84563fc65d
### What this PR does / why we need it? This PR adds sleep mode feature for vllm-ascend, when sleeps, we do mainly two things: - offload model weights - discard kv cache RLHF tools(such as https://github.com/volcengine/verl and https://github.com/OpenRLHF/OpenRLHF) have a strong need of sleep mode to accelerate the training process. This PR may solve #375 and #320 . ### Does this PR introduce _any_ user-facing change? No existing user interfaces changed. Users will have two new methods(`sleep()` and `wake_up()`) to use. ### How was this patch tested? This PR is tested with Qwen/Qwen2.5-0.5B-Instruct. At first, we have free NPU memory M1. After `llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)` executed, we have free NPU memory M2. M2 < M1. Then we call `llm.sleep(level=1)`, we have free NPU memory M3. We have M3 > M2, M3 is very close to M1. Plus, we have the same output tokens before sleep and after wake up, with the config of `SamplingParams(temperature=0, max_tokens=10)` and with the same input tokens of course. This PR is utilizing the CMake procedure of #371 , thanks a lot. Signed-off-by: Shuqiao Li <celestialli@outlook.com>
93 lines
3.0 KiB
Python
93 lines
3.0 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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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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try:
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import torch_npu # noqa: F401
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except ImportError:
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print("Failed to import torch_npu.")
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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.npu.mem_get_info()[0]
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allocator.sleep()
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free_bytes_after_sleep = torch_npu.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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os.environ["VLLM_USE_V1"] = "0"
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free, total = torch_npu.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.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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