unifying the input shape of the text-only branch and the text-image branch
#19
by
cardcounter
- opened
modeling_internlm_xcomposer2.py
CHANGED
@@ -423,6 +423,7 @@ class InternLMXComposer2ForCausalLM(InternLM2PreTrainedModel):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Returns:
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"""
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@@ -458,7 +459,7 @@ class InternLMXComposer2ForCausalLM(InternLM2PreTrainedModel):
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image, text, image_nums)
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else:
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to_regress_tokens, targets = self.text2emb(
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-
text, add_special_tokens=True)
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to_regress_embeds = self.model.tok_embeddings(
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to_regress_tokens.input_ids)
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attention_mask = to_regress_tokens.attention_mask
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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+
kwargs['samples']['text_input] should have dimension 1 x bs
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Returns:
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"""
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image, text, image_nums)
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else:
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to_regress_tokens, targets = self.text2emb(
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+
text[0], add_special_tokens=True)
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to_regress_embeds = self.model.tok_embeddings(
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to_regress_tokens.input_ids)
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attention_mask = to_regress_tokens.attention_mask
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