Intel® Low Precision Optimization Tool v1.4.1 Release

Intel® Low Precision Optimization Tool v1.4.1 release is featured by:

1. Support TensorFlow 2.5.0
2. Support PyTorch 1.8.0
3. Support TensorFlow Object Detection YOLO-V3 model

Validated Configurations:

• Python 3.6 & 3.7 & 3.8
• Centos 7 & Ubuntu 18.04
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0, 2.5.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
• MxNet 1.7.0
• ONNX Runtime 1.6.0, 1.7.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.4 Release

Intel® Low Precision Optimization Tool v1.4 release is featured by:

Quantization

1. PyTorch FX-based quantization support
2. TensorFlow & ONNX RT quantization enhancement

Pruning

1. Pruning/sparsity API refinement
2. Magnitude-based pruning on PyTorch

Model Zoo

1. INT8 key models updated (BERT on TensorFlow, DLRM on PyTorch, etc.)
2. 20+ HuggingFace model quantization

User Experience

1. More comprehensive logging message
2. UI enhancement with FP32 optimization, auto-mixed precision (BF16/FP32), and graph visualization
3. Online document: https://intel.github.io/lpot

Extended Capabilities

1. Model conversion from QAT to Intel Optimized TensorFlow model

Validated Configurations:

• Python 3.6 & 3.7 & 3.8
• Centos 7 & Ubuntu 18.04
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
• MxNet 1.7.0
• ONNX Runtime 1.6.0, 1.7.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.3.1 Release

Intel® Low Precision Optimization Tool v1.3 release is featured by:

1. Improve graph optimization without explicit input/output setting

Validated Configurations:

• Python 3.6 & 3.7 & 3.8
• Centos 7 & Ubuntu 18.04
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
• MxNet 1.7.0
• ONNX Runtime 1.6.0, 1.7.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.3 Release

Intel® Low Precision Optimization Tool v1.3 release is featured by:

1. FP32 optimization & auto-mixed precision (BF16/FP32) for TensorFlow
2. Dynamic quantization support for PyTorch
3. ONNX Runtime v1.7 support
4. Configurable benchmarking support (multi-instances, warmup, etc.)
5. Multiple batch size calibration & mAP metrics for object detection models
6. Experimental user facing APIs for better usability
7. Various HuggingFace models support

Validated Configurations:

• Python 3.6 & 3.7 & 3.8
• Centos 7 & Ubuntu 18.04
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
• MxNet 1.7.0
• ONNX Runtime 1.6.0, 1.7.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.2.1 Release

Intel® Low Precision Optimization Tool v1.2.1 release is featured by:

1. user-facing APIs backward compatibility with v1.1 and v1.0.
2. refined experimental user-facing APIs for better out-of-box experience.

Validated Configurations:

• Python 3.6 & 3.7 & 3.8
• Centos 7 & Ubuntu 18.04
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
• MxNet 1.7.0
• ONNX Runtime 1.6.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.2 Release

Intel® Low Precision Optimization Tool v1.2 release is featured by:

• Broad TensorFlow model type support
• operator-wise quantization scheme for ONNX RT
• MSE driven tuning for metric-free use cases
• UX improvement, including UI web server preview support
• More key model supports

Validated Configurations:

• Python 3.6 & 3.7 & 3.8
• Centos 7 & Ubuntu 18.04
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0, 2.4.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu, 1.6.0+cpu, ipex
• MxNet 1.7.0
• ONNX Runtime 1.6.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.1 Release

Intel® Low Precision Optimization Tool v1.1 release is featured by:

• New backends (PyTorch/IPEX, ONNX Runtime) backend preview support
• Add built-in industry dataset/metric and custom registration
• Preliminary input/output node auto-detection on TensorFlow models
• New INT8 quantization recipes: bias correction and label balance

Validated Configurations:

• Python 3.6 & 3.7
• Centos 7
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0 and 1.15.0 UP1 & UP2
• PyTorch 1.5.0+cpu
• MxNet 1.7.0
• ONNX Runtime 1.6.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lpot.git	$ git clone https://github.com/intel/lpot.git
Binary	Pip	https://pypi.org/project/lpot	$ pip install lpot
Binary	Conda	https://anaconda.org/intel/lpot	$ conda install lpot -c conda-forge -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.0 Release

Intel® Low Precision Optimization Tool v1.0 release is featured by:

• Refined user facing APIs for best OOB.
• Add TPE tuning strategies (Experimental).
• Pruning POC support on PyTorch
• TensorBoard POC support for tuning analysis.
• Built-in INT8/Dummy dataloader Support.
• Built-in Benchmarking support.
• Tuning history for strategy finetune.
• Support TF Keras and checkpoint model type as input.

Validated Configurations:

• Python 3.6 & 3.7
• Centos 7
• Intel TensorFlow 1.15.2, 2.1.0, 2.2.0, 2.3.0 and 1.15UP1
• PyTorch 1.5.0+cpu
• MxNet 1.7.0

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lp-opt-tool.git	$ git clone https://github.com/intel/lp-opt-tool.git
Binary	Pip	https://pypi.org/project/ilit	$ pip install ilit
Binary	Conda	https://anaconda.org/intel/ilit	$ conda install ilit -c intel

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Tool v1.0 Beta Release

Intel® Low Precision Optimization Tool v1.0 beta release is featured by:

• Built-in dataloaders and evaluators
• Add random and exhaustive tuning strategies
• Mix precision tuning support on TensorFlow (INT8/BF16/FP32)
• Quantization-aware training POC support on Pytorch
• TensorFlow mainstream version support, including 1.15.2, 1.15UP1 and 2.1.0
• 50+ models validated

Supported Models:

TensorFlow Model	Category
ResNet50 V1	Image Recognition
ResNet50 V1.5	Image Recognition
ResNet101	Image Recognition
Inception V1	Image Recognition
Inception V2	Image Recognition
Inception V3	Image Recognition
Inception V4	Image Recognition
ResNetV2_50	Image Recognition
ResNetV2_101	Image Recognition
ResNetV2_152	Image Recognition
Inception ResNet V2	Image Recognition
SSD ResNet50 V1	Object Detection
Wide & Deep	Recommendation
VGG16	Image Recognition
VGG19	Image Recognition
Style_transfer	Style Transfer

PyTorch Model	Category
BERT-Large RTE	Language Translation
BERT-Large QNLI	Language Translation
BERT-Large CoLA	Language Translation
BERT-Base SST-2	Language Translation
BERT-Base RTE	Language Translation
BERT-Base STS-B	Language Translation
BERT-Base CoLA	Language Translation
BERT-Base MRPC	Language Translation
DLRM	Recommendation
BERT-Large MRPC	Language Translation
ResNext101_32x8d	Image Recognition
BERT-Large SQUAD	Language Translation
ResNet50 V1.5	Image Recognition
ResNet18	Image Recognition
Inception V3	Image Recognition
YOLO V3	Object Detection
Peleenet	Image Recognition
ResNest50	Image Recognition
SE_ResNext50_32x4d	Image Recognition
ResNet50 V1.5 QAT	Image Recognition
ResNet18 QAT	Image Recognition

MxNet Model	Category
ResNet50 V1	Image Recognition
MobileNet V1	Image Recognition
MobileNet V2	Image Recognition
SSD-ResNet50	Object Detection
SqueezeNet V1	Image Recognition
ResNet18	Image Recognition
Inception V3	Image Recognition

Known Issues:

• TensorFlow ResNet50 v1.5 int8 model will crash on TensorFlow 1.15 UP1 branch

Validated Configurations:

• Python 3.6 & 3.7
• Centos 7
• Intel TensorFlow 1.15.2, 2.1.0 and 1.15UP1
• PyTorch 1.5
• MxNet 1.6

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lp-opt-tool.git	$ git clone https://github.com/intel/lp-opt-tool.git
Binary	Pip	https://pypi.org/project/ilit	$ pip install ilit
Binary	Conda	https://anaconda.org/intel/ilit	$ conda config --add channels intel $ conda install ilit

Contact:

Please feel free to contact [email protected], if you get any questions.

Intel® Low Precision Optimization Toolkit (iLiT) v1.0 Alpha Release

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

Feature List:

• Unified low precision quantization interface cross multiple Intel optimized frameworks (TensorFlow, PyTorch, and MXNet)
• Built-in tuning strategies, including Basic, Bayesian, and MSE
• Built-in evaluation metrics, including TopK (image classification), F1 (NLP), and CocoMAP (object detection)
• Built-in tuning objectives, including Performance, ModelSize, and Footprint
• Extensible API design to add new strategy, framework backend, metric, and objective
• KL-divergence calibration for TensorFlow and MXNet
• Tuning process resume from certain checkpoint

Supported Models:

Model	Framework	Model	Framework	Model	Framework
ResNet50 V1	MXNet	BERT-Large RTE	PyTorch	ResNet18	PyTorch
MobileNet V1	MXNet	BERT-Large QNLI	PyTorch	ResNet50 V1	TensorFlow
MobileNet V2	MXNet	BERT-Large CoLA	PyTorch	ResNet50 V1.5	TensorFlow
SSD-ResNet50	MXNet	BERT-Base SST-2	PyTorch	ResNet101	TensorFlow
SqueezeNet V1	MXNet	BERT-Base RTE	PyTorch	Inception V1	TensorFlow
ResNet18	MXNet	BERT-Base STS-B	PyTorch	Inception V2	TensorFlow
Inception V3	MXNet	BERT-Base CoLA	PyTorch	Inception V3	TensorFlow
DLRM	PyTorch	BERT-Base MRPC	PyTorch	Inception V4	TensorFlow
BERT-Large MRPC	PyTorch	ResNet101	PyTorch	Inception ResNet V2	TensorFlow
BERT-Large SQUAD	PyTorch	ResNet50 V1.5	PyTorch	SSD ResNet50 V1	TensorFlow

Known Issues:

• Statistics collection for KL algorithm is slow in TensorFlow due to lack of tensor inspector APIs
• MSE tuning strategy is not supported in PyTorch

Validated Configurations:

• Python 3.6 & 3.7
• Centos 7
• TensorFlow 1.15, 2.0 and 2.1
• PyTorch 1.5
• MxNet 1.6

Distribution:

	Channel	Links	Install Command
Source	Github	https://github.com/intel/lp-opt-tool.git	$ git clone https://github.com/intel/lp-opt-tool.git
Binary	Pip	https://pypi.org/project/ilit	$ pip install ilit
Binary	Conda	https://anaconda.org/intel/ilit	$ conda config --add channels intel $ conda install ilit

Contact:

Please feel free to contact [email protected], if you get any questions.