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

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

tests/v1/shutdown/test_startup_error.py

@@ -5,8 +5,10 @@
 import pytest
 
 from tests.utils import wait_for_gpu_memory_to_clear
-from tests.v1.shutdown.utils import (SHUTDOWN_TEST_THRESHOLD_BYTES,
-                                     SHUTDOWN_TEST_TIMEOUT_SEC)
+from tests.v1.shutdown.utils import (
+    SHUTDOWN_TEST_THRESHOLD_BYTES,
+    SHUTDOWN_TEST_TIMEOUT_SEC,
+)
 from vllm import LLM
 from vllm.distributed import get_tensor_model_parallel_rank
 from vllm.engine.arg_utils import AsyncEngineArgs
@@ -30,9 +32,9 @@ def evil_method(self, *args, **kwargs):
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("tensor_parallel_size", [2, 1])
 @pytest.mark.parametrize("failing_method", ["forward", "load_weights"])
-def test_async_llm_startup_error(monkeypatch, model: str,
-                                 tensor_parallel_size: int,
-                                 failing_method: str) -> None:
+def test_async_llm_startup_error(
+    monkeypatch, model: str, tensor_parallel_size: int, failing_method: str
+) -> None:
     """Test that AsyncLLM propagates an __init__ error & frees memory.
     Test profiling (forward()) and load weights failures.
     AsyncLLM always uses an MP client.
@@ -43,9 +45,9 @@ def test_async_llm_startup_error(monkeypatch, model: str,
     # Monkeypatch an error in the model.
     monkeypatch.setattr(LlamaForCausalLM, failing_method, evil_method)
 
-    engine_args = AsyncEngineArgs(model=model,
-                                  enforce_eager=True,
-                                  tensor_parallel_size=tensor_parallel_size)
+    engine_args = AsyncEngineArgs(
+        model=model, enforce_eager=True, tensor_parallel_size=tensor_parallel_size
+    )
 
     # Confirm we get an exception.
     with pytest.raises(Exception, match="initialization failed"):
@@ -63,9 +65,13 @@ def test_async_llm_startup_error(monkeypatch, model: str,
 @pytest.mark.parametrize("tensor_parallel_size", [2, 1])
 @pytest.mark.parametrize("enable_multiprocessing", [True])
 @pytest.mark.parametrize("failing_method", ["forward", "load_weights"])
-def test_llm_startup_error(monkeypatch, model: str, tensor_parallel_size: int,
-                           enable_multiprocessing: bool,
-                           failing_method: str) -> None:
+def test_llm_startup_error(
+    monkeypatch,
+    model: str,
+    tensor_parallel_size: int,
+    enable_multiprocessing: bool,
+    failing_method: str,
+) -> None:
     """Test that LLM propagates an __init__ error and frees memory.
     Test profiling (forward()) and load weights failures.
     TODO(andy) - LLM without multiprocessing.
@@ -76,7 +82,6 @@ def test_llm_startup_error(monkeypatch, model: str, tensor_parallel_size: int,
         pytest.skip(reason="Not enough CUDA devices")
 
     with monkeypatch.context() as m:
-
         MP_VALUE = "1" if enable_multiprocessing else "0"
         m.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", MP_VALUE)
 
@@ -84,12 +89,16 @@ def test_llm_startup_error(monkeypatch, model: str, tensor_parallel_size: int,
         monkeypatch.setattr(LlamaForCausalLM, failing_method, evil_method)
 
         with pytest.raises(
-                Exception,
-                match="initialization failed"
-                if enable_multiprocessing else "Simulated Error in startup!"):
-            _ = LLM(model=model,
-                    enforce_eager=True,
-                    tensor_parallel_size=tensor_parallel_size)
+            Exception,
+            match="initialization failed"
+            if enable_multiprocessing
+            else "Simulated Error in startup!",
+        ):
+            _ = LLM(
+                model=model,
+                enforce_eager=True,
+                tensor_parallel_size=tensor_parallel_size,
+            )
 
         # Confirm all the processes are cleaned up.
         wait_for_gpu_memory_to_clear(