Introduction

The Transformer model has revolutionized natural language processing (NLP) by introducing self-attention mechanisms, enabling parallel processing and better context understanding. This tutorial guides you through implementing a basic Transformer from scratch.

Key Concepts

  • Self-Attention: Allows the model to weigh the importance of different words in a sentence.
  • Positional Encoding: Adds position information to token embeddings since Transformers lack inherent sequential information.
  • Feed-Forward Networks: Simple fully connected layers applied to each position separately.

Implementation Steps

  1. Tokenization
    Convert input text into tokens using a tokenizer like BPE or WordPiece.

    Tokenization_Process

  2. Embedding Layer
    Map tokens to dense vectors (embeddings) using a lookup table.

    Token_Embedding

  3. Positional Encoding
    Add positional information to embeddings using sine and cosine functions.

    Positional_Encoding

  4. Encoder-Decoder Architecture
    Build stacked self-attention and feed-forward blocks for encoding and decoding.

    Transformer_Model_Structure

Code Example (Simplified)

class Transformer(nn.Module):
    def __init__(self, vocab_size, d_model, nhead, num_layers):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, d_model)
        self.positional_encoding = PositionalEncoding(d_model)
        self.encoder = nn.TransformerEncoder(TransformerEncoderLayer(d_model, nhead), num_layers)
        self.decoder = nn.TransformerDecoder(TransformerDecoderLayer(d_model, nhead), num_layers)
        self.output_layer = nn.Linear(d_model, vocab_size)

Extend Your Learning

For a deeper dive into attention mechanisms, check out our Attention Mechanism Tutorial.

Note: All images are illustrative and generated for demonstration purposes.