add new e2e tests case for aclgraph memory to v0.11.0 (#3880)

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### What this PR does / why we need it?
add new e2e tests case for aclgraph memory to v0.11.0

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?
ut

Signed-off-by: lilinsiman <lilinsiman@gmail.com>

.github/workflows/_e2e_test.yaml

@@ -89,6 +89,7 @@ jobs:
           # the test separately.
 
           pytest -sv tests/e2e/singlecard/test_aclgraph.py
+          pytest -sv tests/e2e/singlecard/test_aclgraph_mem.py
           pytest -sv tests/e2e/singlecard/test_ascend_scheduler.py
           pytest -sv tests/e2e/singlecard/test_bge_model.py
           pytest -sv tests/e2e/singlecard/test_camem.py

tests/e2e/singlecard/test_aclgraph_mem.py

@@ -0,0 +1,100 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import multiprocessing
+import os
+from unittest.mock import patch
+
+import pytest
+import torch
+from modelscope import snapshot_download  # type: ignore
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
+
+MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
+
+
+@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", [4])
+@patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1"})
+def test_aclgraph_mem_use(model: str, max_tokens: int) -> None:
+    del os.environ["VLLM_WORKER_MULTIPROC_METHOD"]
+    capture_called = multiprocessing.Value("i", 0)  # int, 0 or 1
+    capture_mem_before = multiprocessing.Value("q", -1)  # long long (64-bit)
+    capture_mem_after = multiprocessing.Value("q", -1)  # long long
+
+    def capture_model_wrapper(original_method):
+
+        def wrapped(self):
+            mem_before = torch.npu.mem_get_info()[0]  # free memory
+            result = original_method(self)
+            mem_after = torch.npu.mem_get_info()[0]
+            with capture_called.get_lock():
+                capture_called.value = 1
+                capture_mem_before.value = mem_before
+                capture_mem_after.value = mem_after
+            return result
+
+        return wrapped
+
+    original_capture = NPUModelRunner._capture_model
+
+    with patch.object(NPUModelRunner,
+                      '_capture_model',
+                      new=capture_model_wrapper(original_capture)):
+        prompts = [
+            "Hello, my name is", "The president of the United States is",
+            "The capital of France is", "The future of AI is"
+        ]
+        sampling_params = SamplingParams(max_tokens=max_tokens,
+                                         temperature=0.0)
+        if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
+            vllm_model = VllmRunner(snapshot_download(model),
+                                    max_model_len=1024,
+                                    quantization="ascend")
+        else:
+            vllm_model = VllmRunner(snapshot_download(model))
+        _ = vllm_model.generate(prompts, sampling_params)
+
+    assert capture_called.value == 1, "_capture_model was not called during test"
+    assert capture_mem_before.value != -1, "capture_mem_before not set"
+    assert capture_mem_after.value != -1, "capture_mem_after not set"
+
+    print("capture_mem_before =", capture_mem_before.value)
+    print("capture_mem_after =", capture_mem_after.value)
+
+    mem_used_by_capture = capture_mem_before.value - capture_mem_after.value
+    # Empirical observation: capturing ACL graphs for Qwen3-0.6B uses ~0.20 GiB of NPU memory.
+    # DeepSeek-V2-Lite-W8A8 uses ~0.68 GiB of NPU memory
+    # a 1.3x tolerance is applied to account for runtime variance.
+    if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
+        baseline_capture_mem = 0.68
+        capture_mem_tolerance = 1.5
+    else:
+        baseline_capture_mem = 0.20
+        capture_mem_tolerance = 1.3
+    max_capture_mem_gib = baseline_capture_mem * capture_mem_tolerance
+    max_mem_expected = max_capture_mem_gib * (1024**3)
+    assert mem_used_by_capture < max_mem_expected, (
+        f"_capture_model used more memory than expected. "
+        f"Used: {mem_used_by_capture / (1024**3):.2f} GiB, "
+        f"Expected: < {max_capture_mem_gib:.2f} GiB")
+    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = 'spawn'