amx
tinygrad
amx | tinygrad | |
---|---|---|
18 | 58 | |
859 | 17,800 | |
- | - | |
4.1 | 9.7 | |
2 months ago | 10 months ago | |
C | Python | |
MIT License | MIT License |
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amx
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Optimize sgemm on RISC-V platform
I am talking about the matrix/vector coprocessor (AMX). You can find some reverse-engineered documentation here: https://github.com/corsix/amx
On M3 a singe matrix block can achieve ~ 1TFLOP on DGEMM, I assume it will be closer to 4TFLOPS for SGEMM. The Max variants have two such blocks. Didn't do precise benchmarking myself, but switching Python/R matrix libraries to use Apple's BLAS result in 5-6x perf improvement on matrix heavy code for me.
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Intel AMX
It's really cool. I hope it becomes more common for training/inference/numerics capable accelerators to be included in consumer hardware.
Apple's AMX is really under-documented, while the instructions were reverse engineered, Virtually no benchmarks are available comparing current chip generations, models and variants.
https://github.com/corsix/amx
- Why do x86 processors take up so much energy when compared to ARM?
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Bfloat16 support coming to Apple's Metal and PyTorch [video]
Visible in the unofficial documentation for AMX instructions too - M2 only bf16 functionality - https://github.com/corsix/amx/blob/main/matfp.md
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LLaMA-7B in Pure C++ with full Apple Silicon support
Confusingly there are 2 mechanisms to do matrix operations on the new apple hardware - AMX (https://github.com/corsix/amx) - and the ANE (apple neural engine) - which is enabled by CoreML. This code does not run on the neural engine but the author has a branch for his whisper.cpp project which uses it here: https://github.com/ggerganov/whisper.cpp/pull/566 - so it may not be long before we see it applied here as well. All of this is to say that it actually could get significantly faster if some of this work was able to be handed to the ANE with CoreML.
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Linux 6.2: The first mainstream Linux kernel for Apple M1 chips arrives
really? seems pretty well documented here: https://github.com/corsix/amx
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AMX: The Secret Apple M1 Coprocessor
Article is almost two years old, and has a huge correction at the bottom. It's just a proprietary ISA extension, there's even a repo documenting what's been reverse engineered.
- corsix/amx: Apple AMX Instruction Set
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Show HN: Port of OpenAI's Whisper model in C/C++
You are correct, in that those are the four
My understanding is that the AMX is more tightly wound with the CPU, ultimately being accessible via an instruction set (https://github.com/corsix/amx), and it is useful if you need to do matrix multiplications interleaved with other CPU tasks. A common example would be a VIO loop or something where you want that data in the CPU caches.
The GPU and Neural Engine are not that – they take some time to set up and initialize. They also can parallelize tasks to a much higher degree. The GPU is more generalizable, because you can write compute shaders to do anything in parallel, but it uses a lot of resources. I'll have to check out the PR to see how exactly the MPS shaders match up with the task at hand, because you could also consider writing Metal compute shaders by hand.
I know the least about the ANE, but it has specific hardware for running ML models, and you have to process the weights ahead of time to make sure they are in the right format. It can run ML models very efficiently and is the most battery friendly.
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Ask HN: Are there any undocumented ISA extensions used in Linux systems?
If someone were to build a Linux system with proprietary ISA extensions, how would they do it given Linux is open source? Are there any examples of this being done? Would it be possible at all?
I got inspiration from this (https://github.com/corsix/amx) and I wondered if someone has done it before on a Linux-based system. I understand a userspace library could be created to access those instructions from userspace, but how would then they be implemented in the kernel? Through a proprietary kernel module built using a custom compiler? Or is that not needed at all and the library could just run on the processor taking advantage of the proprietary extensions?
tinygrad
- tinygrad: extreme simplicity, easiest framework to add new accelerators to
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GGML – AI at the Edge
Might be a silly question but is GGML a similar/competing library to George Hotz's tinygrad [0]?
[0] https://github.com/geohot/tinygrad
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Render neural network into CUDA/HIP code
at first glance i thought may its like tinygrad. but looks has many ops than that tiny grad but most maps to underlying hardware provided ops?
i wonder how well tinygrad's apporach will work out, ops fusion sounds easy, just a walk a graph, pattern match it and lower to hardware provided ops?
Anyway if anyone wants to understand the philosophy behind tinygrad, this file is great start https://github.com/geohot/tinygrad/blob/master/docs/abstract...
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llama.cpp now officially supports GPU acceleration.
There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)
- George Hotz building an AMD competitor to Nvidia.
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George Hotz ROCm adventures
Hopefully we will see now full support with AMD hardware on https://github.com/geohot/tinygrad. You can read more about it on https://tinygrad.org/
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The Coming of Local LLMs
tinygrad
https://github.com/geohot/tinygrad/tree/master/accel/ane
But I have not tested it on Linux since Asahi has not yet added support.
llama.cpp runs at 18ms per token (7B) and 200ms per token (65B) without quantization.
- Everything we know about Apple's Neural Engine
- Everything we know about the Apple Neural Engine (ANE)
- How 'Open' Is OpenAI, Really?
What are some alternatives?
emacs-pure
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
whisper.cpp - Port of OpenAI's Whisper model in C/C++
llama.cpp - LLM inference in C/C++
sentencepiece - Unsupervised text tokenizer for Neural Network-based text generation.
openpilot - openpilot is an open source driver assistance system. openpilot performs the functions of Automated Lane Centering and Adaptive Cruise Control for 250+ supported car makes and models.
whisper.cpp - Port of OpenAI's Whisper model in C/C++
llama - Inference code for Llama models
llama-mps - Experimental fork of Facebooks LLaMa model which runs it with GPU acceleration on Apple Silicon M1/M2
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
amx-rs - Rust wrapper for Apple Matrix Coprocessor (AMX) instructions
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ