Will these optimization integrate into hf's code? about gpt-fast HOT 7 OPEN

@SunMarc I think there might still be some gaps in how the kv-cache is handled during inference. Specifically, the link you sent is about vision models, not text generation.

We should chat more about this - i'd love to see the techniques here integrated.

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Thanks for the interest ! We already support most of the optimization described here:

• Torch.compile with pytorch blog here
• 4-bit quant with GPTQ and recently AWQ which is faster
• Speculative Decoding.

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Yes, absolutely! cc @younesbelkada for visibility

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Most of these features are already supported in Lit-GPT (if you're looking for finetuning LLMs) and more of this will be supported soon. You can use LLMs from HF model hub.

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These opt should already in hf. Moreover, some specific opt made for hardware like writing your cuda knerl for GPTQ and paged attention (e.g. flash_attn2) would make inference even faster.

https://github.com/turboderp/exllamav2 has bench marked llama-7b with 190+ t/s on single 3090Ti which matches this repo on 8xA100, but 3090Ti is only about 1/3 flops of a single A100. So hardware opt also plays as another drive.

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Hi， does torch.complie works with AWQ?

(seems hf already supports AWQ, but quantization way might not same as this repo)

How to enable speculative decoding in hf?

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@yhyu13

https://github.com/turboderp/exllamav2 has bench marked llama-7b with 190+ t/s on single 3090Ti which matches this repo on 8xA100, but 3090Ti is only about 1/3 flops of a single A100.

To be clear, the benchmark on this repo is at 197 t/s on a single A100 with a groupsize of 32, while exllamav2 is running a single 4090 with a groupsize of 128.

Still certainly very good results from exllamav2 :)

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