iree VS vello

How is this different than the mlir infrastructure of llvm and xla implemented in https://iree.dev/?

So I have been developing this GDExtension called iree.gd. It is mission to embed IREE, another cool project that compiles and runs ML models, into Godot. It took me quite a while, but finally It has reached alpha. Hope you guys could check it out the sample.

I am going to paste a cousin comment:

StableHLO[1] is an interesting project that might help AMD here:

> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).

From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.

[1] https://github.com/openxla/stablehlo

[2] https://github.com/openxla/iree

[3] https://www.tensorflow.org/xla

I want to promote that the Google project https://github.com/openxla/iree exists and IREE acts as a way to turn Tensorflow, Pytorch, and MLIR workflows to compute on cpu, vulkan compute, cuda, rocm, metal and others.

https://github.com/RechieKho/IREE.gd -- RechieKho and I collaborate on making this work for Godot Engine, but IREE.gd is at a proof of concept stage.

To a first approximation, Kompute[1] is that. It doesn't seem to be catching on, I'm seeing more buzz around WebGPU solutions, including wonnx[2] and more hand-rolled approaches, and IREE[3], the latter of which has a Vulkan back-end.

[1]: https://kompute.cc/

[2]: https://github.com/webonnx/wonnx

[3]: https://github.com/openxla/iree

In the ML section you mentioned Kompute and MediaPipe. Have you seen IREE? It has a Vulkan-like compute-only HAL. https://github.com/iree-org/iree

Exactly the kind of things we've been talking about! A fun and challenging tradeoff space and it's always great to connect with others!

Ahh linebender - I hadn't connected the name with your github account - piet-gpu is great, as is your blog! Also, for anyone skimming the comments this talk is fantastic and I share it with anyone new to the GPGPU space: https://www.youtube.com/watch?v=DZRn_jNZjbw

We waffled a bit with the API granularity in the beginning and it's taken building out most of the rest of the project in order to nail it down (the big refactor still pending). The biggest issue is that in simple models we'll end up emitting a single command buffer but anything with control flow (that we can't predicate), data dependencies (sparsity, thresholding, etc), or CPU work in the middle (IO, custom user code, etc) can break that up. We also hit cases where we need to flush work - such as if we run out of usable memory and need to defragment or resize our pools. We want to be able to (but aren't yet) reuse command buffers (CUDA graphs, etc) and that requires being able to both cache them and recreate them on demand (if we resize a pool we have to invalidate all cached command buffers using those resources, as update-after-bind is not universally available and if shapes change there's big ripples). Since most models beyond simple vision ones are ~thousands of dispatches it also lets us better integrate into multithreaded applications like you mention as apps can record commands for themselves in parallel without synchronization. It still would be nice to have certain operations inlined, though, and for that we want to allow custom hooks that we call into to add commands to the command buffers, turning things inside-out to make small amounts of work like image transformations in-between model layers possible (I'm really hoping we can avoid modeling the entire graphics pipeline in the compiler and this would be a way around that :). We haven't yet started on scheduling across queues but that's also very interesting especially in multi-GPU cases (with x4/x8 GPUs being common in datacenters, or NUMA CPU clusters that can be scheduled similarly).

We're fully open source (https://github.com/google/iree) but have been operating quietly while we get the groundwork in place - it's taken some time but now we're finally starting to stumble into success on certain problem categories (like transformers as in the post). Right now it's mostly just organized as a systems/compiler nerd honeypot for people looking for an ML/number crunching framework that (purposefully) doesn't look like any of the existing ones :)

Would love to chat more - even if just to commiserate over GPU APIs and such - everyone is welcome on the discord where a bunch of us nerds have gathered or we could grab virtual coffee (realized just now that this hn acct is ancient - I'm [email protected] :)

If you're interested in really pushing yourself, perhaps you can look at https://github.com/google/iree?

This doesn't answer your question, but it would be cool if we had something based on MLIR for GPU compute. From what I've read, it closes the gap between NVIDIA and other GPU vendors a lot more than pure compute shaders. e.g. ONNX-MLIR, PlaidML, and IREE.

I'm looking forward to doing careful benchmarking, as this renderer absolutely looks like it will be competitive. It turns out that is really hard to do, if you want meaningful results.

My initial take is that performance will be pretty dependent on hardware, in particular support for pixel local storage[1]. From what I've seen so far, Apple Silicon is the sweet spot, as there is hardware support for binning and sorting to tiles, and then asking for fragment shader execution to be serialized within a tile works well. On other hardware, I expect the cost of serializing those invocations to be much higher.

One reason we haven't done deep benchmarking on the Vello side is that our performance story is far from done. We know one current issue is the use of device atomics for aggregating bounding boxes. We have a prototype implementation [2] that uses monoids for segmented reduction. Additionally, we plan to do f16 math (which should be a major win especially on mobile), as well as using subgroups for various prefix sum steps (subgroups are in the process of landing in WebGPU[3]).

Overall, I'm thrilled to see this released as open source, and that there's so much activity in fast GPU vector graphics rendering. I'd love to see a future in which CPU path rendering is seen as being behind the times, and this moves us closer to that future.

[1]: https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/da...

[2]: https://github.com/linebender/vello/issues/259

[3]: https://github.com/gpuweb/gpuweb/issues/4306

Dioxus is working on this with blitz. It's leveraging wgpu through the linebender group's Vello renderer. Still in early stages.

If you're doing advanced compute work (including lock-free data structures), then it's best effort.

https://github.com/linebender/vello/issues/42 is an issue from when Vello (then piet-gpu) had a single-pass prefix sum algorithm. Looking back, I'm fairly confident that it's a shader translation issue and that it wouldn't work with MoltenVK either, but we stopped investigating when we moved to a more robustly portable approach.

There are a number of up-and-coming Rust-based frameworks in this niche:

- https://github.com/iced-rs/iced (probably the most usable today)

- https://github.com/vizia/vizia

- https://github.com/marc2332/freya

- https://github.com/linebender/xilem (currently very incomplete but exciting because it's from a team with a strong track record)

What is also exciting to me is that the Rust GUI ecosystem is in many cases building itself up with modular libraries. So while we have umpteen competing frameworks they are to a large degree all building and collaborating on the same foundations. For example, we have:

- https://github.com/rust-windowing/winit (cross-platform window creation)

- https://github.com/gfx-rs/wgpu (abstraction on top of vulkan/metal/dx12)

- https://github.com/linebender/vello (a canvas like imperative drawing API on top of wgpu)

- https://github.com/DioxusLabs/taffy (UI layout algorithms)

- https://github.com/pop-os/cosmic-text (text rendering and editing)

- https://github.com/AccessKit/accesskit (cross-platform accessibility APIs)

In many cases there a see https://blessed.rs/crates#section-graphics-subsection-gui for a more complete list of frameworks and foundational libraries)

blessed.rs lists these three crates for 2D drawing: - https://lib.rs/crates/femtovg - https://lib.rs/crates/skia-safe - https://github.com/linebender/vello

Vello (https://github.com/linebender/vello) which uses wgpu to render Edit: just saw you require images. Vello doesn't support those yet

How does this relate to Vello? Both target raw-window-handle for winit compatibility. Vello uses WGPU vs piet-glow using GL.

Finally, maybe vello could help you with ideas. It's not production ready yet, but they have some interesting ideas for 2D rendering using wgpu.