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[V1][Core] using cached vocab_size for Structured Outputs #14630

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Merged
merged 2 commits into from
Mar 13, 2025
Merged

[V1][Core] using cached vocab_size for Structured Outputs #14630

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e248036
fix(v1): support for vision models
aarnphm Mar 11, 2025
8872481
chore: use max_tokens_id
aarnphm Mar 11, 2025
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2 changes: 1 addition & 1 deletion vllm/v1/structured_output/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,6 @@
class StructuredOutputManager:

def __init__(self, vllm_config: VllmConfig):
self.vocab_size = vllm_config.model_config.get_vocab_size()
self.vllm_config = vllm_config
self.init_complete = False

Expand All @@ -41,6 +40,7 @@ def _delayed_init(self):
tokenizer_group.ping()

tokenizer = tokenizer_group.get_lora_tokenizer(None)
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@njhill njhill Mar 12, 2025

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Not directly related to this PR but it looks like structured output currently isn't compatible with custom vocab LoRAs.

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@aarnphm aarnphm Mar 12, 2025

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Sg, will add to the list for compatibility

Fwiw i don't think it ever worked with LoRA

self.vocab_size = tokenizer.max_token_id
if isinstance(tokenizer, MistralTokenizer):
# NOTE: ideally, xgrammar should handle this accordingly.
# refer to https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98
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