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[generate] Fix vocab_size access for multimodal models (#37937)

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Implements last migrations for generation from `config.vocab_size` to `config.get_text_config().vocab.size`

In doing so, we enable multimodal models to fully leverage all existing generation features.
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Jonas 2025-05-05 16:56:56 +02:00 committed by GitHub
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commit d80f53fa50
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1 changed files with 6 additions and 4 deletions
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src/transformers/generation/utils.py
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@ -968,7 +968,7 @@ class GenerationMixin:
atm_translator = AssistantVocabTranslatorCache.get_translator( atm_translator = AssistantVocabTranslatorCache.get_translator(
target_tokenizer, target_tokenizer,
assistant_tokenizer, assistant_tokenizer,
self.config.vocab_size, self.config.get_text_config().vocab_size,
assistant_model=assistant_model, assistant_model=assistant_model,
assistant_prune_lm_head=True, # prune LM head of assistant model assistant_prune_lm_head=True, # prune LM head of assistant model
) )
@ -1234,7 +1234,9 @@ class GenerationMixin:
# Watermarking should be after all logits processing is finished (see #34630) # Watermarking should be after all logits processing is finished (see #34630)
if generation_config.watermarking_config is not None: if generation_config.watermarking_config is not None:
processors.append( processors.append(
generation_config.watermarking_config.construct_processor(self.config.vocab_size, device) generation_config.watermarking_config.construct_processor(
self.config.get_text_config().vocab_size, device
)
) )
# `LogitNormalization` should always be the last logit processor, when present # `LogitNormalization` should always be the last logit processor, when present
@ -1412,7 +1414,7 @@ class GenerationMixin:
# 3. Optionally normalize the logits (across the vocab dimension) # 3. Optionally normalize the logits (across the vocab dimension)
if normalize_logits: if normalize_logits:
scores = scores.reshape(-1, self.config.vocab_size, scores.shape[-1]) scores = scores.reshape(-1, self.config.get_text_config().vocab_size, scores.shape[-1])
scores = torch.nn.functional.log_softmax(scores, dim=1) scores = torch.nn.functional.log_softmax(scores, dim=1)
scores = scores.reshape(-1, scores.shape[-1]) scores = scores.reshape(-1, scores.shape[-1])
@ -1426,7 +1428,7 @@ class GenerationMixin:
beam_indices[beam_indices_mask] = 0 beam_indices[beam_indices_mask] = 0
# 6. multiply beam_indices with vocab size to gather correctly from scores # 6. multiply beam_indices with vocab size to gather correctly from scores
beam_sequence_indices = beam_indices * self.config.vocab_size beam_sequence_indices = beam_indices * self.config.get_text_config().vocab_size
# 7. Define which indices contributed to scores # 7. Define which indices contributed to scores
cut_idx = sequences.shape[-1] - max_beam_length cut_idx = sequences.shape[-1] - max_beam_length