[Chore][1/2] Drop v0.14 deprecations (#31285)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

docs/api/README.md

@@ -72,7 +72,6 @@ Internal data structures.
 - [vllm.multimodal.inputs.MultiModalFieldConfig][]
 - [vllm.multimodal.inputs.MultiModalKwargsItem][]
 - [vllm.multimodal.inputs.MultiModalKwargsItems][]
-- [vllm.multimodal.inputs.MultiModalKwargs][]
 - [vllm.multimodal.inputs.MultiModalInputs][]
 
 ### Data Parsing

tests/entrypoints/openai/test_chat_error.py

@@ -13,7 +13,7 @@ from vllm.entrypoints.openai.protocol import ChatCompletionRequest, ErrorRespons
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_models import BaseModelPath, OpenAIServingModels
 from vllm.outputs import CompletionOutput, RequestOutput
-from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.tokenizers import get_tokenizer
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"

tests/entrypoints/openai/test_completion_error.py

@@ -13,7 +13,7 @@ from vllm.entrypoints.openai.protocol import CompletionRequest, ErrorResponse
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_models import BaseModelPath, OpenAIServingModels
 from vllm.outputs import CompletionOutput, RequestOutput
-from vllm.transformers_utils.tokenizer import get_tokenizer
+from vllm.tokenizers import get_tokenizer
 from vllm.v1.engine.async_llm import AsyncLLM
 
 MODEL_NAME = "openai-community/gpt2"

tests/entrypoints/openai/test_lora_resolvers.py

@@ -61,13 +61,13 @@ class MockLoRAResolver(LoRAResolver):
             return LoRARequest(
                 lora_name="test-lora",
                 lora_int_id=1,
-                lora_local_path="/fake/path/test-lora",
+                lora_path="/fake/path/test-lora",
             )
         elif lora_name == "invalid-lora":
             return LoRARequest(
                 lora_name="invalid-lora",
                 lora_int_id=2,
-                lora_local_path="/fake/path/invalid-lora",
+                lora_path="/fake/path/invalid-lora",
             )
         return None
 

vllm/entrypoints/context.py

@@ -41,9 +41,8 @@ from vllm.entrypoints.tool import Tool
 from vllm.entrypoints.tool_server import ToolServer
 from vllm.outputs import RequestOutput
 from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
-from vllm.tokenizers.protocol import TokenizerLike
+from vllm.tokenizers import TokenizerLike
 from vllm.tool_parsers.abstract_tool_parser import ToolParser
-from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import random_uuid
 
 if TYPE_CHECKING:
@@ -259,8 +258,8 @@ class ParsableContext(ConversationContext):
         self,
         *,
         response_messages: list[ResponseInputOutputItem],
-        tokenizer: AnyTokenizer,
+        tokenizer: TokenizerLike,
-        reasoning_parser_cls: Callable[[AnyTokenizer], ReasoningParser] | None,
+        reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser] | None,
         request: ResponsesRequest,
         available_tools: list[str] | None,
         tool_parser_cls: Callable[[TokenizerLike], ToolParser] | None,

vllm/entrypoints/openai/parser/responses_parser.py

@@ -19,9 +19,8 @@ from vllm.entrypoints.constants import MCP_PREFIX
 from vllm.entrypoints.openai.protocol import ResponseInputOutputItem, ResponsesRequest
 from vllm.outputs import CompletionOutput
 from vllm.reasoning.abs_reasoning_parsers import ReasoningParser
-from vllm.tokenizers.protocol import TokenizerLike
+from vllm.tokenizers import TokenizerLike
 from vllm.tool_parsers.abstract_tool_parser import ToolParser
-from vllm.transformers_utils.tokenizer import AnyTokenizer
 from vllm.utils import random_uuid
 
 logger = logging.getLogger(__name__)
@@ -33,8 +32,8 @@ class ResponsesParser:
     def __init__(
         self,
         *,
-        tokenizer: AnyTokenizer,
+        tokenizer: TokenizerLike,
-        reasoning_parser_cls: Callable[[AnyTokenizer], ReasoningParser],
+        reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser],
         response_messages: list[ResponseInputOutputItem],
         request: ResponsesRequest,
         tool_parser_cls: Callable[[TokenizerLike], ToolParser] | None,
@@ -150,8 +149,8 @@ class ResponsesParser:
 
 def get_responses_parser_for_simple_context(
     *,
-    tokenizer: AnyTokenizer,
+    tokenizer: TokenizerLike,
-    reasoning_parser_cls: Callable[[AnyTokenizer], ReasoningParser],
+    reasoning_parser_cls: Callable[[TokenizerLike], ReasoningParser],
     response_messages: list[ResponseInputOutputItem],
     request: ResponsesRequest,
     tool_parser_cls,

vllm/entrypoints/openai/serving_models.py

@@ -119,7 +119,7 @@ class OpenAIServingModels:
         lora_cards = [
             ModelCard(
                 id=lora.lora_name,
-                root=lora.local_path,
+                root=lora.path,
                 parent=lora.base_model_name
                 if lora.base_model_name
                 else self.base_model_paths[0].name,

vllm/entrypoints/openai/tool_parsers/__init__.py

@@ -1,33 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import warnings
-
-
-def __getattr__(name: str):
-    if name == "ToolParser":
-        from vllm.tool_parsers import ToolParser
-
-        warnings.warn(
-            "`vllm.entrypoints.openai.tool_parsers.ToolParser` has been moved to "
-            "`vllm.tool_parsers.ToolParser`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return ToolParser
-    if name == "ToolParserManager":
-        from vllm.tool_parsers import ToolParserManager
-
-        warnings.warn(
-            "`vllm.entrypoints.openai.tool_parsers.ToolParserManager` "
-            "has been moved to `vllm.tool_parsers.ToolParserManager`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return ToolParserManager
-
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/lora/request.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import warnings
 
 import msgspec
 
@@ -21,7 +20,6 @@ class LoRARequest(
     lora_name: str
     lora_int_id: int
     lora_path: str = ""
-    lora_local_path: str | None = msgspec.field(default=None)
     long_lora_max_len: int | None = None
     base_model_name: str | None = msgspec.field(default=None)
     tensorizer_config_dict: dict | None = None
@@ -29,16 +27,6 @@ class LoRARequest(
     def __post_init__(self):
         if self.lora_int_id < 1:
             raise ValueError(f"id must be > 0, got {self.lora_int_id}")
-        if self.lora_local_path:
-            warnings.warn(
-                "The 'lora_local_path' attribute is deprecated "
-                "and will be removed in a future version. "
-                "Please use 'lora_path' instead.",
-                DeprecationWarning,
-                stacklevel=2,
-            )
-            if not self.lora_path:
-                self.lora_path = self.lora_local_path or ""
 
         # Ensure lora_path is not empty
         assert self.lora_path, "lora_path cannot be empty"
@@ -55,28 +43,6 @@ class LoRARequest(
     def path(self):
         return self.lora_path
 
-    @property
-    def local_path(self):
-        warnings.warn(
-            "The 'local_path' attribute is deprecated "
-            "and will be removed in a future version. "
-            "Please use 'path' instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        return self.lora_path
-
-    @local_path.setter
-    def local_path(self, value):
-        warnings.warn(
-            "The 'local_path' attribute is deprecated "
-            "and will be removed in a future version. "
-            "Please use 'path' instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        self.lora_path = value
-
     def __eq__(self, value: object) -> bool:
         """
         Overrides the equality method to compare LoRARequest

vllm/model_executor/models/interfaces.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from collections.abc import Callable, Iterable, Mapping, MutableSequence, Set
+from collections.abc import Callable, Iterable, Mapping, MutableSequence
 from typing import (
     TYPE_CHECKING,
     ClassVar,
@@ -100,17 +100,6 @@ class SupportsMultiModal(Protocol):
     in their raw form and not input embeddings.
     """
 
-    merge_by_field_config: ClassVar[bool | None] = None
-    """
-    [DEPRECATED] A flag that indicates which implementation of
-    `vllm.multimodal.utils.group_mm_kwargs_by_modality` to use.
-    """
-
-    multimodal_cpu_fields: ClassVar[Set[str] | None] = None
-    """
-    [DEPRECATED] A set indicating CPU-only multimodal fields.
-    """
-
     _processor_factory: ClassVar[_ProcessorFactories]
     """
     Set internally by `MultiModalRegistry.register_processor`.
@@ -277,35 +266,7 @@ def supports_multimodal(model: object) -> TypeIs[SupportsMultiModal]: ...
 def supports_multimodal(
     model: type[object] | object,
 ) -> TypeIs[type[SupportsMultiModal]] | TypeIs[SupportsMultiModal]:
-    res = getattr(model, "supports_multimodal", False)
+    return getattr(model, "supports_multimodal", False)
-
-    if res:
-        # We can remove this starting from v0.14
-        merge_by_field_config = getattr(model, "merge_by_field_config", None)
-        if merge_by_field_config is False:
-            raise ValueError(
-                "`merge_by_field_config=False` is no longer effective, "
-                "please update your model to consider the new batching logic "
-                "in `group_mm_kwargs_by_modality` (refer to "
-                "https://github.com/vllm-project/vllm/issues/26149), "
-                "and then remove the override from your model."
-            )
-        if merge_by_field_config is True:
-            logger.warning_once(
-                "`merge_by_field_config=True` is redundant, "
-                "please remove the override from your model."
-            )
-
-        multimodal_cpu_fields = getattr(model, "multimodal_cpu_fields", None)
-        if multimodal_cpu_fields is not None:
-            raise ValueError(
-                "`multimodal_cpu_fields` is no longer effective, "
-                "please set `keep_on_cpu=True` in `MultiModalFieldConfig` "
-                "(refer to https://github.com/vllm-project/vllm/pull/30181), "
-                "and then remove the override from your model."
-            )
-
-    return res
 
 
 def supports_multimodal_raw_input_only(model: type[object] | object) -> bool:

vllm/multimodal/__init__.py

@@ -6,7 +6,6 @@ from .inputs import (
     ModalityData,
     MultiModalDataBuiltins,
     MultiModalDataDict,
-    MultiModalKwargs,
     MultiModalKwargsItems,
     MultiModalPlaceholderDict,
     MultiModalUUIDDict,
@@ -30,7 +29,6 @@ __all__ = [
     "MultiModalDataBuiltins",
     "MultiModalDataDict",
     "MultiModalHasher",
-    "MultiModalKwargs",
     "MultiModalKwargsItems",
     "MultiModalPlaceholderDict",
     "MultiModalUUIDDict",

vllm/multimodal/inputs.py

@@ -20,7 +20,7 @@ from typing import (
 )
 
 import numpy as np
-from typing_extensions import NotRequired, TypeVar, deprecated
+from typing_extensions import NotRequired, TypeVar
 
 from vllm.utils.collection_utils import full_groupby, is_list_of
 from vllm.utils.import_utils import LazyLoader
@@ -356,8 +356,8 @@ class MultiModalFeatureSpec:
 @dataclass
 class MultiModalFieldElem:
     """
-    Represents a keyword argument corresponding to a multi-modal item
+    Represents a keyword argument inside a
-    in [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs].
+    [`MultiModalKwargsItem`][vllm.multimodal.inputs.MultiModalKwargsItem].
     """
 
     modality: str
@@ -369,14 +369,14 @@ class MultiModalFieldElem:
     key: str
     """
     The key of this field in
-    [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs],
+    [`MultiModalKwargsItem`][vllm.multimodal.inputs.MultiModalKwargsItem],
     i.e. the name of the keyword argument to be passed to the model.
     """
 
     data: NestedTensors
     """
     The tensor data of this field in
-    [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs],
+    [`MultiModalKwargsItem`][vllm.multimodal.inputs.MultiModalKwargsItem],
     i.e. the value of the keyword argument to be passed to the model.
 
     It may be set to `None` if it is determined that the item is cached
@@ -410,9 +410,9 @@ class MultiModalFieldElem:
 @dataclass(frozen=True, kw_only=True)
 class BaseMultiModalField(ABC):
     """
-    Defines how to interpret tensor data belonging to a keyword argument in
+    Defines how to interpret tensor data belonging to a keyword argument for
-    [`MultiModalKwargs`][vllm.multimodal.inputs.MultiModalKwargs] for multiple
+    [`MultiModalKwargsItems`][vllm.multimodal.inputs.MultiModalKwargsItems],
-    multi-modal items, and vice versa.
+    and vice versa.
     """
 
     keep_on_cpu: bool = False
@@ -985,62 +985,6 @@ MultiModalKwargsOptionalItems: TypeAlias = (
 )
 
 
-@deprecated("`MultiModalKwargs` is deprecated and will be removed in v0.14.")
-class MultiModalKwargs(UserDict[str, NestedTensors]):
-    """
-    A dictionary that represents the keyword arguments to
-    [`torch.nn.Module.forward`][].
-    """
-
-    @staticmethod
-    @deprecated(
-        "`MultiModalKwargs.from_hf_inputs` is deprecated and "
-        "will be removed in v0.14. "
-        "Please use `MultiModalKwargsItems.from_hf_inputs` and "
-        "access the tensor data using `.get_data()`."
-    )
-    def from_hf_inputs(
-        hf_inputs: "BatchFeature",
-        config_by_key: Mapping[str, MultiModalFieldConfig],
-    ):
-        return MultiModalKwargsItems.from_hf_inputs(hf_inputs, config_by_key).get_data()
-
-    @staticmethod
-    @deprecated(
-        "`MultiModalKwargs.from_items` is deprecated and "
-        "will be removed in v0.14. "
-        "Please use `MultiModalKwargsItems.from_seq` and "
-        "access the tensor data using `.get_data()`."
-    )
-    def from_items(
-        items: Sequence[MultiModalKwargsItem],
-        *,
-        pin_memory: bool = False,
-    ):
-        return MultiModalKwargsItems.from_seq(items).get_data(pin_memory=pin_memory)
-
-    def __getitem__(self, key: str):
-        if key not in self:
-            raise KeyError(
-                f"Keyword argument {key!r} not found. "
-                f"Available keys: {set(self.keys())}"
-            )
-
-        return super().__getitem__(key)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, self.__class__):
-            return False
-
-        for k in self:
-            if k not in other:
-                return False
-            if not nested_tensors_equal(self[k], other[k]):
-                return False
-
-        return True
-
-
 MultiModalPlaceholderDict: TypeAlias = Mapping[str, Sequence[PlaceholderRange]]
 """
 A dictionary containing placeholder ranges for each modality.

vllm/multimodal/utils.py

@@ -4,7 +4,7 @@
 import asyncio
 import atexit
 import mimetypes
-from collections.abc import Generator, Set
+from collections.abc import Generator
 from concurrent.futures import ThreadPoolExecutor
 from itertools import groupby
 from pathlib import Path
@@ -462,8 +462,6 @@ def group_mm_kwargs_by_modality(
     *,
     device: torch.types.Device = None,
     pin_memory: bool = False,
-    merge_by_field_config: bool | None = None,
-    multimodal_cpu_fields: Set[str] | None = None,
 ) -> Generator[tuple[str, int, BatchedTensorInputs], None, None]:
     """Group consecutive `MultiModalKwargsItem`s from `mm_kwargs` with the same
     modality together into the same `MultiModalKwargs` instance.
@@ -476,17 +474,6 @@ def group_mm_kwargs_by_modality(
     Yields:
         A tuple `(modality, num_items, grouped_kwargs)`.
     """
-    if merge_by_field_config is not None:
-        logger.warning_once(
-            "The `merge_by_field_config` argument of `group_mm_kwargs_by_modality` "
-            "is deprecated and will be removed in v0.14."
-        )
-    if multimodal_cpu_fields is not None:
-        logger.warning_once(
-            "The `multimodal_cpu_fields` argument of `group_mm_kwargs_by_modality` "
-            "is deprecated and will be removed in v0.14."
-        )
-
     from vllm.multimodal.inputs import MultiModalKwargsItems
 
     for modality, items in groupby(mm_kwargs, key=lambda item: item.modality):

vllm/tokenizers/__init__.py

@@ -7,7 +7,6 @@ from .registry import (
     cached_get_tokenizer,
     cached_tokenizer_from_config,
     get_tokenizer,
-    init_tokenizer_from_config,
 )
 
 __all__ = [
@@ -16,5 +15,4 @@ __all__ = [
     "cached_get_tokenizer",
     "get_tokenizer",
     "cached_tokenizer_from_config",
-    "init_tokenizer_from_config",
 ]

vllm/tokenizers/registry.py

@@ -7,7 +7,7 @@ from pathlib import Path
 from typing import TYPE_CHECKING
 
 import huggingface_hub
-from typing_extensions import TypeVar, assert_never, deprecated
+from typing_extensions import TypeVar, assert_never
 
 import vllm.envs as envs
 from vllm.logger import init_logger
@@ -224,10 +224,3 @@ def cached_tokenizer_from_config(model_config: "ModelConfig", **kwargs):
         trust_remote_code=model_config.trust_remote_code,
         **kwargs,
     )
-
-
-@deprecated(
-    "Renamed to `cached_tokenizer_from_config`. The old name will be removed in v0.14."
-)
-def init_tokenizer_from_config(model_config: "ModelConfig"):
-    return cached_tokenizer_from_config(model_config)

vllm/transformers_utils/tokenizer.py

@@ -1,127 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
 import warnings
-from typing import Any
-
-from typing_extensions import deprecated
-
-from vllm.logger import init_logger
-from vllm.tokenizers import TokenizerLike
-
-logger = init_logger(__name__)
 
 
 def __getattr__(name: str):
-    if name == "AnyTokenizer":
+    # Keep until lm-eval is updated
-        warnings.warn(
-            "`vllm.transformers_utils.tokenizer.AnyTokenizer` has been moved to "
-            "`vllm.tokenizers.TokenizerLike`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return TokenizerLike
     if name == "get_tokenizer":
         from vllm.tokenizers import get_tokenizer
 
         warnings.warn(
             "`vllm.transformers_utils.tokenizer.get_tokenizer` "
             "has been moved to `vllm.tokenizers.get_tokenizer`. "
-            "The old name will be removed in v0.14.",
+            "The old name will be removed in a future version.",
             DeprecationWarning,
             stacklevel=2,
         )
 
         return get_tokenizer
-    if name == "cached_get_tokenizer":
-        from vllm.tokenizers import cached_get_tokenizer
-
-        warnings.warn(
-            "`vllm.transformers_utils.tokenizer.cached_get_tokenizer` "
-            "has been moved to `vllm.tokenizers.cached_get_tokenizer`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return cached_get_tokenizer
-    if name == "cached_tokenizer_from_config":
-        from vllm.tokenizers import cached_tokenizer_from_config
-
-        warnings.warn(
-            "`vllm.transformers_utils.tokenizer.cached_tokenizer_from_config` "
-            "has been moved to `vllm.tokenizers.cached_tokenizer_from_config`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return cached_tokenizer_from_config
-    if name == "init_tokenizer_from_configs":
-        from vllm.tokenizers import cached_tokenizer_from_config
-
-        warnings.warn(
-            "`vllm.transformers_utils.tokenizer.init_tokenizer_from_configs` "
-            "has been moved to `vllm.tokenizers.cached_tokenizer_from_config`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return cached_tokenizer_from_config
-
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-@deprecated("Will be removed in v0.14. Please use `tokenizer.decode()` instead.")
-def decode_tokens(
-    tokenizer: TokenizerLike,
-    token_ids: list[int],
-    *,
-    skip_special_tokens: bool | None = None,
-) -> str:
-    """
-    Backend-agnostic equivalent of HF's
-    `tokenizer.decode(token_ids, ...)`.
-
-    `skip_special_tokens=None` means to use the backend's default
-    settings.
-    """
-    kw_args: dict[str, Any] = {}
-
-    if skip_special_tokens is not None:
-        kw_args["skip_special_tokens"] = skip_special_tokens
-
-    return tokenizer.decode(token_ids, **kw_args)
-
-
-@deprecated("Will be removed in v0.14. Please use `tokenizer.encode()` instead.")
-def encode_tokens(
-    tokenizer: TokenizerLike,
-    text: str,
-    *,
-    truncation: bool | None = None,
-    max_length: int | None = None,
-    add_special_tokens: bool | None = None,
-) -> list[int]:
-    """
-    Backend-agnostic equivalent of HF's
-    `tokenizer.encode(text, ...)`.
-
-    `add_special_tokens=None` means to use the backend's default
-    settings.
-    """
-
-    kw_args: dict[str, Any] = {}
-    if max_length is not None:
-        kw_args["max_length"] = max_length
-
-    if truncation is not None:
-        kw_args["truncation"] = truncation
-
-    if add_special_tokens is not None:
-        kw_args["add_special_tokens"] = add_special_tokens
-
-    return tokenizer.encode(text, **kw_args)

vllm/transformers_utils/tokenizer_base.py

@@ -1,33 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import warnings
-
-
-def __getattr__(name: str):
-    if name == "TokenizerBase":
-        from vllm.tokenizers import TokenizerLike
-
-        warnings.warn(
-            "`vllm.transformers_utils.tokenizer_base.TokenizerBase` has been "
-            "moved to `vllm.tokenizers.TokenizerLike`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return TokenizerLike
-    if name == "TokenizerRegistry":
-        from vllm.tokenizers import TokenizerRegistry
-
-        warnings.warn(
-            "`vllm.transformers_utils.tokenizer_base.TokenizerRegistry` has been "
-            "moved to `vllm.tokenizers.TokenizerRegistry`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return TokenizerRegistry
-
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/utils/__init__.py

@@ -2,39 +2,9 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import uuid
-import warnings
-from typing import Any
 
 import torch
 
-_DEPRECATED_MAPPINGS = {
-    "cprofile": "profiling",
-    "cprofile_context": "profiling",
-    # Used by lm-eval
-    "get_open_port": "network_utils",
-}
-
-
-def __getattr__(name: str) -> Any:  # noqa: D401 - short deprecation docstring
-    """Module-level getattr to handle deprecated utilities."""
-    if name in _DEPRECATED_MAPPINGS:
-        submodule_name = _DEPRECATED_MAPPINGS[name]
-        warnings.warn(
-            f"vllm.utils.{name} is deprecated and will be removed in a future version. "
-            f"Use vllm.utils.{submodule_name}.{name} instead.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-        module = __import__(f"vllm.utils.{submodule_name}", fromlist=[submodule_name])
-        return getattr(module, name)
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
-
-
-def __dir__() -> list[str]:
-    # expose deprecated names in dir() for better UX/tab-completion
-    return sorted(list(globals().keys()) + list(_DEPRECATED_MAPPINGS.keys()))
-
-
 MASK_64_BITS = (1 << 64) - 1
 
 

vllm/v1/engine/async_llm.py

@@ -11,7 +11,6 @@ from typing import Any, cast
 
 import numpy as np
 import torch
-from typing_extensions import deprecated
 
 import vllm.envs as envs
 from vllm.config import VllmConfig
@@ -190,14 +189,6 @@ class AsyncLLM(EngineClient):
         else:
             self.profiler = None
 
-    @property
-    @deprecated(
-        "`AsyncLLM.processor` has been renamed to `AsyncLLM.input_processor`. "
-        "The old name will be removed in v0.14."
-    )
-    def processor(self):
-        return self.input_processor
-
     @classmethod
     def from_vllm_config(
         cls,

vllm/v1/engine/llm_engine.py

@@ -7,7 +7,7 @@ from copy import copy
 from typing import Any, cast
 
 import torch.nn as nn
-from typing_extensions import TypeVar, deprecated
+from typing_extensions import TypeVar
 
 import vllm.envs as envs
 from vllm.config import ParallelConfig, VllmConfig
@@ -136,14 +136,6 @@ class LLMEngine:
         # Don't keep the dummy data in memory
         self.reset_mm_cache()
 
-    @property
-    @deprecated(
-        "`LLMEngine.processor` has been renamed to `LLMEngine.input_processor`. "
-        "The old name will be removed in v0.14."
-    )
-    def processor(self):
-        return self.input_processor
-
     @classmethod
     def from_vllm_config(
         cls,

vllm/v1/engine/processor.py

@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import warnings
-
-
-def __getattr__(name: str):
-    if name == "Processor":
-        from .input_processor import InputProcessor
-
-        warnings.warn(
-            "`vllm.v1.engine.processor.Processor` has been moved to "
-            "`vllm.v1.engine.input_processor.InputProcessor`. "
-            "The old name will be removed in v0.14.",
-            DeprecationWarning,
-            stacklevel=2,
-        )
-
-        return InputProcessor
-
-    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

vllm/v1/worker/gpu_model_runner.py

@@ -1090,13 +1090,11 @@ class GPUModelRunner(
                     mm_kwargs.append(feature.data)
 
         # Input all modalities at once
-        model = cast(SupportsMultiModal, self.model)
         mm_kwargs_combined: BatchedTensorInputs = {}
         for _, _, mm_kwargs_group in group_mm_kwargs_by_modality(
             mm_kwargs,
             device=self.device,
             pin_memory=self.pin_memory,
-            merge_by_field_config=model.merge_by_field_config,
         ):
             mm_kwargs_combined.update(mm_kwargs_group)