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fix transformers 4.52 device_map ddp #4424

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Merged
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May 30, 2025
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fix transformers 4.52 device_map ddp #4424

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8 changes: 5 additions & 3 deletions swift/llm/model/patcher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -298,8 +298,10 @@ def patch_mp_ddp():
This should be called before any training starts.
"""
global _mp_ddp_patched
if is_mp_ddp() and not _mp_ddp_patched:
_mp_ddp_patched = True
if _mp_ddp_patched:
return
_mp_ddp_patched = True
if is_mp_ddp():
from accelerate.utils.modeling import get_balanced_memory, infer_auto_device_map

@wraps(infer_auto_device_map)
Expand All @@ -321,7 +323,7 @@ def _infer_auto_device_map_patch(model: nn.Module,
_old_ddp_init = DDP.__init__
accelerate.accelerator.torch.nn.parallel.DistributedDataParallel.__init__ = (
lambda self, model, device_ids, output_device, *args, **kwargs: _old_ddp_init(self, model, *args, **kwargs))
transformers.modeling_utils.get_balanced_memory = lambda *args, **kwargs: None
transformers.modeling_utils.get_balanced_memory = lambda *args, **kwargs: {}
transformers.modeling_utils.infer_auto_device_map = _infer_auto_device_map_patch

if is_mp_ddp() or use_torchacc():
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5 changes: 2 additions & 3 deletions swift/trainers/trainers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -176,13 +176,12 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
logger.info_once(f'use_logits_to_keep: {use_logits_to_keep}')

if use_logits_to_keep:
if inputs['labels'].shape[0] == 1:
if inputs['labels'].shape[0] == 1 and not is_mp():
# device_map may encounter device mismatch issues.
loss_mask = (inputs['labels'] != -100)[0]
inputs['labels'] = inputs['labels'][:, loss_mask]
inputs['labels'] = nn.functional.pad(inputs['labels'], (1, 0), value=-100)
inputs['logits_to_keep'] = nn.functional.pad(loss_mask[1:], (0, 1), value=True)
if is_mp():
inputs['logits_to_keep'] = inputs['logits_to_keep'].cpu()
else:
inputs['logits_to_keep'] = (inputs['labels'].shape[-1] -
(torch.ne(inputs['labels'], -100).int().argmax(-1))).max().item() + 1
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