Revert "[CI] improve test_aclgraph.py and make sure replay is called as expected when inferencing"

This reverts commit 36519ff.

Signed-off-by: Yizhou Liu <liu_yizhou@outlook.com>

Author: yiz-liu

tests/singlecard/test_aclgraph.py

@@ -20,9 +20,7 @@
 Run `pytest tests/compile/test_aclgraph.py`.
 """
 
-import multiprocessing
 import os
-from unittest.mock import patch
 
 import pytest
 import torch
@@ -32,64 +30,32 @@
 from tests.model_utils import check_outputs_equal
 
 MODELS = ["Qwen/Qwen2.5-0.5B-Instruct"]
-PROMPTS = [
-    "Hello, my name is",
-    "The president of the United States is",
-    "The capital of France is",
-    "The future of AI is",
-]
-
-original_replay = torch.npu.NPUGraph.replay
-replay_counter = multiprocessing.Value("i", 0)
-
-
-def replay_wrapper(self):
-    with replay_counter.get_lock():
-        replay_counter.value += 1
-    return original_replay(self)
 
 
 @pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
                     reason="aclgraph only support on v1")
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("max_tokens", [32])
-@pytest.mark.parametrize("prompts", [PROMPTS, PROMPTS[:3]])
 def test_models(
     model: str,
     max_tokens: int,
-    prompts: list[str],
     monkeypatch: pytest.MonkeyPatch,
 ) -> None:
     with monkeypatch.context() as m:
+        prompts = [
+            "Hello, my name is", "The president of the United States is",
+            "The capital of France is", "The future of AI is"
+        ]
+
         # aclgraph only support on v1
         m.setenv("VLLM_USE_V1", "1")
 
         sampling_params = SamplingParams(max_tokens=max_tokens,
                                          temperature=0.0)
-
         # TODO: change to use vllmrunner when the registry of custom op is solved
         # while running pytest
-        with patch.object(torch.npu.NPUGraph, "replay", replay_wrapper):
-            vllm_model = LLM(model)
-            vllm_aclgraph_outputs = vllm_model.generate(
-                prompts, sampling_params)
-
-        num_hidden_layers = vllm_model.llm_engine.model_config.hf_config.num_hidden_layers
-
-        # Calculate expected replay call count
-        # Number of ACL graphs = hidden layers + 1 (only for piecewise scenario)
-        num_acl_graphs = num_hidden_layers + 1
-        # Number of inference steps (first step only includes one prompt, hence +1)
-        num_inference_steps = max_tokens + 1
-        expected_replay_calls = num_acl_graphs * num_inference_steps
-
-        # Verify replay call count
-        actual_replay_calls = replay_counter.value
-        assert actual_replay_calls == expected_replay_calls, (
-            f"NPUGraph.replay call count mismatch. "
-            f"Expected: {expected_replay_calls}, Actual: {actual_replay_calls}"
-        )
-
+        vllm_model = LLM(model)
+        vllm_aclgraph_outputs = vllm_model.generate(prompts, sampling_params)
         del vllm_model
         torch.npu.empty_cache()