47 lines
1.1 KiB
Python
47 lines
1.1 KiB
Python
"""Custom normalization layers."""
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from typing import Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from vllm import layernorm_ops
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class RMSNorm(nn.Module):
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"""Root mean square normalization.
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Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
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Refer to https://arxiv.org/abs/1910.07467
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"""
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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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) -> None:
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super().__init__()
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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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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if residual is not None:
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layernorm_ops.fused_add_rms_norm(
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x,
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residual,
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self.weight.data,
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self.variance_epsilon,
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)
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return x, residual
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out = torch.empty_like(x)
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layernorm_ops.rms_norm(
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out,
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x,
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self.weight.data,
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self.variance_epsilon,
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)
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return out
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