Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model here.
I have been greatly inspired by the work of Dr. Phil 'Lucid' Wang. Please check out his open-source implementations of multiple different transformer architectures and support his work.
Developer updates can be found on:
lamda_base = LaMDA(
num_tokens = 20000,
dim = 512,
dim_head = 64,
depth = 12,
heads = 8
)
lamda = AutoregressiveWrapper(lamda_base, max_seq_len = 512)
tokens = torch.randint(0, 20000, (1, 512)) # mock token data
logits = lamda(tokens)
print(logits)- T5 Relative Positional Bias in Attention
- Gated GELU Activation in the Feed forward layer
- GPT-like Decoder Only architecture
- Autoregressive with Top-k sampling
- Sentencepiece Byte-pair encoded tokenizer
- Finish building pre-training model architecture
- Add pre-training script
- Integrate Huggingface datasets
- Implement GPT-2 tokenizer
- Add Sentencepiece tokenizer training script and integration
- Add detailed documentation
- Add logging with Weights And Biases
- Add scaling with ColossalAI.
- Add finetuning script
- Add pip installer with PyPI
- Add inference only if someone wants to open-source LaMDA model weights
- Enrico Shippole
@article{DBLP:journals/corr/abs-2201-08239,
author = {Romal Thoppilan and
Daniel De Freitas and
Jamie Hall and
Noam Shazeer and
Apoorv Kulshreshtha and
Heng{-}Tze Cheng and
Alicia Jin and
Taylor Bos and
Leslie Baker and
Yu Du and
YaGuang Li and
Hongrae Lee and
Huaixiu Steven Zheng and
Amin Ghafouri and
Marcelo Menegali and
Yanping Huang and
Maxim Krikun and
Dmitry Lepikhin and
James Qin and
Dehao Chen and
Yuanzhong Xu and
Zhifeng Chen and
Adam Roberts and
Maarten Bosma and
Yanqi Zhou and
Chung{-}Ching Chang and
Igor Krivokon and
Will Rusch and
Marc Pickett and
Kathleen S. Meier{-}Hellstern and
Meredith Ringel Morris and
Tulsee Doshi and
Renelito Delos Santos and
Toju Duke and
Johnny Soraker and
Ben Zevenbergen and
Vinodkumar Prabhakaran and
Mark Diaz and
Ben Hutchinson and
Kristen Olson and
Alejandra Molina and
Erin Hoffman{-}John and
Josh Lee and
Lora Aroyo and
Ravi Rajakumar and
Alena Butryna and
Matthew Lamm and
Viktoriya Kuzmina and
Joe Fenton and
Aaron Cohen and
Rachel Bernstein and
Ray Kurzweil and
Blaise Aguera{-}Arcas and
Claire Cui and
Marian Croak and
Ed H. Chi and
Quoc Le},
title = {LaMDA: Language Models for Dialog Applications},
journal = {CoRR},
volume = {abs/2201.08239},
year = {2022},
url = {https://arxiv.org/abs/2201.08239},
eprinttype = {arXiv},
eprint = {2201.08239},
timestamp = {Fri, 22 Apr 2022 16:06:31 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2201-08239.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@misc{https://doi.org/10.48550/arxiv.1706.03762,
doi = {10.48550/ARXIV.1706.03762},
url = {https://arxiv.org/abs/1706.03762},
author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Attention Is All You Need},
publisher = {arXiv},
year = {2017},
copyright = {arXiv.org perpetual, non-exclusive license}
}@misc{https://doi.org/10.48550/arxiv.1910.10683,
doi = {10.48550/ARXIV.1910.10683},
url = {https://arxiv.org/abs/1910.10683},
author = {Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J.},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
publisher = {arXiv},
year = {2019},
copyright = {arXiv.org perpetual, non-exclusive license}
}@misc{https://doi.org/10.48550/arxiv.2002.05202,
doi = {10.48550/ARXIV.2002.05202},
url = {https://arxiv.org/abs/2002.05202},
author = {Shazeer, Noam},
keywords = {Machine Learning (cs.LG), Neural and Evolutionary Computing (cs.NE), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {GLU Variants Improve Transformer},
publisher = {arXiv},
year = {2020},
copyright = {arXiv.org perpetual, non-exclusive license}
}@article{DBLP:journals/corr/abs-2101-00027,
author = {Leo Gao and
Stella Biderman and
Sid Black and
Laurence Golding and
Travis Hoppe and
Charles Foster and
Jason Phang and
Horace He and
Anish Thite and
Noa Nabeshima and
Shawn Presser and
Connor Leahy},
title = {The Pile: An 800GB Dataset of Diverse Text for Language Modeling},
journal = {CoRR},
volume = {abs/2101.00027},
year = {2021},
url = {https://arxiv.org/abs/2101.00027},
eprinttype = {arXiv},
eprint = {2101.00027},
timestamp = {Thu, 14 Oct 2021 09:16:12 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-00027.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@article{DBLP:journals/corr/abs-1808-06226,
author = {Taku Kudo and
John Richardson},
title = {SentencePiece: {A} simple and language independent subword tokenizer
and detokenizer for Neural Text Processing},
journal = {CoRR},
volume = {abs/1808.06226},
year = {2018},
url = {http://arxiv.org/abs/1808.06226},
eprinttype = {arXiv},
eprint = {1808.06226},
timestamp = {Sun, 02 Sep 2018 15:01:56 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1808-06226.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}@inproceedings{sennrich-etal-2016-neural,
title = "Neural Machine Translation of Rare Words with Subword Units",
author = "Sennrich, Rico and
Haddow, Barry and
Birch, Alexandra",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P16-1162",
doi = "10.18653/v1/P16-1162",
pages = "1715--1725",
}