init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

tests/e2e/weekly/single_node/models/test_qwen3_30b_acc.py

@@ -0,0 +1,143 @@
+# 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.network_utils import get_open_port
+
+from tests.e2e.conftest import MooncakeLauncher, RemoteOpenAIServer
+from tools.aisbench import maybe_download_from_modelscope, run_aisbench_cases
+
+MODELS = [
+    "vllm-ascend/Qwen3-30B-A3B-W8A8",
+]
+
+eagle_model = maybe_download_from_modelscope("vllm-ascend/Qwen3-a3B_eagle3")
+
+TENSOR_PARALLELS = [1, 4]
+
+prompts = [
+    "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her "
+    "friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. "
+    "How much in dollars does she make every day at the farmers' market?",
+]
+
+api_keyword_args = {
+    "max_tokens": 10,
+}
+
+mooncake_json = {
+    "local_hostname": "localhost",
+    "metadata_server": "P2PHANDSHAKE",
+    "protocol": "ascend",
+    "device_name": "",
+    "master_server_address": "",
+    "global_segment_size": 30000000000,
+}
+
+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,
+        "baseline": 95,
+        "threshold": 5,
+    }
+]
+
+
+@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()
+    mooncake_port = get_open_port()
+    mooncake_metrics_port = get_open_port()
+    mooncake_json["master_server_address"] = f"127.0.0.1:{mooncake_port}"
+    with open("mooncake.json", "w") as f:
+        json.dump(mooncake_json, f)
+    env_dict = {
+        "PYTHONHASHSEED": "0",
+        "ASCEND_CONNECT_TIMEOUT": "10000",
+        "ASCEND_TRANSFER_TIMEOUT": "10000",
+        "VLLM_USE_V1": "1",
+        "OMP_PROC_BIND": "false",
+        "HCCL_OP_EXPANSION_MODE": "AIV",
+        "HCCL_BUFFSIZE": "1024",
+        "OMP_NUM_THREADS": "1",
+        "PYTORCH_NPU_ALLOC_CONF": "expandable_segments:True",
+        "VLLM_ASCEND_ENABLE_NZ": "2",
+        "MOONCAKE_CONFIG_PATH": "mooncake.json",
+    }
+    if tp_size != 1:
+        env_dict["VLLM_ASCEND_ENABLE_FLASHCOMM1"] = "1"
+    kv_transfer_config = {
+        "kv_connector": "AscendStoreConnector",
+        "kv_role": "kv_both",
+        "kv_connector_extra_config": {"register_buffer": True, "use_layerwise": False, "mooncake_rpc_port": "0"},
+    }
+    speculative_config = {"method": "eagle3", "model": eagle_model, "num_speculative_tokens": 3}
+    server_args = [
+        "--trust-remote-code",
+        "--max-num-seqs",
+        "100",
+        "--max-model-len",
+        "37364",
+        "--max-num-batched-tokens",
+        "16384",
+        "--tensor-parallel-size",
+        str(tp_size),
+        "--enable-expert-parallel",
+        "--port",
+        str(port),
+        "--distributed_executor_backend",
+        "mp",
+        "--quantization",
+        "ascend",
+        "--compilation-config",
+        '{"cudagraph_mode": "FULL_DECODE_ONLY"}',
+        "--gpu-memory-utilization",
+        "0.95",
+        "--speculative-config",
+        json.dumps(speculative_config),
+        "--kv-transfer-config",
+        json.dumps(kv_transfer_config),
+    ]
+    request_keyword_args: dict[str, Any] = {
+        **api_keyword_args,
+    }
+    with (
+        MooncakeLauncher(mooncake_port, mooncake_metrics_port),
+        RemoteOpenAIServer(model, server_args, server_port=port, env_dict=env_dict, auto_port=False) as server,
+    ):
+        client = server.get_async_client()
+        for _ in range(2):
+            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)
+        run_aisbench_cases(model, port, aisbench_cases)