# Copyright 2022 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch.nn as nn
[docs]class AdaLayerNorm(nn.Module):
[docs] def __init__(self, normalized_shape, eps=1e-05, device=None, dtype=None):
factory_kwargs = {"device": device, "dtype": dtype}
super(AdaLayerNorm, self).__init__()
self.layernorm = nn.LayerNorm(
normalized_shape=normalized_shape,
eps=eps,
elementwise_affine=False,
**factory_kwargs,
)
self.scale_linear = nn.Sequential(
nn.SiLU(), nn.Linear(normalized_shape, normalized_shape, bias=True)
)
self.shift_linear = nn.Sequential(
nn.SiLU(), nn.Linear(normalized_shape, normalized_shape, bias=True)
)
self.reset_parameters()
def reset_parameters(self):
for param in self.parameters():
param.data.zero_()
def forward(self, input, context):
shift = self.shift_linear(context)
scale = self.scale_linear(context)
output = (1 + scale.unsqueeze(1)) * self.layernorm(
input
) + shift.unsqueeze(1)
return output