r"""
The MIT License (MIT)
Copyright (c) 2020 Varuna Jayasiri
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---
title: Position-wise Feed-Forward Network (FFN)
summary: Documented reusable implementation of the position wise feedforward network.
---
# Position-wise Feed-Forward Network (FFN)
This is a [PyTorch](https://pytorch.org) implementation
of position-wise feedforward network used in transformer.
FFN consists of two fully connected layers.
Number of dimensions in the hidden layer $d_{ff}$, is generally set to around
four times that of the token embedding $d_{model}$.
So it is sometime also called the expand-and-contract network.
There is an activation at the hidden layer, which is
usually set to ReLU (Rectified Linear Unit) activation, $$\max(0, x)$$
That is, the FFN function is,
$$FFN(x, W_1, W_2, b_1, b_2) = \max(0, x W_1 + b_1) W_2 + b_2$$
where $W_1$, $W_2$, $b_1$ and $b_2$ are learnable parameters.
Sometimes the
GELU (Gaussian Error Linear Unit) activation is also used instead of ReLU.
$$x \Phi(x)$$ where $\Phi(x) = P(X \le x), X \sim \mathcal{N}(0,1)$
### Gated Linear Units
This is a generic implementation that supports different variants including
[Gated Linear Units](https://papers.labml.ai/paper/2002.05202) (GLU).
"""
import torch
from torch import nn as nn
from darts.utils.torch import MonteCarloDropout
[docs]class FeedForward(nn.Module):
"""
## FFN module
source
[FeedForward network](https://arxiv.org/abs/2002.05202)
"""
def __init__(
self,
d_model: int,
d_ff: int,
dropout: float = 0.1,
activation=nn.ReLU(),
is_gated: bool = False,
bias1: bool = True,
bias2: bool = True,
bias_gate: bool = True,
):
"""
* `d_model` is the number of features in a token embedding
* `d_ff` is the number of features in the hidden layer of the FFN
* `dropout` is dropout probability for the hidden layer,
compatible with Monte Carlo dropout at inference time
* `is_gated` specifies whether the hidden layer is gated
* `bias1` specified whether the first fully connected layer should have a learnable bias
* `bias2` specified whether the second fully connected layer should have a learnable bias
* `bias_gate` specified whether the fully connected layer for the gate should have a learnable bias
"""
super().__init__()
# Layer one parameterized by weight $W_1$ and bias $b_1$
self.layer1 = nn.Linear(d_model, d_ff, bias=bias1)
# Layer one parameterized by weight $W_1$ and bias $b_1$
self.layer2 = nn.Linear(d_ff, d_model, bias=bias2)
# Hidden layer dropout
self.dropout = MonteCarloDropout(dropout)
# Activation function $f$
self.activation = activation
# Whether there is a gate
self.is_gated = is_gated
if is_gated:
# If there is a gate the linear layer to transform inputs to
# be multiplied by the gate, parameterized by weight $V$ and bias $c$
self.linear_v = nn.Linear(d_model, d_ff, bias=bias_gate)
[docs] def forward(self, x: torch.Tensor):
# $f(x W_1 + b_1)$
g = self.activation(self.layer1(x))
# If gated, $f(x W_1 + b_1) \otimes (x V + b) $
if self.is_gated:
x = g * self.linear_v(x)
# Otherwise
else:
x = g
# Apply dropout
x = self.dropout(x)
# $(f(x W_1 + b_1) \otimes (x V + b)) W_2 + b_2$ or $f(x W_1 + b_1) W_2 + b_2$
# depending on whether it is gated
return self.layer2(x)