[Frontend] Batch inference for llm.chat() API

examples/offline_inference_chat.py

@@ -39,6 +39,33 @@ def print_outputs(outputs):
                    use_tqdm=False)
 print_outputs(outputs)
 
+# You can run batch inference with llm.chat API
+conversation = [
+    {
+        "role": "system",
+        "content": "You are a helpful assistant"
+    },
+    {
+        "role": "user",
+        "content": "Hello"
+    },
+    {
+        "role": "assistant",
+        "content": "Hello! How can I assist you today?"
+    },
+    {
+        "role": "user",
+        "content": "Write an essay about the importance of higher education.",
+    },
+]
+conversations = [conversation for _ in range(10)]
+
+# We turn on tqdm progress bar to verify it's indeed running batch inference
+outputs = llm.chat(messages=conversations,
+                   sampling_params=sampling_params,
+                   use_tqdm=True)
+print_outputs(outputs)
+
 # A chat template can be optionally supplied.
 # If not, the model will use its default chat template.
 

tests/entrypoints/llm/test_generate.py

@@ -162,6 +162,41 @@ def test_chat():
     assert len(outputs) == 1
 
 
+def test_multi_chat():
+
+    llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
+
+    prompt1 = "Explain the concept of entropy."
+    prompt2 = "Explain what among us is."
+
+    conversation1 = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant"
+        },
+        {
+            "role": "user",
+            "content": prompt1
+        },
+    ]
+
+    conversation2 = [
+        {
+            "role": "system",
+            "content": "You are a helpful assistant"
+        },
+        {
+            "role": "user",
+            "content": prompt2
+        },
+    ]
+
+    messages = [conversation1, conversation2]
+
+    outputs = llm.chat(messages)
+    assert len(outputs) == 2
+
+
 @pytest.mark.parametrize("image_urls",
                          [[TEST_IMAGE_URLS[0], TEST_IMAGE_URLS[1]]])
 def test_chat_multi_image(image_urls: List[str]):

vllm/entrypoints/llm.py

@@ -485,7 +485,8 @@ def beam_search(
 
     def chat(
         self,
-        messages: List[ChatCompletionMessageParam],
+        messages: Union[List[ChatCompletionMessageParam],
+                        List[List[ChatCompletionMessageParam]]],
         sampling_params: Optional[Union[SamplingParams,
                                         List[SamplingParams]]] = None,
         use_tqdm: bool = True,
@@ -505,8 +506,9 @@ def chat(
         to the OpenAI API.
 
         Args:
-            messages: A single conversation represented as a list of messages.
-                Each message is a dictionary with 'role' and 'content' keys.
+            messages: A list of conversations or a single conversation. 
+                - Each conversation is represented as a list of messages.
+                - Each message is a dictionary with 'role' and 'content' keys.
             sampling_params: The sampling parameters for text generation.
                 If None, we use the default sampling parameters. When it
                 is a single value, it is applied to every prompt. When it
@@ -523,42 +525,56 @@ def chat(
             A list of ``RequestOutput`` objects containing the generated
             responses in the same order as the input messages.
         """
+        list_of_messages: List[List[ChatCompletionMessageParam]]
 
-        tokenizer = self.get_tokenizer()
-        model_config = self.llm_engine.get_model_config()
-
-        conversation, mm_data = parse_chat_messages(messages, model_config,
-                                                    tokenizer)
-
-        prompt: Union[str, List[int]]
-        if isinstance(tokenizer, MistralTokenizer):
-            prompt = apply_mistral_chat_template(
-                tokenizer,
-                messages=messages,
-                chat_template=chat_template,
-                add_generation_prompt=add_generation_prompt,
-                tools=tools,
-            )
+        # Handle multi and single conversations
+        if is_list_of(messages, list):
+            # messages is List[List[...]]
+            list_of_messages = messages
         else:
-            prompt = apply_hf_chat_template(
-                tokenizer,
-                conversation=conversation,
-                chat_template=chat_template,
-                add_generation_prompt=add_generation_prompt,
-                tools=tools,
-            )
+            # messages is List[...]
+            list_of_messages = [messages]
+
+        prompts: List[Union[TokensPrompt, TextPrompt]] = []
+
+        for msgs in list_of_messages:
+            tokenizer = self.get_tokenizer()
+            model_config = self.llm_engine.get_model_config()
+
+            conversation, mm_data = parse_chat_messages(
+                msgs, model_config, tokenizer)
+
+            prompt_data: Union[str, List[int]]
+            if isinstance(tokenizer, MistralTokenizer):
+                prompt_data = apply_mistral_chat_template(
+                    tokenizer,
+                    messages=msgs,
+                    chat_template=chat_template,
+                    add_generation_prompt=add_generation_prompt,
+                    tools=tools,
+                )
+            else:
+                prompt_data = apply_hf_chat_template(
+                    tokenizer,
+                    conversation=conversation,
+                    chat_template=chat_template,
+                    add_generation_prompt=add_generation_prompt,
+                    tools=tools,
+                )
+
+            prompt: Union[TokensPrompt, TextPrompt]
+            if is_list_of(prompt_data, int):
+                prompt = TokensPrompt(prompt_token_ids=prompt_data)
+            else:
+                prompt = TextPrompt(prompt=prompt_data)
 
-        inputs: PromptInputs
-        if is_list_of(prompt, int):
-            inputs = TokensPrompt(prompt_token_ids=prompt)
-        else:
-            inputs = TextPrompt(prompt=prompt)
+            if mm_data is not None:
+                prompt["multi_modal_data"] = mm_data
 
-        if mm_data is not None:
-            inputs["multi_modal_data"] = mm_data
+            prompts.append(prompt)
 
         return self.generate(
-            inputs,
+            prompts,
             sampling_params=sampling_params,
             use_tqdm=use_tqdm,
             lora_request=lora_request,