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|
| | import torch.nn as nn |
| | import torch |
| |
|
| | num_parallel = 1 |
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|
| | 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] |
| |
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| |
|
| | 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)] |