update test_basic_correctness.py

tests/basic_correctness/test_basic_correctness.py

@@ -74,63 +74,63 @@ def test_models(
     )
 
 
-@multi_gpu_test(num_gpus=2)
-@pytest.mark.parametrize(
-    "model, distributed_executor_backend, attention_backend, "
-    "test_suite", [
-        ("facebook/opt-125m", "ray", "", "L4"),
-        ("facebook/opt-125m", "mp", "", "L4"),
-        ("meta-llama/Llama-2-7b-hf", "ray", "", "L4"),
-        ("meta-llama/Llama-2-7b-hf", "mp", "", "L4"),
-        ("facebook/opt-125m", "ray", "", "A100"),
-        ("facebook/opt-125m", "mp", "", "A100"),
-        ("facebook/opt-125m", "mp", "FLASHINFER", "A100"),
-        ("meta-llama/Meta-Llama-3-8B", "ray", "FLASHINFER", "A100"),
-    ])
-def test_models_distributed(
-    hf_runner,
-    vllm_runner,
-    example_prompts,
-    model: str,
-    distributed_executor_backend: str,
-    attention_backend: str,
-    test_suite: str,
-) -> None:
-
-    if test_suite != TARGET_TEST_SUITE:
-        pytest.skip(f"Skip test for {test_suite}")
-
-    if model == "meta-llama/Llama-2-7b-hf" and distributed_executor_backend == "ray" and attention_backend == "" and test_suite == "L4":  # noqa
-        # test ray adag
-        os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
-        os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"
-
-    if attention_backend:
-        os.environ["VLLM_ATTENTION_BACKEND"] = attention_backend
-
-    dtype = "half"
-    max_tokens = 5
-
-    # NOTE: take care of the order. run vLLM first, and then run HF.
-    # vLLM needs a fresh new process without cuda initialization.
-    # if we run HF first, the cuda initialization will be done and it
-    # will hurt multiprocessing backend with fork method (the default method).
-    with vllm_runner(model,
-                     dtype=dtype,
-                     tensor_parallel_size=2,
-                     distributed_executor_backend=distributed_executor_backend
-                     ) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
-
-    with hf_runner(model, dtype=dtype) as hf_model:
-        hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
-
-    check_outputs_equal(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-    )
+# @multi_gpu_test(num_gpus=2)
+# @pytest.mark.parametrize(
+#     "model, distributed_executor_backend, attention_backend, "
+#     "test_suite", [
+#         ("facebook/opt-125m", "ray", "", "L4"),
+#         ("facebook/opt-125m", "mp", "", "L4"),
+#         ("meta-llama/Llama-2-7b-hf", "ray", "", "L4"),
+#         ("meta-llama/Llama-2-7b-hf", "mp", "", "L4"),
+#         ("facebook/opt-125m", "ray", "", "A100"),
+#         ("facebook/opt-125m", "mp", "", "A100"),
+#         ("facebook/opt-125m", "mp", "FLASHINFER", "A100"),
+#         ("meta-llama/Meta-Llama-3-8B", "ray", "FLASHINFER", "A100"),
+#     ])
+# def test_models_distributed(
+#     hf_runner,
+#     vllm_runner,
+#     example_prompts,
+#     model: str,
+#     distributed_executor_backend: str,
+#     attention_backend: str,
+#     test_suite: str,
+# ) -> None:
+
+#     if test_suite != TARGET_TEST_SUITE:
+#         pytest.skip(f"Skip test for {test_suite}")
+
+#     if model == "meta-llama/Llama-2-7b-hf" and distributed_executor_backend == "ray" and attention_backend == "" and test_suite == "L4":  # noqa
+#         # test ray adag
+#         os.environ['VLLM_USE_RAY_SPMD_WORKER'] = "1"
+#         os.environ['VLLM_USE_RAY_COMPILED_DAG'] = "1"
+
+#     if attention_backend:
+#         os.environ["VLLM_ATTENTION_BACKEND"] = attention_backend
+
+#     dtype = "half"
+#     max_tokens = 5
+
+#     # NOTE: take care of the order. run vLLM first, and then run HF.
+#     # vLLM needs a fresh new process without cuda initialization.
+#     # if we run HF first, the cuda initialization will be done and it
+#     # will hurt multiprocessing backend with fork method (the default method).
+#     with vllm_runner(model,
+#                      dtype=dtype,
+#                      tensor_parallel_size=2,
+#                      distributed_executor_backend=distributed_executor_backend
+#                      ) as vllm_model:
+#         vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+
+#     with hf_runner(model, dtype=dtype) as hf_model:
+#         hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+
+#     check_outputs_equal(
+#         outputs_0_lst=hf_outputs,
+#         outputs_1_lst=vllm_outputs,
+#         name_0="hf",
+#         name_1="vllm",
+#     )
 
 
 def test_model_with_failure(vllm_runner) -> None: