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[v1] fix device_mesh and sp for fsdp2 (#10429)

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sunyi0505
2026-04-28 11:20:11 +08:00
committed by GitHub
parent 2f0bef207a
commit 50945ef850
3 changed files with 23 additions and 30 deletions
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23
src/llamafactory/v1/core/base_trainer.py
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@@ -269,26 +269,13 @@ class BaseTrainer:
# deepspeed: engine.step() already ran inside backward at the sync boundary
grad_norm = self._deepspeed_engine.get_grad_norm()
else:
grad_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args.max_grad_norm).item()
if self.args.dist_config and self.args.dist_config.get("cp_size", 1) > 1:
from torch.nn.utils.clip_grad import _clip_grads_with_norm_, _get_total_norm
grad_norm = grad_norm**2
grad_norm = DistributedInterface().all_reduce(grad_norm, op=ReduceOp.SUM, dim=Dim.CP)
grad_norm = grad_norm**0.5
parameters = self.model.parameters()
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
else:
parameters = list(parameters)
grads = [p.grad for p in parameters if p.grad is not None]
grad_norm = _get_total_norm(grads)
grad_norm = grad_norm.to(self.device)
_clip_grads_with_norm_(parameters, self.args.max_grad_norm, grad_norm)
if isinstance(grad_norm, torch.distributed._tensor.DTensor):
grad_norm = grad_norm.full_tensor().item()
else:
grad_norm = torch.nn.utils.clip_grad_norm_(
self.model.parameters(), self.args.max_grad_norm
).item()
# isfinite(): argument 'input' (position 1) must be Tensor, not float
if not torch.isfinite(torch.tensor(grad_norm)): # type: ignore # pyright: ignore [reportUnknownReturnType]
logger.warning_rank0(f"Gradient norm is not finite: {grad_norm}")
else: