[philipkglass]

What was the "vanilla post-training quantization" used for comparison? There are 22 GGUF quantization variants smaller than 16 bits per weight and I can't tell which one is being compared with:

https://huggingface.co/docs/hub/en/gguf#quantization-types

It might even mean a non-GGUF quantization scheme; I'm just an intermediate user of local models, not an expert user or developer.

[formalsystem]

Please ignore my previous comments - I double checked with the model developers and here's the correction. Vanilla PTQ means no fancy quantization algorithm like SpinQuant, AWQ, etc. was applied. It just applied the same quantization scheme mentioned in the post (4bit per-group with g_size=32 symmetric weight, 8bit dynamic per token activation).

[formalsystem]

So this should be referring to w8a8 (weights and activations in 8 bit)

So this is gonna be 8 bit weights, 8 bit activations, group size of 256, symmetric quantization. Not sure how to map this to the GGUF variants because they don't mention how they don't do activation quantization

[imjonse]

Were there comparisons made to AWS, Smoothquant, GPTQ or other non-vanilla PTQ methods? Thanks.

[formalsystem]

Not that I know of for this study, at least for the specific scope torchao we want to make it easier for researchers to create new quantization algorithms in python and have those algorithms run fast and you can see a lot of those algorithms here https://github.com/pytorch/ao/tree/main/torchao/prototype

So for example for AWQ and GPTQ we can accelerate them by using a fast int4 kernel called tinygemm