Source code for fairseq.modules.fp32_group_norm

# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
Layer norm done in fp32 (for fp16 training)

import torch.nn as nn
import torch.nn.functional as F

[docs]class Fp32GroupNorm(nn.GroupNorm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
[docs] def forward(self, input): output = F.group_norm( input.float(), self.num_groups, self.weight.float() if self.weight is not None else None, self.bias.float() if self.bias is not None else None, self.eps, ) return output.type_as(input)