e1ab6d318e
More and more config options are added to additional_config. This PR provide a new AscendConfig to manage these config options by an easier way to make code cleaner and readable. This PR also added the `additional_config` doc for users. Added the test_ascend_config.py to make sure the new AscendConfig works as expect. TODO: Add e2e test with torchair and deepseek once the CI resource is available. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
58 lines
1.8 KiB
Python
58 lines
1.8 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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import torch
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from vllm import LLM, SamplingParams
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MODELS = [
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"Qwen/Qwen2.5-0.5B-Instruct",
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]
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TENSOR_PARALLELS = [2]
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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("max_tokens", [64])
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@pytest.mark.parametrize("temperature", [0.0])
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@pytest.mark.parametrize("ignore_eos", [True])
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def test_models(model: str, tp_size: int, max_tokens: int, temperature: int,
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ignore_eos: bool) -> None:
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# Create an LLM.
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llm = LLM(
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model=model,
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tensor_parallel_size=tp_size,
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)
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# Prepare sampling_parames
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sampling_params = SamplingParams(
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max_tokens=max_tokens,
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temperature=temperature,
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ignore_eos=ignore_eos,
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)
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# Generate texts from the prompts.
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# The output is a list of RequestOutput objects
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outputs = llm.generate(prompts, sampling_params)
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torch.npu.synchronize()
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# The output length should be equal to prompts length.
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assert len(outputs) == len(prompts)
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