Update rmsnorm.py

Modifies the forward pass of RMSNorm to avoid mixed precision issues as described in https://github.com/chandar-lab/AMPLIFY/issues/19

rmsnorm.py

@@ -6,29 +6,27 @@ class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-6):
         """
         Initialize the RMSNorm normalization layer.
-
         Args:
             dim (int): The dimension of the input tensor.
             eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
-
         Attributes:
             eps (float): A small value added to the denominator for numerical stability.
             weight (nn.Parameter): Learnable scaling parameter.
-
         """
         super().__init__()
         self.eps = eps
         self.weight = nn.Parameter(torch.ones(dim))
 
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
     def forward(self, x):
         """
         Forward pass through the RMSNorm layer.
-
         Args:
             x (torch.Tensor): The input tensor.
-
         Returns:
             torch.Tensor: The output tensor after applying RMSNorm.
-
         """
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
+        output = self._norm(x.float()).type_as(x) # Avoids mixed precision issues as in https://github.com/chandar-lab/AMPLIFY/issues/19
+        return output * self.weight