iree
gpuweb
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iree | gpuweb | |
---|---|---|
10 | 56 | |
2,379 | 4,580 | |
4.4% | 1.7% | |
10.0 | 9.1 | |
5 days ago | 4 days ago | |
C++ | Bikeshed | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
iree
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Calyx, a Compiler Infrastructure for Accelerator Generators
How is this different than the mlir infrastructure of llvm and xla implemented in https://iree.dev/?
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Running pre-trained ML models in Godot
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.
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Nvidia H200 Tensor Core GPU
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
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Nvidia reveals new A.I. chip, says costs of running LLMs will drop significantly
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.
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VkFFT: Vulkan/CUDA/Hip/OpenCL/Level Zero/Metal Fast Fourier Transform Library
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
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Requiem for Piet-GPU-Hal
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
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PyTorch on Apple M1 Faster Than TensorFlow-Metal
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] :)
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WONNX: Deep Learning on WebGPU using the ONNX format.
If you're interested in really pushing yourself, perhaps you can look at https://github.com/google/iree?
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GPU computing on Apple Silicon
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.
gpuweb
- WGSL Is Terrible
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WebGPU now available for testing in Safari Technology Preview
People keep spreading this incredibly misleading statement, and yours is even more misleading (suggesting Apple opposed a 'GPU WASM')
By all accounts, Apple's /only/ stance was that if WebGPU used SPIR-V it would be a non-starter for them, due to ongoing legal issues between Apple and Khronos.
Apple actually proposed WebHLSL in collaboration with Microsoft, to have HLSL be the standard.
Mozilla employee's stance[0] was that SPIRV was too low level, did not fit with the goals of WebGPU portability and security, and expressed concern that Khronos may add functionality to SPIRV they cannot support in WebGPU like raytracing instructions .. 'So we'd always be on the verge of forking SPIR-V in some way.'
It was also noted by many people that even if a bytecode format was used, it would still have to be translated to the target (HLSL/DXIL, MSL, etc.) in almost the same way a text format would.
Nobody proposed a 'GPU WASM equivalent' or an alternative bytecode format.
The hard truth is that shader compilation is a fucking nightmare, people do not realize how bad it is across the different native APIs. SPIR-V is good, but it doesn't solve that - and presents other challenges if you are a web browser API. Vulkan and SPIRV are not the golden goose many make them out to be.
[0] https://github.com/gpuweb/gpuweb/issues/847#issuecomment-642...
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Show HN: WebGPU Particles Simulation
Yes it is still a bit new. WebGPU is not finished and is still being worked on: https://webgpu.io/
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Capturing the WebGPU Ecosystem
WebGPU currently doesn't support the "bindless" resource access model (see: https://github.com/gpuweb/gpuweb/issues/380).
The "max number of sampled texture per shader stage" is a runtime device limit, and the minimal value for that seems to be 16. So texture atlasses are still a thing in WebGPU.
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Why aren't we using highly efficient int8 calcualtions in quants? (maybe eli14?)
There's even an implementation under discussion to have the dp4a instruction added to WebGPU (https://github.com/gpuweb/gpuweb/issues/2677)
- WebGPU – All of the cores, none of the canvas
- How to get Chromium working with the Vulkan driver on a RPi4?
- Anyone has Chromium WebGPU working?
- [Rust_Gamedev] WGSL est-il un bon choix?
- I want to talk about WebGPU
What are some alternatives?
onnx-mlir - Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure
wgsl.vim - WGSL syntax highlight for vim
torch-mlir - The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
pyodide - Pyodide is a Python distribution for the browser and Node.js based on WebAssembly
onnx - Open standard for machine learning interoperability
noclip.website - A digital museum of video game levels
cutlass - CUDA Templates for Linear Algebra Subroutines
BestBuy-GPU-Bot - BestBuy Bot is an Add to cart and Auto Checkout Bot. This auto buying bot can search the item repeatedly on the ITEM page using one keyword. Once the desired item is available it can add to cart and checkout very fast. This auto purchasing BestBuy Bot can work on Firefox Browser so it can run in all Operating Systems. It can run for multiple items simultaneously.
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
wgpu-rs - Rust bindings to wgpu native library
plaidml - PlaidML is a framework for making deep learning work everywhere.
WASI - WebAssembly System Interface