[Feature] Support MiniMax-M1 function calls features

[qscqesze]

Support MiniMax-M1 function calls features

How to use:

export SAFETENSORS_FAST_GPU=1
export VLLM_USE_V1=0

python3 -m vllm.entrypoints.openai.api_server \
--model MiniMax-M1-80k \
--tensor-parallel-size 8 \
--trust-remote-code \
--quantization experts_int8  \
--enable-auto-tool-choice \
--tool-call-parser minimax \
--chat-template vllm/examples/tool_chat_template_minimax_m1.jinja \
--max_model_len 4096 \
--dtype bfloat16 \
--gpu-memory-utilization 0.85

test_scripts:

from openai import OpenAI
import json

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

def get_weather(location: str, unit: str):
    return f"Getting the weather for {location} in {unit}..."
tool_functions = {"get_weather": get_weather}

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location", "unit"]
        }
    }
}]

response = client.chat.completions.create(
    model=client.models.list().data[0].id,
    messages=[{"role": "user", "content": "What's the weather like in San Francisco? use celsius."}],
    tools=tools,
    tool_choice="auto"
)

print(response)

tool_call = response.choices[0].message.tool_calls[0].function
print(f"Function called: {tool_call.name}")
print(f"Arguments: {tool_call.arguments}")
print(f"Result: {get_weather(**json.loads(tool_call.arguments))}")

Outpt:

Function called: get_weather
Arguments: {"location": "San Francisco, CA", "unit": "celsius"}
Result: Getting the weather for San Francisco, CA in celsius...

Streaming Test:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")

class bcolors:
    HEADER = '\033[95m'
    OKBLUE = '\033[94m'
    OKCYAN = '\033[96m'
    OKGREEN = '\033[92m'
    WARNING = '\033[93m'
    FAIL = '\033[91m'
    ENDC = '\033[0m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'


tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_temperature",
            "description": "Get current temperature at a location.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": 'The location to get the temperature for, in the format "City, State, Country".',
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": 'The unit to return the temperature in. Defaults to "celsius".',
                    },
                },
                "required": ["location"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "get_temperature_date",
            "description": "Get temperature at a location and date.",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": 'The location to get the temperature for, in the format "City, State, Country".',
                    },
                    "date": {
                        "type": "string",
                        "description": 'The date to get the temperature for, in the format "Year-Month-Day".',
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"],
                        "description": 'The unit to return the temperature in. Defaults to "celsius".',
                    },
                },
                "required": ["location", "date"],
            },
        },
    },
]


tool_calls_stream = client.chat.completions.create(
    model=client.models.list().data[0].id,
    messages=[
        {
            "role": "system",
            "content": "You are a helpful assistant.\n\nCurrent Date: 2024-09-30",
        },
        {
            "role": "user",
            "content": "What's the temperature in San Francisco now? How about tomorrow? use celsius.",
        },
    ],
    tools=tools,
    tool_choice="auto",
    stream=True
)

print("reasoning content(Blue) and content(Green):")
chunks = []
arguments = []
tool_call_idx = -1

response = ""

for chunk in tool_calls_stream:
    chunks.append(chunk)
    delta = chunk.choices[0].delta
    
    
    if hasattr(delta, "reasoning_content") and delta.reasoning_content:
        print(bcolors.OKBLUE + delta.reasoning_content, end="", flush=True)
    
    if hasattr(delta, "content") and delta.content:
        print(bcolors.OKGREEN + delta.content, end="", flush=True)
        response += delta.content
    
    if delta.tool_calls:
        tool_call = delta.tool_calls[0]
        
        if tool_call.index != tool_call_idx:
            tool_call_idx = tool_call.index
            arguments.append({"id": "", "name": "", "arguments": ""})
        
        if tool_call.id:
            if not arguments[tool_call_idx]["id"]:
                arguments[tool_call_idx]["id"] = tool_call.id
        
        if tool_call.function:
            if tool_call.function.name:
                if not arguments[tool_call_idx]["name"]: 
                    arguments[tool_call_idx]["name"] = tool_call.function.name
            
            if tool_call.function.arguments:
                current_args = arguments[tool_call_idx]["arguments"]
                new_args = tool_call.function.arguments
                
                if new_args.startswith(current_args):
                    delta_args = new_args[len(current_args):]
                
                arguments[tool_call_idx]["arguments"] = new_args

print(bcolors.ENDC + "\n\n### Streaming Output End ###\n")

if response.strip():
    print(f"{bcolors.OKGREEN}Complete Response:{bcolors.ENDC}")
    print(response)

if arguments:
    print(f"\n{bcolors.HEADER}Tool Calls Summary:{bcolors.ENDC}")
    for i, tool_call_info in enumerate(arguments):
        print(f"Tool {i+1}: {tool_call_info.get('name', 'Unknown')}")
        if tool_call_info.get('arguments'):
            print(f"  Full Arguments: {tool_call_info['arguments']}")
        print()

Output:

Tool Calls Summary:
Tool 1: get_current_temperature
  Full Arguments: {"location": "San Francisco, California, USA", "unit": "celsius"}

Tool 2: get_temperature_date
  Full Arguments: {"location": "San Francisco, California, USA", "date": "2024-10-01", "unit": "celsius"}

[gemini-code-assist]

Summary of Changes

Hello @qscqesze, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances vLLM's capabilities by adding native support for Minimax-M1 model's function calling features. It involves creating a specialized chat template to format prompts according to Minimax-M1's requirements and developing a dedicated tool parser to interpret the model's tool call responses, including robust handling for streaming scenarios. This integration allows users to leverage Minimax-M1 models for advanced tool-use applications within the vLLM framework.

Highlights

• Minimax-M1 Function Call Support: Introduces comprehensive support for Minimax-M1's function calling features, enabling vLLM to correctly format prompts for tool use and parse the model's tool call responses.
• New Chat Template: Adds a new Jinja chat template (examples/tool_chat_template_minimax_m1.jinja) specifically designed for Minimax-M1, defining its unique prompt format for system messages, user inputs, assistant responses, and the structure for tool definitions and tool calls using XML tags.
• Dedicated Tool Parser: Implements a new MinimaxToolParser to accurately extract and stream tool calls from Minimax-M1 model outputs. This parser handles the model's specific XML-like tag structure (<tool_calls>) and uses partial JSON parsing for incremental argument extraction during streaming.
• Integration with OpenAI API: Integrates the new MinimaxToolParser into vLLM's OpenAI API entrypoint, allowing users to enable Minimax-M1 function calling via the --tool-call-parser minimax command-line argument.

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[gemini-code-assist]

Code Review

This pull request introduces support for Minimax-M1 function calls, including a new Jinja chat template and a dedicated tool parser. The non-streaming tool extraction logic appears sound. However, the streaming tool call parsing in minimax_tool_parser.py has significant design flaws related to handling multiple concurrent tool calls and extracting argument deltas, which will lead to incorrect behavior. Additionally, there are opportunities to improve error handling and code clarity for better maintainability.

[github-actions]

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🚀

[mgoin]

Great work, thanks for sharing examples! It would be nice to add a unit test, especially to test complex features like streaming - see xlam for an example https://github.com/vllm-project/vllm/blob/02cabff207ca68094a73ba21296c82cdbcb1d1a5/tests/tool_use/test_xlam_tool_parser.py

[qscqesze]

@heeju-kim2 @aarnphm Hi. Would you have some time to help review this RP when convenient? It looks like we’ll need someone with write access to approve it in order to move forward with the CI/CD process.

[aarnphm]

Can you add tests similar to Michael's suggestion here?

[aarnphm]

note for self: one more tools to keep in mind of 😃

[qscqesze]

@aarnphm @mgoin Thanks for your suggestions. I added the tests.

It can run with:python3 -m pytest tests/tool_use/test_minimax_tool_parser.py -v

[aarnphm]

Thanks for this

[qscqesze]

@aandyw I noticed that the CI/CD pipeline for this auto-merge has been running for over 17 hours. It seems to be stuck at the "Intel HPU Test" stage — is this expected, or could something be wrong?
Just wanted to check in case it needs a manual retry or intervention?