Source code for fairseq.modules.layer_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.

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

    from apex.normalization import FusedLayerNorm as _FusedLayerNorm

    has_fused_layernorm = True

    class FusedLayerNorm(_FusedLayerNorm):
        def forward(self, x):
            if not x.is_cuda:
                return super().forward(x)
                with torch.cuda.device(x.device):
                    return super().forward(x)

except ImportError:
    has_fused_layernorm = False

[docs]def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False): if torch.jit.is_scripting() or torch.jit.is_tracing(): export = True if not export and torch.cuda.is_available() and has_fused_layernorm: return FusedLayerNorm(normalized_shape, eps, elementwise_affine) return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
[docs]class Fp32LayerNorm(nn.LayerNorm): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs)
[docs] def forward(self, input): output = F.layer_norm( input.float(), self.normalized_shape, 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)