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

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

tests/v1/tpu/test_topk_topp_sampler.py

@@ -10,8 +10,7 @@ from vllm.platforms import current_platform
 from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
 
 # isort: off
-from vllm.v1.sample.tpu.sampler import (apply_top_k_top_p as
-                                        apply_top_k_top_p_tpu)
+from vllm.v1.sample.tpu.sampler import apply_top_k_top_p as apply_top_k_top_p_tpu
 # isort: on
 
 if not current_platform.is_tpu():
@@ -30,11 +29,10 @@ def test_topk_equivalence_to_native_impl():
         logits = torch.rand((BATCH_SIZE, VOCAB_SIZE))
 
         # Random top-k values between 1 and 10.
-        k = torch.randint(1, 10, (BATCH_SIZE, ))
+        k = torch.randint(1, 10, (BATCH_SIZE,))
 
         # Set k=vocab_size for ~50% of requests in the batch (top-k disabled).
-        k.masked_fill_(torch.randint(0, 2, (BATCH_SIZE, ), dtype=bool),
-                       VOCAB_SIZE)
+        k.masked_fill_(torch.randint(0, 2, (BATCH_SIZE,), dtype=bool), VOCAB_SIZE)
 
         result_tpu = apply_top_k_top_p_tpu(logits=logits.clone(), k=k, p=None)
 
@@ -50,15 +48,13 @@ def test_topp_result_sums_past_p():
         probs = logits.softmax(dim=-1)
 
         # Random top-p values between 0 and 1.
-        p = torch.rand((BATCH_SIZE, ))
+        p = torch.rand((BATCH_SIZE,))
 
         # Set p=1 for ~50% of requests in the batch (top-p disabled).
-        p.masked_fill_(torch.randint(0, 2, (BATCH_SIZE, ), dtype=bool), 1)
+        p.masked_fill_(torch.randint(0, 2, (BATCH_SIZE,), dtype=bool), 1)
 
         no_op_k = torch.tensor([VOCAB_SIZE])
-        logits_masked = apply_top_k_top_p_tpu(logits=logits.clone(),
-                                              k=no_op_k,
-                                              p=p)
+        logits_masked = apply_top_k_top_p_tpu(logits=logits.clone(), k=no_op_k, p=p)
 
         # Verify that the masked logit's probability sums to at least p.
         probs.masked_fill_(logits_masked.isinf(), 0)
@@ -72,16 +68,16 @@ def test_topp_result_sums_past_p():
 
 def test_topp_basic():
     with torch.device(xm.xla_device()):
-        logits = torch.tensor([[math.log(0.2),
-                                math.log(0.3),
-                                math.log(0.5)],
-                               [math.log(0.5),
-                                math.log(0.1),
-                                math.log(0.4)]])
+        logits = torch.tensor(
+            [
+                [math.log(0.2), math.log(0.3), math.log(0.5)],
+                [math.log(0.5), math.log(0.1), math.log(0.4)],
+            ]
+        )
 
-        result = apply_top_k_top_p_tpu(logits=logits.clone(),
-                                       k=torch.tensor([3, 3]),
-                                       p=torch.tensor([0.79, 0.79]))
+        result = apply_top_k_top_p_tpu(
+            logits=logits.clone(), k=torch.tensor([3, 3]), p=torch.tensor([0.79, 0.79])
+        )
 
         torch_xla.sync()
 
@@ -94,16 +90,16 @@ def test_topp_basic():
 
 def test_topp_select_all():
     with torch.device(xm.xla_device()):
-        logits = torch.tensor([[math.log(0.2),
-                                math.log(0.3),
-                                math.log(0.5)],
-                               [math.log(0.5),
-                                math.log(0.1),
-                                math.log(0.4)]])
+        logits = torch.tensor(
+            [
+                [math.log(0.2), math.log(0.3), math.log(0.5)],
+                [math.log(0.5), math.log(0.1), math.log(0.4)],
+            ]
+        )
 
-        result = apply_top_k_top_p_tpu(logits=logits.clone(),
-                                       k=torch.tensor([3, 3]),
-                                       p=torch.tensor([1.0, 1.0]))
+        result = apply_top_k_top_p_tpu(
+            logits=logits.clone(), k=torch.tensor([3, 3]), p=torch.tensor([1.0, 1.0])
+        )
 
         torch_xla.sync()
 
@@ -114,14 +110,12 @@ def test_topp_with_ties():
     with torch.device(xm.xla_device()):
         # Input has multiple math.log(0.3).
         logits = torch.tensor(
-            [[math.log(0.3),
-              math.log(0.3),
-              math.log(0.3),
-              math.log(0.1)]])
+            [[math.log(0.3), math.log(0.3), math.log(0.3), math.log(0.1)]]
+        )
 
-        result = apply_top_k_top_p_tpu(logits=logits.clone(),
-                                       k=torch.tensor([4]),
-                                       p=torch.tensor([0.2]))
+        result = apply_top_k_top_p_tpu(
+            logits=logits.clone(), k=torch.tensor([4]), p=torch.tensor([0.2])
+        )
 
         torch_xla.sync()
 
@@ -135,17 +129,17 @@ def test_topp_with_ties():
 
 def test_both_topk_topp():
     with torch.device(xm.xla_device()):
-        logits = torch.tensor([[math.log(0.2),
-                                math.log(0.3),
-                                math.log(0.5)],
-                               [math.log(0.5),
-                                math.log(0.1),
-                                math.log(0.4)]])
+        logits = torch.tensor(
+            [
+                [math.log(0.2), math.log(0.3), math.log(0.5)],
+                [math.log(0.5), math.log(0.1), math.log(0.4)],
+            ]
+        )
 
         # Set k=1 for the first batch.
-        result = apply_top_k_top_p_tpu(logits=logits.clone(),
-                                       k=torch.tensor([1, 3]),
-                                       p=torch.tensor([0.79, 0.79]))
+        result = apply_top_k_top_p_tpu(
+            logits=logits.clone(), k=torch.tensor([1, 3]), p=torch.tensor([0.79, 0.79])
+        )
 
         torch_xla.sync()