4fb3d5e1b2
### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
| vllm_ascend/ops/\_\_init\_\_.py |
| vllm_ascend/ops/activation.py |
| vllm_ascend/ops/flashcomm2_oshard_manager.py |
| vllm_ascend/ops/layernorm.py |
| vllm_ascend/ops/mla.py |
| vllm_ascend/ops/mm_encoder_attention.py |
| vllm_ascend/ops/register_custom_ops.py |
| vllm_ascend/ops/vocab_parallel_embedding.py |
| vllm_ascend/ops/weight_prefetch.py |
| vllm_ascend/spec_decode/\_\_init\_\_.py |
| vllm_ascend/spec_decode/eagle_proposer.py |
| vllm_ascend/spec_decode/interface.py |
| vllm_ascend/spec_decode/mtp_proposer.py |
| vllm_ascend/spec_decode/ngram_proposer.py |
| vllm_ascend/spec_decode/suffix_proposer.py |
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
d68209402d
Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: SILONG ZENG <2609716663@qq.com>
43 lines
1.5 KiB
Python
43 lines
1.5 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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from vllm_ascend.utils import get_weight_prefetch_method
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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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weight_prefetch_method = get_weight_prefetch_method()
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if weight_prefetch_method:
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weight_prefetch_method.maybe_prefetch_mlp_weight_preprocess(weight_prefetch_method.MLP_DOWN, x)
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out = torch_npu.npu_swiglu(x)
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if weight_prefetch_method:
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weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
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return out
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