> CoreNet evolved from CVNets, to encompass a broader range of applications beyond computer vision. Its expansion facilitated the training of foundational models, including LLMs.
It looks like a mid-level implementations of training and inference. You can see in their "default_trainer.py"[1] that the engine uses Tensors from torch but implements its own training method. They implement their own LR scheduler and optimizer; the caller can optionally use Adam from torch.
It's an interesting (maybe very Apple) choice to build from the ground up instead of partnering with existing frameworks to provide first class support in them.
The interface looks very Apple as well. Looks like you create a config file, and you already have a model in mind with the hyperparameters and it provides a simple interface. How useful is this to researchers trying to hack the model architecture?
Not much. But if you just want to adapt/optimize hyperparams, this is a useful approach. So I can certainly see a possible, less technical audience. If you actually want to hack and adapt architectures it's probably not worth it.
> It looks like a mid-level implementations of training and inference
I’m not familiar with how any of this works but what does state of the art training look like? Almost no models release their training source code or data sets or pre processing or evaluation code. So is it known what the high level implementation even is?
> It's an interesting (maybe very Apple) choice to build from the ground up instead of partnering with existing frameworks to provide first class support in them.
It smells of a somewhat panicked attempt to prepare for WWDC to me. Apple has really dropped the ball on AI and now they're trying to catch up.
I don’t get the idea that Apple dropped the ball on AI. They were fairly early with adding neural engine hardware to their chips and have been using ML extensively on-device for a long time now
They haven’t put an LLM assistant out there. But they don’t make their own search engine either so I don’t think “online LLM assistant” is something they’ll ever put much effort into unless it’s part of a bigger effort to launch their own AI-based search engine as well.
As for generative AI I don’t think the quality is up to a level that would be reasonable for Apple.
The only area where i would expect Apple to keep up is the kind of Copilot integration Microsoft is working on. And we know Apple is working on on-device AI assistant, and probably have for a long time. It’ll be launched when they can get good quality results on-device. Something nobody else has achieved anyway, so we can’t say that they’re behind anyone yet.
Curious then, why they keep recruiting search engineers[1]. And why they run a web crawler[2]. And why typing "Taylor Swift" into safari offers a Siri Suggested website before Google.
I guess what people mean by search engine is "show ads alongside web search to as many people as possible"?
> They were fairly early with adding neural engine hardware to their chips
If that's all it takes to stay ahead of the curve, then Rockchip and Qualcomm are arguably right up there alongside them. Tons of vendors shipped their own AI silicon, and of those vendors, it seems like Nvidia is the only one that shipped anything truly usable. Medium-sized LLMs, Stable Diffusion and probably even stuff like OAI Whisper is faster run on Apple's GPUs than their AI coprocessor.
> and of those vendors, it seems like Nvidia is the only one that shipped anything truly usable. Medium-sized LLMs, Stable Diffusion and probably even stuff like OAI Whisper is faster run on Apple's GPUs than their AI coprocessor.
Be careful not to have NVIDIA-shaped tunnel vision. Performance isn't the whole story. It's very telling that approximately everybody making SoCs for battery powered devices (phones, tablets, laptops) has implemented an AI coprocessor that's separate from the GPU. NVIDIA may take exception, but the industry consensus is that GPUs aren't always the right solution to every AI/ML-related problem.
Ideally, you're right. Realistically, Apple has to choose between using their powerful silicon (the GPU) for high-quality results or their weaker silicon (the Neural engine) for lower-power inference. Devices that are designed around a single power profile (eg. desktop GPUs) can integrate the AI logic into the GPU and have both high-quality and high-speed inference. iPhones gotta choose one or the other.
There's not nothing you can run on that Neural Engine, but it's absolutely being misunderstood relative to the AI applications people are excited for today. Again; if chucking a few TOPS of optimized AI compute onto a mobile chipset is all we needed, then everyone would be running float16 Llama on their smartphone already. Very clearly, something must change.
I agree with 1. For 2, have they ever been a company big into research? They're very consumer focused and it can take time to integrate new tech into consumer products at scale. Especially the way Apple likes to do it: polished and seamlessly integrated into the rest of their ecosystem.
I would say not doing AI research (or buying another big company that does) is tantamount to dropping the ball on AI, if it turns out that AI is a capability they should have had and must have to succeed.
You could argue that publishing research is not the same thing as doing it. But they don't seem to have done much of it until fairly recently.
I agree that Apple does less research than other big tech companies. But they do it where they think it matters. Their M-series CPUs are more than just integration and polishing. And they have been doing some research in health AI as well, I think.
Dearth relative to Apple's size and relative to the amount of research that competitors have been doing.
But I think part of the problem is that Apple simply hasn't focused on the tasks and the methods and the people that have now turned out to be so impactful.
They have clearly been course correcting for a while now as some of the more recent papers show, and they have done successful research in areas such as health AI.
They've been using them, too. Auto OCR so selecting text in images Just Works, image enhancements, Siri. I'm sure LLM Siri is in the works. Scanning your photos for CSAM. Let's hope that last one is more reliable than Siri :/
They added it back in as an optional thing for child accounts managed in a family in messages. It sure sounds like it's AI based and not hash based in this case.
Wouldn’t WWDC-related endeavors be more product-facing? I’m not so sure this has to do with their efforts to incorporate ai into products, and tbh I would say their ai research has been pretty strong generally speaking.
I expect that a lot of WWDC will be Apple trying to get more developers to build AI products for their platforms, because at the moment, Apple products don't have much AI. The other tech companies have integrated user facing LLM products into a significant part of their ecosystem - Google and Microsoft have them up front and center in search. Apple's AI offerings for end users are what exactly? The camera photos app that does minor tweaks to photos (composing from multiple frames). What else actually is there in the first party ecosystem that significantly leverages AI? Siri is still the same trash it's been for the last 10 years - in fact IMO it's become even less useful, often refusing to even do web searches for me. (I WANT Siri to work very well).
So because their first party AI products are so non-existent, I think WWDC is a desperate attempt by Apple to get third party developers to build compelling AI products. I say desperate because they're already a year behind the competition in this space.
(I can imagine they'll be trying to get developers to build Vision Pro software too, though I hear sales there have collapsed so again, way too little, too late)
They have tons of computer vision, NN inference and natural language processing in their products. It's reductive to say Apple products don't have much AI.
For one thing, I can search for any text I’ve ever take a photo of. Finding a picture of someone I took 20+ years ago by searching for a single work I remember on their t-shirt is pretty cool, and is all done on-device.
I think it’s important to remember that there are a bunch of actual useful AI-driven features out there that aren’t just GenAI chatbots.
What AI products are present in other ecosystems (eg. Android, Samsung, whatever) and missing from Apple?
Honest question: I find the platform distinction largely meaningless in most cases apart from “what your phone looks like” and “can you side load apps”…
I am guessing you are not familiar with the AI-powered vision features that already ship since a few years. Mostly accessibility related, so I am not surprised you missed it.
Yep. Google, the AI company, only recently launched image descriptions in TalkBack, which VoiceOver has had for years now. Google still doesn't have Screen Recognition, which basically does OCR and image/UI classification to make inaccessible apps more accessible.
Don't get me even started on TalkBack and Android. It was never on-par with VoiceOver, and is still a few years behind... However, VoiceOver is also getting slowly, but surely, worse and worse over time when it comes to small subtle bugs...
It's interesting that Apple also actively develops https://github.com/apple/axlearn, which is a library on top of Jax. Seems like half the ML teams at Apple use PyTorch, and the other half uses Jax. Maybe its split between Google Cloud and AWS?
In my experience, this is pretty normal in large companies like Apple. Coordination costs are real. Unless there's a good reason to standardize on a single tool, its usually easier for teams to just pick whichever tool makes the most sense based on the problem they're solving and what the team has experience with.
I don’t know as haven’t worked there, but have always heard Apple described more as a series of companies/startups than one coherent entity like Meta or whatever. Each is allowed a large degree of autonomy from what I’ve heard.
Metal was released before Vulkan, and had a different design philosophy. So that’s not a good argument.
They follow the licenses.
Apple has paid developers to work on WebKit, Clamg/LLVM, and CUPS. That’s off the top of my head. All open source. All available to you. I remember reading how they contributed fixes to a huge number of packages in the OSS ecosystem when working to get POSIX certification as they fixed issues.
They released Swift as open source when they didn’t need to, and keep opening more libraries for it as they work to reimplement foundation.
You may not like Apple, but they’re not a leech. They give back.
Apple also contributed substantially to OpenCL (I mean, they were the originators of it), though CUDA (the closed, proprietary solution) ate that world.
CoreData is an object graph and persistence framework. It is more like an ORM toolkit than like a database engine. Indeed, SQLite is one of the possible 'backends', but it is not limited to SQLite. It also supports XML, binary, and in-memory stores out of the box, and you can also create your own if none of those suit.
The first sentence has almost no meaning. Basically anyone who has a 401k or SP500/VOO/etc owns significant amount of Apple stock. Not something worth pointing out.
It looks like MLX is a part of this initiative. https://github.com/apple/corenet lists "MLX examples" as one of the components being released in April.
As mentioned in the "mlx_examples/open_elm": "MLX is an Apple deep learning framework similar in spirit to PyTorch, which is optimized for Apple Silicon based hardware."
"MLX examples demonstrate how to run CoreNet models efficiently on Apple Silicon. Please find further information in the README.md file within the corresponding example directory."
> mlx_example/clip: ... an example to convert CoreNet's CLIP model implementation to MLX's CLIP example with some customized modification.
- FP16 Base variant: 60% speedup over PyTorch
- FP16 Huge variant: 12% speedup
> mlx_example/open_elm: ... an MLX port of OpenELM model trained with CoreNet. MLX is an Apple deep learning framework similar in spirit to PyTorch, which is optimized for Apple Silicon based hardware.
Seems like an advantage is extra speedups thanks to specialization for Apple Silicon. This might be the most power-efficient DNN training framework (for small models) out there. But we won't really know until someone benchmarks it.
I would love an LLM agent that could generate small api examples (reliably) from a repo like this for the various different models and ways to use them.
Would such a capability (training) be useful for anything other than small scale experimentation? Apple doesn’t make server products anymore and even when they did, they were overpriced. Unless they have private Apple silicon based servers for their own training needs?
> Unless they have private Apple silicon based servers for their own training needs?
Id be SHOCKED if so. Its been 15 years, but I was there when xserve died. Priorities were iphone > other mobile devices >>> laptops > displays & desktops >>> literally anything else. When xserve died we still needed osx for OD & similar. Teams moved on to 3P rack mount trays of mac minis as a stop gap. Any internal support/preference for server style hardware was a lolwut response. Externally I see no reason to suspect thats changed.
I can understand the inference part being useful and practical for Apple devs. I’m just wondering about the training part, for which there Apple silicon devices don’t seem very useful.
My M2 Max significantly outperforms my 3090 Ti for training a Mistral-7B LoRA. Its sort of a case-by-case situation though, as it depends on how optimized the CUDA kernels happen to be for whatever workload you're doing (i.e. for inference, theres a big delta between standard transformers vs exllamav2, apple silicon may outperform the former, but certainly not the latter).
I went through their folders, they have have a lot of classes that just inherit from pytorch and torchvision classes and seemingly do nothing new. All optimizers, schedulers and most layers do that. They do however have a reasonable amount of blocks, i.e. specific combinations of layers from various papers, similar to monai.networks.blocks. Out of "building pieces" they also have a few newly implemented losses, metrics.
What library would you recommend for neural net training and inference on Apple M1? I want to use it from C++ or maybe Rust. The neural net will have 5M params at most.
I would use Pytorch as your starting point. Its metal backend is pretty quick on Apple Silicon, and it's the most widely used library for everyone from hackers to foundation model builders.
I haven't looked at Homebrew since that got started. The philosophical difference at that time was using macports and having a consistent and managed */local/ collection of tools with self contained dependencies vs. adding new tools with dependencies tied to the current Mac OS release.
I still use MacPorts for that reason and it is easy enough to create a local portfile for whatever isn't in Macports.
I find this to be the easy way to manage networked development computers.
I used MacPorts a decade ago, but at some point realized that Homebrew had more packages that were kept consistently up-to-date. Switched and never looked back.
I switched away back to macports when homebrew decided to get rid of formula options. To be honest, I always find homebrew frustrating, it feels that they've often made technical decisions that are not necessarily the best but they've been much more successful at marketing themselves than macports.
oh, I actually hadn't realized that this is what they settled on in the end. ffmpeg is the quintessential package where options make sense so good that that's still supported.
The other issue I experienced with homebrew around that time were related to having different versions of openssl installed because I had some old codebase I had to run (and for performance reasons didn't want to use docker). But that's definitely a edge case.
I’m a full-time Mac and iOS developer, have been for almost 20 years, and this is the first I’ve heard of it. Might just be my bubble, but I don’t think it’s a huge thing yet. (I’m going to check it out now!)
it's worth noting for Homebrew users that it also has a nice built-in module for managing a Homebrew installation by generating a Brewfile for you. So you can transition at your own pace, if you like
>Why is it funny? Homebrew is the de facto standard terminal packaging tool for macOS.
It's funny because a multi-trillion dollar company can't be bothered to release a native package manager or an official binary repository for their OS after decades of pleading from developers.
They released "App Store" for the average Joe. We can all agree it is not suitable for power users, but at the same time what would power users gain over existing solutions if they were to introduce something?
"Sherlocking" can be unfortunate for a developer, but it's odd to view it as an inherently bad thing. A package manager is a core OS feature, even Microsoft has WinGet now.
It has become a core OS feature. Historically, you see the set of core OS features expand tremendously. Back in the 80’s drawing lines and circles wasn’t even a core OS feature (not on many home computers, and certainly not on early PCs), bit-mapped fonts were third part add-ons for a while, vector-based fonts were an Adobe add-on (https://en.wikipedia.org/wiki/Adobe_Type_Manager), printer drivers were third party, etc.
I think that’s natural. As lower layers become commodities (try making money selling an OS that only manages memory and processes), OS sellers have to add higher layer stuff to their products to make money on them.
As to Sherlocking, big companies cannot do well there in the eyes of “the angry internet”:
- don’t release feature F: “They don’t even support F out of the box. On the competitor’s product, you get that for free”
- release a minimal implementation: “They have F, but it doesn’t do F1, F2, or F3”
- release a fairly full implementation: “Sherlocking!” and/or nitpicking about their engineering choices.
Or even more terrible, open the image full screen with no way to close it until it feels it has been open long enough to close on its own. maybe show some sort of timer counting down, and then before dismissing itself, it opens the App Store listing for the app. it'll be very convenient for the user as no user interaction will be required for any of this
To be fair, Apple has a long tradition of naming frameworks Core Something, e.g. Core Foundation, Core Graphics etc.
I think these had their initial releases even before .NET Framework 1.0 (and so even longer before .NET Core) so Apple could probably claim "prior art" or whatever this would be called (IANAL).
> CoreNet evolved from CVNets, to encompass a broader range of applications beyond computer vision. Its expansion facilitated the training of foundational models, including LLMs.
We can expect it to have grown from here: https://apple.github.io/ml-cvnets/index.html
It looks like a mid-level implementations of training and inference. You can see in their "default_trainer.py"[1] that the engine uses Tensors from torch but implements its own training method. They implement their own LR scheduler and optimizer; the caller can optionally use Adam from torch.
It's an interesting (maybe very Apple) choice to build from the ground up instead of partnering with existing frameworks to provide first class support in them.
The MLX examples seem to be inference only at this point. It does look like this might be a landing ground for more MLX specific implementations: e.g. https://github.com/apple/corenet/blob/5b50eca42bc97f6146b812...
It will be interesting to see how it tracks over the next year; especially with their recent acquisitions:
Datakalab https://news.ycombinator.com/item?id=40114350
DarwinAI https://news.ycombinator.com/item?id=39709835
1: https://github.com/apple/corenet/blob/main/corenet/engine/de...
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