Convert formatting to use ruff instead of yapf + isort (#26247)

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

tests/kernels/quantization/test_nvfp4_quant.py

@@ -8,15 +8,27 @@ from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 
 if not current_platform.has_device_capability(100):
-    pytest.skip(reason="Nvfp4 Requires compute capability of 10 or above.",
-                allow_module_level=True)
+    pytest.skip(
+        reason="Nvfp4 Requires compute capability of 10 or above.",
+        allow_module_level=True,
+    )
 
 DTYPES = [torch.float16, torch.bfloat16]
 SHAPES = [(128, 64), (128, 128), (256, 64), (256, 128)]
-PAD_SHAPES = [(90, 64), (150, 64), (128, 48), (128, 80), (150, 80), (90, 48),
-              (90, 128), (150, 128), (150, 48), (90, 80)]
+PAD_SHAPES = [
+    (90, 64),
+    (150, 64),
+    (128, 48),
+    (128, 80),
+    (150, 80),
+    (90, 48),
+    (90, 128),
+    (150, 128),
+    (150, 48),
+    (90, 80),
+]
 SEEDS = [42]
-CUDA_DEVICES = ['cuda:0']
+CUDA_DEVICES = ["cuda:0"]
 
 FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
 FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
@@ -31,7 +43,22 @@ FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
 # 0001 -> 0.5
 # 0000 -> 0
 E2M1_TO_FLOAT32 = [
-    0., 0.5, 1., 1.5, 2., 3., 4., 6., 0., -0.5, -1., -1.5, -2., -3., -4., -6.
+    0.0,
+    0.5,
+    1.0,
+    1.5,
+    2.0,
+    3.0,
+    4.0,
+    6.0,
+    0.0,
+    -0.5,
+    -1.0,
+    -1.5,
+    -2.0,
+    -3.0,
+    -4.0,
+    -6.0,
 ]
 BLOCK_SIZE = 16
 
@@ -74,8 +101,7 @@ def ref_nvfp4_quant(x, global_scale):
     assert x.ndim == 2
     m, n = x.shape
     x = torch.reshape(x, (m, n // BLOCK_SIZE, BLOCK_SIZE))
-    vec_max = torch.max(torch.abs(x), dim=-1,
-                        keepdim=True)[0].to(torch.float32)
+    vec_max = torch.max(torch.abs(x), dim=-1, keepdim=True)[0].to(torch.float32)
     scale = global_scale * (vec_max * get_reciprocal(FLOAT4_E2M1_MAX))
     scale = scale.to(torch.float8_e4m3fn).to(torch.float32)
     output_scale = get_reciprocal(scale * get_reciprocal(global_scale))
@@ -131,7 +157,7 @@ def test_quantize_to_fp4(
 def test_quantize_to_fp4_padded(pad_shape: tuple[int, int]) -> None:
     dtype = torch.float16
     current_platform.seed_everything(42)
-    torch.set_default_device('cuda:0')
+    torch.set_default_device("cuda:0")
 
     m, n = pad_shape