Fix per file ruff ignores related to typing (vllm-project#26254)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

Author: hmellor

pyproject.toml

@@ -115,6 +115,7 @@ include = ["vllm*"]
 "vllm/distributed/parallel_state.py" = ["SIM108"]
 "vllm/entrypoints/chat_utils.py" = ["SIM108"]
 "vllm/entrypoints/llm.py" = ["SIM108"]
+"vllm/executor/ray_distributed_executor.py" = ["SIM108", "SIM112"]
 "vllm/model_executor/layers/batch_invariant.py" = ["SIM108"]
 "vllm/model_executor/layers/fla/ops/chunk_o.py" = ["SIM108"]
 "vllm/model_executor/layers/fused_moe/fused_moe.py" = ["SIM108"]
@@ -134,23 +135,6 @@ include = ["vllm*"]
 "tools/profiler/print_layerwise_table.py" = ["SIM118"]
 ## Loop variable binding issues
 "tests/kernels/mamba/test_mamba_ssm_ssd.py" = ["B023"]
-## Type annotation modernization and other rules
-"vllm/attention/backends/abstract.py" = ["UP035", "UP006"]
-"vllm/attention/layer.py" = ["UP035", "UP006"]
-"vllm/attention/layers/chunked_local_attention.py" = ["UP035", "UP006"]
-"vllm/attention/ops/flashmla.py" = ["UP035", "UP006"]
-"vllm/attention/ops/paged_attn.py" = ["UP035", "UP006"]
-"vllm/engine/arg_utils.py" = ["UP035", "UP006"]
-"vllm/engine/metrics.py" = ["UP035", "UP006"]
-"vllm/engine/metrics_types.py" = ["UP035", "UP006"]
-"vllm/executor/executor_base.py" = ["UP035", "UP006"]
-"vllm/executor/msgspec_utils.py" = ["UP035", "UP006"]
-"vllm/executor/ray_distributed_executor.py" = ["UP035", "UP006", "SIM108", "SIM112"]
-"vllm/executor/ray_utils.py" = ["UP035", "UP006"]
-"vllm/executor/uniproc_executor.py" = ["UP035", "UP006"]
-"vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py" = ["UP035"]
-## Type comparison issues
-"vllm/multimodal/inputs.py" = ["E721"]
 # End of temporary ignores
 
 [tool.ruff.lint]

tests/compile/test_full_graph.py

@@ -5,7 +5,7 @@
 
 import logging
 import tempfile
-from typing import Any, Optional, Union
+from typing import Any, Union
 
 import pytest
 import torch
@@ -21,7 +21,7 @@
 from ..utils import create_new_process_for_each_test
 
 
-def models_list(*, all: bool = True, keywords: Optional[list[str]] = None):
+def models_list(*, all: bool = True, keywords: list[str] | None = None):
     TEST_MODELS: list[tuple[str, dict[str, Any]]] = [
         ("facebook/opt-125m", {}),
         (

tests/entrypoints/openai/test_serving_chat.py

@@ -6,7 +6,7 @@
 import asyncio
 from contextlib import suppress
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 from unittest.mock import AsyncMock, MagicMock
 
 import pytest
@@ -233,9 +233,9 @@ class MockModelConfig:
     multimodal_config = MultiModalConfig()
     hf_config = MockHFConfig()
     logits_processor_pattern = None
-    diff_sampling_param: Optional[dict] = None
+    diff_sampling_param: dict | None = None
     allowed_local_media_path: str = ""
-    allowed_media_domains: Optional[list[str]] = None
+    allowed_media_domains: list[str] | None = None
     encoder_config = None
     generation_config: str = "auto"
     media_io_kwargs: dict[str, dict[str, Any]] = field(default_factory=dict)

tests/plugins/prithvi_io_processor_plugin/prithvi_io_processor/prithvi_processor.py

@@ -9,7 +9,7 @@
 import tempfile
 import urllib.request
 from collections.abc import Sequence
-from typing import Any, Optional, Union
+from typing import Any, Union
 
 import albumentations
 import numpy as np
@@ -98,9 +98,9 @@ def _convert_np_uint8(float_image: torch.Tensor):
 
 
 def read_geotiff(
-    file_path: Optional[str] = None,
-    path_type: Optional[str] = None,
-    file_data: Optional[bytes] = None,
+    file_path: str | None = None,
+    path_type: str | None = None,
+    file_data: bytes | None = None,
 ) -> tuple[torch.Tensor, dict, tuple[float, float] | None]:
     """Read all bands from *file_path* and return image + meta info.
 
@@ -114,8 +114,8 @@ def read_geotiff(
 
     if all([x is None for x in [file_path, path_type, file_data]]):
         raise Exception("All input fields to read_geotiff are None")
-    write_to_file: Optional[bytes] = None
-    path: Optional[str] = None
+    write_to_file: bytes | None = None
+    path: str | None = None
     if file_data is not None:
         # with tempfile.NamedTemporaryFile() as tmpfile:
         #     tmpfile.write(file_data)
@@ -162,9 +162,9 @@ def read_geotiff(
 def load_image(
     data: Union[list[str]],
     path_type: str,
-    mean: Optional[list[float]] = None,
-    std: Optional[list[float]] = None,
-    indices: Optional[Union[list[int], None]] = None,
+    mean: list[float] | None = None,
+    std: list[float] | None = None,
+    indices: Union[list[int], None] | None = None,
 ):
     """Build an input example by loading images in *file_paths*.
 
@@ -278,7 +278,7 @@ def output_to_response(
     def pre_process(
         self,
         prompt: IOProcessorInput,
-        request_id: Optional[str] = None,
+        request_id: str | None = None,
         **kwargs,
     ) -> Union[PromptType, Sequence[PromptType]]:
         image_data = dict(prompt)
@@ -359,7 +359,7 @@ def pre_process(
     def post_process(
         self,
         model_output: Sequence[PoolingRequestOutput],
-        request_id: Optional[str] = None,
+        request_id: str | None = None,
         **kwargs,
     ) -> IOProcessorOutput:
         pred_imgs_list = []

tests/v1/engine/test_llm_engine.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import random
-from typing import TYPE_CHECKING, Optional
+from typing import TYPE_CHECKING
 
 import pytest
 
@@ -78,7 +78,7 @@ def vllm_model_skip_tokenizer_init(vllm_runner, request, monkeypatch):
 
 def _get_test_sampling_params(
     prompt_list: list[str],
-    seed: Optional[int] = 42,
+    seed: int | None = 42,
     structured_outputs: bool = False,
 ) -> tuple[list[SamplingParams], list[int]]:
     """Generate random sampling params for a batch."""

vllm/attention/backends/abstract.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from abc import ABC, abstractmethod
-from typing import Generic, List, Optional, Protocol, Tuple, Type, TypeVar
+from typing import Generic, Optional, Protocol, TypeVar
 
 import torch
 
@@ -48,12 +48,12 @@ def get_name() -> str:
 
     @staticmethod
     @abstractmethod
-    def get_impl_cls() -> Type["AttentionImpl"]:
+    def get_impl_cls() -> type["AttentionImpl"]:
         raise NotImplementedError
 
     @staticmethod
     @abstractmethod
-    def get_metadata_cls() -> Type["AttentionMetadata"]:
+    def get_metadata_cls() -> type["AttentionMetadata"]:
         raise NotImplementedError
 
     @classmethod
@@ -73,11 +73,11 @@ def get_kv_cache_shape(
         num_kv_heads: int,
         head_size: int,
         cache_dtype_str: str = "auto",
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         raise NotImplementedError
 
     @staticmethod
-    def get_kv_cache_stride_order() -> Tuple[int, ...]:
+    def get_kv_cache_stride_order() -> tuple[int, ...]:
         raise NotImplementedError
 
     @classmethod
@@ -147,7 +147,7 @@ def __init__(
         head_size: int,
         scale: float,
         num_kv_heads: Optional[int] = None,
-        alibi_slopes: Optional[List[float]] = None,
+        alibi_slopes: Optional[list[float]] = None,
         sliding_window: Optional[int] = None,
         kv_cache_dtype: str = "auto",
         logits_soft_cap: Optional[float] = None,

vllm/attention/layer.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Attention layer."""
 
-from typing import Callable, List, Optional
+from typing import Callable, Optional
 
 import torch
 import torch.nn as nn
@@ -126,7 +126,7 @@ def __init__(
         head_size: int,
         scale: float,
         num_kv_heads: Optional[int] = None,
-        alibi_slopes: Optional[List[float]] = None,
+        alibi_slopes: Optional[list[float]] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         logits_soft_cap: Optional[float] = None,
@@ -586,7 +586,7 @@ def wait_for_kv_layer_from_connector(layer_name: str):
 
 def maybe_save_kv_layer_to_connector(
     layer_name: str,
-    kv_cache_layer: List[torch.Tensor],
+    kv_cache_layer: list[torch.Tensor],
 ):
     if not has_kv_transfer_group() or not is_v1_kv_transfer_group():
         return

vllm/attention/layers/chunked_local_attention.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import functools
-from typing import ClassVar, List, Optional
+from typing import ClassVar, Optional
 
 import torch
 
@@ -61,7 +61,7 @@ def __init__(
         scale: float,
         attention_chunk_size: int,
         num_kv_heads: Optional[int] = None,
-        alibi_slopes: Optional[List[float]] = None,
+        alibi_slopes: Optional[list[float]] = None,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
         kv_sharing_target_layer_name: Optional[str] = None,

vllm/attention/ops/flashmla.py

@@ -1,7 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # adapted from: https://github.com/deepseek-ai/FlashMLA/blob/main/flash_mla/flash_mla_interface.py
-from typing import Optional, Tuple
+from typing import Optional
 
 import torch
 
@@ -31,7 +31,7 @@
     _flashmla_extension_C_AVAILABLE = False
 
 
-def is_flashmla_supported() -> Tuple[bool, Optional[str]]:
+def is_flashmla_supported() -> tuple[bool, Optional[str]]:
     """
     Return: is_supported_flag, unsupported_reason (optional).
     """
@@ -57,7 +57,7 @@ def get_mla_metadata(
     num_heads_q: Optional[int] = None,
     is_fp8_kvcache: bool = False,
     topk: Optional[int] = None,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> tuple[torch.Tensor, torch.Tensor]:
     """
     Arguments:
     - cache_seqlens: (batch_size), dtype torch.int32.
@@ -101,7 +101,7 @@ def flash_mla_with_kvcache(
     descale_k: Optional[torch.Tensor] = None,
     is_fp8_kvcache: bool = False,
     indices: Optional[torch.Tensor] = None,
-) -> Tuple[torch.Tensor, torch.Tensor]:
+) -> tuple[torch.Tensor, torch.Tensor]:
     """
     Arguments:
     - q: (batch_size, seq_len_q, num_heads_q, head_dim).
@@ -183,7 +183,7 @@ def flash_mla_sparse_prefill(
     indices: torch.Tensor,
     sm_scale: float,
     d_v: int = 512,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """
     Sparse attention prefill kernel
 

vllm/attention/ops/paged_attn.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from dataclasses import dataclass
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import torch
 
@@ -41,7 +41,7 @@ class PagedAttentionMetadata:
 
 class PagedAttention:
     @staticmethod
-    def get_supported_head_sizes() -> List[int]:
+    def get_supported_head_sizes() -> list[int]:
         return [32, 64, 80, 96, 112, 120, 128, 192, 256]
 
     @staticmethod
@@ -51,15 +51,15 @@ def get_kv_cache_shape(
         num_kv_heads: int,
         head_size: int,
         cache_dtype_str: str = "auto",
-    ) -> Tuple[int, ...]:
+    ) -> tuple[int, ...]:
         return (2, num_blocks, block_size * num_kv_heads * head_size)
 
     @staticmethod
     def split_kv_cache(
         kv_cache: torch.Tensor,
         num_kv_heads: int,
         head_size: int,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         x = 16 // kv_cache.element_size()
         num_blocks = kv_cache.shape[1]
 
@@ -255,7 +255,7 @@ def swap_blocks(
 
     @staticmethod
     def copy_blocks(
-        kv_caches: List[torch.Tensor],
+        kv_caches: list[torch.Tensor],
         src_to_dists: torch.Tensor,
     ) -> None:
         key_caches = [kv_cache[0] for kv_cache in kv_caches]