[Mistral Grammar] Support Grammar Factory (#38150)

Signed-off-by: juliendenize <julien.denize@mistral.ai>

requirements/common.txt

@@ -31,7 +31,7 @@ partial-json-parser # used for parsing partial JSON outputs
 pyzmq >= 25.0.0
 msgspec
 gguf >= 0.17.0
-mistral_common[image] >= 1.10.0
+mistral_common[image] >= 1.11.0
 opencv-python-headless >= 4.13.0    # required for video IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12

requirements/rocm-test.txt

@@ -604,7 +604,7 @@ mcp==1.27.0
     # via -r requirements/common.txt
 mdurl==0.1.2
     # via markdown-it-py
-mistral-common==1.10.0
+mistral-common==1.11.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/common.txt

requirements/test.txt

@@ -508,7 +508,7 @@ mbstrdecoder==1.1.3
     #   typepy
 mdurl==0.1.2
     # via markdown-it-py
-mistral-common==1.10.0
+mistral-common==1.11.0
     # via
     #   -c requirements/common.txt
     #   -r requirements/test.in

tests/tokenizers_/test_mistral.py

@@ -3,8 +3,10 @@
 
 from typing import Any
 
+import llguidance
 import pytest
 from mistral_common.exceptions import InvalidMessageStructureException
+from mistral_common.guidance.grammar_factory import GrammarFactory
 from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy
 
 from vllm.tokenizers.mistral import (
@@ -2407,3 +2409,29 @@ class TestMistralTokenizer:
         assert actual_tokens == expected_tokens
 
         assert mistral_tokenizer.convert_ids_to_tokens([]) == []
+
+    def test_grammar_factory(self, mistral_tokenizer: MistralTokenizer) -> None:
+        # works in this case cause Mistral 7B is < v11 and SPM
+        if not mistral_tokenizer.is_tekken:
+            with pytest.raises(AttributeError):
+                mistral_tokenizer.grammar_factory  # noqa: B018
+            return
+        factory = mistral_tokenizer.grammar_factory
+        assert isinstance(factory, GrammarFactory)
+
+        # Test caching
+        factory_2 = mistral_tokenizer.grammar_factory
+        assert factory is factory_2
+
+    def test_llg_tokenizer(self, mistral_tokenizer: MistralTokenizer) -> None:
+        if not mistral_tokenizer.is_tekken:
+            with pytest.raises(ValueError):
+                mistral_tokenizer.llg_tokenizer  # noqa: B018
+            return
+
+        llg_tokenizer = mistral_tokenizer.llg_tokenizer
+        assert isinstance(llg_tokenizer, llguidance.LLTokenizer)
+
+        # Test caching
+        llg_tokenizer_2 = mistral_tokenizer.llg_tokenizer
+        assert llg_tokenizer is llg_tokenizer_2

tests/tool_parsers/test_mistral_tool_parser.py

@@ -3,19 +3,43 @@
 
 import json
 from collections.abc import Generator
+from unittest.mock import MagicMock, patch
 
 import partial_json_parser
 import pytest
 from mistral_common.protocol.instruct.messages import AssistantMessage
 from mistral_common.protocol.instruct.request import InstructRequest
-from mistral_common.protocol.instruct.tool_calls import FunctionCall, ToolCall
+from mistral_common.protocol.instruct.tool_calls import (
+    FunctionCall,
+    ToolCall,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    NamedToolChoice as MistralNamedToolChoice,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    ToolChoice as MistralToolChoice,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    ToolChoiceEnum as MistralToolChoiceEnum,
+)
 from partial_json_parser.core.options import Allow
 
-from vllm.entrypoints.openai.engine.protocol import DeltaMessage, DeltaToolCall
+from vllm.entrypoints.openai.chat_completion.protocol import (
+    ChatCompletionRequest,
+)
+from vllm.entrypoints.openai.engine.protocol import (
+    DeltaMessage,
+    DeltaToolCall,
+    StructuralTagResponseFormat,
+)
+from vllm.sampling_params import StructuredOutputsParams
 from vllm.tokenizers import TokenizerLike, get_tokenizer
 from vllm.tokenizers.detokenizer_utils import detokenize_incrementally
 from vllm.tokenizers.mistral import MistralTokenizer
-from vllm.tool_parsers.mistral_tool_parser import MistralToolParser
+from vllm.tool_parsers.mistral_tool_parser import (
+    _DEFAULT_JSON_SCHEMA,
+    MistralToolParser,
+)
 
 
 @pytest.fixture(scope="module")
@@ -40,6 +64,13 @@ def mistral_tool_parser(mistral_tokenizer):
     return MistralToolParser(mistral_tokenizer)
 
 
+@pytest.fixture
+def non_mistral_parser() -> MistralToolParser:
+    mock_tokenizer = MagicMock()
+    mock_tokenizer.get_vocab.return_value = {"[TOOL_CALLS]": 1}
+    return MistralToolParser(mock_tokenizer)
+
+
 def assert_tool_calls(
     actual_tool_calls: list[ToolCall] | list[DeltaToolCall],
     expected_tool_calls: list[ToolCall],
@@ -951,3 +982,313 @@ def test_fast_detokenization_text_detection_pre_v11(
     assert len(delta_message.tool_calls) > 0
     assert delta_message.tool_calls[0].function is not None
     assert delta_message.tool_calls[0].function.name == "add"
+
+
+SAMPLE_TOOLS_DICTS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "get_weather",
+            "description": "Get the weather",
+            "parameters": {
+                "type": "object",
+                "properties": {"city": {"type": "string"}},
+                "required": ["city"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "add",
+            "description": "Add two numbers",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "a": {"type": "number"},
+                    "b": {"type": "number"},
+                },
+                "required": ["a", "b"],
+            },
+        },
+    },
+]
+
+
+def _make_request(**kwargs) -> ChatCompletionRequest:
+    defaults: dict = {
+        "messages": [],
+        "model": "mistralai/Mistral-Small-3.2-24B-Instruct-2506",
+        "tools": SAMPLE_TOOLS_DICTS,
+        "tool_choice": "auto",
+    }
+    defaults.update(kwargs)
+    return ChatCompletionRequest(**defaults)
+
+
+@pytest.mark.parametrize(
+    "request_kwargs,expected_mode,expected_parallel",
+    [
+        ({"tool_choice": "auto"}, MistralToolChoiceEnum.auto, True),
+        ({"tool_choice": "none"}, MistralToolChoiceEnum.none, True),
+        ({"tool_choice": "required"}, MistralToolChoiceEnum.required, True),
+        ({"tool_choice": None, "tools": None}, MistralToolChoiceEnum.auto, True),
+        (
+            {
+                "tool_choice": {
+                    "type": "function",
+                    "function": {"name": "get_weather"},
+                }
+            },
+            MistralNamedToolChoice.model_validate(
+                {"type": "function", "function": {"name": "get_weather"}}
+            ),
+            True,
+        ),
+        (
+            {"tool_choice": "auto", "parallel_tool_calls": False},
+            MistralToolChoiceEnum.auto,
+            False,
+        ),
+        (
+            {"tool_choice": "auto", "response_format": {"type": "text"}},
+            MistralToolChoiceEnum.auto,
+            True,
+        ),
+    ],
+    ids=[
+        "auto",
+        "none",
+        "required",
+        "null_tool_choice",
+        "named_tool_choice",
+        "parallel_false",
+        "response_format_text",
+    ],
+)
+def test_adjust_request_grammar_factory(
+    mistral_tool_parser: MistralToolParser,
+    request_kwargs: dict,
+    expected_mode: MistralToolChoice,
+    expected_parallel: bool,
+) -> None:
+    request = _make_request(**request_kwargs)
+    factory = mistral_tool_parser.model_tokenizer.grammar_factory
+
+    with patch.object(
+        factory,
+        "get_lark_from_jinja",
+        wraps=factory.get_lark_from_jinja,
+    ) as mock_get_lark:
+        result = mistral_tool_parser.adjust_request(request)
+
+        mock_get_lark.assert_called_once()
+        call_kwargs = mock_get_lark.call_args
+
+        assert call_kwargs.kwargs["mode"] == expected_mode
+        assert call_kwargs.kwargs["json_schema"] is None
+        assert call_kwargs.kwargs["parallel_tool_calls"] == expected_parallel
+
+    assert result.structured_outputs is not None
+    assert isinstance(result.structured_outputs.grammar, str)
+    assert len(result.structured_outputs.grammar) > 0
+
+
+def test_adjust_request_unsupported_grammar_for_tokenizer(mistral_tokenizer) -> None:
+    with patch.object(
+        type(mistral_tokenizer),
+        "supports_grammar",
+        new_callable=lambda: property(lambda self: False),
+    ):
+        parser = MistralToolParser(mistral_tokenizer)
+        request = _make_request()
+        result = parser.adjust_request(request)
+
+        assert result.structured_outputs is None
+
+
+@pytest.mark.parametrize(
+    "tool_choice,expected_skip",
+    [("auto", False), ("none", True)],
+    ids=["auto_skip_false", "none_skip_true"],
+)
+def test_adjust_request_non_mistral_tokenizer(
+    non_mistral_parser: MistralToolParser,
+    tool_choice: str,
+    expected_skip: bool,
+) -> None:
+    request = _make_request(tool_choice=tool_choice)
+    result = non_mistral_parser.adjust_request(request)
+
+    assert result.skip_special_tokens is expected_skip
+
+
+@pytest.mark.parametrize(
+    "so_kwargs",
+    [
+        {"regex": r"\d+"},
+        {"choice": ["a", "b"]},
+        {"structural_tag": '{"key": "value"}'},
+        {"grammar": "start: 'hello'"},
+    ],
+    ids=["regex", "choice", "structural_tag", "grammar"],
+)
+def test_adjust_request_unsupported_structured_outputs(
+    mistral_tool_parser: MistralToolParser,
+    so_kwargs: dict,
+) -> None:
+    request = _make_request(
+        structured_outputs=StructuredOutputsParams(**so_kwargs),
+    )
+    result = mistral_tool_parser.adjust_request(request)
+
+    assert result.structured_outputs == request.structured_outputs
+
+
+def test_adjust_request_unsupported_response_format(
+    mistral_tool_parser: MistralToolParser,
+) -> None:
+    request = _make_request(
+        response_format=StructuralTagResponseFormat(
+            type="structural_tag", format={"some": "config"}
+        ),
+    )
+    result = mistral_tool_parser.adjust_request(request)
+    assert result.structured_outputs is None
+    assert result.response_format == request.response_format
+
+
+@pytest.mark.parametrize(
+    "so_kwargs,expected_json_schema",
+    [
+        ({"json_object": True}, _DEFAULT_JSON_SCHEMA),
+        ({"json": '{"type": "object"}'}, {"type": "object"}),
+        (
+            {"json": {"type": "object", "properties": {"x": {"type": "integer"}}}},
+            {"type": "object", "properties": {"x": {"type": "integer"}}},
+        ),
+    ],
+    ids=["json_object", "json_str", "json_dict"],
+)
+def test_adjust_request_structured_outputs_generates_grammar(
+    mistral_tool_parser: MistralToolParser,
+    so_kwargs: dict,
+    expected_json_schema: str,
+) -> None:
+    request = _make_request(
+        structured_outputs=StructuredOutputsParams(**so_kwargs),
+    )
+    factory = mistral_tool_parser.model_tokenizer.grammar_factory
+
+    with patch.object(
+        factory,
+        "get_lark_from_jinja",
+        wraps=factory.get_lark_from_jinja,
+    ) as mock_get_lark:
+        result = mistral_tool_parser.adjust_request(request)
+
+        mock_get_lark.assert_called_once()
+        assert mock_get_lark.call_args.kwargs["json_schema"] == expected_json_schema
+
+    assert result.structured_outputs is not None
+    assert isinstance(result.structured_outputs.grammar, str)
+    assert len(result.structured_outputs.grammar) > 0
+
+
+@pytest.mark.parametrize(
+    "response_format_kwargs,expected_json_schema",
+    [
+        ({"type": "json_object"}, _DEFAULT_JSON_SCHEMA),
+        (
+            {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": "my_schema",
+                    "schema": {
+                        "type": "object",
+                        "properties": {"x": {"type": "integer"}},
+                    },
+                },
+            },
+            {"type": "object", "properties": {"x": {"type": "integer"}}},
+        ),
+    ],
+    ids=["json_object", "json_schema_with_schema"],
+)
+def test_adjust_request_response_format_generates_grammar(
+    mistral_tool_parser: MistralToolParser,
+    response_format_kwargs: dict,
+    expected_json_schema: str,
+) -> None:
+    request = _make_request(response_format=response_format_kwargs)
+    factory = mistral_tool_parser.model_tokenizer.grammar_factory
+
+    with patch.object(
+        factory,
+        "get_lark_from_jinja",
+        wraps=factory.get_lark_from_jinja,
+    ) as mock_get_lark:
+        result = mistral_tool_parser.adjust_request(request)
+
+        mock_get_lark.assert_called_once()
+        assert mock_get_lark.call_args.kwargs["json_schema"] == expected_json_schema
+
+    assert result.structured_outputs is not None
+    assert isinstance(result.structured_outputs.grammar, str)
+    assert len(result.structured_outputs.grammar) > 0
+
+
+def test_adjust_request_tool_choice_none_with_json_schema_uses_json_schema_factory(
+    mistral_tool_parser: MistralToolParser,
+) -> None:
+    request = _make_request(
+        tool_choice="none",
+        structured_outputs=StructuredOutputsParams(json='{"type": "object"}'),
+    )
+    factory = mistral_tool_parser.model_tokenizer.grammar_factory
+
+    with patch.object(
+        factory,
+        "get_lark_for_json_schema",
+        wraps=factory.get_lark_for_json_schema,
+    ) as mock_json_schema:
+        result = mistral_tool_parser.adjust_request(request)
+
+        mock_json_schema.assert_called_once()
+        assert mock_json_schema.call_args.kwargs["json_schema"] == {"type": "object"}
+
+    assert result.structured_outputs is not None
+    assert isinstance(result.structured_outputs.grammar, str)
+    assert len(result.structured_outputs.grammar) > 0
+
+
+def test_adjust_request_tool_choice_auto_with_json_schema_uses_jinja_factory(
+    mistral_tool_parser: MistralToolParser,
+) -> None:
+    request = _make_request(
+        tool_choice="auto",
+        structured_outputs=StructuredOutputsParams(json='{"type": "object"}'),
+    )
+    factory = mistral_tool_parser.model_tokenizer.grammar_factory
+
+    with (
+        patch.object(
+            factory,
+            "get_lark_for_json_schema",
+            wraps=factory.get_lark_for_json_schema,
+        ) as mock_json_schema,
+        patch.object(
+            factory,
+            "get_lark_from_jinja",
+            wraps=factory.get_lark_from_jinja,
+        ) as mock_jinja,
+    ):
+        result = mistral_tool_parser.adjust_request(request)
+
+        mock_jinja.assert_called_once()
+        assert mock_jinja.call_args.kwargs["json_schema"] == {"type": "object"}
+        mock_json_schema.assert_not_called()
+
+    assert result.structured_outputs is not None
+    assert isinstance(result.structured_outputs.grammar, str)
+    assert len(result.structured_outputs.grammar) > 0

tests/v1/structured_output/test_backend_guidance.py

@@ -11,6 +11,7 @@ from vllm.config.model import ModelConfig
 from vllm.config.parallel import ParallelConfig
 from vllm.config.speculative import SpeculativeConfig
 from vllm.sampling_params import SamplingParams, StructuredOutputsParams
+from vllm.tokenizers import get_tokenizer
 from vllm.v1.request import Request
 from vllm.v1.structured_output import StructuredOutputManager
 from vllm.v1.structured_output.backend_guidance import GuidanceBackend
@@ -19,6 +20,14 @@ from vllm.v1.structured_output.backend_types import StructuredOutputOptions
 TOKENIZER = "gpt2"
 
 
+@pytest.fixture(scope="module")
+def mistral_tokenizer():
+    return get_tokenizer(
+        tokenizer_name="mistralai/Mistral-Small-3.2-24B-Instruct-2506",
+        tokenizer_mode="mistral",
+    )
+
+
 def test_backend_guidance_rollback_terminated():
     # Test that the backend guidance successfully rollbacks from a
     # terminated state. This can happen with speculative decoding,
@@ -187,3 +196,38 @@ def test_grammar_init_async_and_sync(async_grammar):
 
     # Verify the grammar can accept valid tokens
     assert grammar.accept_tokens(request.request_id, prompt)
+
+
+@pytest.mark.parametrize(
+    "request_type,grammar_spec",
+    [
+        pytest.param(
+            StructuredOutputOptions.JSON,
+            '{"type": "object"}',
+            id="json",
+        ),
+        pytest.param(
+            StructuredOutputOptions.GRAMMAR,
+            'start: "hello" | "world"',
+            id="lark",
+        ),
+    ],
+)
+def test_mistral_tokenizer_compile_grammar(
+    mistral_tokenizer,
+    request_type: StructuredOutputOptions,
+    grammar_spec: str,
+) -> None:
+    vllm_config = VllmConfig(
+        structured_outputs_config=StructuredOutputsConfig(backend="guidance"),
+    )
+    backend = GuidanceBackend(
+        vllm_config,
+        tokenizer=mistral_tokenizer,
+        vocab_size=mistral_tokenizer.vocab_size,
+    )
+    assert backend.ll_tokenizer is mistral_tokenizer.llg_tokenizer
+
+    grammar = backend.compile_grammar(request_type, grammar_spec)
+    assert grammar is not None
+    assert not grammar.is_terminated()

vllm/sampling_params.py

@@ -153,6 +153,10 @@ class RequestOutputKind(Enum):
     FINAL_ONLY = 2
 
 
+def _is_non_tekken_mistral(tokenizer: TokenizerLike) -> bool:
+    return is_mistral_tokenizer(tokenizer) and not tokenizer.is_tekken
+
+
 class SamplingParams(
     PydanticMsgspecMixin,
     msgspec.Struct,
@@ -801,16 +805,17 @@ class SamplingParams(
             # xgrammar with no fallback
             validate_xgrammar_grammar(self)
         elif backend.startswith("guidance"):
+            if _is_non_tekken_mistral(tokenizer=tokenizer):
+                raise ValueError(
+                    "Non-tekken Mistral tokenizers are not supported for the 'guidance'"
+                    " structured output backend. Please either use a more recent "
+                    "Mistral model, the ['xgrammar', 'outlines'] "
+                    "backends or tokenizer_mode='hf' instead."
+                )
             # TODO: ideally we would have the LLTokenizer here as Lark syntax
             # allows <|special_token|> and similar, see
             # https://github.com/guidance-ai/llguidance/blob/main/docs/syntax.md#special-tokens
             # Without tokenizer these are disallowed in grammars.
-            if is_mistral_tokenizer(tokenizer):
-                raise ValueError(
-                    "Mistral tokenizer is not supported for the 'guidance' "
-                    "structured output backend. Please use ['xgrammar', 'outlines'] "
-                    "backends or tokenizer_mode='hf' instead."
-                )
             validate_guidance_grammar(self, tokenizer=None)
         elif backend == "outlines":
             # outlines backend
@@ -839,19 +844,20 @@ class SamplingParams(
                 # or includes some jsonschema feature(s) that
                 # are not supported in xgrammar.
 
+                skip_guidance = _is_non_tekken_mistral(tokenizer)
+
                 # Check if schema has features unsupported by guidance
                 so_params = self.structured_outputs
-                skip_guidance = False
-                if so_params.json:
+                if not skip_guidance and so_params.json:
                     if isinstance(so_params.json, str):
                         schema = json_mod.loads(so_params.json)
                     else:
                         schema = so_params.json
                     skip_guidance = has_guidance_unsupported_json_features(schema)
 
-                if is_mistral_tokenizer(tokenizer) or skip_guidance:
-                    # Fall back to outlines if the tokenizer is Mistral
-                    # or if schema contains features unsupported by guidance
+                if skip_guidance:
+                    # Fall back to outlines if the tokenizer is non-tekken Mistral or
+                    # the schema contains features unsupported by guidance
                     validate_structured_output_request_outlines(self)
                     self.structured_outputs._backend = "outlines"
                 else:

vllm/tokenizers/mistral.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Sequence
+from functools import cached_property
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, cast, overload
 
+from mistral_common.guidance.grammar_factory import GrammarFactory
+from mistral_common.guidance.tokenizer import from_mistral_tokenizer
 from mistral_common.protocol.instruct.request import (
     ChatCompletionRequest as MistralChatCompletionRequest,
 )
@@ -45,6 +48,7 @@ except ImportError:
     )
 
 if TYPE_CHECKING:
+    import llguidance
     from transformers import BatchEncoding
 
 logger = init_logger(__name__)
@@ -574,3 +578,24 @@ class MistralTokenizer(TokenizerLike):
             ]
 
         return tokens
+
+    @property
+    def supports_grammar(self) -> bool:
+        return GrammarFactory.is_supported(self.mistral)
+
+    @cached_property
+    def grammar_factory(self) -> GrammarFactory:
+        if not self.supports_grammar:
+            raise AttributeError(
+                "This tokenizer does not support `grammar_factory`. "
+                "This is only supported for tekken tokenizers with "
+                "version >= 11."
+            )
+        # Cache grammar factory to avoid creating a llguidance tokenizer at every usage.
+        return GrammarFactory(self.mistral)
+
+    @cached_property
+    def llg_tokenizer(self) -> "llguidance.LLTokenizer":
+        if not self.is_tekken:
+            raise ValueError("`llg_tokenizer` is only supported for Tekkenizers.")
+        return from_mistral_tokenizer(self.mistral)

vllm/tool_parsers/mistral_tool_parser.py

@@ -10,6 +10,18 @@ from typing import Any
 
 import ijson
 import regex as re
+from mistral_common.protocol.instruct.tool_calls import (
+    NamedToolChoice as MistralNamedToolChoice,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    Tool as MistralTool,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    ToolChoice as MistralToolChoice,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    ToolChoiceEnum as MistralToolChoiceEnum,
+)
 from pydantic import Field
 
 from vllm.entrypoints.openai.chat_completion.protocol import (
@@ -25,6 +37,7 @@ from vllm.entrypoints.openai.engine.protocol import (
 )
 from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
 from vllm.logger import init_logger
+from vllm.sampling_params import StructuredOutputsParams
 from vllm.tokenizers import TokenizerLike
 from vllm.tool_parsers.abstract_tool_parser import (
     Tool,
@@ -36,6 +49,8 @@ logger = init_logger(__name__)
 
 ALPHANUMERIC = ascii_letters + digits
 
+_DEFAULT_JSON_SCHEMA = {"anyOf": [{"type": "object"}, {"type": "array"}]}
+
 
 class StreamingState(Enum):
     """Enum for tracking the current streaming parsing state."""
@@ -80,6 +95,9 @@ class MistralToolParser(ToolParser):
     Used when --enable-auto-tool-choice --tool-call-parser mistral are all set
     """
 
+    # Used to generate correct grammar in `adjust_request`
+    model_can_reason: bool = False
+
     def __init__(self, tokenizer: TokenizerLike, tools: list[Tool] | None = None):
         super().__init__(tokenizer, tools)
 
@@ -115,12 +133,34 @@ class MistralToolParser(ToolParser):
     def adjust_request(
         self, request: ChatCompletionRequest | ResponsesRequest
     ) -> ChatCompletionRequest | ResponsesRequest:
-        request = super().adjust_request(request)
+        so_non_supported_attributes = [
+            "regex",
+            "choice",
+            "grammar",
+            # whitespace_pattern is not a constraint type but an option;
+            # Mistral grammar factory does not support it.
+            "whitespace_pattern",
+            "structural_tag",
+        ]
+        any_so_non_supported_active = request.structured_outputs is not None and any(
+            getattr(request.structured_outputs, attribute) is not None
+            for attribute in so_non_supported_attributes
+        )
+        response_format_non_supported_active = (
+            isinstance(request, ResponsesRequest)
+            or request.response_format is not None
+            and request.response_format.type == "structural_tag"
+        )
+
         if (
             not is_mistral_tokenizer(self.model_tokenizer)
-            and request.tools
-            and request.tool_choice != "none"
+            or isinstance(request, ResponsesRequest)
+            or not self.model_tokenizer.supports_grammar
+            or any_so_non_supported_active
+            or response_format_non_supported_active
         ):
+            request = super().adjust_request(request)
+            if request.tools and request.tool_choice != "none":
                 # Do not skip special tokens when using chat template
                 # with Mistral parser as TOOL_CALL token is needed
                 # for tool detection.
@@ -129,6 +169,90 @@ class MistralToolParser(ToolParser):
                 request.skip_special_tokens = False
             return request
 
+        json_schema: dict[str, Any] | None = None
+        if request.structured_outputs is not None:
+            if request.structured_outputs.json_object is not None:
+                json_schema = _DEFAULT_JSON_SCHEMA
+            elif request.structured_outputs.json is not None:
+                if isinstance(request.structured_outputs.json, str):
+                    json_schema = json.loads(request.structured_outputs.json)
+                else:
+                    json_schema = request.structured_outputs.json
+            else:
+                raise ValueError(
+                    "Unsupported request.structured_outputs for MistralToolParser. "
+                    "Only `json` and `json_object` are supported."
+                )
+        elif (
+            request.response_format is not None
+            and request.response_format.type != "text"
+        ):
+            if request.response_format.type == "json_object":
+                json_schema = _DEFAULT_JSON_SCHEMA
+            elif request.response_format.type == "json_schema":
+                if request.response_format.json_schema is not None:
+                    json_schema = request.response_format.json_schema.json_schema
+                else:
+                    json_schema = _DEFAULT_JSON_SCHEMA
+            else:
+                raise ValueError(
+                    "MistralToolParser only accepts `text`, `json_object` or "
+                    f"`json_schema`, got {request.response_format=}"
+                )
+            # Structured Outputs will be defined.
+            request.response_format = None
+
+        grammar_factory = self.model_tokenizer.grammar_factory
+
+        # TODO: Once unified parser, improve this.
+        # The issue is figuring out when a model is a reasoning one or not.
+        template = grammar_factory.select_jinja_template(
+            reasoning=self.model_can_reason
+        )
+
+        tools = (
+            [
+                MistralTool.from_openai(openai_tool=tool.model_dump())
+                for tool in request.tools
+            ]
+            if request.tools is not None
+            else None
+        )
+
+        tool_choice: MistralToolChoice
+        match request.tool_choice:
+            case "none" | "auto" | "required":
+                tool_choice = MistralToolChoiceEnum(request.tool_choice)
+            case None:
+                tool_choice = MistralToolChoiceEnum.auto
+            # _ == Named tool choice
+            case _:
+                tool_choice = MistralNamedToolChoice.model_validate(
+                    {
+                        "type": "function",
+                        "function": {"name": request.tool_choice.function.name},
+                    }
+                )
+
+        # Rendering grammar is cached in mistral-common given tools, template and mode.
+        match tool_choice, json_schema is not None:
+            case MistralToolChoiceEnum.none, True:
+                lark_grammar = grammar_factory.get_lark_for_json_schema(
+                    template=template, json_schema=json_schema
+                )
+            case _, _:
+                lark_grammar = grammar_factory.get_lark_from_jinja(
+                    template=template,
+                    mode=tool_choice,
+                    tools=tools,
+                    json_schema=json_schema,
+                    parallel_tool_calls=request.parallel_tool_calls,
+                    json_only=False,
+                )
+
+        request.structured_outputs = StructuredOutputsParams(grammar=lark_grammar)
+        return request
+
     def extract_tool_calls(
         self,
         model_output: str,

vllm/v1/structured_output/backend_guidance.py

@@ -12,6 +12,7 @@ import torch
 from vllm.logger import init_logger
 from vllm.sampling_params import SamplingParams
 from vllm.utils.import_utils import LazyLoader
+from vllm.utils.mistral import is_mistral_tokenizer
 from vllm.v1.structured_output.backend_types import (
     StructuredOutputBackend,
     StructuredOutputGrammar,
@@ -92,6 +93,9 @@ class GuidanceBackend(StructuredOutputBackend):
             self.vllm_config.structured_outputs_config.disable_additional_properties
         )
 
+        if is_mistral_tokenizer(self.tokenizer):
+            self.ll_tokenizer = self.tokenizer.llg_tokenizer
+        else:
             self.ll_tokenizer = llguidance_hf.from_tokenizer(
                 self.tokenizer, max(self.vocab_size, len(self.tokenizer))
             )