hlb-gpt VS nanoGPT

As a practitioner specializing in extremely fast-training neural networks, seeing a paper in 2023 considering fp32 as a gold standard over pure non-mixed fp16/bp16 is a bit shocking to me and feels dated/distracting from the discussion. They make good points but unless I am hopelessly misinformed, it's been pretty well established at this point in a number of circles that fp32 is overkill for the majority of uses for many modern-day practitioners. Loads of networks train directly in bfloat16 as the standard -- a lot of the modern LLMs among them. Mixed precision is very much no longer needed, not even with fp16 if you're willing to tolerate some range hacks. If you don't want the range hacks, just use bfloat16 directly. The complexity is not worth it, adds not much at all, and the dynamic loss scaler a lot of people use is just begging for more issues.

Both of the main repos that I've published in terms of speed benchmarks train directly in pure fp16 and bf16 respectively without any fp32 frippery, if you want to see an example of both paradigms successfully feel free to take a look (I'll note that bf16 is simpler on the whole for a few reasons, generally seamless): https://github.com/tysam-code/hlb-CIFAR10 [for fp16] and https://github.com/tysam-code/hlb-gpt [for bf16]

Personally from my experience, I think fp16/bf16 is honestly a bit too expressive for what we need, fp8 seems to do just fine and I think will be quite alright with some accommodations, just as with pure fp16. The what and the how of that is a story for a different day (and at this point, the max pooling operation is basically one of the slowest now).

You'll have to excuse my frustration a bit, it just is a bit jarring to see a streetsign from way in the past fly forward in the wind to hit you in the face before tumbling on its merry way. And additionally in the comment section the general discussion doesn't seem to talk about what seems to be a pretty clearly-established consensus in certain research circles. It's not really too much of a debate anymore, it works and we're off to bigger and better problems that I think we should talk about. I guess in one sense it does justify the paper's utility, but also a bit frustrating because it normalizes the conversation as a few notches back from where I personally feel that it actually is at the moment.

We've got to move out of the past, this fp32 business to me personally is like writing a Relu-activated VGG network in Keras on Tensorflow. Phew.

And while we're at it, if I shall throw my frumpy-grumpy hat right back into the ring, this is an information-theoretic problem! Not enough discussion of Shannon and co. Let's please fix that too. See my other rants for x-references to that, should you be so-inclined to punish yourself in that manner.

I made a smaller GPT model that started from Andrej's code that converges to a decent loss in a short amount of time on an A100 -- just under 2.5 minutes or so: https://github.com/tysam-code/hlb-gpt

With the original hyperparameters, it was 30-60 minutes, with a pruned down network and adjusted hyperparameters, about 6 minutes, and a variety of optimizations beyond that to bring it down.

If you want the nano-GPT basically feature-identical (but pruned down) version, 0.0.0 at ~6 minutes or so is your best bet.

You can get A100s cheaply and securely through Colab or LambdaLabs.

Check it out! If LLMs are your thing, I did basically the same thing, but for 3.8 val loss on WikiText-103 in maybe 2.3ish minutes or so on an A100: https://github.com/tysam-code/hlb-gpt.

https://github.com/tysam-code/hlb-gpt

Both of these implementations are pretty straightforward for what they do but CIFAR-10 has less dynamic scheduling and stuff so it might be easier to fit in your head. However, both are meant to be simple (and extremely hackable if you want to poke around and take apart some pieces/add different watchpoints to see how different pieces evolve, etc. I am partially inspired by, among many things, one of those see-through engine kits that I saw in a magazine growing up as a child that I thought was a very cool, dynamic, and hands-on way to just watch how the pieces moved in a difficult topic. Sometimes that is the best way that our brains can learn, though we are all different and learn best differently through different mediums in my experience).

Feel free to let me know if you have any specific questions and I'll endeavor to do my best to help you here. Welcome to an interest in the field!

I guess to briefly touch on one topic -- some people focus on the technical only first, like backprop, and though math is required heavily for more advanced research, I don't learn concepts very well through details only. Knowing that backprop is "Calculate the slope for the error in this high-dimensional space for how a neural network was wrong at a certain point, then take a tiny step towards minimizing the error. After N steps, we converge to a representation that is like a zip file of our input data within a mathematical function" is probably enough for 90-95% of the usecases you will do as a ML practitioner, if you do so. The math is cool but there are more important things to sweat over IMO, and I think messaging to the contrary raises the barrier to entry to the field and distracts from the important things, which we do not need as much. It's good to learn after you have space in your brain for it after you understand how the whole thing works together, though that is just my personal opinion after all.

Much love and care and all that and again feel free to let me know if you have any questions please. :) <3

You can find the code for hlb-gpt here: https://github.com/tysam-code/hlb-gpt

Nice work! I built something similar years ago and I did compile the probabilities based on a corpus of text (public domain books) in an attempt to produce writing in the style of various authors. The results were actually quite similar to the output of nanoGPT[0]. It was very unoptimized and everything was kept in memory. I also knew nothing about embeddings at the time and only a little about NLP techniques that would certainly have helped. Using a graph database would have probably been better than the datastructure I came up with at the time. You should look into stuff like Datalog, Tries[1], and N-Triples[2] for more inspiration.

You're idea of splitting the probabilities based on whether you're starting the sentence or finishing it is interesting but you might be able to benefit from an approach that creates a "window" of text you can use for lookup, using an LCS[3] algorithm could do that. There's probably a lot of optimization you could do based on the probabilities of different sequences, I think this was the fundamental thing I was exploring in my project.

Seeing this has inspired me further to consider working on that project again at some point.

[0] https://github.com/karpathy/nanoGPT

[1] https://en.wikipedia.org/wiki/Trie

[2] https://en.wikipedia.org/wiki/N-Triples

[3] https://en.wikipedia.org/wiki/Longest_common_subsequence

where x1 denotes the 1st token, x2 denotes the 2nd token and so on, respectively.

I understand the conditional terms p(x_n|...) where we use cross-entropy to calculate their losses. However, I'm unsure about the probability of the very first token p(x1). How is it calculated? Is it in some configurations of the training process, or in the model architecture, or in the loss function?

IMHO, if the model doesn't learn p(x1) properly, the entire formula for Bayes' rule cannot be completed, and we can't refer to LLMs as "truly generative". Am I missing something here?

I asked the same question on nanoGPT repo: https://github.com/karpathy/nanoGPT/issues/432, but I haven't found the answer I'm looking for yet. Could someone please enlighten me.

This repo, albeit "old" in regards to how much progress there's been in LLMs, has great simple tutorials right there eg. fine-tuning GPT2 with Shakespeare: https://github.com/karpathy/nanoGPT

For training from scratch, maybe a small model like https://github.com/karpathy/nanoGPT or tinyllama. Perhaps with quantization.

It does remind me of a project [1] Andrej Karpathy did, writing a neural network and training code in ~600 lines (although networks have easier logic to code than a compiler).

[1] https://github.com/karpathy/nanoGPT

But I'm still not convinced that it can't in theory. Maybe the training set or transformer size I'm using is too small. I'm using nanoGPT implementation (https://github.com/karpathy/nanoGPT) with layers 24, heads 12, and embeddings per head 32. I'm using character-based vocab: every digit is a separate token, +, = and EOL.

https://github.com/karpathy/nanoGPT/blob/f08abb45bd2285627d1...

At training time, probabilities for the next token are computed for each position, so if we feed in a sequence of n tokens, we basically get n training examples, one for each position, but at inference time, we only compute the next token since we’ve already output the preceding ones.

And there are a bunch of other efforts at making training more efficient. Here's a cool model by Karpathy (OpenAI/used to head up Tesla's efforts): https://github.com/karpathy/nanoGPT

Just being a part of any auto-regressive system does not contradict his statement.

Go look at the GPT training code, here is the exact line: https://github.com/karpathy/nanoGPT/blob/master/train.py#L12...

The model is only trained to predict the next token. The training regime is purely next-token prediction. There is no loopiness whatsoever here, strange or ordinary.

Just because you take that feedforward neural network and wrap it in a loop to feed it its own output does not change the architecture of the neural net itself. The neural network was trained in one direction and runs in one direction. Hofstadter is surprised that such an architecture yields something that looks like intelligence.

He specifically used the correct term "feedforward" to constrast with recurrent neural networks, which GPT is not: https://en.wikipedia.org/wiki/Feedforward_neural_network