[BugFix][Frontend] Fix `LLM.chat()` tokenization (#16081)

Signed-off-by: Nick Hill <nhill@redhat.com>

tests/entrypoints/llm/test_chat.py

@@ -89,3 +89,31 @@ def test_chat_multi_image(image_urls: list[str]):
     }]
     outputs = llm.chat(messages)
     assert len(outputs) >= 0
+
+
+def test_llm_chat_tokenization_no_double_bos():
+    """
+    LLM.chat() should not add special tokens when using chat templates.
+    Check we get a single BOS token for llama chat.
+    """
+    llm = LLM(model="meta-llama/Llama-3.2-1B-Instruct", enforce_eager=True)
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant"
+        },
+        {
+            "role": "user",
+            "content": "Hello!"
+        },
+    ]
+    outputs = llm.chat(messages)
+    assert len(outputs) == 1
+    prompt_token_ids = getattr(outputs[0], "prompt_token_ids", None)
+    assert prompt_token_ids is not None
+
+    bos_token = llm.get_tokenizer().bos_token_id
+
+    # Ensure we have a single BOS
+    assert prompt_token_ids[0] == bos_token
+    assert prompt_token_ids[1] != bos_token, "Double BOS"

vllm/entrypoints/llm.py

@@ -251,8 +251,12 @@ class LLM:
         self.request_counter = Counter()
         self.default_sampling_params: Union[dict[str, Any], None] = None
 
-    def get_tokenizer(self) -> AnyTokenizer:
-        return self.llm_engine.get_tokenizer_group().tokenizer
+    def get_tokenizer(
+        self,
+        lora_request: Optional[LoRARequest] = None,
+    ) -> AnyTokenizer:
+        return self.llm_engine.get_tokenizer_group().get_lora_tokenizer(
+            lora_request)
 
     def set_tokenizer(self, tokenizer: AnyTokenizer) -> None:
         tokenizer_group = self.llm_engine.get_tokenizer_group()
@@ -712,7 +716,7 @@ class LLM:
                 cast(list[ChatCompletionMessageParam], messages)
             ]
 
-        tokenizer = self.get_tokenizer()
+        tokenizer = self.get_tokenizer(lora_request)
         model_config = self.llm_engine.get_model_config()
         resolved_content_format = resolve_chat_template_content_format(
             chat_template,
@@ -735,9 +739,8 @@ class LLM:
                 content_format=resolved_content_format,
             )
 
-            prompt_data: Union[str, list[int]]
             if isinstance(tokenizer, MistralTokenizer):
-                prompt_data = apply_mistral_chat_template(
+                prompt_token_ids = apply_mistral_chat_template(
                     tokenizer,
                     messages=msgs,
                     chat_template=chat_template,
@@ -746,7 +749,7 @@ class LLM:
                     continue_final_message=continue_final_message,
                 )
             else:
-                prompt_data = apply_hf_chat_template(
+                prompt_str = apply_hf_chat_template(
                     tokenizer,
                     trust_remote_code=model_config.trust_remote_code,
                     conversation=conversation,
@@ -755,12 +758,12 @@ class LLM:
                     add_generation_prompt=add_generation_prompt,
                     continue_final_message=continue_final_message,
                 )
+                # Special tokens are already included in chat templates so
+                # should not be added by the tokenizer in this case.
+                prompt_token_ids = tokenizer.encode(prompt_str,
+                                                    add_special_tokens=False)
 
-            prompt: Union[TokensPrompt, TextPrompt]
-            if is_list_of(prompt_data, int):
-                prompt = TokensPrompt(prompt_token_ids=prompt_data)
-            else:
-                prompt = TextPrompt(prompt=prompt_data)
+            prompt = TokensPrompt(prompt_token_ids=prompt_token_ids)
 
             if mm_data is not None:
                 prompt["multi_modal_data"] = mm_data
@@ -1059,8 +1062,6 @@ class LLM:
         if len(encoded_output_1) == 1:
             encoded_output_1 = encoded_output_1 * len(encoded_output_2)
 
-        scores: list[PoolingRequestOutput] = []
-
         scores = _cosine_similarity(tokenizer=tokenizer,
                                     embed_1=encoded_output_1,
                                     embed_2=encoded_output_2)