neural-compressor 0.1

Repository of Intel® Low Precision Optimization Tool

Intel® Low Precision Optimization Tool

Intel® Low Precision Optimization Tool is an open-source python library which is intended to deliver a unified low-precision inference interface cross multiple Intel optimized DL frameworks on both CPU and GPU. It supports automatic accuracy-driven tuning strategies, along with additional objectives like optimizing for performance, model size and memory footprint. It also provides the easy extension capability for new backends, tuning strategies, metrics and objectives.

WARNING

GPU support is under development.

Infrastructure	Workflow

Supported Intel optimized DL frameworks are:

• Tensorflow*, including 1.15, 1.15UP1, 2.1, 2.2, 2.3
• PyTorch*, including 1.5.0+cpu, 1.6.0+cpu
• Apache* MXNet, including 1.6.0, 1.7.0

Supported tuning strategies are:

• Basic
• Bayesian
• MSE
• Exhaustive
• Random
• TPE - experimental

Documents

• Introduction explains the API of Intel® Low Precision Optimization Tool.
• Hello World demonstrates the simple steps to utilize Intel® Low Precision Optimization Tool for quanitzation, which can help you quick start with the tool.
• Tutorial provides comprehensive instructions of how to utilize diffrennt features of Intel® Low Precision Optimization Tool. In examples, there are a lot of examples to demonstrate the usage of Intel® Low Precision Optimization Tool in TensorFlow, PyTorch and MxNet for diffrent categories.
• Strategies provides comprehensive explanation on the details of how every tuning strategy works.
• PTQ and QAT explains how Intel® Low Precision Optimization Tool works with post-training quantization and quantization-ware training.
• Pruning on PyTorch explains how Intel® Low Precision Optimization Tool works with magnitude pruning on PyTorch.
• Tensorboard explains how Intel® Low Precision Optimization Tool helps developer to analyze tensor distribution and the impact to final accuracy during tuning process.
• Quantized Model Deployment on PyTorch explains how Intel® Low Precision Optimization Tool quantizes a FP32 PyTorch model, save and deploy quantized model through lpot utils.
• BF16 Mix-Precision on TensorFlow explains how Intel® Low Precision Optimization Tool supports INT8/BF16/FP32 mix precision model tuning on TensorFlow backend.
• Supported Model Types on TensorFlow explains the TensorFlow model types supported by Intel® Low Precision Optimization Tool.

Install from source

git clone https://github.com/intel/lpot.git
cd lpot
python setup.py install

Install from binary

# install from pip
pip install lpot

# install from conda
conda install lpot -c intel -c conda-forge

System Requirements

Intel® Low Precision Optimization Tool supports systems based on Intel 64 architecture or compatible processors, specially optimized for the following CPUs:

• Intel Xeon Scalable processor (formerly Skylake, Cascade Lake, and Cooper Lake)
• future Intel Xeon Scalable processor (code name Sapphire Rapids)

Intel® Low Precision Optimization Tool requires to install Intel optimized framework version for TensorFlow, PyTorch, and MXNet.

Validated Hardware/Software Environment

Platform	OS	Python	Framework	Version
Cascade Lake Cooper Lake Skylake	CentOS 7.8 Ubuntu 18.04	3.6 3.7	tensorflow	2.2.0
				1.15UP1
				2.3.0
				2.1.0
				1.15.2
			pytorch	1.5.0+cpu
			mxnet	1.7.0
				1.6.0

Model Zoo

Intel® Low Precision Optimization Tool provides a lot of examples to show promising accuracy loss with best performance gain.

Framework	version	model	dataset	TOP-1 Accuracy	Performance Speedup
INT8 Tuning Accuracy	FP32 Accuracy Baseline	Acc Ratio[(INT8-FP32)/FP32]	Realtime Latency Ratio[FP32/INT8]
tensorflow	2.2.0	resnet50v1.0	ImageNet	73.80%	74.30%	-0.67%	2.25x
tensorflow		resnet50v1.5	ImageNet	76.80%	76.50%	0.39%	2.32x
tensorflow		resnet101	ImageNet	77.20%	76.40%	1.05%	2.75x
tensorflow		inception_v1	ImageNet	70.10%	69.70%	0.57%	1.56x
tensorflow		inception_v2	ImageNet	74.00%	74.00%	0.00%	1.68x
tensorflow		inception_v3	ImageNet	77.20%	76.70%	0.65%	2.05x
tensorflow		inception_v4	ImageNet	80.00%	80.30%	-0.37%	2.52x
tensorflow		inception_resnet_v2	ImageNet	80.20%	80.40%	-0.25%	1.75x
tensorflow		mobilenetv1	ImageNet	71.10%	71.00%	0.14%	1.88x
tensorflow		ssd_resnet50_v1	Coco	37.72%	38.01%	-0.76%	2.88x
tensorflow		mask_rcnn_inception_v2	Coco	28.75%	29.13%	-1.30%	4.14x
tensorflow		wide_deep_large_ds	criteo-kaggle	77.61%	77.67%	-0.08%	1.41x
tensorflow		vgg16	ImageNet	72.10%	70.90%	1.69%	3.71x
tensorflow		vgg19	ImageNet	72.30%	71.00%	1.83%	3.78x
tensorflow		resnetv2_50	ImageNet	70.20%	69.60%	0.86%	1.52x
tensorflow		resnetv2_101	ImageNet	72.50%	71.90%	0.83%	1.59x
tensorflow		resnetv2_152	ImageNet	72.70%	72.40%	0.41%	1.62x
tensorflow		densenet121	ImageNet	72.60%	72.90%	-0.41%	1.84x
tensorflow		densenet161	ImageNet	76.10%	76.30%	-0.26%	1.44x
tensorflow		densenet169	ImageNet	74.40%	74.60%	-0.27%	1.22x

Framework	Version	Model	Dataset	TOP-1 Accuracy	Performance Speedup
INT8 Tuning Accuracy	FP32 Accuracy Baseline	Acc Ratio[(INT8-FP32)/FP32]	Realtime Latency Ratio[FP32/INT8]
mxnet	1.7.0	resnet50v1	ImageNet	76.03%	76.33%	-0.39%	3.18x
mxnet		inceptionv3	ImageNet	77.80%	77.64%	0.21%	2.65x
mxnet		mobilenet1.0	ImageNet	71.72%	72.22%	-0.69%	2.62x
mxnet		mobilenetv2_1.0	ImageNet	70.77%	70.87%	-0.14%	2.89x
mxnet		resnet18_v1	ImageNet	69.99%	70.14%	-0.21%	3.08x
mxnet		squeezenet1.0	ImageNet	56.88%	56.96%	-0.14%	2.55x
mxnet		ssd-resnet50_v1	VOC	80.21%	80.23%	-0.02%	4.16x
mxnet		ssd-mobilenet1.0	VOC	74.94%	75.54%	-0.79%	3.31x
mxnet		resnet152_v1	ImageNet	78.32%	78.54%	-0.28%	3.16x

Known Issues

1. MSE tuning strategy doesn't work with PyTorch adaptor layer MSE tuning strategy requires to compare FP32 tensor and INT8 tensor to decide which op has impact on final quantization accuracy. PyTorch adaptor layer doesn't implement this inspect tensor interface. So if the model to tune is a PyTorch model, please do not choose MSE tuning strategy.

Support

Please submit your questions, feature requests, and bug reports on the GitHub issues page. You may also reach out to [e-mail].

Contributing

We welcome community contributions to Intel® Low Precision Optimization Tool. If you have an idea on how to improve the library:

• For changes impacting the public API, submit an RFC pull request.
• Ensure that the changes are consistent with the code contribution guidelines and coding style.
• Ensure that you can run all the examples with your patch.
• Submit a pull request.

For additional details, see contribution guidelines.

This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

License

Intel® Low Precision Optimization Tool is licensed under Apache License Version 2.0. This software includes components with separate copyright notices and license terms. Your use of the source code for these components is subject to the terms and conditions of the following licenses.

Apache License Version 2.0:

• Intel TensorFlow Quantization Tool

MIT License:

• bayesian-optimization

See accompanying LICENSE file for full license text and copyright notices.

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Legal Information

Citing

If you use Intel® Low Precision Optimization Tool in your research or wish to refer to the tuning results published in the Tuning Zoo, please use the following BibTeX entry.

@misc{Intel® Low Precision Optimization Tool,
  author =       {Feng Tian, Chuanqi Wang, Guoming Zhang, Penghui Cheng, Pengxin Yuan, Haihao Shen, and Jiong Gong},
  title =        {Intel® Low Precision Optimization Tool},
  howpublished = {\url{https://github.com/intel/lpot}},
  year =         {2020}
}