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

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

tests/models/quantization/test_gptq_marlin.py

@@ -9,6 +9,7 @@ Note: Marlin internally uses locks to synchronize the threads. This can
 result in very slight nondeterminism for Marlin. As a result, we re-run the test
 up to 3 times to see if we pass.
 """
+
 import os
 
 import pytest
@@ -26,20 +27,20 @@ MAX_MODEL_LEN = 1024
 MODELS = [
     # act_order==True, group_size=128
     ("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "main"),
-
     # 8-bit, act_order==True, group_size=channelwise
     ("TheBloke/TinyLlama-1.1B-Chat-v1.0-GPTQ", "gptq-8bit--1g-actorder_True"),
-
     # 4-bit, act_order==True, group_size=128
-    ("TechxGenus/gemma-1.1-2b-it-GPTQ", "main")
+    ("TechxGenus/gemma-1.1-2b-it-GPTQ", "main"),
 ]
 
 
 @pytest.mark.flaky(reruns=3)
-@pytest.mark.skipif(not is_quant_method_supported("gptq_marlin")
-                    or current_platform.is_rocm()
-                    or not current_platform.is_cuda(),
-                    reason="gptq_marlin is not supported on this GPU type.")
+@pytest.mark.skipif(
+    not is_quant_method_supported("gptq_marlin")
+    or current_platform.is_rocm()
+    or not current_platform.is_cuda(),
+    reason="gptq_marlin is not supported on this GPU type.",
+)
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["half", "bfloat16"])
 @pytest.mark.parametrize("max_tokens", [32])
@@ -55,29 +56,34 @@ def test_models(
     model_name, revision = model
 
     # Run marlin.
-    with vllm_runner(model_name=model_name,
-                     revision=revision,
-                     dtype=dtype,
-                     quantization="marlin",
-                     max_model_len=MAX_MODEL_LEN,
-                     tensor_parallel_size=1) as gptq_marlin_model:
-
+    with vllm_runner(
+        model_name=model_name,
+        revision=revision,
+        dtype=dtype,
+        quantization="marlin",
+        max_model_len=MAX_MODEL_LEN,
+        tensor_parallel_size=1,
+    ) as gptq_marlin_model:
         gptq_marlin_outputs = gptq_marlin_model.generate_greedy_logprobs(
-            example_prompts[:-1], max_tokens, num_logprobs)
+            example_prompts[:-1], max_tokens, num_logprobs
+        )
     _ROPE_DICT.clear()  # clear rope cache to avoid rope dtype error
 
     # Run gptq.
     # The naive gptq kernel doesn't support bf16 yet.
     # Here we always compare fp16/bf16 gpt marlin kernel
     # to fp16 gptq kernel.
-    with vllm_runner(model_name=model_name,
-                     revision=revision,
-                     dtype="half",
-                     quantization="gptq",
-                     max_model_len=MAX_MODEL_LEN,
-                     tensor_parallel_size=1) as gptq_model:
+    with vllm_runner(
+        model_name=model_name,
+        revision=revision,
+        dtype="half",
+        quantization="gptq",
+        max_model_len=MAX_MODEL_LEN,
+        tensor_parallel_size=1,
+    ) as gptq_model:
         gptq_outputs = gptq_model.generate_greedy_logprobs(
-            example_prompts[:-1], max_tokens, num_logprobs)
+            example_prompts[:-1], max_tokens, num_logprobs
+        )
 
     check_logprobs_close(
         outputs_0_lst=gptq_outputs,