ba3dfbd59e
### What this PR does / why we need it?
A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:
Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;
### Does this PR introduce _any_ user-facing change?
No change at user-facing.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
57c22e57f9
Signed-off-by: zzzzwwjj <1183291235@qq.com>
55 lines
2.4 KiB
Python
55 lines
2.4 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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