eeedf7c503
### What this PR does / why we need it?
This PR upgrades the vLLM dependency from `v0.14.1` to `v0.15.0`. This
involves:
- Updating the `VLLM_TAG` in all `Dockerfile`.
- Updating the vLLM version in `docs/source/conf.py`.
- Removing conditional code paths specific to `v0.14.1` across the
codebase, which simplifies maintenance.
- Fix `TypeError: MMEncoderAttention.__init__() got an unexpected
keyword argument 'multimodal_config'` due to
https://github.com/vllm-project/vllm/pull/31972.
- Fix `_shared_experts: 'NoneType' object is not callable` due to
https://github.com/vllm-project/vllm/pull/32082 by
https://github.com/vllm-project/vllm-ascend/pull/6335.
- Fix `ReshapeAndCacheOperation setup failed!` due to
https://github.com/vllm-project/vllm/pull/25954 by overriding attention
metadata slots.
This upgrade is necessary to keep the project aligned with the latest
features, bug fixes, and API changes in the vLLM project.
### Does this PR introduce _any_ user-facing change?
No, this is an internal dependency update and does not introduce any
user-facing changes.
### How was this patch tested?
CI is expected to pass with these changes, ensuring that all existing
tests are successful with the new vLLM version.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
co-authored-by: shen-shanshan <467638484@qq.com>
---------
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/aclgraph_utils.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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from contextlib import contextmanager
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from typing import Any
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import torch
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import torch.nn as nn
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from vllm.config import VllmConfig
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.worker.gpu.block_table import BlockTables
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from vllm.v1.worker.gpu.cudagraph_utils import CudaGraphManager
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from vllm.v1.worker.gpu.cudagraph_utils import \
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prepare_inputs_to_capture as prepare_inputs_to_capture_gpu
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from vllm.v1.worker.gpu.input_batch import InputBuffers
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from vllm.v1.attention.backend import AttentionMetadataBuilder
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from vllm_ascend.worker.v2.utils import torch_cuda_wrapper
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class AclGraphManager(CudaGraphManager):
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"""ACL Graph Manager for Ascend NPUs."""
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def __init__(self, vllm_config: VllmConfig, device: torch.device):
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with torch_cuda_wrapper():
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super().__init__(vllm_config, device)
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def capture_graph(
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self,
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num_tokens: int,
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model: nn.Module,
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input_buffers: InputBuffers,
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block_tables: BlockTables,
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attn_metadata_builders: list[AttentionMetadataBuilder],
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kv_cache_config: KVCacheConfig,
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) -> None:
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with (torch_cuda_wrapper(), prepare_capture_inputs_wrapper()):
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super().capture_graph(
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num_tokens,
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model,
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input_buffers,
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block_tables,
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attn_metadata_builders,
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kv_cache_config,
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)
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@contextmanager
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def prepare_capture_inputs_wrapper():
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"""Context manager to override input preparation for NPU graph capture."""
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# TODO(Ronald1995): make prepare_inputs_to_capture as static method
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# in CudaGraphManager.
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global prepare_inputs_to_capture_gpu
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try:
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ori_func = prepare_inputs_to_capture_gpu
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prepare_inputs_to_capture_gpu = prepare_inputs_to_capture
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yield
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finally:
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prepare_inputs_to_capture_gpu = ori_func
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def prepare_inputs_to_capture(
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num_reqs: int,
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num_tokens: int,
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input_buffers: InputBuffers,
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block_tables: BlockTables,
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attn_metadata_builders: list[AttentionMetadataBuilder],
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max_model_len: int,
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kv_cache_config: KVCacheConfig,
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) -> dict[str, Any]:
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# TODO(Ronald1995): Implement NPU specific input preparation.
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return {}
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