The article starts with NLP models and then mentions the successes of increasingly smaller vision models. NLP seems to be an outlier in increasingly becoming a pissing contest. The models are too big and not particularly useful. openAI spread FUD about their model but after their release , it's rather underwhelming. Yeah you can output some text that's readable and paraphrasing reddit, but what about understanding , intention, doing actual useful stuff with text? Hallucinating text in itself isn't interesting. It seems this line of nlp with transformers has hit some kind of deadend and they are trying to brute force the next breakthrough - doubtful that this will happen though. And then we have bizarre decisions like microsoft releasing dialoGPT yesterday without including a generaiton script because "it might be racist". This whole seems more like marketing than research
Large transformer-based models like BERT and its ilk are not only useful to hallucinate text. They have achieved measurable improvements in various (although not all) classic NLP tasks, such as parsing, entailment recognition or question answering. Google has reportedly used BERT to improve their search algorithm, so indeed it's being used to do "actual useful stuff with text".
It pains me to say this, as I'm a researcher from an institution without the huge resources of the big tech companies, so I can't compete in the pretrained model arms race (and also, it has made the field more boring, as creative solutions to problems become outperformed by approaches that just pile up more millions of parameters). But it's the truth. Although I think it will only be a stage of things: at some point, performance will plateau and we will need to put our minds to work again, rather than our GPUs.
google seemed to make a genuine effort to make a model that is useful rather than record-breaking with bert. But i think it's wrong to consider it the "final" model upon which everything else will be built.
As someone who was able to generate a model for production based on BERT that outperformed all our previous attempts, I have to say transformers really are a game changer. They are not the end all be all, but they are really, really good as being the basis of many different classification tasks.
To be honest it was straight forward. We used BERT-base (although maybe albert would be better choice now). We fine tuned on our corpus (took ~1 month on a V100, if you do it in cloud with multiple GPUs would be faster, but we were just playing around/dumped it on one or our ml dev servers). Finally we do inference using a containerized BERT serving
https://pypi.org/project/bert-serving-server/ this output is consumed by some other elements of ml pipeline.
There is at least one simple reason for obsessing over efficiency for computer vision models. It takes a lot of bandwidth to transmit an image (even a small one) over the air, whereas text is cheap.
A picture may be with a thousand words, but you can send an entire book in the same amount of space as a single holiday snap at low resolution.
IMO this is not a problem. The people building insanely huge models are expanding the set of tasks that can be done by a computer. Who cares how much memory it takes?
Historically, computationally expensive methods eventually become cheap. In the 1980's, researchers had access to Crays to develop physics model, graphics, etc. requiring lots of floating point math and memory. Meanwhile, for the home computers, game programmers had to implement all their math in fixed point. Nowadays, game engines run the same algorithms that were running on the Crays before.
Same with learning. It's great to use tricks to make models fit on phones. Even better: use tricks to make training new models within the budget of a small academic research lab. That doesn't mean we should invalidate all the work that requires a huge cluster.
IMO this is not a problem. The people building insanely huge models are expanding the set of tasks that can be done by a computer. Who cares how much memory it takes?
But are they? The example in the article describes an incremental improvement in a benchmark in exchange for a massive increasing in training time.
Deep learning has achieved success on a number of tasks that previously computers had been unable to do. Since the initial period of success, it is an area of debate whether deep learning has expanded it's basic area of applicability or whether is has incrementally on it's initial achievements.
And if it is true that deep learning is stuck on just expanding what it's already doing, it might be the fundamental next advance might come from one person with one machine rather than a massive team with a massive machine. Consider that neural nets as a theory had been around since the 1990s if not the 1960s but the fundamental advantage of DL came when grad students could use GPU in the 2010s, not when massively parallel machines came into existence (quite a bit earlier).
Here, the further wrinkle is that moore's law is gradually ending. We won't access to that much more computing power twenty years hence - so making less do more does make sense.
> And if it is true that deep learning is stuck on just expanding what it's already doing, it might be the fundamental next advance might come from one person with one machine rather than a massive team with a massive machine. Consider that neural nets as a theory had been around since the 1990s if not the 1960s but the fundamental advantage of DL came when grad students could use GPU in the 2010s, not when massively parallel machines came into existence (quite a bit earlier).
One thing that I can't help wondering, however sci-fi it sounds, is if model simplifications like in this post might lead to models humans can fully understand, which then might lead to new styles of traditional programing - opening up whole new ways of doing things.
I disagree. There are lots of advancements that DL has yet to fully realize with even the current technology. You're focused on commercial applications but applying neural network models, especially CV models to many types of scientific research has yet to be explored due to lack of funding.
I'd like to think I put my comments "as potential problems" since I can't claim to follow everything that's done as deep learning.
Still, to continue the devil's advocate position. Deep learning comes up with a lot of things that are suggestive but not tight enough in their approximation to be useful.
I would guess there are huge number of correlations that seems plausible but aren't really causations. You can apply employ a monster stream of sort of intelligent seeming claims and predictions and find they don't yield any progress in any firm scientific domain. The application of deep learning to finding cancer and related diagnosis processes has been "exciting and promising" for a long time but effectively yielded nothing so far because "quite accurate in highly controlled situations" turns out to seldom be that useful, at least not so far.
I find this weird too, question of "miniaturization" should come after theoretical stage is satisfied. Is this coming from a line of thinking where capitalistic sense avoids high costs or strict design sensibility where optimizition is a primary concern? The nuance is tiny but very important.
I agree, but the main reason why "miniaturization" exists is that it can be done in parallel with theoretical developments and allows you to make money off the results (therefore funding more R&D).
How did they use an elephant as cover image without mentioning von Neumann's famous and relevant quote: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk."
> Given the power requirements per card, a back of the envelope estimate put the amount of energy used to train this model at over 3X the yearly energy consumption of the average American.
So what? Training model is the hardest part, then you just reuse results
> First, it hinders democratization. If we believe in a world where millions of engineers are going to use deep learning to make every application and device better, we won’t get there with massive models that take large amounts of time and money to train.
So what? I can't run weather simulation on my laptop.
So what? Training model is the hardest part, then you just reuse results
I doubt anyone is going to want to run a 33GB model on their phone.
So what? I can't run weather simulation on my laptop.
You only need to run the weather simulation once and then broadcast your forecast to everyone’s devices. You can’t do that with NLP. In order to be useful, NLP models need to run on different input data for every user. With a giant 33GB model, that means round-tripping to the data centre.
If you have to run everything in the cloud, your applications are limited. The cost is also very high, given that there are way more user devices than servers in the world. That means you need to build more data centres if you plan to run these giant models for every application you want to offer your users.
That’s for one application. Phones have dozens of apps. If they all use different, giant models like this, then 512GB won’t be nearly enough.
Moreover, what is the performance going to be like? It can’t be too spectacular if your model doesn’t fit in RAM. 33GB is manageable on a beefy server with a ton of RAM. You’re not going to have the same luxury on your phone.
The other major aspect of it is memory bandwidth. If the model was designed to run on a high end GPU, with all 33GB stored in graphics memory, then it’s going to perform terribly if it has to be paged in and out of flash on a phone.
What are the applications of deep learning that look like weather simulations (as in one run -> results to 10m people?) In my experience deep learning systems are aimed at applications that are single use 1 run -> 1 person.
The training cost is more important than you think as well. To train a model normally requires 10's or 100's of experiments, meaning that we are consuming 30 -> 3k people's carbon, and the application of the model is typically narrow, so we end up doing 4 or 5 projects per year per group... meaning that we could spend 10's of k carbon per team to produce $10m's benefit. I wonder if we can justify this at all?
It's not such a problem, except if you want to train from scratch a large model (NLP or CV), not if you want to fine-tune it for a related task. So one trained model can be reused many times. In general training data is scarce, only in a few situations it is abundant.
The MegatronLM example is a weird one. Neural network language models are replacing n-gram language models that grow to several terabytes for SotA results; 8 billion parameters is tiny by comparison.
We are already past the point of no return. RTX 8000 is now an entry-level GPU that allows training some of the latest NLP models. Attention is spreading over to computer vision models as well, so one could expect memory bloat coming there quickly. Only large companies that can deploy thousands of GPUs in parallel will be able to compete.
Seems to focus on reducing the size of existing models through optimization. Better would be to find ways to train smaller models to start with. Still interesting.
Compressing it means it may take less storage, but not having to look at it in the first place it the win. It simply takes time to process all the data. Less data: faster computation.
Size matters. If you want intelligent neural network, you need some watts. There is nothing astonishing in that. It is also because of constant progress in hardware performance that deep learning has become what it is.
> I don’t mean to single out this particular project. There are many examples of massive models being trained to achieve ever-so-slightly higher accuracy on various benchmarks.
Sounds like particle colliders and Big Science in general.
Actually it parallelizes extremely well, so that large companies are able to create monster models like mentioned in the article in the first place by just throwing money at the problem with TPUs and similar highly parallelized accelerators. It just doesn't lend itself well to distributed computing due to e.g. throughput requirements.
I've read that you can't split up large layers to be trained on separate processors either horizontally (one layer per processor) or vertically (parts of many layers).
On a shared memory system there's little need to do that - there's much more parallelism to be had from accelerating fine grained operations, like matrix multiplications to compute each layer's output.
On a distributed system, splitting up layers between machines to do distributed training is pretty much what Google initially designed Tensorflow for. Generally it scales less well due to the need to communicate massive amounts of data between nodes and much lower network throughput than what GPU/TPU memory provides.
