Applied Transformers (PyTorch)

A playground-like experimental project to explore various transformer architectures from scratch.

Resources:

Intuitions:

1. Intuition behind Attention Mechanism | Notebook
2. Intuition behind individual Transformer Blocks | Notebook
3. Intuition behind Chunked Cross-attention by RETRO Deepmind | Notebook

Implementations from Scratch:

Create virtual environment:

conda create -n applied-transformers python=3.10
conda activate applied-transformers

Install Dependencies:

pip install -r requirements.txt

1. Transformer Model from Scratch {Vaswani et. al, 2017} | Dataset Sample | Python Code

# example training run
python transformer_architectures/vanilla/run.py --num_layers=5\
 --d_model=256 --d_ff=1024 --num_heads=4 --dropout=0.2 \
--train_path=<PATH_TO_TRAIN_DATASET>.csv  --valid_path=<PATH_TO_VALIDATION_DATASET>.csv

2. GPT Model from Scratch {Radford et. al, 2018} | Coming Soon
3. BERT Model from Scratch {Lewis et. al, 2019} | Coming Soon
4. RETRO Model from Scratch {Borgeaud et. al, 2021} | Coming Soon
5. BART Model from Scratch {Lewis et. al, 2019} | Coming Soon

TODO:

• Text Generation Schemes
• Text Generation Eval Metrics
• Sequence Tokenization Algorithms
• Optimized Einsum Implementation

References

1. http://nlp.seas.harvard.edu/annotated-transformer/
2. https://nn.labml.ai/transformers/models.html
3. Transformers from scratch | CodeEmporium