Transformers: How They Work and How to Code One Yourself
Transformers have transformed (pun intended) the world of natural language processing (NLP). They power many of today’s most advanced AI applications, from machine translation to text generation. But what exactly are transformers, and how do they work?
In this article, we’ll dive into the newly acclaimed world of transformers, explaining the mechanics in simple terms, and then show you how to code one from scratch. Whether you’re a seasoned machine learning enthusiast or someone new to the field, this guide will help you understand and implement your own.
What Are Transformers?
Introduced in the landmark paper “Attention is All You Need” by Vaswani et al. in 2017, transformers are deep learning models designed for sequence-to-sequence tasks, especially in NLP. Unlike previous models like recurrent neural networks (RNNs) and LSTMs, transformers do not rely on sequential data processing. Instead, they use an attention mechanism, specifically self-attention, to capture the relationships between different elements in a sequence.
The power of transformers lies in their ability to process data in parallel and look at the entire input at once, making them faster and more effective at understanding long-range dependencies in text. This is a big deal, as earlier models like LSTMs often struggled with long sentences or paragraphs.
The Core Concepts of Transformers
To truly grasp how transformers work, we need to break down three key concepts: self-attention, multi-head attention, and positional encoding. These components work together to enable transformers to understand the structure of language.
Self-Attention Mechanism
At the heart of a transformer lies the self-attention mechanism. In simple terms, self-attention allows the model to focus on different parts of a sentence when making predictions. It’s like reading a sentence and deciding which words are most relevant to understanding the meaning of the entire sentence.
Here’s how self-attention works:
- For each word, the model generates three vectors: query (Q), key (K), and value (V).
- The query and key vectors are used to measure the relationship between words. In other words, they help the model decide how much focus (or attention) each word should give to other words.
- Once the attention scores are calculated, they’re applied to the value vectors, which represent the actual information in the words.
In essence, self-attention determines the importance of each word relative to others, helping the model understand the context and meaning.
Multi-Head Attention
Transformers take self-attention a step further with multi-head attention. Instead of applying just one attention mechanism, transformers use multiple attention heads, each focusing on different aspects of the sentence.
Why? Because different parts of a sentence might carry different types of information. For example, one attention head might focus on relationships between nouns, while another could focus on verbs. By combining the outputs from multiple heads, the transformer builds a richer representation of the input data.
Positional Encoding
Transformers are inherently non-sequential, which means they don’t process data in the order it appears, unlike RNNs. To overcome this, positional encoding is added to the input embeddings to provide information about the position of each word in a sentence.
Think of positional encoding as a signal that tells the model where each word is located in the sequence. Without this, the transformer wouldn’t know the difference between “The cat chased the dog” and “The dog chased the cat.”
How Transformers Work: Encoders and Decoders
A transformer consists of two main components: the encoder and the decoder. The encoder processes the input data (e.g., a sentence in English) and converts it into an internal representation. The decoder then takes that representation and generates the output (e.g., a translated sentence in French).
Each encoder and decoder block consists of several layers of multi-head attention and feed-forward neural networks. The magic happens when these blocks are stacked on top of each other, allowing the transformer to learn more complex relationships in the data.
- Encoder: Maps the input sequence into an intermediate representation using layers of self-attention and feed-forward networks.
- Decoder: Takes the encoded representation and generates the output sequence by applying additional attention and feed-forward layers.
Coding a Transformer from Scratch
Now that we’ve covered the theory, let’s get practical. Here’s a simple implementation of the transformer model using PyTorch. We’ll focus on building the core components step by step.
Step 1: Install PyTorch
First, make sure you have PyTorch installed. You can install it using pip:
pip3 install torchStep 2: Implement the Self-Attention Mechanism
We’ll start by implementing the self-attention mechanism, which calculates attention scores for each word in the input sequence.
import torch
import torch.nn as nn
import math
class SelfAttention(nn.Module):
def __init__(self, embed_size, heads):
super(SelfAttention, self).__init__()
self.embed_size = embed_size
self.heads = heads
self.head_dim = embed_size // heads
assert self.head_dim * heads == embed_size, "Embedding size must be divisible by heads"
self.values = nn.Linear(self.head_dim, embed_size, bias=False)
self.keys = nn.Linear(self.head_dim, embed_size, bias=False)
self.queries = nn.Linear(self.head_dim, embed_size, bias=False)
self.fc_out = nn.Linear(embed_size, embed_size)
def forward(self, values, keys, query, mask):
N = query.shape[0] # Number of examples
value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]
values = values.reshape(N, value_len, self.heads, self.head_dim)
keys = keys.reshape(N, key_len, self.heads, self.head_dim)
queries = query.reshape(N, query_len, self.heads, self.head_dim)
energy = torch.einsum("nqhd,nkhd->nhqk", [queries, keys]) # (N, heads, query_len, key_len)
scale = self.embed_size ** (1/2)
energy = energy / scale
attention = torch.softmax(energy, dim=3) # Attention scores
out = torch.einsum("nhql,nlhd->nqhd", [attention, values]).reshape(N, query_len, self.embed_size)
out = self.fc_out(out)
return outStep 3: Build the Transformer Block
Next, we combine self-attention with a feed-forward neural network to form the basic transformer block.
class TransformerBlock(nn.Module):
def __init__(self, embed_size, heads, dropout, forward_expansion):
super(TransformerBlock, self).__init__()
self.attention = SelfAttention(embed_size, heads)
self.norm1 = nn.LayerNorm(embed_size)
self.norm2 = nn.LayerNorm(embed_size)
self.feed_forward = nn.Sequential(
nn.Linear(embed_size, forward_expansion * embed_size),
nn.ReLU(),
nn.Linear(forward_expansion * embed_size, embed_size)
)
self.dropout = nn.Dropout(dropout)
def forward(self, value, key, query, mask):
attention = self.attention(value, key, query, mask)
x = self.dropout(self.norm1(attention + query))
forward = self.feed_forward(x)
out = self.dropout(self.norm2(forward + x))
return outWrapping It Up
In this guide, we’ve explored the inner workings of transformers, from self-attention to multi-head attention and positional encoding. We’ve also walked through the process of coding a transformer from scratch using PyTorch. Understanding how transformers function is crucial if you want to dive deeper into cutting-edge NLP tasks, and implementing one yourself is a great way to solidify your knowledge.
If you’re interested in learning more or expanding on this implementation, I encourage you to explore resources like the original “Attention is All You Need” paper or experiment with building your own transformer-based models.
Thanks for reading! If you have any questions or want to share your own experience with transformers, feel free to leave a comment below!
