# MIT License # Copyright (c) Microsoft # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # Copyright (c) [2025] [Microsoft] # SPDX-License-Identifier: MIT import torch import torch.nn as nn class LayerNorm32(nn.LayerNorm): def forward(self, x: torch.Tensor) -> torch.Tensor: return super().forward(x.float()).type(x.dtype) class GroupNorm32(nn.GroupNorm): """ A GroupNorm layer that converts to float32 before the forward pass. """ def forward(self, x: torch.Tensor) -> torch.Tensor: return super().forward(x.float()).type(x.dtype) class ChannelLayerNorm32(LayerNorm32): def forward(self, x: torch.Tensor) -> torch.Tensor: DIM = x.dim() x = x.permute(0, *range(2, DIM), 1).contiguous() x = super().forward(x) x = x.permute(0, DIM-1, *range(1, DIM-1)).contiguous() return x