| | |
| | import gc |
| | from functools import partial |
| |
|
| | import torch |
| | from torch.distributed.fsdp import FullyShardedDataParallel as FSDP |
| | from torch.distributed.fsdp import MixedPrecision, ShardingStrategy |
| | from torch.distributed.fsdp.wrap import lambda_auto_wrap_policy |
| | from torch.distributed.utils import _free_storage |
| |
|
| |
|
| | def shard_model( |
| | model, |
| | device_id, |
| | param_dtype=torch.bfloat16, |
| | reduce_dtype=torch.float32, |
| | buffer_dtype=torch.float32, |
| | process_group=None, |
| | sharding_strategy=ShardingStrategy.FULL_SHARD, |
| | sync_module_states=True, |
| | ): |
| | model = FSDP( |
| | module=model, |
| | process_group=process_group, |
| | sharding_strategy=sharding_strategy, |
| | auto_wrap_policy=partial( |
| | lambda_auto_wrap_policy, lambda_fn=lambda m: m in model.blocks), |
| | mixed_precision=MixedPrecision( |
| | param_dtype=param_dtype, |
| | reduce_dtype=reduce_dtype, |
| | buffer_dtype=buffer_dtype), |
| | device_id=device_id, |
| | sync_module_states=sync_module_states) |
| | return model |
| |
|
| |
|
| | def free_model(model): |
| | for m in model.modules(): |
| | if isinstance(m, FSDP): |
| | _free_storage(m._handle.flat_param.data) |
| | del model |
| | gc.collect() |
| | torch.cuda.empty_cache() |
| |
|
