remove support for multiple chats

examples/offline_inference_chat.py

@@ -14,9 +14,8 @@ def print_outputs(outputs):
 
 print("=" * 80)
 
-# In this script, we demonstrate two ways to pass input to the chat method:
+# In this script, we demonstrate how to pass input to the chat method:
 
-# Conversation with a list of dictionaries
 conversation = [
     {"role": "system", "content": "You are a helpful assistant"},
     {"role": "user", "content": "Hello"},
@@ -31,30 +30,6 @@ def print_outputs(outputs):
 )
 print_outputs(outputs)
 
-# Multiple conversations
-conversations = [
-    [
-        {"role": "system", "content": "You are a helpful assistant"},
-        {"role": "user", "content": "What is dark matter?"},
-    ],
-    [
-        {"role": "system", "content": "You are a helpful assistant"},
-        {"role": "user", "content": "How are you?"},
-        {
-            "role": "assistant",
-            "content": "I'm an AI without feelings, but I'm here to help!",
-        },
-        {"role": "user", "content": "Tell me a joke."},
-    ],
-]
-
-outputs = llm.chat(
-    conversations,
-    sampling_params=sampling_params,
-    use_tqdm=False,
-)
-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

@@ -159,25 +159,3 @@ def test_chat():
     ]
     outputs = llm.chat(messages)
     assert len(outputs) == 1
-
-    prompt2 = "Describe Bangkok in 150 words."
-    multiple_messages = [messages] + [[
-        {
-            "role": "system",
-            "content": "You are a helpful assistant"
-        },
-        {
-            "role": "user",
-            "content": prompt2
-        },
-    ]]
-    outputs = llm.chat(multiple_messages)
-    assert len(outputs) == len(multiple_messages)
-
-    sampling_params = [
-        SamplingParams(temperature=0.01, top_p=0.95),
-        SamplingParams(temperature=0.3, top_p=0.95),
-    ]
-
-    outputs = llm.chat(multiple_messages, sampling_params=sampling_params)
-    assert len(outputs) == len(messages)

vllm/entrypoints/llm.py

@@ -1,13 +1,15 @@
 from contextlib import contextmanager
-from typing import (ClassVar, Dict, List, Optional, Sequence, Union, cast,
+from typing import (ClassVar, List, Optional, Sequence, Union, cast,
                     overload)
 
 from tqdm import tqdm
 from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
 
 from vllm.engine.arg_utils import EngineArgs
 from vllm.engine.llm_engine import LLMEngine
-from vllm.entrypoints.chat_utils import ChatCompletionMessageParam, parse_chat_messages
+from vllm.entrypoints.chat_utils import (ChatCompletionMessageParam,
+                                         parse_chat_messages,
+                                         apply_chat_template)
 from vllm.inputs import (PromptInputs, TextPrompt, TokensPrompt,
                          parse_and_batch_prompt)
 from vllm.logger import init_logger
@@ -360,13 +362,9 @@ def generate(
 
     def chat(
         self,
-        messages: Union[
-            List[ChatCompletionMessageParam], 
-            List[List[ChatCompletionMessageParam]]
-        ],
-        sampling_params: Optional[
-            Union[SamplingParams, List[SamplingParams]]
-        ] = None,
+        messages: List[ChatCompletionMessageParam],
+        sampling_params: Optional[Union[SamplingParams,
+                                        List[SamplingParams]]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[LoRARequest] = None,
         chat_template: Optional[str] = None,
@@ -398,33 +396,17 @@ def chat(
 
         tokenizer = self.get_tokenizer()
         model_config = self.llm_engine.get_model_config()
-
-        if isinstance(messages[0], dict):
-            conversations, _ = parse_chat_messages(
-                messages,
-                model_config,
-                tokenizer
-            )
-
-            prompts = tokenizer.apply_chat_template(
-                conversations,
-                tokenize=False,
-                add_generation_template=add_generation_template,
-                chat_template=chat_template,
-            )
 
-        elif isinstance(messages[0], list):
+        conversations, _ = parse_chat_messages(messages, model_config,
+                                               tokenizer)
+
+        prompts = apply_chat_template(
+            tokenizer,
+            conversations,
+            chat_template=chat_template,
+            add_generation_template=add_generation_template
+        )
 
-            prompts = [
-                tokenizer.apply_chat_template(
-                    parse_chat_messages(message, model_config, tokenizer)[0],
-                    tokenize=False,
-                    add_generation_template=add_generation_template,
-                    chat_template=chat_template,
-                )
-                for message in messages
-            ]
-
         return self.generate(
             prompts,
             sampling_params,
@@ -436,9 +418,8 @@ def chat(
     def encode(
         self,
         prompts: str,
-        pooling_params: Optional[
-            Union[PoolingParams, Sequence[PoolingParams]]
-        ] = None,
+        pooling_params: Optional[Union[PoolingParams,
+                                       Sequence[PoolingParams]]] = None,
         prompt_token_ids: Optional[List[int]] = None,
         use_tqdm: bool = True,
         lora_request: Optional[Union[List[LoRARequest], LoRARequest]] = None,
@@ -672,9 +653,8 @@ def _validate_and_add_requests(
             self._add_request(
                 request_inputs,
                 params[i] if isinstance(params, Sequence) else params,
-                lora_request=lora_request[i]
-                if isinstance(lora_request, Sequence)
-                else lora_request,
+                lora_request=lora_request[i] if isinstance(
+                    lora_request, Sequence) else lora_request,
                 prompt_adapter_request=prompt_adapter_request,
             )