Getting Started with Transformers in PyTorch

Introduction to Transformers

Transformers revolutionized natural language processing when Google introduced the "Attention is All You Need" paper in 2017. In this guide, we will build a Transformer from scratch using PyTorch.

What is Self-Attention?

Self-attention allows the model to look at other positions in the input sequence when encoding a specific position. It computes a weighted sum of all positions.

import torch
import torch.nn as nn

class MultiHeadAttention(nn.Module):
    def __init__(self, d_model, num_heads):
        super().__init__()
        self.num_heads = num_heads
        self.d_model   = d_model
        self.d_k       = d_model // num_heads
        self.W_q = nn.Linear(d_model, d_model)
        self.W_k = nn.Linear(d_model, d_model)
        self.W_v = nn.Linear(d_model, d_model)
        self.W_o = nn.Linear(d_model, d_model)

This is just the beginning. The full Transformer architecture includes positional encoding, feed-forward layers, and layer normalization.