[Model] Add mistral function calling format to all models loaded with "mistral" format (#8515)

Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>

examples/offline_chat_with_tools.py

+# ruff: noqa
+import json
+import random
+import string
+
+from vllm import LLM
+from vllm.sampling_params import SamplingParams
+
+# This script is an offline demo for function calling
+#
+# If you want to run a server/client setup, please follow this code:
+#
+# - Server:
+#
+# ```bash
+# vllm serve mistralai/Mistral-7B-Instruct-v0.3 --tokenizer-mode mistral --load-format mistral --config-format mistral
+# ```
+#
+# - Client:
+#
+# ```bash
+# curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
+# --header 'Content-Type: application/json' \
+# --header 'Authorization: Bearer token' \
+# --data '{
+#     "model": "mistralai/Mistral-7B-Instruct-v0.3"
+#     "messages": [
+#       {
+#         "role": "user",
+#         "content": [
+#             {"type" : "text", "text": "Describe this image in detail please."},
+#             {"type": "image_url", "image_url": {"url": "https://s3.amazonaws.com/cms.ipressroom.com/338/files/201808/5b894ee1a138352221103195_A680%7Ejogging-edit/A680%7Ejogging-edit_hero.jpg"}},
+#             {"type" : "text", "text": "and this one as well. Answer in French."},
+#             {"type": "image_url", "image_url": {"url": "https://www.wolframcloud.com/obj/resourcesystem/images/a0e/a0ee3983-46c6-4c92-b85d-059044639928/6af8cfb971db031b.png"}}
+#         ]
+#       }
+#     ]
+#   }'
+# ```
+#
+# Usage:
+#     python demo.py simple
+#     python demo.py advanced
+
+model_name = "mistralai/Mistral-7B-Instruct-v0.3"
+# or switch to "mistralai/Mistral-Nemo-Instruct-2407"
+# or "mistralai/Mistral-Large-Instruct-2407"
+# or any other mistral model with function calling ability
+
+sampling_params = SamplingParams(max_tokens=8192, temperature=0.0)
+llm = LLM(model=model_name,
+          tokenizer_mode="mistral",
+          config_format="mistral",
+          load_format="mistral")
+
+
+def generate_random_id(length=9):
+    characters = string.ascii_letters + string.digits
+    random_id = ''.join(random.choice(characters) for _ in range(length))
+    return random_id
+
+
+# simulate an API that can be called
+def get_current_weather(city: str, state: str, unit: 'str'):
+    return (f"The weather in {city}, {state} is 85 degrees {unit}. It is "
+            "partly cloudly, with highs in the 90's.")
+
+
+tool_funtions = {"get_current_weather": get_current_weather}
+
+tools = [{
+    "type": "function",
+    "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "city": {
+                    "type":
+                    "string",
+                    "description":
+                    "The city to find the weather for, e.g. 'San Francisco'"
+                },
+                "state": {
+                    "type":
+                    "string",
+                    "description":
+                    "the two-letter abbreviation for the state that the city is"
+                    " in, e.g. 'CA' which would mean 'California'"
+                },
+                "unit": {
+                    "type": "string",
+                    "description": "The unit to fetch the temperature in",
+                    "enum": ["celsius", "fahrenheit"]
+                }
+            },
+            "required": ["city", "state", "unit"]
+        }
+    }
+}]
+
+messages = [{
+    "role":
+    "user",
+    "content":
+    "Can you tell me what the temperate will be in Dallas, in fahrenheit?"
+}]
+
+outputs = llm.chat(messages, sampling_params=sampling_params, tools=tools)
+output = outputs[0].outputs[0].text.strip()
+
+# append the assistant message
+messages.append({
+    "role": "assistant",
+    "content": output,
+})
+
+# let's now actually parse and execute the model's output simulating an API call by using the
+# above defined function
+tool_calls = json.loads(output)
+tool_answers = [
+    tool_funtions[call['name']](**call['arguments']) for call in tool_calls
+]
+
+# append the answer as a tool message and let the LLM give you an answer
+messages.append({
+    "role": "tool",
+    "content": "\n\n".join(tool_answers),
+    "tool_call_id": generate_random_id(),
+})
+
+outputs = llm.chat(messages, sampling_params, tools=tools)
+
+print(outputs[0].outputs[0].text.strip())
+# yields
+#   'The weather in Dallas, TX is 85 degrees fahrenheit. '
+#   'It is partly cloudly, with highs in the 90's.'

tests/models/decoder_only/language/test_mistral.py

@@ -4,13 +4,61 @@ Run `pytest tests/models/test_mistral.py`.
 """
 import pytest
 
+from vllm import SamplingParams
+
 from ...utils import check_logprobs_close
 
 MODELS = [
     "mistralai/Mistral-7B-Instruct-v0.1",
     "mistralai/Mistral-7B-Instruct-v0.3",
+    # Mistral-Nemo is to big for CI, but passes locally
+    # "mistralai/Mistral-Nemo-Instruct-2407"
 ]
 
+SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
+
+# for function calling
+TOOLS = [{
+    "type": "function",
+    "function": {
+        "name": "get_current_weather",
+        "description": "Get the current weather in a given location",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "city": {
+                    "type":
+                    "string",
+                    "description":
+                    "The city to find the weather for, e.g. 'San Francisco'"
+                },
+                "state": {
+                    "type":
+                    "string",
+                    "description":
+                    "the two-letter abbreviation for the state that the city is"
+                    " in, e.g. 'CA' which would mean 'California'"
+                },
+                "unit": {
+                    "type": "string",
+                    "description": "The unit to fetch the temperature in",
+                    "enum": ["celsius", "fahrenheit"]
+                }
+            },
+            "required": ["city", "state", "unit"]
+        }
+    }
+}]
+MSGS = [{
+    "role":
+    "user",
+    "content": ("Can you tell me what the temperate"
+                " will be in Dallas, in fahrenheit?")
+}]
+EXPECTED_FUNC_CALL = (
+    '[{"name": "get_current_weather", "arguments": '
+    '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}}]')
+
 
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
@@ -81,3 +129,22 @@ def test_mistral_format(
         name_0="hf",
         name_1="mistral",
     )
+
+
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("model", MODELS[1:])  # v1 can't do func calling
+def test_mistral_function_calling(
+    vllm_runner,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model,
+                     dtype=dtype,
+                     tokenizer_mode="mistral",
+                     config_format="mistral",
+                     load_format="mistral") as vllm_model:
+        outputs = vllm_model.model.chat(MSGS,
+                                        tools=TOOLS,
+                                        sampling_params=SAMPLING_PARAMS)
+
+        assert outputs[0].outputs[0].text.strip() == EXPECTED_FUNC_CALL

vllm/entrypoints/llm.py

 from contextlib import contextmanager
-from typing import ClassVar, List, Optional, Sequence, Union, cast, overload
+from typing import (Any, ClassVar, Dict, List, Optional, Sequence, Union, cast,
+                    overload)
 
 from tqdm import tqdm
 
@@ -357,6 +358,7 @@ class LLM:
         lora_request: Optional[LoRARequest] = None,
         chat_template: Optional[str] = None,
         add_generation_prompt: bool = True,
+        tools: Optional[List[Dict[str, Any]]] = None,
     ) -> List[RequestOutput]:
         """
         Generate responses for a chat conversation.
@@ -401,6 +403,7 @@ class LLM:
                 messages=messages,
                 chat_template=chat_template,
                 add_generation_prompt=add_generation_prompt,
+                tools=tools,
             )
         else:
             prompt = apply_hf_chat_template(
@@ -408,6 +411,7 @@ class LLM:
                 conversation=conversation,
                 chat_template=chat_template,
                 add_generation_prompt=add_generation_prompt,
+                tools=tools,
             )
 
         inputs: PromptInputs

vllm/entrypoints/openai/serving_chat.py

@@ -123,7 +123,8 @@ class OpenAIServingChat(OpenAIServing):
             ]
 
             prompt: Union[str, List[int]]
-            if isinstance(tokenizer, MistralTokenizer):
+            is_mistral_tokenizer = isinstance(tokenizer, MistralTokenizer)
+            if is_mistral_tokenizer:
                 prompt = apply_mistral_chat_template(
                     tokenizer,
                     messages=request.messages,
@@ -159,10 +160,10 @@ class OpenAIServingChat(OpenAIServing):
             return self.create_error_response(
                 "tool_choice = \"required\" is not supported!")
 
-            # "auto" tools requires --enable-auto-tool-choice
-            # and --tool-call-parser
-        if request.tool_choice == "auto" and not (
+        if not is_mistral_tokenizer and request.tool_choice == "auto" and not (
                 self.enable_auto_tools and self.tool_parser is not None):
+            # for hf tokenizers, "auto" tools requires
+            # --enable-auto-tool-choice and --tool-call-parser
             return self.create_error_response(
                 "\"auto\" tool choice requires "
                 "--enable-auto-tool-choice and --tool-call-parser to be set")

vllm/transformers_utils/tokenizers/mistral.py

@@ -165,10 +165,9 @@ class MistralTokenizer:
                             messages: List["ChatCompletionMessageParam"],
                             tools: Optional[Dict[str, Any]] = None,
                             **kwargs) -> List[int]:
-        assert tools is None, "`tools` are not yet supported."
 
-        request = ChatCompletionRequest(
-            messages=messages)  # type: ignore[type-var]
+        request = ChatCompletionRequest(messages=messages,
+                                        tools=tools)  # type: ignore[type-var]
         encoded = self.mistral.encode_chat_completion(request)
 
         # encode-decode to get clean prompt
@@ -176,7 +175,8 @@ class MistralTokenizer:
 
     def convert_tokens_to_string(self, tokens: List[str]) -> str:
         if isinstance(self.tokenizer, Tekkenizer):
-            return "".join(tokens)
+            return "".join(t for t in tokens
+                           if t not in self.tokenizer._all_special_tokens)
         else:
             return self.tokenizer.decode(tokens)  # type: ignore[arg-type]