Swin-Transformer VS fairseq

> there is really nothing that "normal" AI requires that is bound to CUDA. pyTorch and Tensorflow are backend agnostic (ideally...).

There are a lot of optimizations that CUDA has that are nowhere near supported in other software or even hardware. Custom cuda kernels also aren't as rare as one might think, they will often just be hidden unless you're looking at libraries. Our more well known example is going to be StyleGAN[0] but it isn't uncommon to see elsewhere, even in research code. Swin even has a cuda kernel[1]. Or find torch here[1] (which github reports that 4% of the code is cuda (and 42% C++ and 2% C)). These things are everywhere. I don't think pytorch and tensorflow could ever be agnostic, there will always be a difference just because you have to spend resources differently (developing kernels is time resource). We can draw evidence by looking at Intel MKL, which is still better than open source libraries and has been so for a long time.

I really do want AMD to compete in this space. I'd even love a third player like Intel. We really do need competition here, but it would be naive to think that there's going to be a quick catchup here. AMD has a lot of work to do and posting a few bounties and starting a company (idk, called "micro grad"?) isn't going to solve the problem anytime soon.

And fwiw, I'm willing to bet that most AI companies would rather run in house servers than from cloud service providers. The truth is that right now just publishing is extremely correlated to compute infrastructure (doesn't need to be but with all the noise we've just said "fuck the poor" because rejecting is easy) and anyone building products has costly infrastructure.

[0] https://github.com/NVlabs/stylegan2-ada-pytorch/blob/d72cc7d...

[1] https://github.com/microsoft/Swin-Transformer/blob/2cb103f2d...

[2] https://github.com/pytorch/pytorch/tree/main/aten/src

ConvNeXt. The A ConvNet for the 2020s paper is a highlight for me because the authors were able to design a purely convolutional architecture that outperformed popular vision transformers such as Swin Transformer (and all convolutional neural networks that came before it, of course).

Found relevant code at https://github.com/microsoft/Swin-Transformer + all code implementations here

Figures aside, you can reform the image from the tokens if you want. This is what's done in SWIN transformers (https://arxiv.org/abs/2103.14030) patches are tokenized, transformed, and then re-assembled into an image-like tensor. The patchification is shifted at every other transformer stage so that there is more information that propagates from one patch to the next.

Give it a few days and lucidrains will have the code up[0].

But in honesty, it is probably how people react. We saw this with Pulse, GPT, and many others. The authors are clear about the limitations but people talk it up too much and others shit on it. There's also a reproducibility crisis in ML (many famous networks, like Swin[1][2][3], can't be reproduced (even worse when reviewers concentrate on benchmarks)). It isn't like many can train a model like this anyways. It gives them benefit of the doubt and maintains good publicity rather than controversial.

Of course, this is extremely bad from an academic perspective and personally I believe you should have your paper revoked if it isn't reproducible. You'd be surprised how many don't track the random seed or measure variance. We have GitHub. You should be able to write training options that get approximately the same results as the paper. Otherwise I don't trust your results.

[0] https://github.com/lucidrains/parti-pytorch

[1] https://github.com/microsoft/Swin-Transformer/issues/183

[2] https://github.com/microsoft/Swin-Transformer/issues/180

[3] https://github.com/microsoft/Swin-Transformer/issues/148

That's about it. I should be able to go to your code and hit run, and reproduce your results (or within the reported variance). If you don't meet any of these criteria them I'm going to be pretty upset and lose a lot of respect for your work. I think we should also put pressure on these papers if they don't meet these conditions, especially if they are pushing the benchmarks (I'm looking at you Swin). If you win on benchmarks due to silicon lottery, then we shouldn't be trusting you.

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data. The MMS models are available at https://github.com/pytorch/fairseq/tree/master/examples/mms.

Found relevant code at https://github.com/pytorch/fairseq + all code implementations here