b917361ca5
torch_npu 2.5.1 support autoload now. This patch does: 1. remove useless torch_npu import 2. replace `torch_npu.npu` to `torch.npu`. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
102 lines
3.4 KiB
Python
102 lines
3.4 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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# This file is a part of the vllm-ascend project.
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# Adapted from vllm-project/vllm/vllm/worker/worker.py
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#
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import torch
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from packaging.version import InvalidVersion, Version
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from vllm.logger import logger
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import vllm_ascend.envs as envs
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def try_register_lib(lib_name: str, lib_info: str = ""):
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import importlib
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import importlib.util
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try:
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module_spec = importlib.util.find_spec(lib_name)
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if module_spec is not None:
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importlib.import_module(lib_name)
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if lib_info:
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logger.info(lib_info)
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except Exception:
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pass
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def find_hccl_library() -> str:
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"""
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We either use the library file specified by the `HCCL_SO_PATH`
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environment variable, or we find the library file brought by PyTorch.
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After importing `torch`, `libhccl.so` can be
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found by `ctypes` automatically.
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"""
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so_file = envs.HCCL_SO_PATH
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# manually load the hccl library
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if so_file:
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logger.info("Found hccl from environment variable HCCL_SO_PATH=%s",
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so_file)
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else:
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if torch.version.cann is not None:
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so_file = "libhccl.so"
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else:
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raise ValueError("HCCL only supports Ascend NPU backends.")
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logger.info("Found hccl from library %s", so_file)
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return so_file
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_current_stream = None
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def current_stream() -> torch.npu.Stream:
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"""
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replace `torch.npu.current_stream()` with `vllm.utils.current_stream()`.
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it turns out that `torch.npu.current_stream()` is quite expensive,
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as it will construct a new stream object at each call.
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here we patch `torch.npu.set_stream` to keep track of the current stream
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directly, so that we can avoid calling `torch.npu.current_stream()`.
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"""
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global _current_stream
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if _current_stream is None:
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# when this function is called before any stream is set,
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# we return the default stream.
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_current_stream = torch.npu.current_stream()
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return _current_stream
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def adapt_patch(is_global_patch: bool = False):
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if is_global_patch:
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from vllm_ascend.patch import platform # noqa: F401
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else:
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from vllm_ascend.patch import worker # noqa: F401
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def vllm_version_is(target_vllm_version: str):
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if envs.VLLM_VERSION is not None:
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vllm_version = envs.VLLM_VERSION
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else:
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import vllm
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vllm_version = vllm.__version__
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try:
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return Version(vllm_version) == Version(target_vllm_version)
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except InvalidVersion:
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raise ValueError(
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f"Invalid vllm version {vllm_version} found. A dev version of vllm "
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"is installed probably. Set the environment variable VLLM_VERSION "
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"to control it by hand. And please make sure the vaule follows the "
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"format of x.y.z.")
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