[Chore] Separate out vllm.utils.collections

tests/conftest.py

@@ -57,7 +57,8 @@
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
 from vllm.transformers_utils.utils import maybe_model_redirect
-from vllm.utils import is_list_of, set_default_torch_num_threads
+from vllm.utils import set_default_torch_num_threads
+from vllm.utils.collections import is_list_of
 
 logger = init_logger(__name__)
 

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -25,7 +25,7 @@
 from transformers.video_utils import VideoMetadata
 
 from vllm.logprobs import SampleLogprobs
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 
 from .....conftest import HfRunner, ImageAsset, ImageTestAssets
 from .types import RunnerOutput

tests/models/multimodal/processing/test_tensor_schema.py

@@ -35,7 +35,7 @@
 from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
 from vllm.multimodal.utils import group_mm_kwargs_by_modality
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 
 from ...registry import _MULTIMODAL_EXAMPLE_MODELS, HF_EXAMPLE_MODELS
 from ...utils import dummy_hf_overrides

tests/utils_/test_collections.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import pytest
+
+from vllm.utils.collections import swap_dict_values
+
+
+@pytest.mark.parametrize(
+    "obj,key1,key2",
+    [
+        # Tests for both keys exist
+        ({1: "a", 2: "b"}, 1, 2),
+        # Tests for one key does not exist
+        ({1: "a", 2: "b"}, 1, 3),
+        # Tests for both keys do not exist
+        ({1: "a", 2: "b"}, 3, 4),
+    ],
+)
+def test_swap_dict_values(obj, key1, key2):
+    original_obj = obj.copy()
+
+    swap_dict_values(obj, key1, key2)
+
+    if key1 in original_obj:
+        assert obj[key2] == original_obj[key1]
+    else:
+        assert key2 not in obj
+    if key2 in original_obj:
+        assert obj[key1] == original_obj[key2]
+    else:
+        assert key1 not in obj

tests/utils_/test_utils.py

@@ -38,7 +38,6 @@
     sha256,
     split_host_port,
     split_zmq_path,
-    swap_dict_values,
     unique_filepath,
 )
 
@@ -516,30 +515,6 @@ def build_ctx():
         _ = placeholder_attr.module
 
 
-@pytest.mark.parametrize(
-    "obj,key1,key2",
-    [
-        # Tests for both keys exist
-        ({1: "a", 2: "b"}, 1, 2),
-        # Tests for one key does not exist
-        ({1: "a", 2: "b"}, 1, 3),
-        # Tests for both keys do not exist
-        ({1: "a", 2: "b"}, 3, 4),
-    ],
-)
-def test_swap_dict_values(obj, key1, key2):
-    original_obj = obj.copy()
-    swap_dict_values(obj, key1, key2)
-    if key1 in original_obj:
-        assert obj[key2] == original_obj[key1]
-    else:
-        assert key2 not in obj
-    if key2 in original_obj:
-        assert obj[key1] == original_obj[key2]
-    else:
-        assert key1 not in obj
-
-
 def test_model_specification(
     parser_with_config, cli_config_file, cli_config_file_with_model
 ):

vllm/entrypoints/llm.py

@@ -75,7 +75,8 @@
     get_cached_tokenizer,
 )
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import Counter, Device, as_iter, is_list_of
+from vllm.utils import Counter, Device
+from vllm.utils.collections import as_iter, is_list_of
 from vllm.v1.engine import EngineCoreRequest
 from vllm.v1.engine.llm_engine import LLMEngine
 from vllm.v1.sample.logits_processor import LogitsProcessor

vllm/entrypoints/openai/serving_chat.py

@@ -70,7 +70,7 @@
     truncate_tool_call_ids,
     validate_request_params,
 )
-from vllm.utils import as_list
+from vllm.utils.collections import as_list
 
 logger = init_logger(__name__)
 

vllm/entrypoints/openai/serving_completion.py

@@ -34,8 +34,8 @@
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams, SamplingParams
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-from vllm.utils import as_list
 from vllm.utils.asyncio import merge_async_iterators
+from vllm.utils.collections import as_list
 
 logger = init_logger(__name__)
 

vllm/entrypoints/openai/serving_embedding.py

@@ -39,8 +39,8 @@
     RequestOutput,
 )
 from vllm.pooling_params import PoolingParams
-from vllm.utils import chunk_list
 from vllm.utils.asyncio import merge_async_iterators
+from vllm.utils.collections import chunk_list
 
 logger = init_logger(__name__)
 

vllm/entrypoints/openai/serving_engine.py

@@ -90,13 +90,14 @@
     log_tracing_disabled_warning,
 )
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import is_list_of, random_uuid
+from vllm.utils import random_uuid
 from vllm.utils.asyncio import (
     AsyncMicrobatchTokenizer,
     collect_from_async_generator,
     make_async,
     merge_async_iterators,
 )
+from vllm.utils.collections import is_list_of
 from vllm.v1.engine import EngineCoreRequest
 
 logger = init_logger(__name__)

vllm/entrypoints/openai/tool_parsers/abstract_tool_parser.py

@@ -12,7 +12,8 @@
 )
 from vllm.logger import init_logger
 from vllm.transformers_utils.tokenizer import AnyTokenizer
-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
 
 logger = init_logger(__name__)
 

vllm/inputs/parse.py

@@ -5,7 +5,7 @@
 
 from typing_extensions import TypeIs
 
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 
 from .data import (
     EmbedsPrompt,

vllm/model_executor/layers/activation.py

@@ -17,7 +17,7 @@
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
-from vllm.utils import LazyDict
+from vllm.utils.collections import LazyDict
 
 logger = init_logger(__name__)
 

vllm/model_executor/layers/quantization/gptq.py

@@ -28,7 +28,7 @@
     RowvLLMParameter,
 )
 from vllm.transformers_utils.config import get_safetensors_params_metadata
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 
 if TYPE_CHECKING:
     from vllm.model_executor.layers.quantization import QuantizationMethods

vllm/model_executor/layers/quantization/gptq_marlin.py

@@ -57,7 +57,7 @@
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 from vllm.transformers_utils.config import get_safetensors_params_metadata
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 
 logger = init_logger(__name__)
 

vllm/model_executor/models/deepseek_vl2.py

@@ -49,7 +49,7 @@
 )
 from vllm.transformers_utils.processors.deepseek_vl2 import DeepseekVLV2Processor
 from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP

vllm/model_executor/models/llava_next_video.py

@@ -33,7 +33,7 @@
 )
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP

vllm/model_executor/models/minicpmv.py

@@ -86,7 +86,7 @@
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.platforms import current_platform
 from vllm.sequence import IntermediateTensors
-from vllm.utils import flatten_2d_lists
+from vllm.utils.collections import flatten_2d_lists
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
 from .idefics2_vision_model import Idefics2VisionTransformer

vllm/model_executor/models/qwen3_vl.py

@@ -79,7 +79,7 @@
 )
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
-from vllm.utils import is_list_of
+from vllm.utils.collections import is_list_of
 
 from .interfaces import (
     MultiModalEmbeddings,

vllm/multimodal/inputs.py

@@ -22,7 +22,8 @@
 import numpy as np
 from typing_extensions import NotRequired, TypeVar, deprecated
 
-from vllm.utils import LazyLoader, full_groupby, is_list_of
+from vllm.utils import LazyLoader
+from vllm.utils.collections import full_groupby, is_list_of
 from vllm.utils.jsontree import json_map_leaves
 
 if TYPE_CHECKING:

vllm/multimodal/parse.py

@@ -19,7 +19,8 @@
 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 @@ def _is_embeddings(
         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 @@ def _parse_audio_data(
         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 @@ def _parse_audio_data(
         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 @@ def _parse_video_data(
         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 @@ def _parse_video_data(
         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 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 @@ def modality(self) -> str: ...
 
 
 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 @@
 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 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 (