[Frontend] Generate valid tool call IDs when using `tokenizer-mode=mi…

…stral` (#12332)

tests/mistral_tool_use/conftest.py

@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import pytest_asyncio
+from huggingface_hub import snapshot_download
+
+from tests.utils import RemoteOpenAIServer
+from vllm.platforms import current_platform
+
+from .utils import ARGS, CONFIGS, ServerConfig
+
+
+# for each server config, download the model and return the config
+@pytest.fixture(scope="session", params=CONFIGS.keys())
+def server_config(request):
+    config = CONFIGS[request.param]
+
+    if current_platform.is_rocm() and not config.get("supports_rocm", True):
+        pytest.skip("The {} model can't be tested on the ROCm platform".format(
+            config["model"]))
+
+    # download model and tokenizer using transformers
+    snapshot_download(config["model"])
+    yield CONFIGS[request.param]
+
+
+# run this for each server config
+@pytest.fixture(scope="session")
+def server(request, server_config: ServerConfig):
+    model = server_config["model"]
+    args_for_model = server_config["arguments"]
+    with RemoteOpenAIServer(model, ARGS + args_for_model,
+                            max_wait_seconds=480) as server:
+        yield server
+
+
+@pytest_asyncio.fixture
+async def client(server: RemoteOpenAIServer):
+    async with server.get_async_client() as async_client:
+        yield async_client

tests/mistral_tool_use/test_mistral_tool_calls.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import openai
+import pytest
+
+from tests.tool_use.utils import MESSAGES_ASKING_FOR_TOOLS, WEATHER_TOOL
+
+
+# test: a tool_choice with mistral-tokenizer results in an ID of length 9
+@pytest.mark.asyncio
+async def test_tool_call_with_tool_choice(client: openai.AsyncOpenAI):
+    models = await client.models.list()
+    model_name: str = models.data[0].id
+    chat_completion = await client.chat.completions.create(
+        messages=MESSAGES_ASKING_FOR_TOOLS,
+        temperature=0,
+        max_completion_tokens=100,
+        model=model_name,
+        tools=[WEATHER_TOOL],
+        tool_choice=WEATHER_TOOL,
+        logprobs=False)
+
+    choice = chat_completion.choices[0]
+
+    assert choice.finish_reason != "tool_calls"  # "stop" or "length"
+    assert choice.message.role == "assistant"
+    assert choice.message.tool_calls is None \
+           or len(choice.message.tool_calls) == 1
+    assert len(choice.message.tool_calls[0].id) == 9  # length of 9 for mistral

tests/mistral_tool_use/utils.py

@@ -0,0 +1,33 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Dict, List, Optional
+
+from typing_extensions import TypedDict
+
+
+class ServerConfig(TypedDict, total=False):
+    model: str
+    arguments: List[str]
+    system_prompt: Optional[str]
+    supports_parallel: Optional[bool]
+    supports_rocm: Optional[bool]
+
+
+ARGS: List[str] = ["--max-model-len", "1024"]
+
+CONFIGS: Dict[str, ServerConfig] = {
+    "mistral": {
+        "model":
+        "mistralai/Mistral-7B-Instruct-v0.3",
+        "arguments": [
+            "--tokenizer-mode", "mistral",
+            "--ignore-patterns=\"consolidated.safetensors\""
+        ],
+        "system_prompt":
+        "You are a helpful assistant with access to tools. If a tool"
+        " that you have would be helpful to answer a user query, "
+        "call the tool. Otherwise, answer the user's query directly "
+        "without calling a tool. DO NOT CALL A TOOL THAT IS IRRELEVANT "
+        "to the user's question - just respond to it normally."
+    },
+}

vllm/entrypoints/openai/serving_chat.py

@@ -28,12 +28,15 @@
 from vllm.entrypoints.openai.serving_engine import OpenAIServing
 from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
+from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
+    MistralToolCall)
 from vllm.logger import init_logger
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.sequence import Logprob
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.transformers_utils.tokenizers import maybe_serialize_tool_calls
+from vllm.transformers_utils.tokenizers import (maybe_serialize_tool_calls,
+                                                truncate_tool_call_ids)
 
 logger = init_logger(__name__)
 
@@ -150,11 +153,12 @@ async def create_chat_completion(
                 return self.create_error_response(
                     "tool_choice = \"required\" is not supported!")
 
-            # because of issues with pydantic we need to potentially
-            # re-serialize the tool_calls field of the request
-            # for more info: see comment in `maybe_serialize_tool_calls`
             if isinstance(tokenizer, MistralTokenizer):
+                # because of issues with pydantic we need to potentially
+                # re-serialize the tool_calls field of the request
+                # for more info: see comment in `maybe_serialize_tool_calls`
                 maybe_serialize_tool_calls(request)
+                truncate_tool_call_ids(request)
 
             if (request.tool_choice == "auto" and
                     not (self.enable_auto_tools and tool_parser is not None)
@@ -745,11 +749,13 @@ async def chat_completion_full_generator(
             elif request.tool_choice and type(
                     request.tool_choice) is ChatCompletionNamedToolChoiceParam:
 
+                tool_call_class = MistralToolCall if isinstance(
+                    tokenizer, MistralTokenizer) else ToolCall
                 message = ChatMessage(
                     role=role,
                     content="",
                     tool_calls=[
-                        ToolCall(function=FunctionCall(
+                        tool_call_class(function=FunctionCall(
                             name=request.tool_choice.function.name,
                             arguments=output.text))
                     ])

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -33,7 +33,7 @@ class MistralToolCall(ToolCall):
 
     @staticmethod
     def generate_random_id():
-        # Mistral Tool Call Ids must be alphanumeric with a maximum length of 9.
+        # Mistral Tool Call Ids must be alphanumeric with a length of 9.
         # https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
         return "".join(choices(ALPHANUMERIC, k=9))
 

vllm/transformers_utils/tokenizers/__init__.py

@@ -1,5 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from .mistral import MistralTokenizer, maybe_serialize_tool_calls
+from .mistral import (MistralTokenizer, maybe_serialize_tool_calls,
+                      truncate_tool_call_ids)
 
-__all__ = ["MistralTokenizer", "maybe_serialize_tool_calls"]
+__all__ = [
+    "MistralTokenizer", "maybe_serialize_tool_calls", "truncate_tool_call_ids"
+]

vllm/transformers_utils/tokenizers/mistral.py

@@ -68,6 +68,36 @@ def maybe_serialize_tool_calls(request: "ChatCompletionRequest"):
             request.messages[i]["tool_calls"] = validated_tool_calls
 
 
+def truncate_tool_call_ids(request: "ChatCompletionRequest"):
+    """Truncates tool call IDs for Mistral's ID requirements."""
+    for i, message in enumerate(request.messages):
+        if message.get("role") == 'assistant':
+            tool_calls = message.get("tool_calls", [])
+            for tool_call in tool_calls:
+                if len(tool_call["id"]) > 9:
+                    logger.warning(
+                        "Truncating tool call ID: %s to %s",
+                        tool_call["id"],
+                        tool_call["id"][-9:],
+                    )
+                    tool_call["id"] = tool_call["id"][-9:]
+
+            request.messages[i]["tool_calls"] = tool_calls
+
+        elif message.get("role") in {"tool_results", "tool"}:
+            if "tool_call_id" in message:
+                tool_call_id = message["tool_call_id"]
+
+                if len(tool_call_id) > 9:
+                    logger.warning(
+                        "Truncating tool_call_id: %s to %s",
+                        tool_call_id,
+                        tool_call_id[-9:],
+                    )
+                    tool_call_id = tool_call_id[-9:]
+                request.messages[i]["tool_call_id"] = tool_call_id
+
+
 def list_local_repo_files(repo_id: str, revision: Optional[str]) -> List[str]:
     repo_cache = os.path.join(
         huggingface_hub.constants.HF_HUB_CACHE,