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
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Signed-off-by: Tflowers-0129 <2906339855@qq.com>
40 lines
1.2 KiB
Python
40 lines
1.2 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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from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
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class AscendQuickGELU(QuickGELU):
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def forward_oot(self, x: torch.tensor) -> torch.Tensor:
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import torch_npu
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out = torch_npu.npu_fast_gelu(x)
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return out
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class AscendSiluAndMul(SiluAndMul):
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def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
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import torch_npu
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torch.ops.vllm.maybe_prefetch_mlp_down_proj(x)
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out = torch_npu.npu_swiglu(x)
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torch.ops.vllm.maybe_wait_prefetch_done(out)
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return out
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