69b817ed65
This PR added the unit test framework to enable ut for vLLM Ascend. Unit test runs on CPU machines. It'll be ran once lint check is passed the same as e2e test. For unit test, this PR created a new folder called `ut` under `tests` module. All the test file in `ut` should keep the same with the code in `vllm-ascend`. The file name should be start with `test_` prefix. For example, in this PR. the `test_ascend_config.py` is added for `ascend_config.py` test. A new fille `worker/test_worker_v1.py` is also added as the placeholder. This file should be the unit test for `vllm-ascend/worker/worker_v1.py`. Additional, a new `fake_weight` folder is added, it contains the config.json from `facebook/opt-125m`, so that the test will not always visit huggingface. TODO: We should add all the unit test file one by one in the future. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
86 lines
2.9 KiB
Python
86 lines
2.9 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 torch
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from vllm import LLM, SamplingParams
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from vllm.utils import GiB_bytes
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from tests.utils import fork_new_process_for_each_test
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from vllm_ascend.device_allocator.camem import CaMemAllocator
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@fork_new_process_for_each_test
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def test_basic_camem():
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# some tensors from default memory pool
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shape = (1024, 1024)
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x = torch.empty(shape, device='npu:0')
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x.zero_()
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# some tensors from custom memory pool
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allocator = CaMemAllocator.get_instance()
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with allocator.use_memory_pool():
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# custom memory pool
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y = torch.empty(shape, device='npu:0')
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y.zero_()
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y += 1
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z = torch.empty(shape, device='npu:0')
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z.zero_()
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z += 2
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# they can be used together
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output = x + y + z
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assert torch.allclose(output, torch.ones_like(output) * 3)
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free_bytes = torch.npu.mem_get_info()[0]
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allocator.sleep()
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free_bytes_after_sleep = torch.npu.mem_get_info()[0]
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assert free_bytes_after_sleep > free_bytes
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allocator.wake_up()
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# they can be used together
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output = x + y + z
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assert torch.allclose(output, torch.ones_like(output) * 3)
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@fork_new_process_for_each_test
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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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llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)
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prompt = "How are you?"
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sampling_params = SamplingParams(temperature=0, max_tokens=10)
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output = llm.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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llm.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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llm.wake_up()
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output2 = llm.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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