6f7a81cd9f
1. speed up e2e light test.
2. create `2-cards` and `4-cards` folder in multicard
3. move ops to nightly
4. run test in Alphabetical Order
- vLLM version: v0.13.0
- vLLM main:
8be6432bda
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
86 lines
2.9 KiB
Python
86 lines
2.9 KiB
Python
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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import asyncio
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from typing import Any
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import openai
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import pytest
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from vllm.utils.network_utils import get_open_port
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from tests.e2e.conftest import RemoteOpenAIServer
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MODELS = ["Qwen/Qwen3-0.6B", "vllm-ascend/DeepSeek-V2-Lite-W8A8"]
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DATA_PARALLELS = [2]
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prompts = [
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"San Francisco is a",
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]
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api_keyword_args = {
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"max_tokens": 10,
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}
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@pytest.mark.asyncio
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dp_size", DATA_PARALLELS)
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async def test_models_single_request_aclgraph_dp2(model: str,
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dp_size: int) -> None:
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port = get_open_port()
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env_dict = {
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"TASK_QUEUE_ENABLE": "1",
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"HCCL_OP_EXPANSION_MODE": "AIV",
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}
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if model == "vllm-ascend/DeepSeek-V2-Lite-W8A8":
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server_args = [
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"--no-enable-prefix-caching", "--tensor-parallel-size", "1",
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"--data-parallel-size",
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str(dp_size), "--quantization", "ascend", "--max-model-len",
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"1024", "--port",
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str(port), "--trust-remote-code", "--gpu-memory-utilization", "0.9"
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]
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else:
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server_args = [
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"--no-enable-prefix-caching", "--tensor-parallel-size", "1",
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"--data-parallel-size",
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str(dp_size), "--port",
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str(port), "--trust-remote-code", "--gpu-memory-utilization", "0.9"
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]
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request_keyword_args: dict[str, Any] = {
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**api_keyword_args,
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}
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with RemoteOpenAIServer(model,
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vllm_serve_args=server_args,
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server_port=port,
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env_dict=env_dict,
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auto_port=False) as server:
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client = server.get_async_client()
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try:
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batch = await asyncio.wait_for(client.completions.create(
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model=model,
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prompt=prompts,
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**request_keyword_args,
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),
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timeout=10.0)
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except asyncio.TimeoutError:
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pytest.fail("Model did not return response within 10 seconds")
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choices: list[openai.types.CompletionChoice] = batch.choices
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assert choices[0].text, "empty response"
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