[Feature] Lazy import for the "mistral" tokenizer module. (#34651)

Signed-off-by: Neil Schemenauer <nas@arctrix.com>

tests/models/multimodal/processing/test_common.py

@@ -23,7 +23,7 @@ from vllm.multimodal.cache import MultiModalProcessorOnlyCache
 from vllm.multimodal.inputs import MultiModalInputs, batched_tensors_equal
 from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
 from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
-from vllm.tokenizers.mistral import MistralTokenizer
+from vllm.utils.mistral import is_mistral_tokenizer
 
 from ....multimodal.utils import random_audio, random_image, random_video
 from ...registry import (
@@ -183,7 +183,7 @@ def get_text_token_prompts(
 
     text_prompt: str | None
     token_prompt: list[int]
-    if isinstance(tokenizer, MistralTokenizer):
+    if is_mistral_tokenizer(tokenizer):
         # ChatCompletionRequest only supports ImageChunk natively;
         # for other modalities (e.g. audio), fall back to the model's
         # own dummy inputs builder which knows the right placeholders.

tests/reasoning/utils.py

@@ -4,7 +4,7 @@
 from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.engine.protocol import DeltaMessage
 from vllm.reasoning import ReasoningParser
-from vllm.tokenizers.mistral import MistralTokenizer
+from vllm.utils.mistral import is_mistral_tokenizer
 
 
 class StreamingReasoningReconstructor:
@@ -59,7 +59,7 @@ def run_reasoning_extraction_mistral(
     request: ChatCompletionRequest | None = None,
     streaming: bool = False,
 ) -> tuple[str | None, str | None]:
-    assert isinstance(reasoning_parser.model_tokenizer, MistralTokenizer), type(
+    assert is_mistral_tokenizer(reasoning_parser.model_tokenizer), type(
         reasoning_parser.model_tokenizer
     )
     if streaming:
@@ -130,7 +130,7 @@ def run_reasoning_extraction_streaming_mistral(
     model_deltas: list[int],
     request: ChatCompletionRequest | None = None,
 ) -> StreamingReasoningReconstructor:
-    assert isinstance(reasoning_parser.model_tokenizer, MistralTokenizer), type(
+    assert is_mistral_tokenizer(reasoning_parser.model_tokenizer), type(
         reasoning_parser.model_tokenizer
     )
     request = request or ChatCompletionRequest(messages=[], model="test-model")

vllm/entrypoints/llm.py

@@ -83,9 +83,9 @@ from vllm.renderers.inputs.preprocess import (
 from vllm.sampling_params import BeamSearchParams, RequestOutputKind, SamplingParams
 from vllm.tasks import PoolingTask
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils.counter import Counter
+from vllm.utils.mistral import is_mistral_tokenizer
 from vllm.utils.tqdm_utils import maybe_tqdm
 from vllm.v1.engine.llm_engine import LLMEngine
 from vllm.v1.sample.logits_processor import LogitsProcessor
@@ -891,7 +891,7 @@ class LLM:
                     add_generation_prompt=add_generation_prompt,
                     continue_final_message=continue_final_message,
                     tools=tools,
-                    tokenize=isinstance(renderer.tokenizer, MistralTokenizer),
+                    tokenize=is_mistral_tokenizer(renderer.tokenizer),
                 ),
             ),
         )
@@ -1458,7 +1458,7 @@ class LLM:
         model_config = self.model_config
         tokenizer = self.get_tokenizer()
 
-        if isinstance(tokenizer, MistralTokenizer):
+        if is_mistral_tokenizer(tokenizer):
             raise ValueError("Score API is not supported for Mistral tokenizer")
 
         if len(data_1) == 1:

vllm/entrypoints/openai/chat_completion/serving.py

@@ -75,16 +75,12 @@ from vllm.parser import ParserManager
 from vllm.reasoning import ReasoningParser
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import (
-    MistralTokenizer,
-    maybe_serialize_tool_calls,
-    truncate_tool_call_ids,
-    validate_request_params,
-)
 from vllm.tool_parsers import ToolParser
 from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
 from vllm.tool_parsers.utils import partial_json_loads
 from vllm.utils.collection_utils import as_list
+from vllm.utils.mistral import is_mistral_tokenizer
+from vllm.utils.mistral import mt as _mt
 
 logger = init_logger(__name__)
 
@@ -244,18 +240,18 @@ class OpenAIServingChat(OpenAIServing):
 
             tool_parser = self.tool_parser
 
-            if isinstance(tokenizer, MistralTokenizer):
+            if is_mistral_tokenizer(tokenizer):
                 # 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)  # type: ignore[arg-type]
-                truncate_tool_call_ids(request)  # type: ignore[arg-type]
-                validate_request_params(request)
+                _mt.maybe_serialize_tool_calls(request)  # type: ignore[arg-type]
+                _mt.truncate_tool_call_ids(request)  # type: ignore[arg-type]
+                _mt.validate_request_params(request)
 
             # Check if tool parsing is unavailable (common condition)
             tool_parsing_unavailable = (
                 tool_parser is None
-                and not isinstance(tokenizer, MistralTokenizer)
+                and not is_mistral_tokenizer(tokenizer)
                 and not self.use_harmony
             )
 
@@ -639,8 +635,6 @@ class OpenAIServingChat(OpenAIServing):
         request_metadata: RequestResponseMetadata,
         reasoning_parser: ReasoningParser | None = None,
     ) -> AsyncGenerator[str, None]:
-        from vllm.tokenizers.mistral import MistralTokenizer
-
         created_time = int(time.time())
         chunk_object_type: Final = "chat.completion.chunk"
         first_iteration = True
@@ -955,7 +949,7 @@ class OpenAIServingChat(OpenAIServing):
                                 )
                             else:
                                 # Generate ID based on tokenizer type
-                                if isinstance(tokenizer, MistralTokenizer):
+                                if is_mistral_tokenizer(tokenizer):
                                     tool_call_id = MistralToolCall.generate_random_id()
                                 else:
                                     tool_call_id = make_tool_call_id(
@@ -1516,7 +1510,7 @@ class OpenAIServingChat(OpenAIServing):
                 tool_parser_cls=self.tool_parser,
             )
             tool_call_class = (
-                MistralToolCall if isinstance(tokenizer, MistralTokenizer) else ToolCall
+                MistralToolCall if is_mistral_tokenizer(tokenizer) else ToolCall
             )
             if self.use_harmony:
                 # Harmony models already have parsed content and tool_calls
@@ -1951,7 +1945,7 @@ class OpenAIServingChat(OpenAIServing):
         # 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)  # type: ignore[arg-type]
+        _mt.maybe_serialize_tool_calls(request)  # type: ignore[arg-type]
 
         # Add system message.
         # NOTE: In Chat Completion API, browsing is enabled by default

vllm/entrypoints/openai/engine/serving.py

@@ -128,6 +128,7 @@ from vllm.utils.async_utils import (
     collect_from_async_generator,
     merge_async_iterators,
 )
+from vllm.utils.mistral import is_mistral_tokenizer
 
 
 class GenerationError(Exception):
@@ -976,15 +977,13 @@ class OpenAIServing:
         tool_dicts: list[dict[str, Any]] | None = None,
         tool_parser: Callable[[TokenizerLike], ToolParser] | None = None,
     ) -> tuple[list[ConversationMessage], list[ProcessorInputs]]:
-        from vllm.tokenizers.mistral import MistralTokenizer
-
         renderer = self.renderer
 
         default_template_kwargs = merge_kwargs(
             default_template_kwargs,
             dict(
                 tools=tool_dicts,
-                tokenize=isinstance(renderer.tokenizer, MistralTokenizer),
+                tokenize=is_mistral_tokenizer(renderer.tokenizer),
             ),
         )
 

vllm/entrypoints/pooling/score/serving.py

@@ -41,8 +41,8 @@ from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.utils.async_utils import make_async, merge_async_iterators
+from vllm.utils.mistral import is_mistral_tokenizer
 
 logger = init_logger(__name__)
 
@@ -348,7 +348,7 @@ class ServingScores(OpenAIServing):
         trace_headers: Mapping[str, str] | None = None,
     ) -> list[PoolingRequestOutput] | ErrorResponse:
         tokenizer = self.renderer.get_tokenizer()
-        if isinstance(tokenizer, MistralTokenizer):
+        if is_mistral_tokenizer(tokenizer):
             raise ValueError("MistralTokenizer not supported for cross-encoding")
 
         model_config = self.model_config

vllm/multimodal/processing/context.py

@@ -26,6 +26,7 @@ from vllm.tokenizers import TokenizerLike
 from vllm.transformers_utils.processor import cached_processor_from_config
 from vllm.utils.func_utils import get_allowed_kwarg_only_overrides
 from vllm.utils.jsontree import JSONTree, json_map_leaves
+from vllm.utils.mistral import is_mistral_tokenizer
 
 if TYPE_CHECKING:
     from transformers.configuration_utils import PretrainedConfig
@@ -260,10 +261,8 @@ class InputProcessingContext:
 
             typ = ProcessorMixin
 
-        from vllm.tokenizers.mistral import MistralTokenizer
-
         tokenizer = self.tokenizer
-        if isinstance(tokenizer, MistralTokenizer):
+        if is_mistral_tokenizer(tokenizer):
             tokenizer = tokenizer.transformers_tokenizer
 
         merged_kwargs = self.get_merged_mm_kwargs(kwargs)

vllm/sampling_params.py

@@ -16,6 +16,7 @@ from vllm.config import ModelConfig, SpeculativeConfig, StructuredOutputsConfig
 from vllm.exceptions import VLLMValidationError
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
+from vllm.utils.mistral import is_mistral_tokenizer
 from vllm.v1.serial_utils import PydanticMsgspecMixin
 
 logger = init_logger(__name__)
@@ -731,7 +732,6 @@ class SamplingParams(
         ):
             raise ValueError("structured_outputs.grammar cannot be an empty string")
 
-        from vllm.tokenizers.mistral import MistralTokenizer
         from vllm.v1.structured_output.backend_guidance import (
             has_guidance_unsupported_json_features,
             validate_guidance_grammar,
@@ -752,7 +752,7 @@ class SamplingParams(
             # 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 isinstance(tokenizer, MistralTokenizer):
+            if is_mistral_tokenizer(tokenizer):
                 raise ValueError(
                     "Mistral tokenizer is not supported for the 'guidance' "
                     "structured output backend. Please use ['xgrammar', 'outlines'] "
@@ -764,7 +764,7 @@ class SamplingParams(
             validate_structured_output_request_outlines(self)
         elif backend == "lm-format-enforcer":
             # lm format enforcer backend
-            if isinstance(tokenizer, MistralTokenizer):
+            if is_mistral_tokenizer(tokenizer):
                 raise ValueError(
                     "Mistral tokenizer is not supported for the 'lm-format-enforcer' "
                     "structured output backend. Please use ['xgrammar', 'outlines'] "
@@ -796,7 +796,7 @@ class SamplingParams(
                         schema = so_params.json
                     skip_guidance = has_guidance_unsupported_json_features(schema)
 
-                if isinstance(tokenizer, MistralTokenizer) or skip_guidance:
+                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
                     validate_structured_output_request_outlines(self)

vllm/tokenizers/mistral.py

@@ -210,6 +210,8 @@ def _tekken_token_to_id(tokenizer: "Tekkenizer", t: str | bytes) -> int:
 
 
 class MistralTokenizer(TokenizerLike):
+    IS_MISTRAL_TOKENIZER = True  # used by vllm.utils.mistral
+
     @classmethod
     def from_pretrained(
         cls,

vllm/tool_parsers/hermes_tool_parser.py

@@ -22,10 +22,10 @@ from vllm.entrypoints.openai.engine.protocol import (
 )
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.tool_parsers.abstract_tool_parser import (
     ToolParser,
 )
+from vllm.utils.mistral import is_mistral_tokenizer
 
 logger = init_logger(__name__)
 
@@ -34,7 +34,7 @@ class Hermes2ProToolParser(ToolParser):
     def __init__(self, tokenizer: TokenizerLike):
         super().__init__(tokenizer)
 
-        if isinstance(tokenizer, MistralTokenizer):
+        if is_mistral_tokenizer(tokenizer):
             logger.error("Detected Mistral tokenizer when using a Hermes model")
             self.model_tokenizer = tokenizer.tokenizer
 

vllm/tool_parsers/jamba_tool_parser.py

@@ -22,9 +22,9 @@ from vllm.entrypoints.openai.engine.protocol import (
 )
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.tool_parsers import ToolParser
 from vllm.tool_parsers.utils import extract_intermediate_diff
+from vllm.utils.mistral import is_mistral_tokenizer
 
 logger = init_logger(__name__)
 
@@ -33,7 +33,7 @@ class JambaToolParser(ToolParser):
     def __init__(self, tokenizer: TokenizerLike):
         super().__init__(tokenizer)
 
-        if isinstance(self.model_tokenizer, MistralTokenizer):
+        if is_mistral_tokenizer(self.model_tokenizer):
             raise ValueError(
                 "Detected a MistralTokenizer tokenizer when using a Jamba model"
             )

vllm/tool_parsers/mistral_tool_parser.py

@@ -25,10 +25,10 @@ from vllm.entrypoints.openai.engine.protocol import (
 )
 from vllm.logger import init_logger
 from vllm.tokenizers import TokenizerLike
-from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.tool_parsers.abstract_tool_parser import (
     ToolParser,
 )
+from vllm.utils.mistral import is_mistral_tokenizer
 
 logger = init_logger(__name__)
 
@@ -66,9 +66,7 @@ class MistralToolCall(ToolCall):
 
 
 def _is_pre_v11_tokeniser(model_tokenizer: TokenizerLike) -> bool:
-    return not (
-        isinstance(model_tokenizer, MistralTokenizer) and model_tokenizer.version >= 11
-    )
+    return not (is_mistral_tokenizer(model_tokenizer) and model_tokenizer.version >= 11)
 
 
 class MistralToolParser(ToolParser):
@@ -83,7 +81,7 @@ class MistralToolParser(ToolParser):
     def __init__(self, tokenizer: TokenizerLike):
         super().__init__(tokenizer)
 
-        if not isinstance(self.model_tokenizer, MistralTokenizer):
+        if not is_mistral_tokenizer(self.model_tokenizer):
             logger.info("Non-Mistral tokenizer detected when using a Mistral model...")
 
         # initialize properties used for state when parsing tool calls in
@@ -115,7 +113,7 @@ class MistralToolParser(ToolParser):
     def adjust_request(self, request: ChatCompletionRequest) -> ChatCompletionRequest:
         request = super().adjust_request(request)
         if (
-            not isinstance(self.model_tokenizer, MistralTokenizer)
+            not is_mistral_tokenizer(self.model_tokenizer)
             and request.tools
             and request.tool_choice != "none"
         ):

vllm/utils/mistral.py

@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Provides lazy import of the vllm.tokenizers.mistral module."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, TypeGuard
+
+from vllm.tokenizers import TokenizerLike
+from vllm.utils.import_utils import LazyLoader
+
+if TYPE_CHECKING:
+    # if type checking, eagerly import the module
+    import vllm.tokenizers.mistral as mt
+else:
+    mt = LazyLoader("mt", globals(), "vllm.tokenizers.mistral")
+
+
+def is_mistral_tokenizer(obj: TokenizerLike | None) -> TypeGuard[mt.MistralTokenizer]:
+    """Return true if the tokenizer is a MistralTokenizer instance."""
+    cls = type(obj)
+    # Check for special class attribute, this avoids importing the class to
+    # do an isinstance() check.  If the attribute is True, do an isinstance
+    # check to be sure we have the correct type.
+    return bool(
+        getattr(cls, "IS_MISTRAL_TOKENIZER", False)
+        and isinstance(obj, mt.MistralTokenizer)
+    )

vllm/v1/structured_output/backend_xgrammar.py

@@ -10,8 +10,8 @@ import torch
 import vllm.envs
 from vllm.logger import init_logger
 from vllm.sampling_params import SamplingParams
-from vllm.tokenizers.mistral import MistralTokenizer
 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,
@@ -38,7 +38,7 @@ class XgrammarBackend(StructuredOutputBackend):
             self.vllm_config.structured_outputs_config.disable_any_whitespace
         )
 
-        if isinstance(self.tokenizer, MistralTokenizer):
+        if is_mistral_tokenizer(self.tokenizer):
             # NOTE: ideally, xgrammar should handle this accordingly.
             # refer to https://github.com/mlc-ai/xgrammar/blob/d77c0a0173ef14779c918e3be7966ba852f7910f/python/xgrammar/tokenizer_info.py#L98
             stop_token_ids = [self.tokenizer.eos_token_id]