d96440924a
### What this PR does / why we need it?
This PR aims to adapt to newest commit of vllm main branch for model
runner v2. please refer to
https://github.com/vllm-project/vllm-ascend/issues/5208
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.18.0
- vLLM main:
ed359c497a
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
74 lines
3.1 KiB
Python
74 lines
3.1 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/model_states/default.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 typing import Any
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import torch
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from vllm.config.compilation import CUDAGraphMode
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.worker.gpu.model_states.default import DefaultModelState
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from vllm.v1.worker.utils import AttentionGroup
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from vllm_ascend.worker.v2.attn_utils import build_attn_metadata
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from vllm_ascend.worker.v2.input_batch import AscendInputBatch
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class AscendModelState(DefaultModelState):
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"""Model state for Ascend NPUs."""
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def prepare_attn(
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self,
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input_batch: AscendInputBatch,
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cudagraph_mode: CUDAGraphMode,
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block_tables: tuple[torch.Tensor, ...],
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slot_mappings: torch.Tensor,
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attn_groups: list[list[AttentionGroup]],
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kv_cache_config: KVCacheConfig,
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for_capture: bool = False,
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) -> dict[str, Any]:
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"""Override prepare_attn method because `build_attn_metadata` is different from vllm."""
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if cudagraph_mode == CUDAGraphMode.FULL:
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# Use padded sizes - padding is handled by model_runner.prepare_attn.
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num_reqs = input_batch.num_reqs_after_padding
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num_tokens = input_batch.num_tokens_after_padding
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else:
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# For piecewise cudagraphs and eager, use unpadded sizes.
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num_reqs = input_batch.num_reqs
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num_tokens = input_batch.num_tokens
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query_start_loc_cpu = torch.from_numpy(input_batch.query_start_loc_np)
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max_query_len = input_batch.num_scheduled_tokens.max().item()
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attn_metadata = build_attn_metadata(
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attn_groups=attn_groups,
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num_reqs=num_reqs,
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num_tokens=num_tokens,
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query_start_loc_gpu=input_batch.query_start_loc,
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query_start_loc_cpu=query_start_loc_cpu,
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max_query_len=max_query_len,
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seq_lens=input_batch.seq_lens,
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max_seq_len=self.max_model_len,
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block_tables=block_tables,
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slot_mappings=slot_mappings,
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kv_cache_config=kv_cache_config,
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dcp_local_seq_lens=input_batch.dcp_local_seq_lens,
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# extra attributes for ascend npus.
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seq_lens_np=input_batch.seq_lens_np,
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attn_state=input_batch.attn_state,
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)
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return attn_metadata
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