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

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

tests/kernels/quantization/nvfp4_utils.py

@@ -8,8 +8,9 @@ from vllm.scalar_type import scalar_types
 FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
 FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
 
-kE2M1ToFloat = torch.tensor([0., 0.5, 1., 1.5, 2., 3., 4., 6.],
-                            dtype=torch.float32)
+kE2M1ToFloat = torch.tensor(
+    [0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0], dtype=torch.float32
+)
 
 
 def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
@@ -22,12 +23,9 @@ def convert_swizzled_to_linear(a_sf_swizzled: torch.Tensor, m, k, block_size):
     return out[0:m, 0:k]
 
 
-def dequantize_nvfp4_to_dtype(tensor_fp4,
-                              tensor_sf,
-                              global_scale,
-                              dtype,
-                              device,
-                              block_size=16):
+def dequantize_nvfp4_to_dtype(
+    tensor_fp4, tensor_sf, global_scale, dtype, device, block_size=16
+):
     """Dequantize the fp4 tensor back to high precision."""
     # Two fp4 values are packed into one uint8.
     assert tensor_fp4.dtype == torch.uint8
@@ -69,7 +67,8 @@ def break_fp4_bytes(a, dtype):
 
 
 def quant_nvfp4_tensor(a: torch.Tensor):
-    a_global_scale = ((FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) /
-                      torch.abs(a).max().to(torch.float32))
+    a_global_scale = (FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX) / torch.abs(a).max().to(
+        torch.float32
+    )
     a_quant, a_block_scale = scaled_fp4_quant(a, a_global_scale)
     return a_quant, a_block_scale, a_global_scale