# I-BERT

## Overview

The I-BERT model was proposed in [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by

Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It's a quantized version of RoBERTa running

inference up to four times faster.

The abstract from the paper is the following:

*Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results in many Natural Language

Processing tasks. However, their memory footprint, inference latency, and power consumption are prohibitive for

efficient inference at the edge, and even at the data center. While quantization can be a viable solution for this,

previous work on quantizing Transformer based models use floating-point arithmetic during inference, which cannot

efficiently utilize integer-only logical units such as the recent Turing Tensor Cores, or traditional integer-only ARM

processors. In this work, we propose I-BERT, a novel quantization scheme for Transformer based models that quantizes

the entire inference with integer-only arithmetic. Based on lightweight integer-only approximation methods for

nonlinear operations, e.g., GELU, Softmax, and Layer Normalization, I-BERT performs an end-to-end integer-only BERT

inference without any floating point calculation. We evaluate our approach on GLUE downstream tasks using

RoBERTa-Base/Large. We show that for both cases, I-BERT achieves similar (and slightly higher) accuracy as compared to

the full-precision baseline. Furthermore, our preliminary implementation of I-BERT shows a speedup of 2.4 - 4.0x for

INT8 inference on a T4 GPU system as compared to FP32 inference. The framework has been developed in PyTorch and has

been open-sourced.*

This model was contributed by [kssteven](https://huggingface.co/kssteven). The original code can be found [here](https://github.com/kssteven418/I-BERT).

## Resources

- [Text classification task guide](../tasks/sequence_classification)

- [Token classification task guide](../tasks/token_classification)

- [Question answering task guide](../tasks/question_answering)

- [Masked language modeling task guide](../tasks/masked_language_modeling)

- [Multiple choice task guide](../tasks/masked_language_modeling)

## IBertConfig

[[autodoc]] IBertConfig

## IBertModel

[[autodoc]] IBertModel

    - forward

## IBertForMaskedLM

[[autodoc]] IBertForMaskedLM

    - forward

## IBertForSequenceClassification

[[autodoc]] IBertForSequenceClassification

    - forward

## IBertForMultipleChoice

[[autodoc]] IBertForMultipleChoice

    - forward

## IBertForTokenClassification

[[autodoc]] IBertForTokenClassification

    - forward

## IBertForQuestionAnswering

[[autodoc]] IBertForQuestionAnswering

    - forward