R2 Loss: Range Restriction Loss for Model Compression and Quantization
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910324922253312 |
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| author | Kundu, Arnav Yoo, Chungkuk Mishra, Srijan Cho, Minsik Adya, Saurabh |
| author_facet | Kundu, Arnav Yoo, Chungkuk Mishra, Srijan Cho, Minsik Adya, Saurabh |
| contents | Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable accuracy with higher bit such as 4bit or 8bit, but still it is challenging to quantize/compress a model further, e.g., 1bit or 2bit. To overcome the challenge, we focus on outliers in weights of a pre-trained model which disrupt effective lower bit quantization and compression. In this work, we propose Range Restriction Loss (R2-Loss) for building lower bit quantization and compression friendly models by removing outliers from weights during pre-training. By effectively restricting range of weights, we mold the overall distribution into a tight shape to ensure high quantization bit resolution, therefore allowing model compression and quantization techniques can to utilize their limited numeric representation powers better. We introduce three different, L-inf R2-Loss, its extension Margin R2-Loss and a new Soft-Min-MaxR2-Loss to be used as an auxiliary loss during full-precision model training. These R2-Loss can be used in different cases such as L-inf and Margin R2-Loss would be effective for symmetric quantization, while Soft-Min-Max R2-Loss shows better performance for model compression. In our experiment, R2-Loss improves lower bit quantization accuracy with state-of-the-art post-training quantization (PTQ), quantization-aware training (QAT), and model compression techniques. With R2-Loss, MobileNet-V2 2bit weight and 8bit activation PTQ, MobileNet-V1 2bit weight and activation QAT, ResNet18 1bit weight compression are improved to 59.49% from 50.66%, 59.05% from 55.96%, and 52.58% from 45.54%, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_08253 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | R2 Loss: Range Restriction Loss for Model Compression and Quantization Kundu, Arnav Yoo, Chungkuk Mishra, Srijan Cho, Minsik Adya, Saurabh Machine Learning Computer Vision and Pattern Recognition Performance Image and Video Processing Model quantization and compression is widely used techniques to reduce usage of computing resource at inference time. While state-of-the-art works have been achieved reasonable accuracy with higher bit such as 4bit or 8bit, but still it is challenging to quantize/compress a model further, e.g., 1bit or 2bit. To overcome the challenge, we focus on outliers in weights of a pre-trained model which disrupt effective lower bit quantization and compression. In this work, we propose Range Restriction Loss (R2-Loss) for building lower bit quantization and compression friendly models by removing outliers from weights during pre-training. By effectively restricting range of weights, we mold the overall distribution into a tight shape to ensure high quantization bit resolution, therefore allowing model compression and quantization techniques can to utilize their limited numeric representation powers better. We introduce three different, L-inf R2-Loss, its extension Margin R2-Loss and a new Soft-Min-MaxR2-Loss to be used as an auxiliary loss during full-precision model training. These R2-Loss can be used in different cases such as L-inf and Margin R2-Loss would be effective for symmetric quantization, while Soft-Min-Max R2-Loss shows better performance for model compression. In our experiment, R2-Loss improves lower bit quantization accuracy with state-of-the-art post-training quantization (PTQ), quantization-aware training (QAT), and model compression techniques. With R2-Loss, MobileNet-V2 2bit weight and 8bit activation PTQ, MobileNet-V1 2bit weight and activation QAT, ResNet18 1bit weight compression are improved to 59.49% from 50.66%, 59.05% from 55.96%, and 52.58% from 45.54%, respectively. |
| title | R2 Loss: Range Restriction Loss for Model Compression and Quantization |
| topic | Machine Learning Computer Vision and Pattern Recognition Performance Image and Video Processing |
| url | https://arxiv.org/abs/2303.08253 |