Objective

1. Introduction

2. Supported Objectives Matrix

3. Examples

Introduction

In terms of evaluating the status of a specific model during tuning, we should have general objectives to measure the status of different models.

Intel Extension for Transformers supports optimized low-precision recipes for deep learning models to achieve optimal product objectives like inference performance and memory usage with expected accuracy criteria.

Supported Objectives Matrix:

Argument	Type	Description	Default value
name	string	a objective name in Intel Neural Compressor. Like "performance", "modelsize",......and so on	/
greater_is_better	bool	used to describe the usage of the objective, like: greater is better for performance, but lower is better for modelsize	True
weight_ratio	float	used when there are multiple objective. for example: different weight proportion on performance and modelsize.	None

Examples:

There are two built-in objective instances: performance, modelsize. Users can also build their own objective as below:

from intel_extension_for_transformers.objectives import performance, modelsize

or

from intel_extension_for_transformers.transformers import objectives
performance = objectives.Objective(name="performance", greater_is_better=True, weight_ratio=None)