fbae41697e
### What this PR does / why we need it?
* Refactor the LayerNorm and activation operator classes to decouple the
310P device implementation from the main branch.
* Refactor `mm_encoder_attention` on 310P to use the
`torch_npu._npu_flash_attention_unpad` operator.
* Refactor the QKV inputs in the prefill stage of `attention_v1` on 310P
so they are no longer padded to 16× alignment.
* Refactor `model_runner` on 310P to align the KV-cache initialization
logic with the mainline implementation.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
use the e2e tests.
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
#
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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 einops
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import torch
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import torch_npu
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from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention as _Base
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class AscendMMEncoderAttention310(_Base):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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def forward_oot(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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cu_seqlens: torch.Tensor | None = None,
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max_seqlen: int | None = None,
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**kwargs,
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):
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bsz, q_len = query.size()[:2]
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kv_len = key.size(1)
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q, k, v = self.reshape_qkv_to_3d(query, key, value, bsz, q_len, kv_len)
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if cu_seqlens is None:
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cu_seqlens = torch.arange(
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0,
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(bsz + 1) * q_len,
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step=q_len,
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dtype=torch.int32,
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device=query.device,
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)
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seq_len = torch.diff(cu_seqlens).to("cpu", dtype=torch.int32)
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context_layer = torch.empty_like(q)
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torch_npu._npu_flash_attention_unpad(
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query=q,
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key=k,
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value=v,
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seq_len=seq_len,
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scale_value=self.head_size**-0.5,
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num_heads=self.num_heads,
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num_kv_heads=self.num_kv_heads,
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out=context_layer,
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)
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context_layer = einops.rearrange(context_layer, "(b s) h d -> b s h d", b=bsz).contiguous()
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return context_layer
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