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Comment by lacker | Hacker News Reader

After all the attention OpenAI got for making the GPT-3 API semi-publicly-available, I wonder if Google has considered making some of their research models available via API. It would be pretty neat to be able to try these things out, rather than just reading papers about them.

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You've actually got it backwards.

Releasing model weights has been common for a long time, it was OpenAI that regressed and refused to release its GPT models until it released GPT-3 hidden behind a semi-public API. The google BERT models are released pre-trained in full (as mentioned in the article), you can easily play around with them on your own.

BERT isn't a forward LM like GPT, so it's a little less easy to play with for the uninitiated - although there are papers showing text generation with BERT.

It's really ironic that they are regressing and keeping their research behind a curtain when the entire pretense for their existence (and their name) is to open things up.

It's easier to hire people for a (claimed) mission driven company than for business evil corp that builds killer machines for the us military.

It’s not like it’s a new across-the-board policy for them. They were just being cautious in this particular case because they were worried about social ramifications. Arguably some other research should have been made less readily available like this (think deepfakes for example).

To be fair, IIRC OpenAI did release GPT-2-large after some community pressure, and it was somewhat doable for some people to actually train from scratch. GPT-3 is too large, so even if they released it, nobody apart from large companies like Google could do anything with it. If anything, they've made GPT-3 more open than it would have been if they just released the weights.

At least that's my understanding. Feel free to correct me if I'm wrong.

I'm not really up in arms about OpenAIs decision, but I just don't want people to frame this as "why can't more companies release models like OpenAI does" when in reality the opposite is pretty much true.

GPT-2 wasn't really feasible for most actors to train from scratch, otherwise it would have been released by a third party. GPT-3 is technically feasible for a single actor to do inference with, although not really really.

I agree with your comment here but I think for the type and size of model, OpenAI has done the best they could with such large models. So I wouldn't say they've "regressed and refused to release its GPT models" they just had to take a different route with GPT models.

People who disagree and are downvoting, may I ask why?

They only released GPT-2 large after other language models came out that were even larger, like T5...

I don't know. It has 175 billion parameters, thus, about 500GiB for floating point parameters. If these are bfloat16, it is mere 300GiB. We know CPU is about 50 times slower than GPU for transformer models, hence, we are looking at 4 to 8 minutes per inference (parameter loading on demand from SSD takes 200 seconds or so, and probably the bottleneck here). If the parameters can be loaded into memory (seems you have to be on a >= 8-channel machine with unbuffered RAM or with buffered RAM), it will be probably 1 to 2 minutes per inference.

Still in the realm of doing inference in homelab territory (barely).

> We know CPU is about 50 times slower than GPU for transformer models

Where's this heuristic from? Seems handy if true.

It is from my own experience. But it looks like inference is a bit faster on CPU: https://medium.com/huggingface/benchmarking-transformers-pyt...

Those figures make sense as a rough ballpark -- max FLOPS estimates including impacts from FMA and whatnot roughly correspond to the throughput we would expect for a compute-bound linear algebra problem, and current top of the line consumer grade GPUs are 15-30x better in that metric than equivalent CPUs.

Aside from having to slice and dice things a bit to fit in the accelerator's memory and incurring a higher memory bandwidth cost in doing so, is there any reason a home lab's GPU couldn't be used for GPT-3?

It absolutely can. PCIe 3x16 is at around 16GB/s speed, hence, uploading parameters at full speed would be around 50 seconds. That probably is the bottleneck for this case though. A minor issue is that the NVIDIA consumer cards (or any cards besides the new A100) doesn't support bfloat16, I am uncertain fp16 is sufficient for this model or not.

Edit: It probably is, considering you only need weights to be fp16, while intermediate layers can be fp32 and reuse these memory.

However, @lacker makes a valid point that most research papers lack interactive, well-designed API for people unfamiliar with the domain.

by API we mean pretty UI? if not, huggingface/transformers is a pretty good API for the Google-brand transformers.

I firmly believe what OpenAI is actually doing is building a dataset.

For the uninitiated, WTH does semi-public mean? Did GPT-3 only provide an API but not the model?

They do only have an API and the (trained) model (parameters) are not released. But I believe the "semi-public" here refers to the fact that access to the API is limited to invitees (with the ability to add your name to a waitlist).

Huh, for some reason I had just assumed that it must be open based on the buzz around it. Cool.

Releasing model weights is more powerful, but far less accessible than releasing an API. You can easily waste an hour trying to get a model from a research paper running, even when everything works smoothly, just having the wrong environment or dependencies. And that’s for people who are already savvy with ML. Releasing a Docker container is a lot closer to making things easily accessible but still not close to an API. If you look at the audience using GPT-3, a lot of it is software engineers who are experienced but don’t have AI-centric experience. In practice, making an API available is making the model available to many more people.

Learn to appreciate papers? This piece of research is far more interesting than openai's work which essentially involves spending more money on bigger compute and more data. The API is actually a massive turn off for me as I like to know how things work. While I understand your wanting to try things out via an API, I don't think that its the way things should be moving forward. Furthermore I think its very important that engineers using ML actually read the papers to understand what is going on otherwise you'll be finding idiots who apply facial recognition models to predict criminals running around in the wild saying that ML is some black magic when all its doing is reflecting social bias.

As mentioned in the post, a pretrained model is available here: https://tfhub.dev/google/LaBSE/1

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