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Release Notes

Introduction

Neural Network Compression Framework (NNCF) is a toolset for Neural Networks model compression. The framework organized as a Python module that can be built and used as standalone or within samples distributed with the code. The samples demonstrate the usage of compression methods on public models and datasets for three different use cases: Image Classification, Object Detection, and Semantic Segmentation.

New in Release 1.3.1

  • Now using PyTorch 1.5 and CUDA 10.2 by default
  • Support for exporting quantized models to ONNX checkpoints with standard ONNX v10 QuantizeLinear/DequantizeLinear pairs (8-bit quantization only)
  • Compression algorithm initialization moved to the compressed model creation stage

New in Release 1.3:

  • Filter pruning algorithm added
  • Mixed precision quantization with manual and automatic (HAWQ-powered) precision setup
  • Support for DistilBERT
  • Selecting quantization parameters based on hardware configuration preset (CPU, GPU, VPU)
  • Propagation-based quantizer position setup mode (quantizers are position as early in the network control flow graph as possible while keeping inputs of target operation quantized)
  • Improved model graph tracing with introduction of input nodes and intermediate tensor shape tracking
  • Updated third-party integration patches for consistency with NNCF release v1.3
  • CPU-only installation mode for execution on machines without CUDA GPU hardware installed
  • Docker images supplied for easier setup in container-based environments
  • Usability improvements (NNCF config .JSON file validation by schema, less boilerplate code, separate logging and others)

New in Release 1.2:

  • Support for transformer-based networks quantization (tested on BERT and RoBERTa)
  • Added instructions and Git patches for integrating NNCF into third-party repositories (mmdetection, transformers)
  • Support for GNMT quantization
  • Regular expression format support for specifying ignored/target scopes in config files - prefix the regex-enabled scope with {re}

New in Release 1.1

  • Binary networks using XNOR and DoReFa methods
  • Asymmetric quantization scheme and per-channel quantization of Convolution
  • 3D models support
  • Support of integration into the mmdetection repository
  • Custom search patterns for FakeQuantize operation insertion
  • Quantization of the model input by default
  • Support of quantization of non-ReLU models (ELU, sigmoid, swish, hswish, and others)

New in Release 1.0

  • Support of symmetric quantization and two sparsity algorithms with fine-tuning
  • Automatic model graph transformation. The model is wrapped by the custom class and additional layers are inserted in the graph. The transformations are configurable.
  • Three training samples which demonstrate usage of compression methods from the NNCF:
    • Image Classification: torchvision models for classification and custom models on ImageNet and CIFAR10/100 datasets.
    • Object Detection: SSD300, SSD512, MobileNet SSD on Pascal VOC2007, Pascal VOC2012, and COCO datasets.
    • Semantic Segmentation: UNet, ICNet on CamVid and Mapillary Vistas datasets.
  • Unified interface for compression methods.
  • GPU-accelerated Quantization layer for fast model fine-tuning.
  • Distributed training support in all samples.
  • Configuration file examples for sparsity, quantization and sparsity with quantization for all three samples. Each type of compression requires only one additional stage of fine-tuning.
  • Export models to the ONNX format that is supported by the OpenVINO toolkit.