d9cdc65854
### What this PR does / why we need it? Upgrade to new vllm commit:c9461e05a4- Fix many imports, caused by https://github.com/vllm-project/vllm/pull/26908 - Fix import ```sha256```, caused by https://github.com/vllm-project/vllm/pull/27169 - Remove ```SchedulerConfig.send_delta_data```, caused by https://github.com/vllm-project/vllm/pull/27142 - Fix ```FusedMoE``` because of dual stream execution, caused by https://github.com/vllm-project/vllm/pull/26440 ### Does this PR introduce _any_ user-facing change? N/A ### How was this patch tested? CI passed with new added/existing test. - vLLM version: v0.11.0rc3 - vLLM main:17c540a993--------- Signed-off-by: MengqingCao <cmq0113@163.com> Signed-off-by: Icey <1790571317@qq.com> Co-authored-by: MengqingCao <cmq0113@163.com>
62 lines
2.0 KiB
Python
62 lines
2.0 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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#
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import os
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import torch
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from vllm import LLM, SamplingParams
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from vllm_ascend.utils import vllm_version_is
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if vllm_version_is("0.11.0"):
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from vllm.utils import GiB_bytes
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else:
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from vllm.utils.mem_constants import GiB_bytes
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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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def main():
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prompt = "How are you?"
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free, total = torch.npu.mem_get_info()
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print(f"Free memory before sleep: {free / 1024 ** 3:.2f} GiB")
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# record npu memory use baseline in case other process is running
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used_bytes_baseline = total - free
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llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)
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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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llm.sleep(level=1)
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free_npu_bytes_after_sleep, total = torch.npu.mem_get_info()
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print(
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f"Free memory after sleep: {free_npu_bytes_after_sleep / 1024 ** 3:.2f} GiB"
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)
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used_bytes = total - free_npu_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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if __name__ == "__main__":
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main()
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