23524f2ca4
### What this PR does / why we need it?
This pull request focuses on a significant refactoring effort within the
vllm-ascend project, specifically targeting operations optimized for the
Ascend 310P hardware. The changes aim to streamline the implementation
of core components like quantization and multi-head attention, making
the codebase more maintainable and robust. Concurrently, new unit tests
have been introduced to ensure the correctness and reliability of these
refactored modules.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
E2E test with qwen3-32b w8a8
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: pu-zhe <zpuaa@outlook.com>
62 lines
2.0 KiB
Python
62 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 torch
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import torch_npu
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from vllm_ascend.ops.mm_encoder_attention import AscendMMEncoderAttention
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class AscendMMEncoderAttention310(AscendMMEncoderAttention):
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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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query = query.view(bsz * q_len, self.num_heads, self.head_size)
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key = key.view(bsz * kv_len, self.num_kv_heads, self.head_size)
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value = value.view(bsz * kv_len, self.num_kv_heads, self.head_size)
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if cu_seqlens is None:
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seq_len = torch.tensor([q_len] * bsz, device="cpu", dtype=torch.int32)
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else:
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seq_len = torch.diff(cu_seqlens.to("cpu", dtype=torch.int32))
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output = torch.empty_like(query)
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torch_npu._npu_flash_attention_unpad(
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query=query,
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key=key,
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value=value,
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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=output,
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)
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output = output.view(bsz, -1, self.num_heads, self.head_size)
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return output
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