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News

• 2023.04.27: :fire: Pipeline parallelism is supported for alpaca-qlora which enables fine-tuning llama-65b with 8*2080ti within 13 hours.
• 2023.04.15: :fire: We release alpaca-qlora which reduce a half model size gpu-memory than alpaca-lora. With alpaca-qlora support, you can use a single 2080ti to instruct fine-tuning llama-7b/13b.
• 2023.03.20: :fire: We implemented a GPTQ cuda kernel with groupsize feature and add --single_device_mode to support all quant LLaMAs run in a single GPU(i.e. 2080ti). GPTQ for LLaMA.
• 2023.03.08: Release a mix-precision quantization method based on GPTQ for LLaMA.
• 2023.02.23: Release a PTQ example of GPT2 on wikiText2
• 2022.11.24: Release a QAT example of BEVDet
• 2022.12.13: Release some examples of BERT.
• 2022.12.14: Release a QAT example of BEVDepth
• 2022.12.26: Release a QAT example of BEVDet4D

Introduction

Sparsebit is a toolkit with pruning and quantization capabilities. It is designed to help researchers compress and accelerate neural network models by modifying only a few codes in existing pytorch project.

Quantization

Quantization turns full-precision params into low-bit precision params, which can compress and accelerate the model without changing its structure. This toolkit supports two common quantization paradigms, Post-Training-Quantization and Quantization-Aware-Training, with following features:

• Benefiting from the support of torch.fx, Sparsebit operates on a QuantModel, and each operation becomes a QuantModule.
• Sparsebit can easily be extended by users to accommodate their own researches. Users can register to extend important objects such as QuantModule, Quantizer and Observer by themselves.
• Exporting QDQ-ONNX is supported, which can be loaded and deployed by backends such as TensorRT and OnnxRuntime.

Results

• PTQ results on ImageNet-1k: link
• PTQ results of Vision Transformer on ImageNet-1k: link
• PTQ results of YOLO related works on COCO: link
• QAT results on ImageNet-1k: link

Sparse

Sparse is often used in deep learning to refer to operations such as reducing network parameters or network computation. At present, Sparse supported by the toolbox has the following characteristics:

• Supports two types of pruning: structured/unstructured;
• Supports a variety of operation objects including: weights, activations, model-blocks, model-layers, etc.;
• Supports multiple pruning algorithms: L1-norm/L0-norm/Fisher-pruning/Hrank/Slimming...
• Users can extend a custom pruning algorithm easily by defining a Sparser
• Using ONNX as the export format for the pruned model

Resources

Documentations

Detailed usage and development guidance is located in the document. Refer to: docs

CV-Master

• We maintain a public course on quantification at Bilibili, introducing the basics of quantification and our latest work. Interested users can join the course.video
• Aiming at better enabling users to understand and apply the knowledge related to model compression, we designed related homework based on Sparsebit. Interested users can complete it by themselves.quantization_homework

Plan to re-implement

• https://github.com/megvii-research/SSQL-ECCV2022
• https://github.com/megvii-research/FQ-ViT

Join Us

• Welcome to be a member (or an intern) of our team if you are interested in Quantization, Pruning, Distillation, Self-Supervised Learning and Model Deployment.
• Submit your resume to: sunpeiqin@megvii.com

Acknowledgement

Sparsebit was inspired by several open source projects. We are grateful for these excellent projects and list them as follows:

• torch
• pytorch-quantization
• PPQ
• MQBench

License

Sparsebit is released under the Apache 2.0 license.