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https://github.com/vllm-project/vllm-ascend.git
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787010a637
V1 is enabled by default, no need to set it by hand now. This PR remove
the useless setting in example and tests
- vLLM version: v0.9.2
- vLLM main:
9ad0a4588b
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.e2e.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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