vall-e/vall_e/models/adaln.py
2023-08-02 22:06:39 +00:00

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"""
# https://github.com/enhuiz/vall-e/
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaLN(nn.Module):
def __init__(self, d_model, n_levels, eps=1e-5, k=0.1, c=2):
super().__init__()
self.eps = eps
self.emb = nn.Embedding(n_levels, d_model * 2)
self.k = k
self.c = c
nn.init.zeros_(self.emb.weight)
def forward(self, x, l):
h = F.layer_norm(x, x.shape[-1:], eps=self.eps)
# The initial implementation (https://github.com/enhuiz/vall-e/blob/fbf023448c08e55c0422eefed7fc234cf8b76680/vall_e/vall_e/base.py#L135)
# performed worse than vanilla LayerNorm.
# The authors mentioned another AdaNorm paper (https://openreview.net/pdf?id=HyxndNrxLB) as they introduce AdaLN.
# Did they use AdaNorm inside AdaLN? (as follows)
h = self.c * (1 - (self.k * h).detach()) * h
logγ, β = self.emb(l).unsqueeze(1).chunk(2, dim=-1)
y = logγ.exp() * h + β
return y