Awesome

LaMDA-pytorch

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.

Acknowledgement:

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

Developer updates can be found on:

Basic Usage - Pre-training

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)

Notes on training at scale:

Pipeline parallelism should be used with ZeRO 1, not ZeRO 2.

About LaMDA:

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

TODO:

Author

Enrico Shippole

Citations

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  author    = {Romal Thoppilan and
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               Leslie Baker and
               Yu Du and
               YaGuang Li and
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               Amin Ghafouri and
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               Dmitry Lepikhin and
               James Qin and
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               Igor Krivokon and
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               Meredith Ringel Morris and
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               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,
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  publisher = {arXiv},
  
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@misc{https://doi.org/10.48550/arxiv.1910.10683,
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  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.},
  
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  publisher = {arXiv},
  
  year = {2019},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

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  doi = {10.48550/ARXIV.2002.05202},
  
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  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
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  journal   = {CoRR},
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  year      = {2021},
  url       = {https://arxiv.org/abs/2101.00027},
  eprinttype = {arXiv},
  eprint    = {2101.00027},
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}