Files
xc-llm-ascend/vllm_ascend/ops/activation.py
Nengjun Ma 78fad4e348 [Refactor] MLP weight prefetch to consistency with MoE Model's prefetching in terms of code and usage (#6442)
### What this PR does / why we need it?
Refactor MLP weight prefetch to consistency with MoE Model's prefetching
in terms of code and usage.
Environments VLLM_ASCEND_ENABLE_PREFETCH_MLP,
VLLM_ASCEND_MLP_DOWN_PREFETCH_SIZE and
VLLM_ASCEND_MLP_GATE_UP_PREFETCH_SIZE is removed, usage as following:

--additional-config '{"weight_prefetch_config": { "enabled": true,
"prefetch_ratio": {"mlp": { "gate_up": 1.0, "down": 1.0} }}}'

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: leo-pony <nengjunma@outlook.com>
2026-02-04 09:08:18 +08:00

43 lines
1.5 KiB
Python

#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
#
import torch
from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
from vllm_ascend.utils import get_weight_prefetch_method
class AscendQuickGELU(QuickGELU):
def forward_oot(self, x: torch.tensor) -> torch.Tensor:
import torch_npu
out = torch_npu.npu_fast_gelu(x)
return out
class AscendSiluAndMul(SiluAndMul):
def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
import torch_npu
weight_prefetch_method = get_weight_prefetch_method()
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_mlp_weight_preprocess(weight_prefetch_method.MLP_DOWN, x)
out = torch_npu.npu_swiglu(x)
if weight_prefetch_method:
weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
return out