6874d666fa
### What this PR does / why we need it?
Add e2e test for 310p:
trigger conditions：tag, labels(ready-for-test, e2e-310p-test), schedule
image: m.daocloud.io/quay.io/ascend/cann:8.1.rc1-310p-ubuntu22.04-py3.10
runner: linux-aarch64-310p-1, linux-aarch64-310p-4
model: IntervitensInc/pangu-pro-moe-model, Qwen/Qwen3-0.6B-Base,
Qwen/Qwen2.5-7B-Instruct
- vLLM version: v0.10.0
- vLLM main:
b917da442b
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
#
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# 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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import pytest
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import vllm # noqa: F401
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import vllm_ascend # noqa: F401
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from tests.e2e.conftest import VllmRunner
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# Pangu local model path
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MODELS = [
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"IntervitensInc/pangu-pro-moe-model",
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]
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# set additional config for ascend scheduler and torchair graph
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ADDITIONAL_CONFIG = [{
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"additional_config": {
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"torchair_graph_config": {
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"enabled": True
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},
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"ascend_scheduler_config": {
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"enabled": True,
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}
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}
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}]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float16"])
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@pytest.mark.parametrize("max_tokens", [5])
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@pytest.mark.parametrize("enfore_eager", [True, False])
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@pytest.mark.parametrize("additional_config", ADDITIONAL_CONFIG)
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def test_pangu_model(model: str, dtype: str, max_tokens: int,
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enfore_eager: bool, additional_config: dict) -> None:
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if enfore_eager:
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additional_config = {}
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example_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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with VllmRunner(model,
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tensor_parallel_size=4,
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dtype=dtype,
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max_model_len=1024,
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enforce_eager=True,
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enable_expert_parallel=True,
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additional_config=additional_config,
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distributed_executor_backend="mp") as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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