transformer-cpp: Implementing the Transformer Architecture from Scratch with Modern C++

transformer-cpp is an educational GitHub project maintained by Jean-Marc-NH, released on July 12, 2026. This project implements the Transformer architecture entirely from scratch using modern C++, without relying on external machine learning frameworks like PyTorch or TensorFlow. It aims to help developers gain a deep understanding of core components such as attention mechanisms, multi-head attention, and feed-forward networks, avoiding the "black box" dependency on framework APIs.

In today's era of highly developed deep learning frameworks, developers often rely on ready-made APIs to quickly build models, but this can easily lead to a "black box" understanding of underlying mechanisms. The transformer-cpp project takes the opposite approach: no external dependencies, hand-implementing every component (including matrix operations), and using a hands-on method to help developers gain a deep understanding of the Transformer architecture. It is positioned as an educational project rather than a production tool.

Reasons for Choosing C++: Explicit memory management (visible tensor storage/copy), compile-time type checking (catch dimension errors early), performance transparency (no interpreter overhead), engineering practice (the underlying layers of deep learning systems are mostly implemented in C++).

Implemented Components: Custom linear algebra library (Matrix/Vector classes, random initialization, activation functions), word-based tokenizer.

To-be-Implemented Modules: Embedding layer, positional encoding, multi-head self-attention, feed-forward network, layer normalization, encoder/decoder, Adam optimizer, training pipeline.

Project Structure: Clear modularization: math/ (linear algebra basics), tokenizer/ (tokenizer), test/ (test code), main (entry point).

Reference Resources: Original paper Attention Is All You Need, open-source textbook Dive into Deep Learning, classic textbook Deep Learning (by Goodfellow et al.).

Learning Value: Understand Transformer principles by breaking down layer by layer, track tensor dimension changes, understand gradient flow (if backpropagation is implemented), master engineering trade-offs from algorithm to implementation.

Target Audience: Computer science students (apply mathematical knowledge), deep learning researchers (understand underlying details), C++ developers (enter the AI field), interview candidates (practice hand-implementing components).

Limitations: Positioned for educational use, performance is not as good as mature frameworks (no hardware acceleration), no automatic differentiation, lack of ecosystem support.

Usage Recommendations: Use this project to understand principles rather than replace production frameworks; after learning, using frameworks like PyTorch will allow a deeper understanding of API design and internal implementation.