GPT-Fast: A fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!

programmerChilliB to

LocalLLaMA@poweruser.forumEnglish · 11 months ago

We’re happy to release GPT-Fast, a fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!

Check out the blog post describing the techniques here: https://pytorch.org/blog/accelerating-generative-ai-2/

And check out the code here: https://github.com/pytorch-labs/gpt-fast

To be clear, this is intended more as a minimal “tutorial” of how you get really good inference performance rather than a library. Hopefully y’all find it useful!

Happy to answer any questions.

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GPT-Fast: A fast and hackable implementation of transformer inference in &lt;1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!

GPT-Fast: A fast and hackable implementation of transformer inference in &lt;1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!

GPT-Fast: A fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!

GPT-Fast: A fast and hackable implementation of transformer inference in <1000 lines of native PyTorch with support for quantization, speculative decoding, TP, Nvidia/AMD support, and more!