text-to-text-transfer-transformer VS t5x

Google released the T5 paper about 5 years ago:

https://arxiv.org/abs/1910.10683

This included full model weights along with a detailed description of the dataset, training process, and ablations that led them to that architecture. T5 was state-of-the-art on many benchmarks when it was released, but it was of course quickly eclipsed by GPT-3.

Following GPT-3, it became much more common for labs to not release full details or model weights. Prior to that, it was common practice from Google (BERT, T5), Meta (BART), OpenAI (GPT1, GPT2) and others to release full training details and model weights.

[2] - https://arxiv.org/abs/1910.10683

T5-style relative encodings https://arxiv.org/abs/1910.10683

11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research)

You can find a link to the paper here: https://arxiv.org/abs/1910.10683

The Text-to-Text Transformer is a type of neural network architecture that is particularly well-suited for natural language processing tasks involving the generation of text. It was introduced in the paper "Attention is All You Need" by Vaswani et al. and has since become a popular choice for many NLP tasks, including language translation, summarization, and text generation

Found relevant code at https://github.com/google-research/text-to-text-transfer-transformer + all code implementations here

[3]: https://github.com/google-research/t5x

Asking because I have worked extensively on training a large model on a TPU cluster, and started with Levanter, then tried MaxText, and finally ended up on EasyLM. My thoughts are:

- Levanter is well intentioned but is unproven and lacking in features. For instance, their sharding is odd in that it requires embedding dimension to be a multiple of the number of devices, so I can't test using a model with embedding dimension 768 on a 512-device pod. Lost confidence in Levanter after finding some glaring correctness bugs (and helping get them fixed). Also, while I'm a huge fan of Equinox's approach, it's sadly underdeveloped (for instance, there's no way to specify non-default weight initialization strategies without manually doing model surgery to set weights).

- MaxText was just very difficult to work with. We felt like we were fighting against it every time we needed to change something because we would be digging through numerous needless layers of abstraction. My favorite was after one long day of debugging, I found a function who's only purpose was to pass its arguments to another function untouched; this function's only purpose was to pass its arguments untouched to a new, third function, that then slightly changed them and passed them to a fourth function that did the work.

- EasyLM is, as the name says, easy. But on a deeper dive, the sharding functionality seems to be underdeveloped. What they call "FSDP" is not necessarily true FSDP, it's literally just a certain axis that the JAX mesh is being sharded around that happens to shard some data axes and some model weight axes.

I'm still searching for a "perfect" JAX LLM codebase - any pointers?

> Are you using a normal training script i.e. "continued pretraining" on ALL parameters with just document fragments rather than input output pairs?

Yes, this one.

> do you make a custom dataset that has qa pairs about that particular knowledgebase?

This one. Once you have a checkpoint w knowledge, it makes sense to finetune. You can use either LORA or PEFT. We do it depending on the case. (some orgs have like millions of tokens and i am not that confident that PEFT).

LoRA with raw document text may not work, haven't tried that. Google has a good example of training scripts here: https://github.com/google-research/t5x (under training. and then finetuning). I like this one. Facebook Research also has a few on their repo.

If you are just looking to scrape by, I would suggest just do what they tell you to do. You can offer suggestions, but better let them take the call. A lot of fluff, a lot of chatter online, so everyone is figuring out stuff.

One note about pretraining is that it is costly, so most OSS devs just do direct finetuning/LoRA. Works because their dataset is from the open internet. Orgs aren't finding much value with these. And yet, many communities are filled with these tactics.

Google have released the models and code for the Switch Transformer from Fedus et al. (2021) under the Apache 2.0 licence. [0]

There's also OpenMoE - an open-source effort to train a mixture of experts model. Currently they've released a model with 8 billion parameters. [1]

[0] https://github.com/google-research/t5x/blob/main/docs/models...

[1] https://github.com/XueFuzhao/OpenMoE

You can find the official T5x repository by Google AI here: https://github.com/google-research/t5x

Quick Read: https://www.marktechpost.com/2022/12/20/google-ai-introduces-confident-adaptive-language-modeling-calm-for-3x-faster-text-generation-with-language-models-lms/ Paper: https://arxiv.org/pdf/2207.07061.pdf Code: https://github.com/google-research/t5x/tree/main/t5x/contrib/calm

From Section 9.1 in the paper, it looks like the weights in the Google buckets are associated with the T5X model(s?) here: https://github.com/google-research/t5x