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Allow flexibility for users to pass attention_mask in data_collator #2234

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Allow flexibility for users to pass attention_mask in data_collator #2234

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14 changes: 10 additions & 4 deletions swift/llm/utils/template.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1008,13 +1008,19 @@ def data_collator(self, batch: List[Dict[str, Any]], padding_to: Optional[int] =
if 'inputs_embeds' in batch[0]:
inputs_embeds = [b['inputs_embeds'] for b in batch]
res['inputs_embeds'] = inputs_embeds
res['attention_mask'] = [
torch.ones((inputs_embeds[i].shape[0]), dtype=torch.int64) for i in range(len(inputs_embeds))
]
if 'attention_mask' in batch[0]:
res['attention_mask'] = batch[0]['attention_mask']
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The batch size may be greater than 1.

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Okay, let me think about how to deal with batch size greater than 1.

else:
res['attention_mask'] = [
torch.ones((inputs_embeds[i].shape[0]), dtype=torch.int64) for i in range(len(inputs_embeds))
]
elif 'input_ids' in batch[0]:
input_ids = [torch.tensor(b['input_ids']) for b in batch]
res['input_ids'] = input_ids
res['attention_mask'] = [torch.ones(len(input_ids[i]), dtype=torch.int64) for i in range(len(input_ids))]
if 'attention_mask' in batch[0]:
res['attention_mask'] = batch[0]['attention_mask']
else:
res['attention_mask'] = [torch.ones(len(input_ids[i]), dtype=torch.int64) for i in range(len(input_ids))]

for key in ['labels', 'loss_scale', 'position_ids']:
if key in batch[0]:
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