```
11from typing import Optional
12
13import torch
14from torch import nn
15
16from labml_helpers.module import Module
```

This implements equations $(6) - (9)$.

$s_d^t = h_d^t \odot g_d^t + s_{d - 1}^t \odot c_d^t$

where

and for $0 < d < D$

$\odot$ stands for element-wise multiplication.

Here we have made a couple of changes to notations from the paper. To avoid confusion with time, gate is represented with $g$, which was $t$ in the paper. To avoid confusion with multiple layers we use $d$ for depth and $D$ for total depth instead of $l$ and $L$ from the paper.

We have also replaced the weight matrices and bias vectors from the equations with linear transforms, because that’s how the implementation is going to look like.

We implement weight tying, as described in paper, $c_d^t = 1 - g_d^t$.

`19class RHNCell(Module):`

`input_size`

is the feature length of the input and `hidden_size`

is
the feature length of the cell.
`depth`

is $D$.

`57 def __init__(self, input_size: int, hidden_size: int, depth: int):`

```
63 super().__init__()
64
65 self.hidden_size = hidden_size
66 self.depth = depth
```

We combine $lin_{hs}$ and $lin_{gs}$, with a single linear layer. We can then split the results to get the $lin_{hs}$ and $lin_{gs}$ components. This is the $lin_{hs}^d$ and $lin_{gs}^d$ for $0 \leq d < D$.

`70 self.hidden_lin = nn.ModuleList([nn.Linear(hidden_size, 2 * hidden_size) for _ in range(depth)])`

Similarly we combine $lin_{hx}$ and $lin_{gx}$.

`73 self.input_lin = nn.Linear(input_size, 2 * hidden_size, bias=False)`

`x`

has shape `[batch_size, input_size]`

and
`s`

has shape `[batch_size, hidden_size]`

.

`75 def __call__(self, x: torch.Tensor, s: torch.Tensor):`

Iterate $0 \leq d < D$

`82 for d in range(self.depth):`

We calculate the concatenation of linear transforms for $h$ and $g$

`84 if d == 0:`

The input is used only when $d$ is $0$.

```
86 hg = self.input_lin(x) + self.hidden_lin[d](s)
87 else:
88 hg = self.hidden_lin[d](s)
```

Use the first half of `hg`

to get $h_d^t$

`95 h = torch.tanh(hg[:, :self.hidden_size])`

Use the second half of `hg`

to get $g_d^t$

```
101 g = torch.sigmoid(hg[:, self.hidden_size:])
102
103 s = h * g + s * (1 - g)
104
105 return s
```

`108class RHN(Module):`

Create a network of `n_layers`

of recurrent highway network layers, each with depth `depth`

, $D$.

`113 def __init__(self, input_size: int, hidden_size: int, depth: int, n_layers: int):`

```
118 super().__init__()
119 self.n_layers = n_layers
120 self.hidden_size = hidden_size
```

Create cells for each layer. Note that only the first layer gets the input directly. Rest of the layers get the input from the layer below

```
123 self.cells = nn.ModuleList([RHNCell(input_size, hidden_size, depth)] +
124 [RHNCell(hidden_size, hidden_size, depth) for _ in range(n_layers - 1)])
```

`x`

has shape `[seq_len, batch_size, input_size]`

and
`state`

has shape `[batch_size, hidden_size]`

.

`126 def __call__(self, x: torch.Tensor, state: Optional[torch.Tensor] = None):`

`131 time_steps, batch_size = x.shape[:2]`

Initialize the state if `None`

```
134 if state is None:
135 s = [x.new_zeros(batch_size, self.hidden_size) for _ in range(self.n_layers)]
136 else:
```

Reverse stack the state to get the state of each layer

📝 You can just work with the tensor itself but this is easier to debug

`139 s = torch.unbind(state)`

Array to collect the outputs of the final layer at each time step.

`142 out = []`

Run through the network for each time step

`145 for t in range(time_steps):`

Input to the first layer is the input itself

`147 inp = x[t]`

Loop through the layers

`149 for layer in range(self.n_layers):`

Get the state of the layer

`151 s[layer] = self.cells[layer](inp, s[layer])`

Input to the next layer is the state of this layer

`153 inp = s[layer]`

Collect the output of the final layer

`155 out.append(s[-1])`

Stack the outputs and states

```
158 out = torch.stack(out)
159 s = torch.stack(s)
160
161 return out, s
```