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.
| 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 |
| Compression algorithm | PyTorch | TensorFlow |
|---|---|---|
| Quantization Aware Training | Supported | 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.
- 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.
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.
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.runtime 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
from nncf.torch import disable_patching
# 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)
with nncf.torch.disable_patching():
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)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 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': model.nncf.get_config(),
... # 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 pipelineHere 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.
For a quicker start with NNCF-powered compression, try sample notebooks and scripts presented below.
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 |
| 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 |
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 |
Examples of full pipelines including compression, training, and inference for classification, detection, and segmentation tasks:
| Example Name | Compression Algorithm | Backend | Domain |
|---|---|---|---|
| PyTorch Image Classification | Training-Time Compression | PyTorch | Image Classification |
| PyTorch Object Detection | Training-Time Compression | PyTorch | Object Detection |
| PyTorch Semantic Segmentation | Training-Time Compression | PyTorch | Semantic Segmentation |
| TensorFlow Image Classification | Training-Time Compression | TensorFlow | Image Classification |
| TensorFlow Object Detection | Training-Time Compression | TensorFlow | Object Detection |
| TensorFlow Instance Segmentation | Training-Time Compression | TensorFlow | Instance Segmentation |
NNCF may be easily integrated into training/evaluation pipelines of third-party repositories.
-
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.
-
NNCF is used as a compression backend within the renowned
transformersrepository in HuggingFace Optimum Intel.
For detailed installation instructions, refer to the Installation guide.
NNCF can be installed as a regular PyPI package via pip:
pip install nncfNNCF is also available via conda:
conda install -c conda-forge nncf- Ubuntu* 18.04 or later (64-bit)
- Python* 3.9 or later
- Supported frameworks:
- PyTorch* >=2.4, <2.6
- TensorFlow* >=2.8.4, <=2.15.1
- ONNX* ==1.17.0
- OpenVINO* >=2022.3.0
This repository is tested on Python* 3.10.14, PyTorch* 2.5.0 (NVidia CUDA* Toolkit 12.4) and TensorFlow* 2.12.1 (NVidia CUDA* Toolkit 11.8).
List of models and compression results for them can be found at our NNCF Model Zoo page.
@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}
}Refer to the CONTRIBUTING.md file for guidelines on contributions to the NNCF repository.
- Documentation
- Example scripts (model objects available through links in respective README.md files):
- FAQ
- Notebooks
- HuggingFace Optimum Intel
- OpenVINO Model Optimization Guide
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.