74a1de50a9
### What this PR does / why we need it?
[E2E] Optimize e2e test.
- Remove the test_basic_camem testcase.
- Change Qwen2.5-0.5B-Instruct-W8A8 to Qwen3-0.6B-W8A8
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: menogrey <1299267905@qq.com>
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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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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#
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import os
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from unittest.mock import patch
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import torch
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from vllm import SamplingParams
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from vllm.utils.mem_constants import GiB_bytes
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from tests.e2e.conftest import VllmRunner
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from tests.e2e.utils import fork_new_process_for_each_test
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@fork_new_process_for_each_test
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
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def test_end_to_end():
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free, total = torch.npu.mem_get_info()
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used_bytes_baseline = total - free # in case other process is running
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prompt = "How are you?"
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sampling_params = SamplingParams(temperature=0, max_tokens=10)
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with VllmRunner("Qwen/Qwen3-0.6B", enable_sleep_mode=True) as runner:
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output = runner.model.generate(prompt, sampling_params)
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# the benefit of `llm.sleep(level=2)` is mainly CPU memory usage,
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# which is difficult to measure in the test. therefore, we only
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# test sleep level 1 here.
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runner.model.sleep(level=1)
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free_gpu_bytes_after_sleep, total = torch.npu.mem_get_info()
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used_bytes = total - free_gpu_bytes_after_sleep - used_bytes_baseline
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# now the memory usage should be less than the model weights
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# (0.5B model, 1GiB weights)
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assert used_bytes < 1 * GiB_bytes
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runner.model.wake_up()
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output2 = runner.model.generate(prompt, sampling_params)
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# cmp output
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assert output[0].outputs[0].text == output2[0].outputs[0].text
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