AffineQuant Model Zoo

AffineQuant is a novel quantization method that uses an affine transformation matrix to change the distribution of weights and activations, aimed at optimizing the distribution of weight activations and reducing quantization errors. By introducing an affine transformation matrix, AffineQuant can better align the data distribution with the quantization function, thereby reducing quantization errors. The matrix optimization objective is to minimize the mean squared error between pre- and post-quantization feature map, while introducing the Gradual Mask (GM) method to maintain the strictly diagonal dominance of the affine matrix, ensuring the matrix's invertibility and stable convergence. Experimental results show that AffineQuant performs better than existing quantization methods, such as OmniQuant and SmoothQuant, achieving consistent performance improvements across different quantization configurations and datasets.

Code: https://github.com/bytedance/AffineQuant

Paper: https://arxiv.org/abs/2403.12544

How to use

This repository contains models with various quantization configurations. The types of models include: OPT, LLaMA1&2.

Fake Quantization Accuracy

To reproduce the accuracy reported in the paper, we need to use the --model parameter to load the fake-quantized model. At the same time, we need to specify the bit parameter as 16 to skip the quantization step. For example:

CUDA_VISIBLE_DEVICES=0 python main.py \
--model /path/to/llama-13b-w2a16g128 --eval_ppl \
--output_dir ./log/llama-13b-w2a16g128 \
--wbits 16 --abits 16 

It is worth noting that if your quantization model is trained using the --let parameter, you need to enable the bias in the layernorm layers and specific linear layers within the transformer repository to load the shift parameters. For instance, for the llama model, we make the following modifications in modeling_llama.py:

1. Set the bias of the q,k,v,o,up,gate linear layer to True.

self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=True)

2. Enable the bias in RMSNorm. We directly replace the original RMSNorm with AffineLlamaRMSNorm from AffineQuant.

Inference Overhead

To reproduce the accuracy described in the paper, our weight-only quantization configuration imposes no restrictions on the affine matrices after layernorm. For the weight-activation configuration, such as 4/4 bits, we only update the diagonal elements of the affine matrices after layernorm. Therefore, the model inference with merged parameters incurs no additional overhead.

Benchmarks

We evaluate the quantization performance of LLaMA-7B, 13B, 30B on six zero-shot datasets using 4/4 bit quantization in the following table.

	PIQA($\uparrow$)	ARC-e($\uparrow$)	WinoGrande($\uparrow$)	BoolQ($\uparrow$)	ARC-c($\uparrow$)	HellaSwag($\uparrow$)	Avg.($\uparrow$)
LLaMA-7B, OmniQuant	66.15	45.20	53.43	63.51	31.14	56.44	52.65
LLaMA-7B, AffineQuant	69.37	42.55	55.33	63.73	31.91	57.65	53.42
LLaMA-13B, OmniQuant	69.69	47.39	55.80	62.84	33.10	58.96	54.37
LLaMA-13B, AffineQuant	66.32	43.90	54.70	64.10	29.61	56.88	52.58
LLaMA-30B, OmniQuant	71.21	49.45	59.19	65.33	34.47	64.65	56.63
LLaMA-30B, AffineQuant	70.84	49.41	58.64	70.12	37.12	65.53	58.61

Meanwhile, we compare the 4/4 bit quantization performance of LLaMA1&2 models on WikiText2 and C4 datasets in the following table.

	Methods	WikiText2	C4
LLaMA-7B	OmniQuant	11.26	14.51
	AffineQuant	10.28	13.64
LLaMA-13B	OmniQuant	10.87	13.78
	AffineQuant	10.32	13.44
LLaMA-30B	OmniQuant	10.33	12.49
	AffineQuant	9.35	11.58
LLaMA2-7B	OmniQuant	14.26	18.02
	AffineQuant	12.69	15.76
LLaMA2-13B	OmniQuant	12.30	14.55
	AffineQuant	11.45	13.97

Related Project

SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

GPTQ: Accurate Post-training Compression for Generative Pretrained Transformers

RPTQ: Reorder-Based Post-Training Quantization for Large Language Models

OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

MLC LLM

AutoGPTQ

Citation

@inproceedings{
ma2024affinequant,
title={AffineQuant: Affine Transformation Quantization for Large Language Models},
author={Yuexiao Ma and Huixia Li and Xiawu Zheng and Feng Ling and Xuefeng Xiao and Rui Wang and Shilei Wen and Fei Chao and Rongrong Ji},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=of2rhALq8l}
}