[BugFix] Fix tokenizer out of vocab size (#3685)

tests/tokenization/test_detokenize.py

@@ -83,6 +83,13 @@ def test_decode_streaming(tokenizer_id, truth, with_prompt,
 
     assert decoded_text == generated
 
+    decoded_text = _run_incremental_decode(
+        tokenizer, [len(tokenizer)],
+        skip_special_tokens=skip_special_tokens,
+        starting_index=starting_index)
+
+    assert decoded_text == ''
+
 
 @pytest.fixture
 def detokenizer(tokenizer_name: str) -> Detokenizer:

vllm/engine/llm_engine.py

@@ -222,14 +222,6 @@ class LLMEngine:
         self.tokenizer: BaseTokenizerGroup = get_tokenizer_group(
             self.parallel_config.tokenizer_pool_config, **init_kwargs)
 
-        if len(self.get_tokenizer()) != self.model_config.get_vocab_size():
-            logger.warning(
-                f"The tokenizer's vocabulary size {len(self.get_tokenizer())}"
-                f" does not match the model's vocabulary size "
-                f"{self.model_config.get_vocab_size()}. This might "
-                f"cause an error in decoding. Please change config.json "
-                "to match the tokenizer's vocabulary size.")
-
     def _verify_args(self) -> None:
         self.model_config.verify_with_parallel_config(self.parallel_config)
         self.cache_config.verify_with_parallel_config(self.parallel_config)

vllm/entrypoints/openai/serving_engine.py

@@ -68,14 +68,6 @@ class OpenAIServing:
             tokenizer_mode=engine_model_config.tokenizer_mode,
             trust_remote_code=engine_model_config.trust_remote_code)
 
-        if len(self.tokenizer) != engine_model_config.get_vocab_size():
-            logger.warning(
-                f"The tokenizer's vocabulary size {len(self.tokenizer)}"
-                f" does not match the model's vocabulary size "
-                f"{engine_model_config.get_vocab_size()}. This might "
-                f"cause an error in decoding. Please change config.json "
-                "to match the tokenizer's vocabulary size.")
-
     async def show_available_models(self) -> ModelList:
         """Show available models. Right now we only have one model."""
         model_cards = [

vllm/transformers_utils/tokenizer.py

@@ -232,6 +232,10 @@ def detokenize_incrementally(
              all_input_ids[:-1],
              skip_special_tokens=skip_special_tokens)
 
+    # If the new token id is out of bounds, return an empty string.
+    if new_token_id >= len(tokenizer):
+        new_tokens = [""]
+    else:
         # Put new_token_id in a list so skip_special_tokens is respected
         new_tokens = tokenizer.convert_ids_to_tokens(
             [new_token_id], skip_special_tokens=skip_special_tokens)