Final Project - Transformer implementation

Public repository and stub/testing code for Final Project of 10-714.

To sum up what we learned during 10-714: Deep Learning Systems, we've implemented the Transformer architecture and its corresponding modules with our self-made needle library for deep learning.

The overall goal of our Final Project is to implement the trainable Transformer architecture [1], which can be divided into some ingredients — Multi-Head Attention, Self-Attention and Positional Encoding, and The Transformer Architecture (Positionwise Feed-Forward Networks, Residual Connection and Layer Normalization, Transformer Encoder Block & Encoder, Transformer Decoder Block & Decoder, and Encoder-Decoder Seq2Seq model.)

Project structure

• Final project.ipynb - notebook with report of project results
• python/needle - all source code for needle library and project classes
• src - backend c++ sources
• tests - tests over implemented functionalities

To explore project details, go to Final project.ipynb. To run it, you can follow Setup cell blocks to run locally or in google colab. Running locally might need some changes in makefile depending on your set up.

Authours

• Yuxuan Sun: [email protected]
• Sergey: [email protected]