Video
Generative Pre-trained Transformer models (GPT), set themselves apart through breakthrough performance across complex language modelling tasks, but also by their extremely high computational and storage costs. Specifically, due to their massive size, even inference for large, highly-accurate GPT models may require multiple performant GPUs, which limits the usability of such models. While there is emerging work on relieving this pressure via model compression, the applicability and performance of existing compression techniques is limited by the scale and complexity of GPT models. GPTQ is a new one-shot weight quantization method based on approximate second-order information, that is both highly accurate and highly-efficient. Specifically, GPTQ can quantize GPT models with 175 billion parameters in approximately four GPU hours, reducing the bitwidth down to 3 or 4 bits per weight, with negligible accuracy degradation relative to the uncompressed baseline. GPTQ more than doubles the compression gains relative to previously-proposed one-shot quantization methods, preserving accuracy, allowing us for the first time to execute an 175 billion-parameter model inside a single GPU for generative inference. GPTQ can still provide reasonable accuracy in the extreme quantization regime, in which weights are quantized to 2-bit or even ternary quantization levels. These improvements can be leveraged for end-to-end inference speedups over FP16, of ~3.25x when using high-end GPUs (NVIDIA A100) and 4.5x when using more cost-effective ones (NVIDIA A6000).
In this video, I talk about the following: How does GPTQ work? How does GPTQ perform?
For more details, please look at https://github.com/IST-DASLab/gptq and https://arxiv.org/pdf/2210.17323.pdf
Frantar, Elias, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. "Gptq: Accurate post-training quantization for generative pre-trained transformers." arXiv preprint arXiv:2210.17323 (2022).