7265dc090d
There is a lot torchair specified logic in common code. It results hard
code maintenance. We will create a new torchair module to launch
torchair related logic there. I plan to add 4 PR.
1. Refactor worker
2. Refactor utils (this PR)
- simple change that move all torchair related util function to torchair
module
3. Refactor model_runner
4. Refactor attention
- vLLM version: v0.9.2
- vLLM main:
8188196a1c
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
65 lines
2.8 KiB
Python
65 lines
2.8 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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import torch
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from vllm.logger import logger
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import vllm_ascend.envs as envs_ascend
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from vllm_ascend.torchair.utils import (check_kv_cache_bytes_cache_exist,
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check_torchair_cache_exist,
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delete_torchair_cache_file,
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read_kv_cache_bytes_from_file)
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from vllm_ascend.worker.worker_v1 import NPUWorker
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class NPUTorchairWorker(NPUWorker):
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"""Torchair worker bases on NPUWorker. Only torchair specified code should be added in this class."""
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def determine_available_memory(self) -> int:
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"""Override determine_available_memory to use cached torchair kv_cache_bytes."""
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available_kv_cache_memory = super().determine_available_memory()
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if check_torchair_cache_exist() and check_kv_cache_bytes_cache_exist():
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old_kv_cache_bytes = read_kv_cache_bytes_from_file(
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torch.distributed.get_rank())
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if 0 < old_kv_cache_bytes <= available_kv_cache_memory:
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logger.info(
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f"Use cached torchair kv_cache_bytes: {old_kv_cache_bytes}"
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)
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self.model_runner.new_kv_cache_bytes = old_kv_cache_bytes
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return old_kv_cache_bytes
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else:
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logger.info(
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"Cached torchair kv_cache_bytes is too big, invalidate old torchair_cache"
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)
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delete_torchair_cache_file()
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bytes_floating_tolerance = 1024 * 1024 * envs_ascend.VLLM_ASCEND_KV_CACHE_MEGABYTES_FLOATING_TOLERANCE
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available_kv_cache_memory -= bytes_floating_tolerance
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logger.info(f"Use new kv_cache_bytes: {available_kv_cache_memory}")
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self.model_runner.new_kv_cache_bytes = available_kv_cache_memory
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return available_kv_cache_memory
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def _get_max_num_tokens_and_with_prefill(self):
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"""Override _get_max_num_tokens_and_with_prefill to update max_num_tokens."""
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max_num_tokens, with_prefill = super(
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)._get_max_num_tokens_and_with_prefill()
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if not with_prefill:
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max_num_tokens = self.model_runner.select_torchair_padded_batch_size(
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max_num_tokens)
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return max_num_tokens, with_prefill
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