ruff check --fix --unsafe-fixes (I checked them all)

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

Author: hmellor

tests/compile/test_full_graph.py

@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 import tempfile
-from typing import Any, Optional, Union
+from typing import Any, Union
 
 import pytest
 import torch
@@ -17,7 +17,7 @@
 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/kernels/attention/test_lightning_attn.py

@@ -33,10 +33,7 @@ def reference_lightning_attention(q, k, v, ed, block_size, kv_history):
 
     # More efficient implementation
     # Convert decay factors to matrix form
-    if ed.dim() == 1:
-        decay = torch.exp(-ed).view(1, -1, 1, 1)
-    else:
-        decay = torch.exp(-ed)
+    decay = torch.exp(-ed).view(1, -1, 1, 1) if ed.dim() == 1 else torch.exp(-ed)
 
     for b in range(B):
         for step in range(S):

tests/kernels/quantization/test_cutlass_scaled_mm.py

@@ -88,10 +88,7 @@ def cutlass_fp8_gemm_helper(
     # make scales K-major for blockwise quant, doesn't affect 1D scales
     scale_b = scale_b.t().contiguous().t()
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
@@ -122,10 +119,7 @@ def cutlass_int8_gemm_helper(
     scale_a = torch.randn(a_scales_shape, device=device, dtype=torch.float32)
     scale_b = torch.randn(b_scales_shape, device=device, dtype=torch.float32)
 
-    if use_bias:
-        bias = torch.rand((n,), device=device, dtype=out_dtype) * 10
-    else:
-        bias = None
+    bias = torch.rand((n,), device=device, dtype=out_dtype) * 10 if use_bias else None
 
     out = ops.cutlass_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
     baseline = baseline_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)

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/model_executor/guided_decoding/outlines_logits_processors.py

@@ -8,7 +8,7 @@
 import importlib.metadata
 import json
 import os
-from typing import Optional, Union
+from typing import Union
 
 import regex as re
 import torch
@@ -36,11 +36,11 @@
 class BaseLogitsProcessor:
 
     def __init__(self, guide: Guide, eos_token_id: int,
-                 reasoner: Optional[ReasoningParser]) -> None:
+                 reasoner: ReasoningParser | None) -> None:
         self._guide: Guide = guide
         self._eos_token_id: int = eos_token_id
-        self._reasoner: Optional[ReasoningParser] = reasoner
-        self._mask: Optional[torch.Tensor] = None
+        self._reasoner: ReasoningParser | None = reasoner
+        self._mask: torch.Tensor | None = None
 
     def __call__(self, input_ids: list[int],
                  scores: torch.Tensor) -> torch.Tensor:
@@ -114,7 +114,7 @@ def _get_guide(cls, regex_string: str,
         return Guide(index)
 
     def __init__(self, regex_string: str, tokenizer: PreTrainedTokenizerBase,
-                 reasoner: Optional[ReasoningParser]) -> None:
+                 reasoner: ReasoningParser | None) -> None:
         super().__init__(
             guide=RegexLogitsProcessor._get_guide(regex_string, tokenizer),
             eos_token_id=tokenizer.eos_token_id,  # type: ignore
@@ -126,7 +126,7 @@ class JSONLogitsProcessor(RegexLogitsProcessor):
     def __init__(self, schema: Union[str, dict, BaseModel],
                  tokenizer: PreTrainedTokenizerBase,
                  whitespace_pattern: Union[str, None],
-                 reasoner: Optional[ReasoningParser]) -> None:
+                 reasoner: ReasoningParser | None) -> None:
 
         if isinstance(schema, type(BaseModel)):
             schema_str = json.dumps(schema.model_json_schema())

vllm/reasoning/abs_reasoning_parsers.py

@@ -7,7 +7,7 @@
 from abc import abstractmethod
 from collections.abc import Sequence
 from functools import cached_property
-from typing import TYPE_CHECKING, Any, Callable, Optional, Union
+from typing import TYPE_CHECKING, Any, Callable, Union
 
 from vllm.logger import init_logger
 from vllm.utils import import_from_path, is_list_of
@@ -77,7 +77,7 @@ def extract_reasoning_content(
         self,
         model_output: str,
         request: Union[ChatCompletionRequest, ResponsesRequest],
-    ) -> tuple[Optional[str], Optional[str]]:
+    ) -> tuple[str | None, str | None]:
         """
         Extract reasoning content from a complete model-generated string.
 
@@ -135,7 +135,7 @@ def get_reasoning_parser(cls, name: str | None) -> type[ReasoningParser]:
     def _register_module(
         cls,
         module: type,
-        module_name: Optional[Union[str, list[str]]] = None,
+        module_name: Union[str, list[str]] | None = None,
         force: bool = True,
     ) -> None:
         if not issubclass(module, ReasoningParser):
@@ -155,7 +155,7 @@ def _register_module(
     @classmethod
     def register_module(
         cls,
-        name: Optional[Union[str, list[str]]] = None,
+        name: Union[str, list[str]] | None = None,
         force: bool = True,
         module: Union[type, None] = None,
     ) -> Union[type, Callable]: