nncf

Neural Network Compression Framework for enhanced OpenVINO™ inference

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Neural Network Compression Framework (NNCF) provides a suite of post-training and training-time algorithms for optimizing inference of neural networks in OpenVINO™ with a minimal accuracy drop. It is designed to work with models from PyTorch, TorchFX, TensorFlow, ONNX, and OpenVINO™. NNCF offers samples demonstrating compression algorithms for various use cases and models, with the ability to add different compression algorithms easily. It supports GPU-accelerated layers, distributed training, and seamless combination of pruning, sparsity, and quantization algorithms. NNCF allows exporting compressed models to ONNX or TensorFlow formats for use with OpenVINO™ toolkit, and supports Accuracy-Aware model training pipelines via Adaptive Compression Level Training and Early Exit Training.

README:

Neural Network Compression Framework (NNCF)

Key Features • Installation • Documentation • Usage • Tutorials and Samples • Third-party integration • Model Zoo

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

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

NNCF provides samples that demonstrate the usage of compression algorithms for different use cases and models. See compression results achievable with the NNCF-powered samples on the NNCF Model Zoo page.

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

Compression algorithm	OpenVINO	PyTorch	TorchFX	TensorFlow	ONNX
Post-Training Quantization	Supported	Supported	Experimental	Supported	Supported
Weights Compression	Supported	Supported	Experimental	Not supported	Not supported
Activation Sparsity	Not supported	Experimental	Not supported	Not supported	Not supported

Training-Time Compression Algorithms

Compression algorithm	PyTorch	TensorFlow
Quantization Aware Training	Supported	Supported
Weight-Only Quantization Aware Training with LoRA and NLS	Supported	Not Supported
Mixed-Precision Quantization	Supported	Not supported
Sparsity	Supported	Supported
Filter pruning	Supported	Supported
Movement pruning	Experimental	Not supported

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

NOTE: Limited support for TensorFlow models. Only models created using Sequential or Keras Functional API are 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.
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, while PyTorch, TensorFlow, and ONNX are also supported.

OpenVINO

import nncf
import openvino as ov
import torch
from torchvision import datasets, transforms

# 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", transform=transforms.Compose([transforms.ToTensor()]))
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)

PyTorch

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", transform=transforms.Compose([transforms.ToTensor()]))
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)

NOTE If the Post-Training Quantization algorithm does not meet quality requirements you can fine-tune the quantized pytorch model. You can find an example of the Quantization-Aware training pipeline for a pytorch model here.

TorchFX

import nncf
import torch.fx
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", transform=transforms.Compose([transforms.ToTensor()]))
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: Export model to TorchFX
input_shape = (1, 3, 224, 224)
fx_model = torch.export.export_for_training(model, args=(ex_input,)).module()
# or
# fx_model = torch.export.export(model, args=(ex_input,)).module()

# Step 4: Run the quantization pipeline
quantized_fx_model = nncf.quantize(fx_model, calibration_dataset)

TensorFlow

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)

ONNX

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", transform=transforms.Compose([transforms.ToTensor()]))
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 Quantization

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

PyTorch

import nncf
import nncf.torch
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", transform=transforms.Compose([transforms.ToTensor()]))
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)

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

# Save quantization modules and the quantized model parameters
checkpoint = {
    'state_dict': model.state_dict(),
    'nncf_config': nncf.torch.get_config(model),
    ... # the rest of the user-defined objects to save
}
torch.save(checkpoint, path_to_checkpoint)

# ...

# Load quantization modules and the quantized model parameters
resuming_checkpoint = torch.load(path_to_checkpoint)
nncf_config = resuming_checkpoint['nncf_config']
state_dict = resuming_checkpoint['state_dict']

quantized_model = nncf.torch.load_from_config(model, nncf_config, example_input)
model.load_state_dict(state_dict)
# ... the rest of the usual PyTorch-powered training pipeline

Training-Time Compression

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

PyTorch

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_imagenet_rb_sparsity.json")

# Provide data loaders for compression algorithm initialization, if necessary
import torchvision.datasets as datasets
representative_dataset = datasets.ImageFolder("/path", transform=transforms.Compose([transforms.ToTensor()]))
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")

NOTE (PyTorch): Due to the way NNCF works within the PyTorch backend, import nncf must be done before any other import of torch in your package or in third-party packages that your code utilizes. Otherwise, the compression may be applied incompletely.

Tensorflow

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_imagenet_rb_sparsity.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.

Demos, Tutorials and Samples

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

Jupyter* Notebook Tutorials and Demos

Ready-to-run Jupyter* notebook tutorials and demos are available to explain and display NNCF compression algorithms for optimizing models for inference with the OpenVINO Toolkit:

Notebook Tutorial Name	Compression Algorithm	Backend	Domain
BERT Quantization	Post-Training Quantization	OpenVINO	NLP
MONAI Segmentation Model Quantization	Post-Training Quantization	OpenVINO	Segmentation
PyTorch Model Quantization	Post-Training Quantization	PyTorch	Image Classification
YOLOv11 Quantization with Accuracy Control	Post-Training Quantization with Accuracy Control	OpenVINO	Speech-to-Text, Object Detection
PyTorch Training-Time Compression	Training-Time Compression	PyTorch	Image Classification
TensorFlow Training-Time Compression	Training-Time Compression	Tensorflow	Image Classification
Joint Pruning, Quantization and Distillation for BERT	Joint Pruning, Quantization and Distillation	OpenVINO	NLP

A list of notebooks demonstrating OpenVINO conversion and inference together with NNCF compression for models from various domains:

Demo Model	Compression Algorithm	Backend	Domain
YOLOv8	Post-Training Quantization	OpenVINO	Object Detection, KeyPoint Detection, Instance Segmentation
EfficientSAM	Post-Training Quantization	OpenVINO	Image Segmentation
Segment Anything Model	Post-Training Quantization	OpenVINO	Image Segmentation
OneFormer	Post-Training Quantization	OpenVINO	Image Segmentation
InstructPix2Pix	Post-Training Quantization	OpenVINO	Image-to-Image
CLIP	Post-Training Quantization	OpenVINO	Image-to-Text
BLIP	Post-Training Quantization	OpenVINO	Image-to-Text
Latent Consistency Model	Post-Training Quantization	OpenVINO	Text-to-Image
ControlNet QR Code Monster	Post-Training Quantization	OpenVINO	Text-to-Image
SDXL-turbo	Post-Training Quantization	OpenVINO	Text-to-Image, Image-to-Image
Distil-Whisper	Post-Training Quantization	OpenVINO	Speech-to-Text
Whisper	Post-Training Quantization	OpenVINO	Speech-to-Text
MMS Speech Recognition	Post-Training Quantization	OpenVINO	Speech-to-Text
Grammar Error Correction	Post-Training Quantization	OpenVINO	NLP, Grammar Correction
LLM Instruction Following	Weight Compression	OpenVINO	NLP, Instruction Following
LLM Chat Bots	Weight Compression	OpenVINO	NLP, Chat Bot

Post-Training Quantization Examples

Compact scripts demonstrating quantization and corresponding inference speed boost:

Example Name	Compression Algorithm	Backend	Domain
OpenVINO MobileNetV2	Post-Training Quantization	OpenVINO	Image Classification
OpenVINO YOLOv8	Post-Training Quantization	OpenVINO	Object Detection
OpenVINO YOLOv8 QwAС	Post-Training Quantization with Accuracy Control	OpenVINO	Object Detection
OpenVINO Anomaly Classification	Post-Training Quantization with Accuracy Control	OpenVINO	Anomaly Classification
PyTorch MobileNetV2	Post-Training Quantization	PyTorch	Image Classification
PyTorch SSD	Post-Training Quantization	PyTorch	Object Detection
TorchFX Resnet18	Post-Training Quantization	TorchFX	Image Classification
TensorFlow MobileNetV2	Post-Training Quantization	TensorFlow	Image Classification
ONNX MobileNetV2	Post-Training Quantization	ONNX	Image Classification

Quantization-Aware Training Examples

Example Name	Compression Algorithm	Backend	Domain
PyTorch Resnet18	Quantization-Aware Training	PyTorch	Image Classification
PyTorch Anomalib	Quantization-Aware Training	PyTorch	Anomaly Detection

Third-party Repository Integration

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

Used by

HuggingFace Optimum Intel

NNCF is used as a compression backend within the renowned transformers repository in HuggingFace Optimum Intel. For instance, the command below exports the Llama-3.2-3B-Instruct model to OpenVINO format with INT4-quantized weights:
```
optimum-cli export openvino -m meta-llama/Llama-3.2-3B-Instruct --weight-format int4 ./Llama-3.2-3B-Instruct-int4
```
Ultralytics

NNCF is integrated into the Intel OpenVINO export pipeline, enabling quantization for the exported models.
ExecuTorch

NNCF is used as primary quantization framework for the ExecuTorch OpenVINO integration.
torch.compile

NNCF is used as primary quantization framework for the torch.compile OpenVINO integration.
OpenVINO Training Extensions

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

Installation Guide

For detailed installation instructions, refer to the Installation guide.

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

pip install nncf

NNCF is also available via conda:

conda install -c conda-forge nncf

System requirements of NNCF correspond to the used backend. System requirements for each backend and the matrix of corresponding versions can be found in installation.md.

NNCF Compressed Model Zoo

List of models and compression results for them can be found at our NNCF Model Zoo page.

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

Telemetry

NNCF as part of the OpenVINO™ toolkit collects anonymous usage data for the purpose of improving OpenVINO™ tools. You can opt-out at any time by running the following command in the Python environment where you have NNCF installed:

opt_in_out --opt_out

More information available on OpenVINO telemetry.

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This project is a c++ version of the AI reasoning library that supports the reasoning of tensorrt models. It provides accelerated deployment cases of deep learning CV popular models and supports dynamic-batch image processing, inference, decode, and NMS. The project has been updated with various models and provides tutorials for model exports. It also includes a producer-consumer inference model for specific tasks. The project directory includes implementations for model inference applications, backend reasoning classes, post-processing, pre-processing, and target detection and tracking. Speed tests have been conducted on various models, and onnx downloads are available for different models.

github

: 87

joliGEN

JoliGEN is an integrated framework for training custom generative AI image-to-image models. It implements GAN, Diffusion, and Consistency models for various image translation tasks, including domain and style adaptation with conservation of semantics. The tool is designed for real-world applications such as Controlled Image Generation, Augmented Reality, Dataset Smart Augmentation, and Synthetic to Real transforms. JoliGEN allows for fast and stable training with a REST API server for simplified deployment. It offers a wide range of options and parameters with detailed documentation available for models, dataset formats, and data augmentation.

github

: 248

ai-edge-torch

AI Edge Torch is a Python library that supports converting PyTorch models into a .tflite format for on-device applications on Android, iOS, and IoT devices. It offers broad CPU coverage with initial GPU and NPU support, closely integrating with PyTorch and providing good coverage of Core ATen operators. The library includes a PyTorch converter for model conversion and a Generative API for authoring mobile-optimized PyTorch Transformer models, enabling easy deployment of Large Language Models (LLMs) on mobile devices.

github

: 460

awesome-RK3588

RK3588 is a flagship 8K SoC chip by Rockchip, integrating Cortex-A76 and Cortex-A55 cores with NEON coprocessor for 8K video codec. This repository curates resources for developing with RK3588, including official resources, RKNN models, projects, development boards, documentation, tools, and sample code.

github

: 106

cl-waffe2

cl-waffe2 is an experimental deep learning framework in Common Lisp, providing fast, systematic, and customizable matrix operations, reverse mode tape-based Automatic Differentiation, and neural network model building and training features accelerated by a JIT Compiler. It offers abstraction layers, extensibility, inlining, graph-level optimization, visualization, debugging, systematic nodes, and symbolic differentiation. Users can easily write extensions and optimize their networks without overheads. The framework is designed to eliminate barriers between users and developers, allowing for easy customization and extension.

github

: 119

TensorRT-Model-Optimizer

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

github

: 1.3k

depthai

This repository contains a demo application for DepthAI, a tool that can load different networks, create pipelines, record video, and more. It provides documentation for installation and usage, including running programs through Docker. Users can explore DepthAI features via command line arguments or a clickable QT interface. Supported models include various AI models for tasks like face detection, human pose estimation, and object detection. The tool collects anonymous usage statistics by default, which can be disabled. Users can report issues to the development team for support and troubleshooting.

github

: 927