Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for neural networks inference optimization in OpenVINO™ with minimal accuracy drop.

NNCF is designed to work with models from PyTorch, TensorFlow, ONNX and OpenVINO™.

NNCF provides samples that demonstrate the usage of compression algorithms for different use cases and models. Compression results achievable with the NNCF-powered samples can be found in a table at the end of this document.

The framework is organized as a Python* package that can be built and used in a standalone mode. The framework architecture is unified to make it easy to add different compression algorithms for both PyTorch and TensorFlow deep learning frameworks.

Key Features

Post-Training Compression Algorithms

Training-Time Compression Algorithms

• Automatic, configurable model graph transformation to obtain the compressed model.

  NOTE: Limited support for TensorFlow models. The models created using Sequential or Keras Functional API are only supported.

• Common interface for compression methods.
• GPU-accelerated layers for faster compressed model fine-tuning.
• Distributed training support.
• Git patch for prominent third-party repository (huggingface-transformers) demonstrating the process of integrating NNCF into custom training pipelines
• Seamless combination of pruning, sparsity and quantization algorithms. Please refer to optimum-intel for examples of joint (movement) pruning, quantization and distillation (JPQD), end-to-end from NNCF optimization to compressed OpenVINO IR.
• Exporting PyTorch compressed models to ONNX* checkpoints and TensorFlow compressed models to SavedModel or Frozen Graph format, ready to use with OpenVINO™ toolkit.
• Support for Accuracy-Aware model training pipelines via the Adaptive Compression Level Training and Early Exit Training.

Documentation

This documentation covers detailed information about NNCF algorithms and functions needed for the contribution to NNCF.

The latest user documentation for NNCF is available here.

NNCF API documentation can be found here.

Usage

Post-Training Quantization

The NNCF PTQ is the simplest way to apply 8-bit quantization. To run the algorithm you only need your model and a small (~300 samples) calibration dataset.

OpenVINO is the preferred backend to run PTQ with, and PyTorch, TensorFlow and ONNX are also supported.

import nncf
import openvino.runtime as ov
import torch
from torchvision import datasets

# Instantiate your uncompressed model
model = ov.Core().read_model("/model_path")
# Provide validation part of the dataset to collect statistics needed for the compression algorithm
val_dataset = datasets.ImageFolder("/path")
dataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1)

# Step 1: Initialize transformation function
def transform_fn(data_item):
    images, _ = data_item
    return images

# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(dataset_loader, transform_fn)
# Step 3: Run the quantization pipeline
quantized_model = nncf.quantize(model, calibration_dataset)

import nncf
import torch
from torchvision import datasets, models

# Instantiate your uncompressed model
model = models.mobilenet_v2() 
# Provide validation part of the dataset to collect statistics needed for the compression algorithm
val_dataset = datasets.ImageFolder("/path")
dataset_loader = torch.utils.data.DataLoader(val_dataset)

# Step 1: Initialize the transformation function
def transform_fn(data_item):
    images, _ = data_item
    return images

# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(dataset_loader, transform_fn)
# Step 3: Run the quantization pipeline
quantized_model = nncf.quantize(model, calibration_dataset)

import nncf
import tensorflow as tf
import tensorflow_datasets as tfds

# Instantiate your uncompressed model
model = tf.keras.applications.MobileNetV2()
# Provide validation part of the dataset to collect statistics needed for the compression algorithm
val_dataset = tfds.load("/path", split="validation", 
                        shuffle_files=False, as_supervised=True)

# Step 1: Initialize transformation function
def transform_fn(data_item):
    images, _ = data_item
    return images

# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(val_dataset, transform_fn)
# Step 3: Run the quantization pipeline
quantized_model = nncf.quantize(model, calibration_dataset)

import onnx
import nncf
import torch
from torchvision import datasets

# Instantiate your uncompressed model
onnx_model = onnx.load_model("/model_path")
# Provide validation part of the dataset to collect statistics needed for the compression algorithm
val_dataset = datasets.ImageFolder("/path")
dataset_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1)

# Step 1: Initialize transformation function
input_name = onnx_model.graph.input[0].name
def transform_fn(data_item):
    images, _ = data_item
    return {input_name: images.numpy()}

# Step 2: Initialize NNCF Dataset
calibration_dataset = nncf.Dataset(dataset_loader, transform_fn)
# Step 3: Run the quantization pipeline
quantized_model = nncf.quantize(onnx_model, calibration_dataset)

Training-Time Compression

Below is an example of Accuracy Aware Quantization pipeline where model weights and compression parameters may be fine-tuned to achieve a higher accuracy.

import torch
import nncf.torch  # Important - must be imported before any other external package that depends on torch

from nncf import NNCFConfig
from nncf.torch import create_compressed_model, register_default_init_args

# Instantiate your uncompressed model
from torchvision.models.resnet import resnet50
model = resnet50()

# Load a configuration file to specify compression
nncf_config = NNCFConfig.from_json("resnet50_int8.json")

# Provide data loaders for compression algorithm initialization, if necessary
import torchvision.datasets as datasets
representative_dataset = datasets.ImageFolder("/path")
init_loader = torch.utils.data.DataLoader(representative_dataset)
nncf_config = register_default_init_args(nncf_config, init_loader)

# Apply the specified compression algorithms to the model
compression_ctrl, compressed_model = create_compressed_model(model, nncf_config)

# Now use compressed_model as a usual torch.nn.Module 
# to fine-tune compression parameters along with the model weights

# ... the rest of the usual PyTorch-powered training pipeline

# Export to ONNX or .pth when done fine-tuning
compression_ctrl.export_model("compressed_model.onnx")
torch.save(compressed_model.state_dict(), "compressed_model.pth")

import tensorflow as tf

from nncf import NNCFConfig
from nncf.tensorflow import create_compressed_model, register_default_init_args

# Instantiate your uncompressed model
from tensorflow.keras.applications import ResNet50
model = ResNet50()

# Load a configuration file to specify compression
nncf_config = NNCFConfig.from_json("resnet50_int8.json")

# Provide dataset for compression algorithm initialization
representative_dataset = tf.data.Dataset.list_files("/path/*.jpeg")
nncf_config = register_default_init_args(nncf_config, representative_dataset, batch_size=1)

# Apply the specified compression algorithms to the model
compression_ctrl, compressed_model = create_compressed_model(model, nncf_config)

# Now use compressed_model as a usual Keras model
# to fine-tune compression parameters along with the model weights

# ... the rest of the usual TensorFlow-powered training pipeline

# Export to Frozen Graph, TensorFlow SavedModel or .h5  when done fine-tuning 
compression_ctrl.export_model("compressed_model.pb", save_format="frozen_graph")

For a more detailed description of NNCF usage in your training code, see this tutorial.

Model Compression Tutorials and Samples

For a quicker start with NNCF-powered compression, try sample notebooks and scripts presented below.

Model Compression Tutorials

A collection of ready-to-run Jupyter* notebooks are available to demonstrate how to use NNCF compression algorithms to optimize models for inference with the OpenVINO Toolkit:

• Accelerate Inference of NLP models with Post-Training Qunatization API of NNCF
• Convert and Optimize YOLOv8 with OpenVINO
• Convert and Optimize YOLOv7 with OpenVINO
• NNCF Post-Training Optimization of Segment Anything Model
• Quantize a Segmentation Model and Show Live Inference
• Training to Deployment with TensorFlow and OpenVINO
• Migrate quantization from POT API to NNCF API
• Post-Training Quantization of Pytorch model with NNCF
• Optimizing PyTorch models with NNCF of OpenVINO by 8-bit quantization
• Optimizing TensorFlow models with NNCF of OpenVINO by 8-bit quantization
• Accelerate Inference of Sparse Transformer Models with OpenVINO and 4th Gen Intel Xeon Scalable Processors

Post-Training Quantization Samples

Compact scripts demonstrating quantization and corresponding inference speed boost:

• Post-Training Quantization of MobileNet v2 OpenVINO Model
• Post-Training Quantization of YOLOv8 OpenVINO Model
• Post-Training Quantization of Anomaly Classification OpenVINO model with control of accuracy metric
• Post-Training Quantization of YOLOv8 OpenVINO Model with control of accuracy metric
• Post-Training Quantization of MobileNet v2 PyTorch Model
• Post-Training Quantization of SSD PyTorch Model
• Post-Training Quantization of MobileNet v2 ONNX Model
• Post-Training Quantization of MobileNet v2 TensorFlow Model

Training-Time Compression Samples

These examples provide full pipelines including compression, training and inference for classification, object detection and segmentation tasks.

• PyTorch samples:
  • Image Classification sample
  • Object Detection sample
  • Semantic Segmentation sample
• TensorFlow samples:
  • Image Classification sample
  • Object Detection sample
  • Instance Segmentation sample

Third-party repository integration

NNCF may be straightforwardly integrated into training/evaluation pipelines of third-party repositories.

Used by

• OpenVINO Training Extensions

  NNCF is integrated into OpenVINO Training Extensions as model optimization backend. So you can train, optimize and export new models based on the available model templates as well as run exported models with OpenVINO.

• HuggingFace Optimum Intel

  NNCF is used as a compression backend within the renowned transformers repository in HuggingFace Optimum Intel.

Git patches for third-party repository

See third_party_integration for examples of code modifications (Git patches and base commit IDs are provided) that are necessary to integrate NNCF into the following repositories:

• huggingface-transformers

Installation Guide

For detailed installation instructions please refer to the Installation page.

NNCF can be installed as a regular PyPI package via pip:

pip install nncf

If you want to install both NNCF and the supported PyTorch version in one line, you can do this by simply running:

pip install nncf[torch]

Other viable options besides [torch] are [tf], [onnx] and [openvino].

NNCF is also available via conda:

conda install -c conda-forge nncf

You may also use one of the Dockerfiles in the docker directory to build an image with an environment already set up and ready for running NNCF sample scripts.

System requirements

• Ubuntu* 18.04 or later (64-bit)
• Python* 3.7 or later
• Supported frameworks:
  • PyTorch* >=1.9.1, <1.14
  • TensorFlow* >=2.4.0, <=2.11.1
  • ONNX* ~=1.13.1
  • OpenVINO* >=2022.3.0

This repository is tested on Python* 3.8.10, PyTorch* 1.13.1 (NVidia CUDA* Toolkit 11.6) and TensorFlow* 2.11.1 (NVidia CUDA* Toolkit 11.2).

NNCF Compressed Model Zoo

Results achieved using sample scripts, example patches to third-party repositories and NNCF configuration files provided with this repository. See README.md files for sample scripts and example patches to find instruction and links to exact configuration files and final checkpoints.

• PyTorch models
  • Classification
  • Object detection
  • Semantic segmentation
  • Natural language processing (3rd-party training pipelines)
• TensorFlow models
  • Classification
  • Object detection
  • Instance segmentation

PyTorch models

Classification

Object detection

Semantic segmentation

NLP (HuggingFace Transformers-powered models)

PyTorch Model	Compression algorithm	Dataset	Accuracy (Drop) %
BERT-base-chinese	INT8	XNLI	77.22 (0.46)
BERT-base-cased	INT8	CoNLL2003	99.18 (-0.01)
BERT-base-cased	INT8	MRPC	84.8 (-0.24)
BERT-large (Whole Word Masking)	INT8	SQuAD v1.1	F1: 92.68 (0.53)
RoBERTa-large	INT8	MNLI	matched: 89.25 (1.35)
DistilBERT-base	INT8	SST-2	90.3 (0.8)
MobileBERT	INT8	SQuAD v1.1	F1: 89.4 (0.58)
GPT-2	INT8	WikiText-2 (raw)	perplexity: 20.9 (-1.17)

TensorFlow models

Classification

Object detection

Instance segmentation

ONNX models

Classification

Object Detection

Citing

@article{kozlov2020neural,
    title =   {Neural network compression framework for fast model inference},
    author =  {Kozlov, Alexander and Lazarevich, Ivan and Shamporov, Vasily and Lyalyushkin, Nikolay and Gorbachev, Yury},
    journal = {arXiv preprint arXiv:2002.08679},
    year =    {2020}
}

Contributing Guide

Refer to the CONTRIBUTING.md file for guidelines on contributions to the NNCF repository.

Useful links

• Documentation
• Example scripts (model objects available through links in respective README.md files):
  • PyTorch
  • TensorFlow
• FAQ
• Notebooks
• HuggingFace Optimum Intel
• OpenVINO Model Optimization Guide