SynapseAI_Core
tinygrad
| SynapseAI_Core | tinygrad | |
|---|---|---|
| 1 | 58 | |
| 25 | 17,800 | |
| - | - | |
| 0.0 | 9.7 | |
| over 1 year ago | 10 months ago | |
| C | Python | |
| GNU General Public License v3.0 or later | MIT License |
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SynapseAI_Core
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The hunt for the M1’s neural engine
Habana has their own SynapseAI layer that their TF/PyTorch port runs on. Custom ops are supported too, via a compiler targeting the TPCs, using a C language variant.
Oh, and they have an open-source UM software stack for those but it's really not usable. Doesn't allow access to the systolic arrays (MME), only using the TPCs is just _starting_ to enumerate what it doesn't have. (but, it made the Linux kernel maintainers happy so...):
https://github.com/HabanaAI/SynapseAI_Core#limitations (not to be confused with the closed-source SynapseAI)
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?
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
GibsonEnv - Gibson Environments: Real-World Perception for Embodied Agents
llama.cpp - LLM inference in C/C++
willow - Open source, local, and self-hosted Amazon Echo/Google Home competitive Voice Assistant alternative
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.
llama - Inference code for Llama models
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
minikeyvalue - A distributed key value store in under 1000 lines. Used in production at comma.ai
flameshot - Powerful yet simple to use screenshot software :desktop_computer: :camera_flash: