feat: 新增FSDP并行后端
- FSDPExecutor通过**fsdp_kwargs直传FSDP参数 - unwrap_model同时支持DDP和FSDP - parallel_mode新增fsdp选项
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@@ -2,7 +2,9 @@ from astrai.parallel.executor import (
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AccumOptimizer,
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AccumScheduler,
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BaseExecutor,
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DDPExecutor,
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ExecutorFactory,
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FSDPExecutor,
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GradientState,
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NoneExecutor,
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)
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@@ -31,4 +33,6 @@ __all__ = [
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"AccumOptimizer",
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"AccumScheduler",
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"NoneExecutor",
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"DDPExecutor",
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"FSDPExecutor",
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]
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@@ -7,6 +7,7 @@ from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LRScheduler
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@@ -198,3 +199,33 @@ class DDPExecutor(BaseExecutor):
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if isinstance(model, DDP):
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return model.module
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return model
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@ExecutorFactory.register("fsdp")
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class FSDPExecutor(BaseExecutor):
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def __init__(self, grad_accum_steps: int = 1, **fsdp_kwargs):
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super().__init__(grad_accum_steps=grad_accum_steps)
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self._fsdp_kwargs = fsdp_kwargs
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self._original_model: Optional[nn.Module] = None
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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if not self.use_distributed:
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logger.warning("FSDP backend selected but world_size=1, model not wrapped")
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return model
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self._original_model = model
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device_id = torch.device("cuda", get_rank())
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model = FSDP(model, device_id=device_id, **self._fsdp_kwargs)
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logger.info("Model wrapped with FSDP (world_size=%d)", get_world_size())
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return model
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def _no_sync(self, model: nn.Module):
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if isinstance(model, FSDP):
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return model.no_sync()
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return contextlib.nullcontext()
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def unwrap_model(self, model: nn.Module) -> nn.Module:
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if self._original_model is not None:
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return self._original_model
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if isinstance(model, FSDP):
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return model._fsdp_wrapped_module
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return model
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