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tied linear layer experiment
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import torch, torch.nn as nn, torch.nn.functional as F | |
import numpy as np | |
import torch.optim as optim | |
# tied autoencoder using off the shelf nn modules | |
class TiedAutoEncoderOffTheShelf(nn.Module): | |
def __init__(self, inp, out, weight): | |
super().__init__() | |
self.encoder = nn.Linear(inp, out, bias=False) | |
self.decoder = nn.Linear(out, inp, bias=False) | |
# tie the weights | |
self.encoder.weight.data = weight.clone() | |
self.decoder.weight = self.encoder.weight.transpose(0,1) | |
def forward(self, input): | |
encoded_feats = self.encoder(input) | |
reconstructed_output = self.decoder(encoded_feats) | |
return encoded_feats, reconstructed_output | |
# tied auto encoder using functional calls | |
class TiedAutoEncoderFunctional(nn.Module): | |
def __init__(self, inp, out): | |
super().__init__() | |
self.param = nn.Parameter(torch.randn(out, inp)) | |
def forward(self, input): | |
encoded_feats = F.linear(input, self.param) | |
reconstructed_output = F.linear(encoded_feats, self.param.t()) | |
return encoded_feats, reconstructed_output | |
# mixed approach | |
class MixedAppraochTiedAutoEncoder(nn.Module): | |
def __init__(self, inp, out, weight): | |
super().__init__() | |
self.encoder = nn.Linear(inp, out, bias=False) | |
self.encoder.weight.data = weight.clone() | |
def forward(self, input): | |
encoded_feats = self.encoder(input) | |
reconstructed_output = F.linear(encoded_feats, self.encoder.weight.t()) | |
return encoded_feats, reconstructed_output | |
if __name__ == '__main__': | |
tied_module_F = TiedAutoEncoderFunctional(5, 6) | |
tied_module_offshelf = TiedAutoEncoderOffTheShelf(5, 6, tied_module_F.param.data.detach()) | |
tied_module_mixed = MixedAppraochTiedAutoEncoder(5, 6, tied_module_F.param.data.detach()) | |
assert torch.equal(tied_module_offshelf.encoder.weight.data, tied_module_F.param.data), 'F vs offshelf: param not equal' | |
assert torch.equal(tied_module_mixed.encoder.weight.data, tied_module_F.param.data), 'F vs mixed: param not equal' | |
optim_F = optim.SGD(tied_module_F.parameters(), lr=1) | |
optim_offshelf = optim.SGD(tied_module_offshelf.parameters(), lr=1) | |
optim_mixed = optim.SGD(tied_module_mixed.parameters(), lr=1) | |
# common input | |
input = torch.rand(5, 5) | |
# zero the gradients | |
optim_F.zero_grad() | |
optim_offshelf.zero_grad() | |
optim_mixed.zero_grad() | |
# get output from both modules | |
reconstruction_F = tied_module_F(input) | |
reconstruction_offshelf = tied_module_offshelf(input) | |
reconstruction_mixed = tied_module_mixed(input) | |
# back propagation | |
reconstruction_F[1].sum().backward() | |
reconstruction_offshelf[1].sum().backward() | |
reconstruction_mixed[1].sum().backward() | |
# step | |
optim_F.step() | |
optim_offshelf.step() | |
optim_mixed.step() | |
# check the equality of output and parameters | |
assert torch.equal(reconstruction_offshelf[0], reconstruction_F[0]), 'F vs offshelf: bottleneck not equal' | |
assert torch.equal(reconstruction_offshelf[1], reconstruction_F[1]), 'F vs offshelf: output not equal' | |
assert (tied_module_offshelf.encoder.weight.data - tied_module_F.param.data).pow(2).sum() < 1e-10, 'F vs offshelf: param after step not equal' | |
assert torch.equal(reconstruction_mixed[0], reconstruction_F[0]), 'F vs mixed: bottleneck not equal' | |
assert torch.equal(reconstruction_mixed[1], reconstruction_F[1]), 'F vs mixed: output not equal' | |
assert (tied_module_mixed.encoder.weight.data - tied_module_F.param.data).pow(2).sum() < 1e-10, 'F vs mixed: param after step not equal' | |
print('success!') |
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