[Bugfix] Fix mistral model tests

tests/models/decoder_only/language/test_mistral.py

@@ -10,8 +10,8 @@
 import jsonschema.exceptions
 import pytest
 
-from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (  # noqa
-    MistralToolParser)
+from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
+    MistralToolCall, MistralToolParser)
 from vllm.sampling_params import GuidedDecodingParams, SamplingParams
 
 from ...utils import check_logprobs_close
@@ -194,7 +194,6 @@ def test_models(hf_runner, vllm_runner, example_prompts, model: str,
     )
 
 
-@pytest.mark.skip("RE-ENABLE: test is currently failing on main.")
 @pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])
@@ -246,10 +245,8 @@ def test_mistral_symbolic_languages(vllm_runner, model: str,
             assert "�" not in outputs[0].outputs[0].text.strip()
 
 
-@pytest.mark.skip("RE-ENABLE: test is currently failing on main.")
+@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
-@pytest.mark.parametrize("model",
-                         MISTRAL_FORMAT_MODELS)  # v1 can't do func calling
 def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
     with vllm_runner(model,
                      dtype=dtype,
@@ -270,7 +267,8 @@ def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
         parsed_message = tool_parser.extract_tool_calls(model_output, None)
 
         assert parsed_message.tools_called
-        assert parsed_message.tool_calls[0].id == "0UAqFzWsD"
+
+        assert MistralToolCall.is_valid_id(parsed_message.tool_calls[0].id)
         assert parsed_message.tool_calls[
             0].function.name == "get_current_weather"
         assert parsed_message.tool_calls[
@@ -281,28 +279,38 @@ def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("guided_backend",
                          ["outlines", "lm-format-enforcer", "xgrammar"])
-def test_mistral_guided_decoding(vllm_runner, model: str,
-                                 guided_backend: str) -> None:
-    with vllm_runner(model, dtype='bfloat16',
-                     tokenizer_mode="mistral") as vllm_model:
+def test_mistral_guided_decoding(
+    monkeypatch: pytest.MonkeyPatch,
+    vllm_runner,
+    model: str,
+    guided_backend: str,
+) -> None:
+    with monkeypatch.context() as m:
+        # Guided JSON not supported in xgrammar + V1 yet
+        m.setenv("VLLM_USE_V1", "0")
 
-        guided_decoding = GuidedDecodingParams(json=SAMPLE_JSON_SCHEMA,
-                                               backend=guided_backend)
-        params = SamplingParams(max_tokens=512,
-                                temperature=0.7,
-                                guided_decoding=guided_decoding)
-
-        messages = [{
-            "role": "system",
-            "content": "you are a helpful assistant"
-        }, {
-            "role":
-            "user",
-            "content":
-            f"Give an example JSON for an employee profile that "
-            f"fits this schema: {SAMPLE_JSON_SCHEMA}"
-        }]
-        outputs = vllm_model.model.chat(messages, sampling_params=params)
+        with vllm_runner(
+                model,
+                dtype='bfloat16',
+                tokenizer_mode="mistral",
+                guided_decoding_backend=guided_backend,
+        ) as vllm_model:
+            guided_decoding = GuidedDecodingParams(json=SAMPLE_JSON_SCHEMA)
+            params = SamplingParams(max_tokens=512,
+                                    temperature=0.7,
+                                    guided_decoding=guided_decoding)
+
+            messages = [{
+                "role": "system",
+                "content": "you are a helpful assistant"
+            }, {
+                "role":
+                "user",
+                "content":
+                f"Give an example JSON for an employee profile that "
+                f"fits this schema: {SAMPLE_JSON_SCHEMA}"
+            }]
+            outputs = vllm_model.model.chat(messages, sampling_params=params)
 
         generated_text = outputs[0].outputs[0].text
         json_response = json.loads(generated_text)

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -38,6 +38,10 @@ def generate_random_id():
         # https://github.com/mistralai/mistral-common/blob/21ee9f6cee3441e9bb1e6ed2d10173f90bd9b94b/src/mistral_common/protocol/instruct/validator.py#L299
         return "".join(choices(ALPHANUMERIC, k=9))
 
+    @staticmethod
+    def is_valid_id(id: str) -> bool:
+        return id.isalnum() and len(id) == 9
+
 
 @ToolParserManager.register_module("mistral")
 class MistralToolParser(ToolParser):