lpot 1.6

Repository of Intel® Low Precision Optimization Tool

Introduction to Intel® LPOT

The Intel® Low Precision Optimization Tool (Intel® LPOT) is an open-source Python library that delivers a unified low-precision inference interface across multiple Intel-optimized Deep Learning (DL) frameworks on both CPUs and GPUs. It supports automatic accuracy-driven tuning strategies, along with additional objectives such as optimizing for performance, model size, and memory footprint. It also provides easy extension capability for new backends, tuning strategies, metrics, and objectives.

Note

GPU support is under development.

Visit the Intel® LPOT online document website at: https://intel.github.io/lpot.

Architecture

Intel® LPOT features an infrastructure and workflow that aids in increasing performance and faster deployments across architectures.

Infrastructure

Click the image to enlarge it.

Workflow

Click the image to enlarge it.

Supported Frameworks

Supported Intel-optimized DL frameworks are:

• TensorFlow*, including 1.15.0 UP3, 1.15.0 UP2, 1.15.0 UP1, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0, Official TensorFlow 2.6.0

Note: Intel Optimized TensorFlow 2.5.0 requires to set environment variable TF_ENABLE_MKL_NATIVE_FORMAT=0 before running LPOT quantization or deploying the quantized model.

Note: From Official TensorFlow 2.6.0, oneDNN support has been upstreamed. User just need download official TensorFlow binary for CPU device and set environment variable TF_ENABLE_ONEDNN_OPTS=1 before running LPOT quantization or deploying the quantized model.

• PyTorch*, including 1.5.0+cpu, 1.6.0+cpu, 1.8.0+cpu
• Apache* MXNet, including 1.6.0, 1.7.0, 1.8.0
• ONNX* Runtime, including 1.6.0, 1.7.0, 1.8.0

Installation

Select the installation based on your operating system.

Linux Installation

You can install LPOT using one of three options: Install just the LPOT library from binary or source, or get the Intel-optimized framework together with the LPOT library by installing the Intel® oneAPI AI Analytics Toolkit.

Option 1 Install from binary

# install stable version from pip
pip install lpot

# install nightly version from pip
pip install -i https://test.pypi.org/simple/ lpot

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

Option 2 Install from source

git clone https://github.com/intel/lpot.git
cd lpot
pip install -r requirements.txt
python setup.py install

Option 3 Install from AI Kit

The Intel® LPOT library is released as part of the Intel® oneAPI AI Analytics Toolkit (AI Kit). The AI Kit provides a consolidated package of Intel's latest deep learning and machine optimizations all in one place for ease of development. Along with LPOT, the AI Kit includes Intel-optimized versions of deep learning frameworks (such as TensorFlow and PyTorch) and high-performing Python libraries to streamline end-to-end data science and AI workflows on Intel architectures.

The AI Kit is distributed through many common channels, including from Intel's website, YUM, APT, Anaconda, and more. Select and download the AI Kit distribution package that's best suited for you and follow the Get Started Guide for post-installation instructions.

Download AI Kit	AI Kit Get Started Guide

Windows Installation

Prerequisites

The following prerequisites and requirements must be satisfied for a successful installation:

• Python version: 3.6 or 3.7 or 3.8 or 3.9

• Download and install anaconda.

• Create a virtual environment named lpot in anaconda:

  # Here we install python 3.7 for instance. You can also choose python 3.6, 3.8, or 3.9.
  conda create -n lpot python=3.7
  conda activate lpot
  

Installation options

Option 1 Install from binary

# install stable version from pip
pip install lpot

# install nightly version from pip
pip install -i https://test.pypi.org/simple/ lpot

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

Option 2 Install from source

git clone https://github.com/intel/lpot.git
cd lpot
pip install -r requirements.txt
python setup.py install

Documentation

Get Started

• APIs explains Intel® Low Precision Optimization Tool's API.
• Transform introduces how to utilize LPOT's built-in data processing and how to develop a custom data processing method.
• Dataset introduces how to utilize LPOT's built-in dataset and how to develop a custom dataset.
• Metric introduces how to utilize LPOT's built-in metrics and how to develop a custom metric.
• Tutorial provides comprehensive instructions on how to utilize LPOT's features with examples.
• Examples are provided to demonstrate the usage of LPOT in different frameworks: TensorFlow, PyTorch, MXNet, and ONNX Runtime.
• UX is a web-based system used to simplify LPOT usage.
• Intel oneAPI AI Analytics Toolkit Get Started Guide explains the AI Kit components, installation and configuration guides, and instructions for building and running sample apps.
• AI and Analytics Samples includes code samples for Intel oneAPI libraries.

Deep Dive

• Quantization are processes that enable inference and training by performing computations at low-precision data types, such as fixed-point integers. LPOT supports Post-Training Quantization (PTQ) with different quantization capabilities and Quantization-Aware Training (QAT). Note that (Dynamic Quantization) currently has limited support.
• Pruning provides a common method for introducing sparsity in weights and activations.
• Benchmarking introduces how to utilize the benchmark interface of LPOT.
• Mixed precision introduces how to enable mixed precision, including BFP16 and int8 and FP32, on Intel platforms during tuning.
• Graph Optimization introduces how to enable graph optimization for FP32 and auto-mixed precision.
• Model Conversion introduces how to convert TensorFlow QAT model to quantized model running on Intel platforms.
• TensorBoard provides tensor histograms and execution graphs for tuning debugging purposes.

Advanced Topics

• Adaptor is the interface between LPOT and framework. The method to develop adaptor extension is introduced with ONNX Runtime as example.
• Strategy can automatically optimized low-precision recipes for deep learning models to achieve optimal product objectives like inference performance and memory usage with expected accuracy criteria. The method to develop a new strategy is introduced.

Publications

• MLPerf™ Performance Gains Abound with latest 3rd Generation Intel® Xeon® Scalable Processors (Apr 2021)
• 3D Digital Face Reconstruction Solution enabled by 3rd Gen Intel® Xeon® Scalable Processors (Apr 2021)
• Accelerating Alibaba Transformer model performance with 3rd Gen Intel® Xeon® Scalable Processors (Ice Lake) and Intel® Deep Learning Boost (Apr 2021)
• Using Low-Precision Optimizations for High-Performance DL Inference Applications (Apr 2021)
• DL Boost Quantization with CERN's 3D-GANs model (Feb 2021)

Full publication list please refers to here

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

Intel® Low Precision Optimization Tool requires installing the pertinent Intel-optimized framework version for TensorFlow, PyTorch, MXNet, and ONNX runtime.

Validated Hardware/Software Environment

Platform	OS	Python	Framework	Version
Cascade Lake Cooper Lake Skylake Ice Lake	CentOS 8.3 Ubuntu 18.04	3.6 3.7 3.8 3.9	TensorFlow	2.6.0
				2.5.0
				2.4.0
				2.3.0
				2.2.0
				2.1.0
				1.15.0 UP1
				1.15.0 UP2
				1.15.0 UP3
				1.15.2
			PyTorch	1.5.0+cpu
				1.6.0+cpu
				1.8.0+cpu
				IPEX
			MXNet	1.8.0
				1.7.0
			1.6.0
			ONNX Runtime	1.6.0
				1.7.0
				1.8.0

Validated Models

Intel® Low Precision Optimization Tool provides numerous examples to show promising accuracy loss with the best performance gain. A full quantized model list on various frameworks is available in the Model List.

Validated MLPerf Models

Model	Framework	Support	Example
ResNet50 v1.5	TensorFlow	Yes	Link
	PyTorch	Yes	Link
DLRM	PyTorch	Yes	Link
BERT-large	TensorFlow	Yes	Link
	PyTorch	Yes	Link
SSD-ResNet34	TensorFlow	WIP
	PyTorch	Yes	Link
RNN-T	PyTorch	WIP
3D-UNet	TensorFlow	WIP
	PyTorch	Yes	Link

Validated Quantized Models

Framework	Version	Model	Accuracy	Performance speed up
INT8 Tuning Accuracy	FP32 Accuracy Baseline	Acc Ratio [(INT8-FP32)/FP32]	Realtime Latency Ratio[FP32/INT8]
tensorflow	2.5.0	resnet50v1.0	74.24%	74.27%	-0.04%	2.67x
tensorflow	2.5.0	resnet50v1.5	76.94%	76.46%	0.63%	2.54x
tensorflow	2.5.0	resnet101	77.21%	76.45%	0.99%	2.46x
tensorflow	2.5.0	inception_v1	70.30%	69.74%	0.80%	1.63x
tensorflow	2.5.0	inception_v2	74.27%	73.97%	0.41%	1.74x
tensorflow	2.5.0	inception_v3	77.29%	76.75%	0.70%	2.11x
tensorflow	2.5.0	inception_v4	80.36%	80.27%	0.11%	2.59x
tensorflow	2.5.0	inception_resnet_v2	80.42%	80.40%	0.02%	1.86x
tensorflow	2.5.0	mobilenetv1	73.93%	70.96%	4.19%	2.27x
tensorflow	2.5.0	mobilenetv2	71.96%	71.76%	0.28%	1.78x
tensorflow	2.5.0	vgg16	72.13%	70.89%	1.75%	3.86x
tensorflow	2.5.0	vgg19	72.35%	71.01%	1.89%	3.90x

Framework	Version	Model	Accuracy	Performance speed up
INT8 Tuning Accuracy	FP32 Accuracy Baseline	Acc Ratio [(INT8-FP32)/FP32]	Realtime Latency Ratio[FP32/INT8]
pytorch	1.8.0+cpu	resnet18	69.58%	69.76%	-0.26%	2.13x
pytorch	1.8.0+cpu	resnet50	75.87%	76.13%	-0.34%	3.11x
pytorch	1.8.0+cpu	resnext101_32x8d	79.09%	79.31%	-0.28%	4.99x
pytorch	1.8.0+cpu	bert_base_mrpc	87.92%	88.73%	-0.91%	1.79x
pytorch	1.8.0+cpu	bert_base_cola	58.33%	58.84%	-0.87%	2.01x
pytorch	1.8.0+cpu	bert_base_sts-b	88.46%	89.27%	-0.91%	2.01x
pytorch	1.8.0+cpu	bert_base_sst-2	91.97%	91.86%	0.12%	2.00x
pytorch	1.8.0+cpu	bert_base_rte	69.68%	69.68%	0.00%	1.92x
pytorch	1.8.0+cpu	bert_large_mrpc	87.60%	88.33%	-0.83%	2.47x
pytorch	1.8.0+cpu	bert_large_squad	92.99	93.05	-0.06%	1.85x
pytorch	1.8.0+cpu	bert_large_qnli	91.12%	91.82%	-0.76%	2.47x
pytorch	1.8.0+cpu	bert_large_rte	71.84%	72.56%	-0.99%	1.44x
pytorch	1.8.0+cpu	bert_large_cola	61.97%	62.57%	-0.97%	2.48x

Validated Pruning Models

Tasks	FWK	Model	fp32 baseline	gradient sensitivity with 20% sparsity	+onnx dynamic quantization on pruned model
accuracy%	drop%	perf gain (sample/s)	accuracy%	drop%	perf gain (sample/s)
SST-2	pytorch	bert-base	accuracy = 92.32	accuracy = 91.97	-0.38	1.30x	accuracy = 92.20	-0.13	1.86x
QQP	pytorch	bert-base	[accuracy, f1] = [91.10, 88.05]	[accuracy, f1] = [89.97, 86.54]	[-1.24, -1.71]	1.32x	[accuracy, f1] = [89.75, 86.60]	[-1.48, -1.65]	1.81x

Tasks	FWK	Model	fp32 baseline	Pattern Lock on 70% Unstructured Sparsity	Pattern Lock on 50% 1:2 Structured Sparsity
accuracy%	drop%	accuracy%	drop%
MNLI	pytorch	bert-base	[m, mm] = [84.57, 84.79]	[m, mm] = [82.45, 83.27]	[-2.51, -1.80]	[m, mm] = [83.20, 84.11]	[-1.62, -0.80]
SST-2	pytorch	bert-base	accuracy = 92.32	accuracy = 91.51	-0.88	accuracy = 92.20	-0.13
QQP	pytorch	bert-base	[accuracy, f1] = [91.10, 88.05]	[accuracy, f1] = [90.48, 87.06]	[-0.68, -1.12]	[accuracy, f1] = [90.92, 87.78]	[-0.20, -0.31]
QNLI	pytorch	bert-base	accuracy = 91.54	accuracy = 90.39	-1.26	accuracy = 90.87	-0.73
QnA	pytorch	bert-base	[em, f1] = [79.34, 87.10]	[em, f1] = [77.27, 85.75]	[-2.61, -1.54]	[em, f1] = [78.03, 86.50]	[-1.65, -0.69]

Framework	Model	fp32 baseline	Compression	dataset	acc(drop)%
Pytorch	resnet18	69.76	30% sparsity on magnitude	ImageNet	69.47(-0.42)
Pytorch	resnet18	69.76	30% sparsity on gradient sensitivity	ImageNet	68.85(-1.30)
Pytorch	resnet50	76.13	30% sparsity on magnitude	ImageNet	76.11(-0.03)
Pytorch	resnet50	76.13	30% sparsity on magnitude and post training quantization	ImageNet	76.01(-0.16)
Pytorch	resnet50	76.13	30% sparsity on magnitude and quantization aware training	ImageNet	75.90(-0.30)

Additional Content

• Release Information
• Contribution Guidelines
• Legal
• Security Policy
• Intel® LPOT Website