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

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

tests/v1/sample/test_topk_topp_sampler.py

@@ -5,8 +5,10 @@ import torch
 from torch import Generator
 
 from vllm.platforms import current_platform
-from vllm.v1.sample.ops.topk_topp_sampler import (apply_top_k_top_p,
-                                                  is_flashinfer_available)
+from vllm.v1.sample.ops.topk_topp_sampler import (
+    apply_top_k_top_p,
+    is_flashinfer_available,
+)
 
 DEVICE = current_platform.device_type
 
@@ -30,19 +32,18 @@ def reset_default_device():
 
 
 def test_topk_impl_equivalence():
-
     torch.set_default_device(DEVICE)
     generator = Generator(device=DEVICE).manual_seed(33)
 
     logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)
 
     # Random top-k values between 1 and 9.
-    k = torch.randint(1, 10, (BATCH_SIZE, ), generator=generator)
+    k = torch.randint(1, 10, (BATCH_SIZE,), generator=generator)
 
     # Set k=vocab_size for ~50% of requests in the batch (top-k disabled).
     k.masked_fill_(
-        torch.randint(0, 2, (BATCH_SIZE, ), generator=generator, dtype=bool),
-        VOCAB_SIZE)
+        torch.randint(0, 2, (BATCH_SIZE,), generator=generator, dtype=bool), VOCAB_SIZE
+    )
 
     # Top-k only implementation
     result1 = apply_top_k_top_p(logits=logits.clone(), k=k, p=None)
@@ -55,7 +56,7 @@ def test_topk_impl_equivalence():
 
 
 def test_flashinfer_sampler():
-    '''
+    """
     This test verifies that the FlashInfer top-k and top-p sampling
     implementation produces the same results as the Python implementation.
 
@@ -63,11 +64,10 @@ def test_flashinfer_sampler():
     top-p prob renorm (it did provide fused sampling but we cannot compare
     sampling results due to randomness), so we will compare the probability
     renormed consequently by top-k and then top-p of FlashInfer implementation.
-    '''
+    """
 
     if not FLASHINFER_ENABLED:
-        pytest.skip(
-            "FlashInfer not installed or not available on this platform.")
+        pytest.skip("FlashInfer not installed or not available on this platform.")
 
     torch.set_default_device(DEVICE)
     generator = Generator(device=DEVICE).manual_seed(42)
@@ -76,23 +76,21 @@ def test_flashinfer_sampler():
     logits = torch.rand((BATCH_SIZE, VOCAB_SIZE), generator=generator)
 
     # Generate various top-k and top-p values
-    k_values = torch.randint(1, 1000, (BATCH_SIZE, ), generator=generator)
-    p_values = torch.rand(
-        (BATCH_SIZE, ), generator=generator) * 0.5 + 0.5  # range in [0.5, 1.0]
+    k_values = torch.randint(1, 1000, (BATCH_SIZE,), generator=generator)
+    p_values = (
+        torch.rand((BATCH_SIZE,), generator=generator) * 0.5 + 0.5
+    )  # range in [0.5, 1.0]
 
     # Sometimes disable top-k (k=vocab_size)
     k_values.masked_fill_(
-        torch.randint(0,
-                      2, (BATCH_SIZE, ),
-                      generator=generator,
-                      dtype=torch.bool), VOCAB_SIZE)
+        torch.randint(0, 2, (BATCH_SIZE,), generator=generator, dtype=torch.bool),
+        VOCAB_SIZE,
+    )
 
     # Sometimes disable top-p (p=1.0)
     p_values.masked_fill_(
-        torch.randint(0,
-                      2, (BATCH_SIZE, ),
-                      generator=generator,
-                      dtype=torch.bool), 1.0)
+        torch.randint(0, 2, (BATCH_SIZE,), generator=generator, dtype=torch.bool), 1.0
+    )
 
     python_logits = apply_top_k_top_p(
         logits=logits.clone(),
@@ -113,5 +111,6 @@ def test_flashinfer_sampler():
     )
 
     # Compare the results
-    assert torch.allclose(python_probs, flashinfer_probs, atol=2e-2), \
+    assert torch.allclose(python_probs, flashinfer_probs, atol=2e-2), (
         "FlashInfer and Python sampling implementations do not match!"
+    )