Smooth Quant Recipe Tuning API (Prototype)

Smooth Quantization is a popular method to improve the accuracy of int8 quantization. The autotune API allows automatic global alpha tuning, and automatic layer-by-layer alpha tuning provided by Intel® Neural Compressor for the best INT8 accuracy.

SmoothQuant will introduce alpha to calculate the ratio of input and weight updates to reduce quantization error. SmoothQuant arguments are as below:

Arguments	Default Value	Available Values	Comments
alpha	'auto'	[0-1] / 'auto'	value to balance input and weight quantization error.
init_alpha	0.5	[0-1] / 'auto'	value to get baseline quantization error for auto-tuning.
alpha_min	0.0	[0-1]	min value of auto-tuning alpha search space
alpha_max	1.0	[0-1]	max value of auto-tuning alpha search space
alpha_step	0.1	[0-1]	step_size of auto-tuning alpha search space
shared_criterion	"mean"	["min", "mean","max"]	criterion for input LayerNorm op of a transformer block.
enable_blockwise_loss	False	[True, False]	whether to enable block-wise auto-tuning

Please refer to the LLM examples for complete examples.

Note: When defining dataloaders for calibration, please follow INC’s dataloader format.