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)