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#Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
#
#This program is free software; you can redistribute it and/or modify it under the terms of the BSD 3-Clause License.
#
#This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the BSD 3-Clause License for more details.
import torch.nn as nn
import torch
num_parallel = 2
class TokenExchange(nn.Module):
def __init__(self):
super(TokenExchange, self).__init__()
def forward(self, x, mask, mask_threshold):
# x: [B, N, C], mask: [B, N, 1]
x0, x1 = torch.zeros_like(x[0]), torch.zeros_like(x[1])
x0[mask[0] >= mask_threshold] = x[0][mask[0] >= mask_threshold]
x0[mask[0] < mask_threshold] = x[1][mask[0] < mask_threshold]
x1[mask[1] >= mask_threshold] = x[1][mask[1] >= mask_threshold]
x1[mask[1] < mask_threshold] = x[0][mask[1] < mask_threshold]
return [x0, x1]
class ModuleParallel(nn.Module):
def __init__(self, module):
super(ModuleParallel, self).__init__()
self.module = module
def forward(self, x_parallel):
return [self.module(x) for x in x_parallel]
class LayerNormParallel(nn.Module):
def __init__(self, num_features):
super(LayerNormParallel, self).__init__()
for i in range(num_parallel):
setattr(self, 'ln_' + str(i), nn.LayerNorm(num_features, eps=1e-6))
def forward(self, x_parallel):
return [getattr(self, 'ln_' + str(i))(x) for i, x in enumerate(x_parallel)] |