f5a97e8fa5
### What this PR does / why we need it?
modelslim will generate self.bias for rms norm in quantization, since
RMSNorm in vllm has no this parameter, so its nesscesary
to create a AscendQuantRmsNorm.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
tested by deepseek-v3.1-w8a8
<img width="2496" height="592" alt="image"
src="https://github.com/user-attachments/assets/004c6e76-3d7a-4a1f-b59f-a14304012663"
/>
- vLLM version: main
- vLLM main:
d6249d0699
Signed-off-by: 22dimensions <waitingwind@foxmail.com>
117 lines
4.0 KiB
Python
117 lines
4.0 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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from typing import Optional, Tuple, Union, cast
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import torch
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from vllm.model_executor.layers.layernorm import RMSNorm
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class AddRMSNormW8A8Quant(RMSNorm):
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# Fuse AddRmsNorm and W8A8 quantization ops together
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def __init__(
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self,
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hidden_size: int,
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layer: torch.nn.Module,
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eps: float = 1e-6,
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var_hidden_size: Optional[int] = None,
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has_weight: bool = True,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
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self.layer = layer
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def forward(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
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import torch_npu
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if residual is not None:
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residual = torch.ops.vllm.maybe_chunk_residual(x, residual)
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assert x.size(0) == residual.size(0)
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x, _, residual = torch_npu.npu_add_rms_norm_quant(
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x,
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residual,
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self.weight,
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self.layer.aclnn_input_scale,
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self.layer.aclnn_input_offset,
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epsilon=self.variance_epsilon)
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torch.ops.vllm.maybe_wait_prefetch_done(x)
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return x, residual
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x, residual = torch_npu.npu_rms_norm(x, self.weight,
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self.variance_epsilon)
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return x
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class AscendRMSNorm(RMSNorm):
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def forward_oot(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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import torch_npu
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from vllm_ascend.utils import is_310p
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if residual is not None:
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residual = torch.ops.vllm.maybe_chunk_residual(x, residual)
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assert x.size(0) == residual.size(0)
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if is_310p():
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orig_dtype = residual.dtype
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x = x + residual.to(x.dtype)
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residual = x.to(orig_dtype)
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x, _ = torch_npu.npu_rms_norm(x, self.weight,
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self.variance_epsilon)
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else:
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x, _, residual = torch_npu.npu_add_rms_norm(
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x, residual, self.weight, self.variance_epsilon)
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torch.ops.vllm.maybe_wait_prefetch_done(x)
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return x, residual
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x, residual = torch_npu.npu_rms_norm(x, self.weight,
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self.variance_epsilon)
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return x
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class AscendQuantRMSNorm(AscendRMSNorm):
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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var_hidden_size: Optional[int] = None,
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has_weight: bool = True,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
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self.bias = torch.nn.Parameter(torch.zeros(hidden_size),
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requires_grad=False)
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def forward_oot(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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if residual is not None:
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x, residual = super().forward_oot(x, residual)
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return x.add_(self.bias), residual
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return cast(torch.Tensor, super().forward_oot(x)).add_(self.bias)
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