Rename 'res' in line 514 to avoid with naming conflict with line 365

liugz18

2024-07-18 d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99

 funasr/models/transformer/layer_norm.py

@@ -36,11 +36,7 @@
        """
        if self.dim == -1:
            return super(LayerNorm, self).forward(x)
        return (
            super(LayerNorm, self)
            .forward(x.transpose(self.dim, -1))
            .transpose(self.dim, -1)
        )
        return super(LayerNorm, self).forward(x.transpose(self.dim, -1)).transpose(self.dim, -1)


class GlobalLayerNorm(nn.Module):
@@ -97,10 +93,7 @@
            mean = torch.mean(x, (1, 2), keepdim=True)
            var = torch.mean((x - mean) ** 2, (1, 2), keepdim=True)
            if self.elementwise_affine:
                x = (
                    self.weight * (x - mean) / torch.sqrt(var + self.eps)
                    + self.bias
                )
                x = self.weight * (x - mean) / torch.sqrt(var + self.eps) + self.bias
            else:
                x = (x - mean) / torch.sqrt(var + self.eps)

@@ -108,10 +101,7 @@
            mean = torch.mean(x, (1, 2, 3), keepdim=True)
            var = torch.mean((x - mean) ** 2, (1, 2, 3), keepdim=True)
            if self.elementwise_affine:
                x = (
                    self.weight * (x - mean) / torch.sqrt(var + self.eps)
                    + self.bias
                )
                x = self.weight * (x - mean) / torch.sqrt(var + self.eps) + self.bias
            else:
                x = (x - mean) / torch.sqrt(var + self.eps)
        return x
@@ -165,13 +155,12 @@


class ScaleNorm(nn.Module):
    def __init__(self, dim, eps = 1e-5):
    def __init__(self, dim, eps=1e-5):
        super().__init__()
        self.scale = dim ** -0.5
        self.scale = dim**-0.5
        self.eps = eps
        self.g = nn.Parameter(torch.ones(1))

    def forward(self, x):
        norm = torch.norm(x, dim = -1, keepdim = True) * self.scale
        return x / norm.clamp(min = self.eps) * self.g

        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
        return x / norm.clamp(min=self.eps) * self.g