81 lines
2.8 KiB
Python
81 lines
2.8 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.model_executor.layers.activation import (
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QuickGELU,
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SiluAndMul,
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SiluAndMulWithClamp,
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SwigluOAIAndMul,
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SwigluStepAndMul,
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)
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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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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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weight_prefetch_method = get_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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weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
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return out
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class AscendSiluAndMulWithClamp(SiluAndMulWithClamp):
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def forward_oot(self, x: torch.Tensor) -> torch.Tensor:
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weight_prefetch_method = get_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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d = x.shape[-1] // 2
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gate = torch.clamp(x[..., :d], max=self.swiglu_limit)
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up = torch.clamp(x[..., d:], min=-self.swiglu_limit, max=self.swiglu_limit)
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x = torch.cat([gate, up], dim=-1)
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out = torch_npu.npu_swiglu(x)
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weight_prefetch_method.maybe_prefetch_mlp_weight_postprocess(out)
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return out
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class AscendSwigluOAIAndMul:
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def swiglu_oai_forward(x: torch.Tensor, alpha: float = 1.702, limit: float = 7.0) -> torch.Tensor:
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class MinimalSwigluOAIAndMul:
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def __init__(self):
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self.alpha = alpha
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self.limit = limit
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layer = MinimalSwigluOAIAndMul()
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return SwigluOAIAndMul.forward_native(layer, x)
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class AscendSwigluStepAndMul:
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def swiglustep_forward(x: torch.Tensor, limit: float = 7.0) -> torch.Tensor:
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if limit is None:
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raise ValueError("SwigluStepAndMul requires limit to be set.")
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class MinimalSwigluStepAndMul:
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def __init__(self):
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self.limit = limit
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layer = MinimalSwigluStepAndMul()
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return SwigluStepAndMul.forward_native(layer, x)
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