[Chore] Separate out `vllm.utils.collections` (#26990)

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

vllm/multimodal/parse.py

@@ -19,7 +19,8 @@ import numpy as np
 import torch
 from typing_extensions import assert_never
 
-from vllm.utils import LazyLoader, is_list_of
+from vllm.utils import LazyLoader
+from vllm.utils.collections import is_list_of
 
 from .audio import AudioResampler
 from .inputs import (
@@ -364,7 +365,7 @@ class MultiModalDataParser:
         if isinstance(data, torch.Tensor):
             return data.ndim == 3
         if is_list_of(data, torch.Tensor):
-            return data[0].ndim == 2
+            return data[0].ndim == 2  # type: ignore[index]
 
         return False
 
@@ -422,6 +423,7 @@ class MultiModalDataParser:
         if self._is_embeddings(data):
             return AudioEmbeddingItems(data)
 
+        data_items: list[AudioItem]
         if (
             is_list_of(data, float)
             or isinstance(data, (np.ndarray, torch.Tensor))
@@ -432,7 +434,7 @@ class MultiModalDataParser:
         elif isinstance(data, (np.ndarray, torch.Tensor)):
             data_items = [elem for elem in data]
         else:
-            data_items = data
+            data_items = data  # type: ignore[assignment]
 
         new_audios = list[np.ndarray]()
         for data_item in data_items:
@@ -485,6 +487,7 @@ class MultiModalDataParser:
         if self._is_embeddings(data):
             return VideoEmbeddingItems(data)
 
+        data_items: list[VideoItem]
         if (
             is_list_of(data, PILImage.Image)
             or isinstance(data, (np.ndarray, torch.Tensor))
@@ -496,7 +499,7 @@ class MultiModalDataParser:
         elif isinstance(data, tuple) and len(data) == 2:
             data_items = [data]
         else:
-            data_items = data
+            data_items = data  # type: ignore[assignment]
 
         new_videos = list[tuple[np.ndarray, dict[str, Any] | None]]()
         metadata_lst: list[dict[str, Any] | None] = []

vllm/multimodal/processing.py

@@ -25,7 +25,7 @@ from typing_extensions import TypeVar, assert_never
 from vllm.logger import init_logger
 from vllm.transformers_utils.processor import cached_processor_from_config
 from vllm.transformers_utils.tokenizer import AnyTokenizer, decode_tokens, encode_tokens
-from vllm.utils import flatten_2d_lists, full_groupby
+from vllm.utils.collections import flatten_2d_lists, full_groupby
 from vllm.utils.functools import get_allowed_kwarg_only_overrides
 from vllm.utils.jsontree import JSONTree, json_map_leaves
 
@@ -484,8 +484,11 @@ _M = TypeVar("_M", bound=_HasModalityAttr | _HasModalityProp)
 
 
 def full_groupby_modality(values: Iterable[_M]) -> ItemsView[str, list[_M]]:
-    """Convenience function to apply [`full_groupby`][vllm.utils.full_groupby]
-    based on modality."""
+    """
+    Convenience function to apply
+    [`full_groupby`][vllm.utils.collections.full_groupby]
+    based on modality.
+    """
     return full_groupby(values, key=lambda x: x.modality)
 
 

vllm/multimodal/registry.py

@@ -9,7 +9,7 @@ import torch.nn as nn
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer, cached_tokenizer_from_config
-from vllm.utils import ClassRegistry
+from vllm.utils.collections import ClassRegistry
 
 from .cache import BaseMultiModalProcessorCache
 from .processing import (

vllm/reasoning/abs_reasoning_parsers.py

@@ -8,7 +8,8 @@ from functools import cached_property
 from typing import TYPE_CHECKING, Any
 
 from vllm.logger import init_logger
-from vllm.utils import import_from_path, is_list_of
+from vllm.utils import import_from_path
+from vllm.utils.collections import is_list_of
 
 if TYPE_CHECKING:
     from vllm.entrypoints.openai.protocol import (

vllm/utils/__init__.py

@@ -37,29 +37,19 @@ from argparse import (
     RawDescriptionHelpFormatter,
     _ArgumentGroup,
 )
-from collections import UserDict, defaultdict
+from collections import defaultdict
 from collections.abc import (
     Callable,
     Collection,
     Generator,
-    Hashable,
-    Iterable,
     Iterator,
-    Mapping,
     Sequence,
 )
 from concurrent.futures.process import ProcessPoolExecutor
 from dataclasses import dataclass, field
 from functools import cache, lru_cache, partial, wraps
 from pathlib import Path
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Generic,
-    Literal,
-    TextIO,
-    TypeVar,
-)
+from typing import TYPE_CHECKING, Any, TextIO, TypeVar
 from urllib.parse import urlparse
 from uuid import uuid4
 
@@ -78,7 +68,7 @@ import zmq.asyncio
 from packaging import version
 from packaging.version import Version
 from torch.library import Library
-from typing_extensions import Never, TypeIs, assert_never
+from typing_extensions import Never
 
 import vllm.envs as envs
 from vllm.logger import enable_trace_function_call, init_logger
@@ -170,9 +160,6 @@ def set_default_torch_num_threads(num_threads: int):
 T = TypeVar("T")
 U = TypeVar("U")
 
-_K = TypeVar("_K", bound=Hashable)
-_V = TypeVar("_V")
-
 
 class Device(enum.Enum):
     GPU = enum.auto()
@@ -421,12 +408,6 @@ def update_environment_variables(envs: dict[str, str]):
         os.environ[k] = v
 
 
-def chunk_list(lst: list[T], chunk_size: int):
-    """Yield successive chunk_size chunks from lst."""
-    for i in range(0, len(lst), chunk_size):
-        yield lst[i : i + chunk_size]
-
-
 def cdiv(a: int, b: int) -> int:
     """Ceiling division."""
     return -(a // -b)
@@ -743,53 +724,6 @@ def common_broadcastable_dtype(dtypes: Collection[torch.dtype]):
     )
 
 
-def as_list(maybe_list: Iterable[T]) -> list[T]:
-    """Convert iterable to list, unless it's already a list."""
-    return maybe_list if isinstance(maybe_list, list) else list(maybe_list)
-
-
-def as_iter(obj: T | Iterable[T]) -> Iterable[T]:
-    if isinstance(obj, str) or not isinstance(obj, Iterable):
-        return [obj]  # type: ignore[list-item]
-    return obj
-
-
-# `collections` helpers
-def is_list_of(
-    value: object,
-    typ: type[T] | tuple[type[T], ...],
-    *,
-    check: Literal["first", "all"] = "first",
-) -> TypeIs[list[T]]:
-    if not isinstance(value, list):
-        return False
-
-    if check == "first":
-        return len(value) == 0 or isinstance(value[0], typ)
-    elif check == "all":
-        return all(isinstance(v, typ) for v in value)
-
-    assert_never(check)
-
-
-def flatten_2d_lists(lists: Iterable[Iterable[T]]) -> list[T]:
-    """Flatten a list of lists to a single list."""
-    return [item for sublist in lists for item in sublist]
-
-
-def full_groupby(values: Iterable[_V], *, key: Callable[[_V], _K]):
-    """
-    Unlike [`itertools.groupby`][], groups are not broken by
-    non-contiguous data.
-    """
-    groups = defaultdict[_K, list[_V]](list)
-
-    for value in values:
-        groups[key(value)].append(value)
-
-    return groups.items()
-
-
 # TODO: This function can be removed if transformer_modules classes are
 # serialized by value when communicating between processes
 def init_cached_hf_modules() -> None:
@@ -1578,50 +1512,6 @@ class AtomicCounter:
         return self._value
 
 
-# Adapted from: https://stackoverflow.com/a/47212782/5082708
-class LazyDict(Mapping[str, T], Generic[T]):
-    def __init__(self, factory: dict[str, Callable[[], T]]):
-        self._factory = factory
-        self._dict: dict[str, T] = {}
-
-    def __getitem__(self, key: str) -> T:
-        if key not in self._dict:
-            if key not in self._factory:
-                raise KeyError(key)
-            self._dict[key] = self._factory[key]()
-        return self._dict[key]
-
-    def __setitem__(self, key: str, value: Callable[[], T]):
-        self._factory[key] = value
-
-    def __iter__(self):
-        return iter(self._factory)
-
-    def __len__(self):
-        return len(self._factory)
-
-
-class ClassRegistry(UserDict[type[T], _V]):
-    def __getitem__(self, key: type[T]) -> _V:
-        for cls in key.mro():
-            if cls in self.data:
-                return self.data[cls]
-
-        raise KeyError(key)
-
-    def __contains__(self, key: object) -> bool:
-        return self.contains(key)
-
-    def contains(self, key: object, *, strict: bool = False) -> bool:
-        if not isinstance(key, type):
-            return False
-
-        if strict:
-            return key in self.data
-
-        return any(cls in self.data for cls in key.mro())
-
-
 def weak_ref_tensor(tensor: Any) -> Any:
     """
     Create a weak reference to a tensor.
@@ -2588,22 +2478,6 @@ class LazyLoader(types.ModuleType):
         return dir(self._module)
 
 
-def swap_dict_values(obj: dict[_K, _V], key1: _K, key2: _K) -> None:
-    """
-    Helper function to swap values for two keys
-    """
-    v1 = obj.get(key1)
-    v2 = obj.get(key2)
-    if v1 is not None:
-        obj[key2] = v1
-    else:
-        obj.pop(key2, None)
-    if v2 is not None:
-        obj[key1] = v2
-    else:
-        obj.pop(key1, None)
-
-
 @contextlib.contextmanager
 def cprofile_context(save_file: str | None = None):
     """Run a cprofile

vllm/utils/collections.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Contains helpers that are applied to collections.
+
+This is similar in concept to the `collections` module.
+"""
+
+from collections import UserDict, defaultdict
+from collections.abc import Callable, Generator, Hashable, Iterable, Mapping
+from typing import Generic, Literal, TypeVar
+
+from typing_extensions import TypeIs, assert_never
+
+T = TypeVar("T")
+U = TypeVar("U")
+
+_K = TypeVar("_K", bound=Hashable)
+_V = TypeVar("_V")
+
+
+class ClassRegistry(UserDict[type[T], _V]):
+    """
+    A registry that acts like a dictionary but searches for other classes
+    in the MRO if the original class is not found.
+    """
+
+    def __getitem__(self, key: type[T]) -> _V:
+        for cls in key.mro():
+            if cls in self.data:
+                return self.data[cls]
+
+        raise KeyError(key)
+
+    def __contains__(self, key: object) -> bool:
+        return self.contains(key)
+
+    def contains(self, key: object, *, strict: bool = False) -> bool:
+        if not isinstance(key, type):
+            return False
+
+        if strict:
+            return key in self.data
+
+        return any(cls in self.data for cls in key.mro())
+
+
+class LazyDict(Mapping[str, T], Generic[T]):
+    """
+    Evaluates dictionary items only when they are accessed.
+
+    Adapted from: https://stackoverflow.com/a/47212782/5082708
+    """
+
+    def __init__(self, factory: dict[str, Callable[[], T]]):
+        self._factory = factory
+        self._dict: dict[str, T] = {}
+
+    def __getitem__(self, key: str) -> T:
+        if key not in self._dict:
+            if key not in self._factory:
+                raise KeyError(key)
+            self._dict[key] = self._factory[key]()
+        return self._dict[key]
+
+    def __setitem__(self, key: str, value: Callable[[], T]):
+        self._factory[key] = value
+
+    def __iter__(self):
+        return iter(self._factory)
+
+    def __len__(self):
+        return len(self._factory)
+
+
+def as_list(maybe_list: Iterable[T]) -> list[T]:
+    """Convert iterable to list, unless it's already a list."""
+    return maybe_list if isinstance(maybe_list, list) else list(maybe_list)
+
+
+def as_iter(obj: T | Iterable[T]) -> Iterable[T]:
+    if isinstance(obj, str) or not isinstance(obj, Iterable):
+        return [obj]  # type: ignore[list-item]
+    return obj
+
+
+def is_list_of(
+    value: object,
+    typ: type[T] | tuple[type[T], ...],
+    *,
+    check: Literal["first", "all"] = "first",
+) -> TypeIs[list[T]]:
+    if not isinstance(value, list):
+        return False
+
+    if check == "first":
+        return len(value) == 0 or isinstance(value[0], typ)
+    elif check == "all":
+        return all(isinstance(v, typ) for v in value)
+
+    assert_never(check)
+
+
+def chunk_list(lst: list[T], chunk_size: int) -> Generator[list[T]]:
+    """Yield successive chunk_size chunks from lst."""
+    for i in range(0, len(lst), chunk_size):
+        yield lst[i : i + chunk_size]
+
+
+def flatten_2d_lists(lists: Iterable[Iterable[T]]) -> list[T]:
+    """Flatten a list of lists to a single list."""
+    return [item for sublist in lists for item in sublist]
+
+
+def full_groupby(values: Iterable[_V], *, key: Callable[[_V], _K]):
+    """
+    Unlike [`itertools.groupby`][], groups are not broken by
+    non-contiguous data.
+    """
+    groups = defaultdict[_K, list[_V]](list)
+
+    for value in values:
+        groups[key(value)].append(value)
+
+    return groups.items()
+
+
+def swap_dict_values(obj: dict[_K, _V], key1: _K, key2: _K) -> None:
+    """Swap values between two keys."""
+    v1 = obj.get(key1)
+    v2 = obj.get(key2)
+    if v1 is not None:
+        obj[key2] = v1
+    else:
+        obj.pop(key2, None)
+    if v2 is not None:
+        obj[key1] = v2
+    else:
+        obj.pop(key1, None)

vllm/v1/engine/async_llm.py

@@ -29,8 +29,9 @@ from vllm.tracing import init_tracer
 from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
 from vllm.transformers_utils.tokenizer import AnyTokenizer, init_tokenizer_from_configs
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import Device, as_list, cdiv
+from vllm.utils import Device, cdiv
 from vllm.utils.asyncio import cancel_task_threadsafe
+from vllm.utils.collections import as_list
 from vllm.utils.functools import deprecate_kwargs
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.core_client import EngineCoreClient

vllm/v1/worker/gpu_input_batch.py

@@ -12,7 +12,8 @@ from vllm.lora.request import LoRARequest
 from vllm.multimodal.inputs import MultiModalFeatureSpec
 from vllm.pooling_params import PoolingParams
 from vllm.sampling_params import SamplingParams, SamplingType
-from vllm.utils import length_from_prompt_token_ids_or_embeds, swap_dict_values
+from vllm.utils import length_from_prompt_token_ids_or_embeds
+from vllm.utils.collections import swap_dict_values
 from vllm.v1.outputs import LogprobsTensors
 from vllm.v1.pool.metadata import PoolingMetadata
 from vllm.v1.sample.logits_processor import (

vllm/v1/worker/tpu_input_batch.py

@@ -9,7 +9,8 @@ import torch
 
 from vllm.lora.request import LoRARequest
 from vllm.sampling_params import SamplingType
-from vllm.utils import length_from_prompt_token_ids_or_embeds, swap_dict_values
+from vllm.utils import length_from_prompt_token_ids_or_embeds
+from vllm.utils.collections import swap_dict_values
 from vllm.v1.outputs import LogprobsTensors
 from vllm.v1.worker.block_table import MultiGroupBlockTable
 from vllm.v1.worker.gpu_input_batch import CachedRequestState