Support chat template and echo for chat API

docs/source/getting_started/quickstart.rst

@@ -107,6 +107,7 @@ OpenAI-Compatible Server
 ------------------------
 
 vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.
+By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the above command) and implements `list models <https://platform.openai.com/docs/api-reference/models/list>`_, `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_, and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints. We are actively adding support for more endpoints.
 
 Start the server:
 
@@ -122,14 +123,23 @@ Use model from www.modelscope.cn
     $ VLLM_USE_MODELSCOPE=True python -m vllm.entrypoints.openai.api_server \
     $     --model="qwen/Qwen-7B-Chat" --revision="v1.1.8" --trust-remote-code
 
-By default, it starts the server at ``http://localhost:8000``. You can specify the address with ``--host`` and ``--port`` arguments. The server currently hosts one model at a time (OPT-125M in the above command) and implements `list models <https://platform.openai.com/docs/api-reference/models/list>`_ and `create completion <https://platform.openai.com/docs/api-reference/completions/create>`_ endpoints. We are actively adding support for more endpoints.
+By default, the server uses a predefined chat template stored in the tokenizer. You can override this template by using the ``--chat-template`` argument:
+
+.. code-block:: console
+
+   $ python -m vllm.entrypoints.openai.api_server \
+   $     --model facebook/opt-125m \
+   $     --chat-template ./examples/template_chatml.json
 
 This server can be queried in the same format as OpenAI API. For example, list the models:
 
 .. code-block:: console
 
     $ curl http://localhost:8000/v1/models
 
+Using OpenAI Completions API with vLLM
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
 Query the model with input prompts:
 
 .. code-block:: console
@@ -156,3 +166,45 @@ Since this server is compatible with OpenAI API, you can use it as a drop-in rep
     print("Completion result:", completion)
 
 For a more detailed client example, refer to `examples/openai_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py>`_.
+
+Using OpenAI Chat API with vLLM
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The vLLM server is designed to support the OpenAI Chat API, allowing you to engage in dynamic conversations with the model. The chat interface is a more interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. This is useful for tasks that require context or more detailed explanations.
+
+Querying the model using OpenAI Chat API:
+
+You can use the `create chat completion <https://platform.openai.com/docs/api-reference/chat/completions/create>`_ endpoint to communicate with the model in a chat-like interface:
+
+.. code-block:: console
+
+    $ curl http://localhost:8000/v1/chat/completions \
+    $     -H "Content-Type: application/json" \
+    $     -d '{
+    $         "model": "facebook/opt-125m",
+    $         "messages": [
+    $             {"role": "system", "content": "You are a helpful assistant."},
+    $             {"role": "user", "content": "Who won the world series in 2020?"}
+    $         ]
+    $     }'
+
+Python Client Example:
+
+Using the `openai` python package, you can also communicate with the model in a chat-like manner:
+
+.. code-block:: python
+
+    import openai
+    # Set OpenAI's API key and API base to use vLLM's API server.
+    openai.api_key = "EMPTY"
+    openai.api_base = "http://localhost:8000/v1"
+    chat_response = openai.ChatCompletion.create(
+        model="facebook/opt-125m",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Tell me a joke."},
+        ]
+    )
+    print("Chat response:", chat_response)
+
+For more in-depth examples and advanced features of the chat API, you can refer to the official OpenAI documentation.

examples/template_alpaca.jinja

@@ -0,0 +1,29 @@
+{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}
+
+{% for message in messages %}
+{% if message['role'] == 'user' %}
+### Instruction:
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+
+{% endif %}
+{% elif message['role'] == 'assistant' %}
+### Response:
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+
+{% endif %}
+{% elif message['role'] == 'user_context' %}
+### Input:
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+
+{% endif %}
+{% endif %}
+{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}
+### Response:
+{% endif %}

examples/template_chatml.jinja

@@ -0,0 +1,2 @@
+{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\n'}}{% endif %}{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n' }}{% endif %}

examples/template_inkbot.jinja

@@ -0,0 +1,30 @@
+<#meta#>
+- Date: {{ (messages|selectattr('role', 'equalto', 'meta-current_date')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'meta-current_date')|list) else '' }}
+- Task: {{ (messages|selectattr('role', 'equalto', 'meta-task_name')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'meta-task_name')|list) else '' }}
+<#system#>
+{{ (messages|selectattr('role', 'equalto', 'system')|list|last).content|trim if (messages|selectattr('role', 'equalto', 'system')|list) else '' }}
+<#chat#>
+{% for message in messages %}
+{% if message['role'] == 'user' %}
+<#user#>
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+{% endif %}
+{% elif message['role'] == 'assistant' %}
+<#bot#>
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+{% endif %}
+{% elif message['role'] == 'user_context' %}
+<#user_context#>
+{{ message['content']|trim -}}
+{% if not loop.last %}
+
+{% endif %}
+{% endif %}
+{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}
+<#bot#>
+{% endif %}

tests/async_engine/test_openai_server.py

@@ -0,0 +1,119 @@
+from argparse import Namespace
+from dataclasses import dataclass
+
+import pytest
+from fastapi.testclient import TestClient
+
+from vllm.entrypoints.openai.api_server import *
+
+# Define models, templates, and their corresponding expected outputs
+MODEL_TEMPLATE_GENERATON_OUTPUT = [
+    ("facebook/opt-125m", None, True,
+     "Hello</s>Hi there!</s>What is the capital of</s>"),
+    ("facebook/opt-125m", None, False,
+     "Hello</s>Hi there!</s>What is the capital of</s>"),
+    ("facebook/opt-125m", "../../examples/template_chatml.jinja", True,
+     """<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there!<|im_end|>
+<|im_start|>user
+What is the capital of<|im_end|>
+<|im_start|>assistant
+"""),
+    ("facebook/opt-125m", "../../examples/template_chatml.jinja", False,
+     """<|im_start|>user
+Hello<|im_end|>
+<|im_start|>assistant
+Hi there!<|im_end|>
+<|im_start|>user
+What is the capital of""")
+]
+
+TEST_MESSAGES = [
+    {
+        'role': 'user',
+        'content': 'Hello'
+    },
+    {
+        'role': 'assistant',
+        'content': 'Hi there!'
+    },
+    {
+        'role': 'user',
+        'content': 'What is the capital of'
+    },
+]
+client = TestClient(app)
+
+
+@dataclass
+class MockTokenizer:
+    chat_template = None
+
+
+def test_load_chat_template():
+    # Testing chatml template
+    template = "../../examples/template_chatml.jinja"
+    mock_args = Namespace(chat_template=template)
+    tokenizer = MockTokenizer()
+
+    # Call the function with the mocked args
+    load_chat_template(mock_args, tokenizer)
+
+    template_content = tokenizer.chat_template
+
+    # Test assertions
+    assert template_content is not None
+    # Hard coded value for template_chatml.jinja
+    assert template_content == """{% for message in messages %}{{'<|im_start|>' + message['role'] + '\\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\\n'}}{% endif %}{% endfor %}
+{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\\n' }}{% endif %}"""
+
+
+def test_no_load_chat_template():
+    # Testing chatml template
+    template = "../../examples/does_not_exist"
+    mock_args = Namespace(chat_template=template)
+    tokenizer = MockTokenizer()
+
+    # Call the function with the mocked args
+    load_chat_template(mock_args, tokenizer=tokenizer)
+    template_content = tokenizer.chat_template
+
+    # Test assertions
+    assert template_content is not None
+    # Hard coded value for template_chatml.jinja
+    assert template_content == """../../examples/does_not_exist"""
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model,template,add_generation_prompt,expected_output",
+    MODEL_TEMPLATE_GENERATON_OUTPUT)
+async def test_get_gen_prompt(model, template, add_generation_prompt,
+                              expected_output):
+    # Initialize the tokenizer
+    tokenizer = get_tokenizer(tokenizer_name=model)
+
+    mock_args = Namespace(chat_template=template)
+    load_chat_template(mock_args, tokenizer)
+
+    # Create a mock request object using keyword arguments
+    mock_request = ChatCompletionRequest(
+        model=model,
+        messages=TEST_MESSAGES,
+        add_generation_prompt=add_generation_prompt)
+
+    # Call the function and get the result
+    result = tokenizer.apply_chat_template(
+        conversation=mock_request.messages,
+        tokenize=False,
+        add_generation_prompt=mock_request.add_generation_prompt)
+
+    # Test assertion
+    assert result == expected_output, f"The generated prompt does not match the expected output for model {model} and template {template}"
+
+
+def test_health_endpoint():
+    response = client.get("/health")
+    assert response.status_code == 200