f1ffb5fb19
### What this PR does / why we need it?
vllm model runner v2 use uva buffer to prepare input data, but npu
doesn't support uva yet, this pr implement a uvawrapper class to mimic
gpu's uva backend. what's more, this pr make some modifications to adapt
to the newer main branch.
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM main:
13397841ab
---------
Signed-off-by: Ronald1995 <ronaldautomobile@163.com>
70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
# Adapt from https://github.com/vllm-project/vllm/blob/main/vllm/v1/worker/gpu/states.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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import torch
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from vllm.v1.worker.gpu.states import RequestState
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class AscendRequestState(RequestState):
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"""Request state for Ascend NPUs."""
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def __init__(
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self,
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max_num_reqs: int,
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max_model_len: int,
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max_num_batched_tokens: int,
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num_speculative_steps: int,
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vocab_size: int,
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device: torch.device,
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):
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super().__init__(
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max_num_reqs,
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max_model_len,
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max_num_batched_tokens,
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num_speculative_steps,
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vocab_size,
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device,
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)
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# vllm gpu_model_runner_v2 deprecate the seqs_lens_cpu attribute,
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# because they think most attention backends do not need it.
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# However, Ascend attention backend muse uses seqs_lens_cpu,
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# so we keep num_computed_tokens_cpu here, seq_lens_cpu need to be
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# calculated by num_computed_tokens_cpu + decode_token_per_req outside.
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self.num_computed_tokens_cpu: torch.Tensor = torch.zeros(
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self.max_num_reqs,
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dtype=torch.int32,
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device="cpu",
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)
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def add_request(
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self,
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req_id,
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prompt_len,
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prefill_token_ids,
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num_computed_tokens,
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):
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super().add_request(
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req_id,
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prompt_len,
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prefill_token_ids,
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num_computed_tokens,
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)
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req_idx = self.req_id_to_index[req_id]
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self.num_computed_tokens_cpu[req_idx] = num_computed_tokens
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