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fix: add pull to solve the problem that fsdp2 traninng in shared will timeout #68

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fix: add pull to solve the problem that fsdp2 traninng in shared will timeout #68

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33 changes: 26 additions & 7 deletions mova/engine/trainer/accelerate/accelerate_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -146,8 +146,8 @@ def _setup(self):
if self.use_fsdp:
from accelerate import FullyShardedDataParallelPlugin
from torch.distributed.fsdp.fully_sharded_data_parallel import (
FullOptimStateDictConfig,
FullStateDictConfig,
ShardedOptimStateDictConfig,
ShardedStateDictConfig,
)

for attr_name in ['text_encoder', 'prompter', 'video_vae', 'audio_vae']:
Expand All @@ -170,13 +170,11 @@ def _setup(self):
fsdp_config['reshard_after_forward'] = True

fsdp_plugin = FullyShardedDataParallelPlugin(
state_dict_config=FullStateDictConfig(
state_dict_config=ShardedStateDictConfig(
offload_to_cpu=True,
# rank0_only=True,
),
optim_state_dict_config=FullOptimStateDictConfig(
optim_state_dict_config=ShardedOptimStateDictConfig(
offload_to_cpu=True,
# rank0_only=True,
),
**fsdp_config
)
Expand Down Expand Up @@ -511,6 +509,17 @@ def _save_checkpoint(self, final: bool = False):
os.makedirs(os.path.join(step_dir, "accelerator"), exist_ok=True)
self.accelerator.wait_for_everyone()
self.accelerator.save_state(os.path.join(step_dir, "accelerator"))
if self.use_fsdp:
rank = self.accelerator.process_index
accel_dir = os.path.join(step_dir, "accelerator")
os.makedirs(accel_dir, exist_ok=True)
torch.save(self.optimizer.state_dict(),
os.path.join(accel_dir, f"optimizer_{rank}.bin"))
torch.save(self.lr_scheduler.state_dict(),
os.path.join(accel_dir, f"scheduler_{rank}.bin"))
else:
self.accelerator.save_state(os.path.join(step_dir, "accelerator"))


def _resume_checkpoint(self, checkpoint_path: str):
"""Resume checkpoint"""
Expand All @@ -522,7 +531,17 @@ def _resume_checkpoint(self, checkpoint_path: str):

accelerator_path = os.path.join(checkpoint_path, "accelerator")
if os.path.exists(accelerator_path):
self.accelerator.load_state(accelerator_path)
if self.use_fsdp:
rank = self.accelerator.process_index
optim_path = os.path.join(accelerator_path, f"optimizer_{rank}.bin")
sched_path = os.path.join(accelerator_path, f"scheduler_{rank}.bin")
if os.path.exists(optim_path):
self.optimizer.load_state_dict(torch.load(optim_path, map_location="cpu"))
if os.path.exists(sched_path):
self.lr_scheduler.load_state_dict(torch.load(sched_path, map_location="cpu"))
else:
self.accelerator.load_state(accelerator_path)


if self.use_lora:
from mova.engine.trainer.accelerate.lora_utils import load_lora_weights
Expand Down