TinyTorch/modules/transformer

Transformer Module

Status: 🚧 Coming Soon

Overview

The Transformer module will be a lightweight implementation of transformer architecture, teaching students how modern attention-based models work from the ground up.

Learning Goals

• Understand attention mechanisms and their computational complexity
• Implement multi-head attention from scratch
• Learn about positional encoding and layer normalization
• Explore transformer architecture design patterns
• Understand memory and computational optimization for attention

Module Dependencies

This module builds on:

• tensor - For all computations
• layers - For feed-forward networks
• networks - For composing transformer blocks
• autograd - For training attention models
• training - For training transformer models

Planned Components

• Attention mechanism implementation
• Multi-head attention
• Positional encoding
• Layer normalization
• Transformer blocks
• Complete transformer architecture
• Memory optimization techniques
• Attention visualization tools

Systems Focus

• Memory management for attention matrices
• Computational complexity analysis
• Parallelization of multi-head attention
• Optimization techniques (sparse attention, linear attention)
• Scaling considerations for large sequences

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This module will be implemented after the core modules (tensor, layers, networks, autograd, training) are complete.