[TEST]Add qwen3-235b-w8a8 and qwen3-30b-w8a8 nightly test (#3973)

### What this PR does / why we need it?
This PR adds some qwen3-235b-w8a8 cases qwen3-30b-w8a8 cases, we need
test them daily
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
by running the test

- vLLM version: v0.11.0
- vLLM main:
83f478bb19

Signed-off-by: jiangyunfan1 <jiangyunfan1@h-partners.com>

.github/workflows/vllm_ascend_test_nightly_a3.yaml

@@ -78,6 +78,12 @@ jobs:
           - name: qwq-32b-a3
             os: linux-aarch64-a3-4
             tests: tests/e2e/nightly/models/test_qwq_32b.py
+          - name: qwen3-30b-w8a8
+            os: linux-aarch64-a3-2
+            tests: tests/e2e/nightly/models/test_qwen3_30b_w8a8.py
+          - name: qwen3-235b-w8a8
+            os: linux-aarch64-a3-16
+            tests: tests/e2e/nightly/models/test_qwen3_235b_w8a8.py
     uses: ./.github/workflows/_e2e_nightly_single_node.yaml
     with:
       vllm: v0.11.0

tests/e2e/nightly/models/test_qwen3_235b_w8a8.py

@@ -0,0 +1,107 @@
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+import json
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import run_aisbench_cases
+
+MODELS = [
+    "vllm-ascend/Qwen3-235B-A22B-W8A8",
+]
+
+MODES = ["full_graph", "piecewise"]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+aisbench_cases = [{
+    "case_type": "accuracy",
+    "dataset_path": "vllm-ascend/gsm8k-lite",
+    "request_conf": "vllm_api_general_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_chat_prompt",
+    "max_out_len": 32768,
+    "batch_size": 32,
+    "top_k": 20,
+    "baseline": 95,
+    "threshold": 5
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("mode", MODES)
+async def test_models(model: str, mode: str) -> None:
+    port = get_open_port()
+    env_dict = {
+        "OMP_NUM_THREADS": "10",
+        "OMP_PROC_BIND": "false",
+        "HCCL_BUFFSIZE": "1024",
+        "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
+        "VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"
+    }
+    additional_config: dict[str, Any] = {
+        "ascend_scheduler_config": {
+            "enabled": False
+        },
+    }
+    compilation_config = {"cudagraph_mode": "FULL_DECODE_ONLY"}
+    server_args = [
+        "--quantization", "ascend", "--async-scheduling",
+        "--data-parallel-size", "4", "--tensor-parallel-size", "4",
+        "--enable-expert-parallel", "--port",
+        str(port), "--max-model-len", "40960", "--max-num-batched-tokens",
+        "8192", "--max-num-seqs", "12", "--trust-remote-code",
+        "--gpu-memory-utilization", "0.9"
+    ]
+    if mode == "piecewise":
+        compilation_config["cudagraph_mode"] = "PIECEWISE"
+    server_args.extend(
+        ["--compilation-config",
+         json.dumps(compilation_config)])
+    server_args.extend(["--additional-config", json.dumps(additional_config)])
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        print(choices)
+        # aisbench test
+        run_aisbench_cases(model,
+                           port,
+                           aisbench_cases,
+                           server_args=server_args)

tests/e2e/nightly/models/test_qwen3_30b_w8a8.py

@@ -0,0 +1,92 @@
+# 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.
+# This file is a part of the vllm-ascend project.
+#
+from typing import Any
+
+import openai
+import pytest
+from vllm.utils import get_open_port
+
+from tests.e2e.conftest import RemoteOpenAIServer
+from tools.aisbench import run_aisbench_cases
+
+MODELS = [
+    "vllm-ascend/Qwen3-30B-A3B-W8A8",
+]
+
+TENSOR_PARALLELS = [1]
+
+prompts = [
+    "San Francisco is a",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+aisbench_cases = [{
+    "case_type": "performance",
+    "dataset_path": "vllm-ascend/GSM8K-in3500-bs400",
+    "request_conf": "vllm_api_stream_chat",
+    "dataset_conf": "gsm8k/gsm8k_gen_0_shot_cot_str_perf",
+    "num_prompts": 180,
+    "max_out_len": 1500,
+    "batch_size": 45,
+    "request_rate": 0,
+    "baseline": 1,
+    "threshold": 0.97
+}]
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
+async def test_models(model: str, tp_size: int) -> None:
+    port = get_open_port()
+    env_dict = {
+        "OMP_PROC_BIND": "false",
+        "OMP_NUM_THREADS": "10",
+        "HCCL_BUFFSIZE": "1024",
+        "HCCL_OP_EXPANSION_MODE": "AIV",
+        "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True"
+    }
+    server_args = [
+        "--quantization", "ascend", "--async-scheduling",
+        "--no-enable-prefix-caching", "--tensor-parallel-size",
+        str(tp_size), "--port",
+        str(port), "--max-model-len", "5600", "--max-num-batched-tokens",
+        "16384", "--max-num-seqs", "100", "--trust-remote-code",
+        "--gpu-memory-utilization", "0.9", "--compilation-config",
+        '{"cudagraph_mode": "FULL_DECODE_ONLY"}'
+    ]
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with RemoteOpenAIServer(model,
+                            server_args,
+                            server_port=port,
+                            env_dict=env_dict,
+                            auto_port=False) as server:
+        client = server.get_async_client()
+        batch = await client.completions.create(
+            model=model,
+            prompt=prompts,
+            **request_keyword_args,
+        )
+        choices: list[openai.types.CompletionChoice] = batch.choices
+        assert choices[0].text, "empty response"
+        # aisbench test
+        run_aisbench_cases(model, port, aisbench_cases)