[Misc] Refactor aclgraph accuracy test to use logprob-based comparison (#7455)

### What this PR does / why we need it?

Replace text-match assertions with a two-tier logprob accuracy check:

- Prefill (token 0): assert token ID is identical between eager baseline
and compiled mode, then verify logprob matches within `atol`.
- Decode (tokens 1-2): if chosen tokens match, compare logprobs
directly; if they differ, cross-lookup the baseline token in the
compiled model's top-20 distribution and assert the assigned logprob is
within `decode_atol` (defaults to 2x atol). This tolerates minor argmax
drift caused by floating-point differences while still catching
distribution divergence.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.17.0
- vLLM main:
8a680463fa

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

tests/e2e/singlecard/test_eager_mode_acc.py

@@ -0,0 +1,68 @@
+#
+# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+# 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.
+#
+
+"""
+This file test accuracy via LMEval.
+It uses local-completions, which interacts with vLLM
+through the OAI API with N concurrent connections.
+This simulates real work usage of the API and makes
+sure that the zmq frontend mp RPC message passing and
+AsyncLLMEngine are working correctly.
+"""
+
+import lm_eval
+import pytest
+
+MODEL_NAMES = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
+NUM_CONCURRENT = 500
+TASK = "gsm8k"
+FILTER = "exact_match,strict-match"
+RTOL = 0.03
+EXPECTED_VALUES = {"Qwen/Qwen3-0.6B": 0.414, "vllm-ascend/DeepSeek-V2-Lite-W8A8": 0.34}
+
+
+def run_test(model_name, more_args=None):
+    """Run the end to end accuracy test."""
+
+    # NOTE: Do not add any spaces to the string below, as this will cause parameter parsing errors.
+    model_args = f"pretrained={model_name},max_model_len=4096,enforce_eager=True"
+
+    if more_args is not None:
+        model_args = "{},{}".format(model_args, more_args)
+
+    results = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=model_args,
+        tasks="gsm8k",
+        batch_size="auto",
+    )
+
+    measured_value = results["results"][TASK][FILTER]
+    assert model_name in EXPECTED_VALUES, f"Cannot find the expected value for the model {model_name=}"
+    expected_value = EXPECTED_VALUES[model_name]
+    assert measured_value - RTOL < expected_value and measured_value + RTOL > expected_value, (
+        f"Expected: {expected_value} |  Measured: {measured_value}"
+    )
+
+
+@pytest.mark.parametrize("model", MODEL_NAMES)
+def test_lm_eval_accuracy(model):
+    """Run with the V1 Engine."""
+    more_args = None
+    run_test(model, more_args)