tinygrad VS swift

Not sure if I completely understand what "Ryzen AI" does, but Tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file.

There's also a small tutorial by AMD on how to use the WMMA intrinsic[1] using AMD's hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.)

0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/...

1. https://gpuopen.com/learn/wmma_on_rdna3/

2. https://github.com/ROCm/HIPCC

3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech...

This post describes how I added automatic differentiation to Tensorken. Tensorken is my attempt to build a fully featured yet easy-to-understand and hackable implementation of a deep learning library in Rust. It takes inspiration from the likes of PyTorch, Tinygrad, and JAX.

what do you think about tinygrad? I think its a good example of growing and well written, (partially) well documented library with many close to reference implementations

The idea of model architecture making fast hardware design easier is what makes https://github.com/tinygrad/tinygrad so interesting.

🌟 Support on GitHub Website: https://tinygrad.org/

Before we begin, make sure you have TinyGrad and the required dependencies installed. You can find the installation instructions here.

Try to get something like tinygrad[1] running locally, that way you can tweak things a bit run it again and see how it performs. While doing this you'll pick up most of the concepts and get a feeling of how things work. Also, take a look at projects like llama.cpp[2], you don't have to fully understand what's going on here, tho.

You may need some intermediate knowledge of linear algebra and this thing called "data science" nowadays, which is pretty much knowing how to mangle data and visualize it.

Try creating a small model on your own, it doesn't have to be super fancy just make sure it does something you want it to do. And then ... you'll probably could go on your own then.

1: https://github.com/tinygrad/tinygrad

2: https://github.com/ggerganov/llama.cpp

This algorithm produces biased result with probability 1/2^(32-bitwidth(N)). Using 64 or 128 random bits can make the bias practically undetectable. Comprehensive overview of the approach can be found here: https://github.com/apple/swift/pull/39143

Because of C/C++ interop, and integration with CMake, you can just add Swift to a Zephyr project and it pretty much Just Works. [The docs](https://github.com/apple/swift/blob/main/docs/EmbeddedSwift/...) should mostly apply to the Zephyr SDK as well.

Fortunately, the Observation framework is part of the Swift 5.9 standard library. We can learn more information by examining its source code.

They do! See https://github.com/apple/swift/blob/main/docs/LibraryEvoluti...

You can also see an example of what a different high level language integration with Swift ABI looks like here: https://github.com/dotnet/designs/blob/main/proposed/swift-i...

So is differentiable Swift a package for Swift or is it part of the Swift standard library? The video says go to swift.org but I can't find any info about differentiable Swift on that site.

Swift's Differentiable Programming Manifesto. Swift has a powerful differentiable programming component, integrated with the compiler.

You can do the same thing the other way around - https://github.com/apple/swift/blob/main/docs/Android.md.

Codable (along with other derived conformances like Equatable, Hashable, and RawRepresentable) is indeed built in to the compiler[0], but unlike Serde, it operates during type-checking on a fully-constructed AST (with access to type information), manipulating the AST to insert code. Because it operates at a later stage of compilation and at a much higher level (with access to type information), the work necessary is significantly less.

With ongoing work for Swift macros, it may eventually be possible to rip this code out of the compiler and rewrite it as a macro, though it would need to be a semantic macro[1] rather a syntactic one, which isn't currently possible in Swift[2].

[0] https://github.com/apple/swift/blob/main/lib/Sema/DerivedCon...

`Int` is a regular struct with a single stored property of type `Builtin.Word` . But the latter is a magical compiler built-in. Source for integer types is generated from this template - https://github.com/apple/swift/blob/9da65ca0a15fdf341649c994b0a77ec3b71f2687/stdlib/public/core/IntegerTypes.swift.gyb