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Pure Pytorch Implementation of SRU
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import torch.nn as nn | |
import torch | |
class SRU(nn.Module): | |
""" Simple Recurrent Unit https://arxiv.org/pdf/1709.02755.pdf """ | |
def __init__(self, input_size, hidden_size, activation=F.tanh): | |
super().__init__() | |
self.input_size = input_size | |
self.hidden_size = hidden_size | |
self.linear_transform = nn.Linear(input_size, hidden_size, bias=False) #for x | |
self.gate = nn.Linear(input_size, 2 * hidden_size, bias=True) # Wf and Wr | |
self.activation = activation | |
self.gate_ln = nn.LayerNorm(2 * hidden_size) | |
self.act_ln = nn.LayerNorm(hidden_size) | |
self.v = nn.Parameter(torch.randn(2*hidden_size)) | |
def forward(self, x, c): | |
if c is None: | |
c = torch.zeros((x.size(0), self.hidden_size), dtype=x.dtype, device=x.device) | |
x_tilde = self.linear_transform(x) | |
gate = F.sigmoid(self.gate_ln(self.gate(x) + torch.einsum('bs,bs->b', self.v, c) ) ) | |
f = gate[:, :, :self.hidden_size] | |
r = gate[:, :, self.hidden_size:] | |
new_data = (1 - f) * x_tilde + f*c | |
cell_states = [] | |
for t in range(x.size(1)): | |
# Every timestep | |
c = f[:, t] * c + new_data[:, t] | |
cell_states.append(c) | |
all_c = torch.stack(cell_states, dim=1) | |
h = r * self.activation(self.act_ln(all_c)) + (1 - r) * x | |
return h, c |
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