Package: mct-nightly
Model Compression Toolkit (MCT)Model Compression Toolkit (MCT) is an open-source project for neural network model optimization under efficient, constrained hardware.This project provides researchers, developers, and engineers tools for optimizing and deploying state-of-the-art neural networks on efficient hardware.Specifically, this project aims to apply quantization to compress neural networks.MCT is developed by researchers and engineers working at Sony Semiconductor Israel.Table of ContentsGetting StartedSupported featuresResultsContributionsLicenseGetting StartedThis section provides an installation and a quick starting guide.InstallationTo install the latest stable release of MCT, run the following command:pip install model-compression-toolkitFor installing the nightly version or installing from source, refer to theinstallation guide.Quick start & tutorialsFor an example of how to use MCT with TensorFlow or PyTorch on various models and tasks,
check out thequick-start pageand
theresults CSV.In addition, a set ofnotebooksare provided for an easy start. For example:MobileNet with Tensorflow.MobileNetV2 with PyTorch.Data Generation for ResNet18 with PyTorch.Supported VersionsCurrently, MCT is being tested on various Python, Pytorch and TensorFlow versions:PyTorch 1.13PyTorch 2.0PyTorch 2.1Python 3.9Python 3.10Python 3.11TensorFlow 2.12TensorFlow 2.13TensorFlow 2.14Python 3.9Python 3.10Python 3.11Supported FeaturesMCT offers a range of powerful features to optimize neural network models for efficient deployment. These supported features include:Data GenerationMCT provides tools for generating synthetic images based on the statistics stored in a model's batch normalization layers. These generated images are valuable for various compression tasks where image data is required, such as quantization and pruning.
You can customize data generation configurations to suit your specific needs.Go to the Data Generation page.QuantizationMCT supports different quantization methods:Post-training quantization (PTQ):Keras API,PyTorch APIGradient-based post-training quantization (GPTQ):Keras API,PyTorch APIQuantization-aware training (QAT)*Quantization MethodComplexityComputational CostPTQLowLow (order of minutes)GPTQ (parameters fine-tuning using gradients)MildMild (order of 2-3 hours)QATHighHigh (order of 12-36 hours)In addition, MCT supports different quantization schemes for quantizing weights and activations:Power-Of-Two (hardware-friendly quantization [1])SymmetricUniformMain features:Graph optimizations: Transforming the model to an equivalent (yet, more efficient) model (for example, batch-normalization layer folding to its preceding linear layer).Quantization parameter search: Different methods can be used to minimize the expected added quantization-noise during thresholds search (by default, we use Mean-Square-Error, but other metrics can be used such as No-Clipping, Mean-Average-Error, and more).Advanced quantization algorithms: To prevent a performance degradation some algorithms are applied such as:Shift negative correction: Symmetric activation quantization can hurt the model's performance when some layers output both negative and positive activations, but their range is asymmetric. For more details please visit [1].Outliers filtering: Computing z-score for activation statistics to detect and remove outliers.Clustering: Using non-uniform quantization grid to quantize the weights and activations to match their distributions.*Mixed-precision search: Assigning quantization bit-width per layer (for weights/activations), based on the layer's sensitivity to different bit-widths.Visualization: You can use TensorBoard to observe useful information for troubleshooting the quantized model's performance (for example, the model in different phases of the quantization, collected statistics, similarity between layers of the float and quantized model and bit-width configuration for mixed-precision quantization). For more details, please read thevisualization documentation.Target Platform Capabilities: The Target Platform Capabilities (TPC) describes the target platform (an edge device with dedicated hardware). For more details, please read theTPC README.Enhanced Post-Training Quantization (EPTQ)As part of the GPTQ we provide an advanced optimization algorithm called EPTQ.The specifications of the algorithm are detailed in the paper:"EPTQ: Enhanced Post-Training Quantization via Label-Free Hessian"[4].More details on the how to use EPTQ via MCT can be found in theEPTQ guidelines.Experimental featuresSome features are experimental and subject to future changes.For more details, we highly recommend visiting our project website where experimental features are mentioned as experimental.ResultsKerasGraph ofMobileNetV2accuracy on ImageNet vs average bit-width of weights, using
single-precision quantization, mixed-precision quantization, and mixed-precision quantization with GPTQ.For more results, please see [1]PytorchWe quantized classification networks from the torchvision library.
In the following table we present the ImageNet validation results for these models:Network NameFloat Accuracy8Bit AccuracyData-Free 8Bit AccuracyMobileNet V2 [3]71.88671.44471.29ResNet-18 [3]69.8669.6369.53SqueezeNet 1.1 [3]58.12857.678For more results, please refer toquick start.Structured PruningMCT introduces a structured and hardware-aware model pruning.
This pruning technique is designed to compress models for specific hardware architectures,
taking into account the target platform's Single Instruction, Multiple Data (SIMD) capabilities.
By pruning groups of channels (SIMD groups), our approach not only reduces model size
and complexity, but ensures that better utilization of channels is in line with the SIMD architecture
for a target KPI of weights memory footprint.Note: Currently, only Keras models pruning is supported.ResultsResults for applying pruning to reduce the parameters of the following models by 50%:ModelDense Model AccuracyPruned Model AccuracyResNet50 [2]75.172.4DenseNet121 [2]75.071.15ContributionsMCT aims at keeping a more up-to-date fork and welcomes contributions from anyone.*You will find more information about contributions in theContribution guide.LicenseApache License 2.0.References[1] Habi, H.V., Peretz, R., Cohen, E., Dikstein, L., Dror, O., Diamant, I., Jennings, R.H. and Netzer, A., 2021.HPTQ: Hardware-Friendly Post Training Quantization. arXiv preprint.[2]Keras Applications[3]TORCHVISION.MODELS[4] Gordon, O., Habi, H. V., & Netzer, A., 2023.EPTQ: Enhanced Post-Training Quantization via Label-Free Hessian. arXiv preprint