# Copyright 2021 Ross Wightman . All rights reserved.
#
# 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.
# This code is modified by Zilliz.
from torch import nn
[docs]class GatedMlp(nn.Module):
""" MLP as used in gMLP
"""
[docs] def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, gate_layer=None, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
if gate_layer is not None:
assert hidden_features % 2 == 0
self.gate = gate_layer(hidden_features)
hidden_features = hidden_features // 2 # FIXME base reduction on gate property?
else:
self.gate = nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
[docs] def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.gate(x)
x = self.fc2(x)
x = self.drop(x)
return x
