f60bb474f9
### What this PR does / why we need it?
Currently our workflow run time takes about 3 hours in total, which
seriously affects the developer experience, so it is urgent to have a
optimization, after this pr, It is expected that the running time of the
full CI can be shortened to 1h40min.
- Enable linux-aarch64-a2 (64GB) to replace linux-arm64-npu (32GB)
- Change TP4 ---> TP2 * 2 max-parallel
- Move DeepSeek-V2-Lite-W8A8 to single card test
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.10.0
- vLLM main:
a2480251ec
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
48 lines
1.7 KiB
Python
48 lines
1.7 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 pytest
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from tests.e2e.conftest import VllmRunner
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MODELS = [
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"Qwen/Qwen3-0.6B",
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]
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TENSOR_PARALLELS = [1]
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PIPELINE_PARALLELS = [2]
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DIST_EXECUTOR_BACKEND = ["mp", "ray"]
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prompts = [
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"Hello, my name is",
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"The future of AI is",
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]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("tp_size", TENSOR_PARALLELS)
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@pytest.mark.parametrize("pp_size", PIPELINE_PARALLELS)
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@pytest.mark.parametrize("distributed_executor_backend", DIST_EXECUTOR_BACKEND)
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def test_models(model: str, tp_size: int, pp_size: int,
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distributed_executor_backend: str) -> None:
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with VllmRunner(model,
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tensor_parallel_size=tp_size,
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pipeline_parallel_size=pp_size,
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distributed_executor_backend=distributed_executor_backend,
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enforce_eager=True,
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gpu_memory_utilization=0.7) as vllm_model:
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vllm_model.generate_greedy(prompts, 64)
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