPU we have to take some extra precautions to properly load the states on the right device.
if self.is_fsdp_xla_enabled and not self.is_fsdp_xla_v2_enabled:
optimizer_state = torch.load(
os.path.join(
checkpoint, f"rank{self.args.process_index}-of-{self.args.world_size}-{OPTIMIZER_NAME}"
),
map_location="cpu",
)
# We only need `optimizer` when resuming from checkpoint
optimizer_state = optimizer_state["optimizer"]
optimizer_state = None
if self.args.process_index == 0:
optimizer_state = torch.load(
os.path.join(
checkpoint, f"{OPTIMIZER_NAME}"
),
map_location="cpu",
)

optimizer_state = FSDP.load_optim_state_dict(self.model, optimizer_state, self.optimizer)
else:
optimizer_state = torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location="cpu")
with warnings.catch_warnings(record=True) as caught_warnings:
Expand Down