nncf 1.7.0

Neural Networks Compression Framework

Neural Network Compression Framework (NNCF)

This repository contains a PyTorch*-based framework and samples for neural networks compression.

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 methods.

The samples demonstrate the usage of compression algorithms for three different use cases on public models and datasets: Image Classification, Object Detection and Semantic Segmentation. Compression results achievable with the NNCF-powered samples can be found in a table at the end of this document.

Key Features

• Support of various compression algorithms, applied during a model fine-tuning process to achieve best compression parameters and accuracy:
  • Quantization
  • Binarization
  • Sparsity
  • Filter pruning
• Automatic, configurable model graph transformation to obtain the compressed model. The source model is wrapped by the custom class and additional compression-specific layers are inserted in the graph.
• Common interface for compression methods
• GPU-accelerated layers for faster compressed model fine-tuning
• Distributed training support
• Configuration file examples for each supported compression algorithm.
• Git patches for prominent third-party repositories (mmdetection, huggingface-transformers) demonstrating the process of integrating NNCF into custom training pipelines
• Exporting compressed models to ONNX* checkpoints ready for usage with OpenVINO™ toolkit.

Usage

The NNCF is organized as a regular Python package that can be imported in your target training pipeline script. The basic workflow is loading a JSON configuration script containing NNCF-specific parameters determining the compression to be applied to your model, and then passing your model along with the configuration script to the nncf.create_compressed_model function. This function returns a wrapped model ready for compression fine-tuning, and handle to the object allowing you to control the compression during the training process:

import torch
import nncf  # Important - should be imported directly after torch
from nncf import create_compressed_model, NNCFConfig, 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
nncf_config = register_default_init_args(nncf_config, train_loader, loss_criterion)

# Apply the specified compression algorithms to the model
comp_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
comp_ctrl.export_model("compressed_model.onnx")
torch.save(compressed_model.state_dict(), "compressed_model.pth")

For a more detailed description of NNCF usage in your training code, see Usage.md. For in-depth examples of NNCF integration, browse the sample scripts code, or the example patches to third-party repositories.

For more details about the framework architecture, refer to the NNCFArchitecture.md.

Model Compression Samples

For a quicker start with NNCF-powered compression, you can also try the sample scripts, each of which provides a basic training pipeline for classification, semantic segmentation and object detection neural network training correspondingly.

To run the samples please refer to the corresponding tutorials:

• Image Classification sample
• Object Detection sample
• Semantic Segmentation sample

Third-party repository integration

NNCF may be straightforwardly integrated into training/evaluation pipelines of third-party repositories. See third_party_integration for examples of code modifications (Git patches and base commit IDs are provided) that are necessary to integrate NNCF into select repositories.

System requirements

• Ubuntu* 18.04 or later (64-bit)
• Python* 3.7 or later
• NVidia CUDA* Toolkit 10.2 or later^
• PyTorch* 1.5 or later (1.8.0 not supported, 1.8.1 supported)

NOTE: The best known PyTorch version for the current NNCF is 1.8.1, and it is highly recommended to use it.

^ If a torch package built for specific CUDA version is already present in the environment into which NNCF is being installed, and if it has a matching base version, then the CUDA version for which the present torch package is targeted will be used.

Otherwise NNCF will install the latest available torch version from pip, which is targeted to the CUDA version of PyTorch packaging strategy's choosing. For PyTorch 1.8.1, the default CUDA is 10.2.

Installation

We suggest to install or use the package in the Python virtual environment.

As a package built from a checked-out repository:

1. Install the following system dependencies:

sudo apt-get install python3-dev

2. Install the package and its dependencies by running the following in the repository root directory:

python setup.py install

NB: For launching example scripts in this repository, we recommend replacing the install option above with develop and setting the PYTHONPATH variable to the root of the checked-out repository.

As a PyPI package:

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

sudo apt install python3-dev
pip install nncf

As a Docker image

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.

NOTE: If you want to use sample training scripts provided in the NNCF repository under examples, you should install the corresponding Python package dependencies:

pip install -r examples/requirements.txt

Contributing

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

NNCF Compressed Model Zoo

Results achieved using sample scripts and NNCF configuration files provided with this repository. See README.md files for sample scripts for links to exact configuration files and final PyTorch checkpoints.

Quick jump to sample type:

Classification

Object detection

Semantic segmentation

Natural language processing (3rd-party training pipelines)

Object detection (3rd-party training pipelines)

Instance Segmentation (3rd-party training pipelines)

Classification

Model	Compression algorithm	Dataset	PyTorch FP32 baseline	PyTorch compressed accuracy
ResNet-50	INT8	ImageNet	76.16	76.42
ResNet-50	INT8 (per-tensor only)	ImageNet	76.16	76.37
ResNet-50	Mixed, 44.8% INT8 / 55.2% INT4	ImageNet	76.16	76.2
ResNet-50	INT8 + Sparsity 61% (RB)	ImageNet	76.16	75.43
ResNet-50	INT8 + Sparsity 50% (RB)	ImageNet	76.16	75.55
ResNet-50	Filter pruning, 40%, geometric median criterion	ImageNet	76.16	75.62
Inception V3	INT8	ImageNet	77.34	78.25
Inception V3	INT8 + Sparsity 61% (RB)	ImageNet	77.34	77.58
MobileNet V2	INT8	ImageNet	71.93	71.35
MobileNet V2	INT8 (per-tensor only)	ImageNet	71.93	71.3
MobileNet V2	Mixed, 46.6% INT8 / 53.4% INT4	ImageNet	71.93	70.92
MobileNet V2	INT8 + Sparsity 52% (RB)	ImageNet	71.93	71.11
SqueezeNet V1.1	INT8	ImageNet	58.24	58.28
SqueezeNet V1.1	INT8 (per-tensor only)	ImageNet	58.24	58.26
SqueezeNet V1.1	Mixed, 54.7% INT8 / 45.3% INT4	ImageNet	58.24	58.9
ResNet-18	XNOR (weights), scale/threshold (activations)	ImageNet	69.8	61.63
ResNet-18	DoReFa (weights), scale/threshold (activations)	ImageNet	69.8	61.61
ResNet-18	Filter pruning, 40%, magnitude criterion	ImageNet	69.8	69.26
ResNet-18	Filter pruning, 40%, geometric median criterion	ImageNet	69.8	69.32
ResNet-34	Filter pruning, 40%, geometric median criterion	ImageNet	73.3	72.73
GoogLeNet	Filter pruning, 40%, geometric median criterion	ImageNet	69.75	68.82

Object detection

Model	Compression algorithm	Dataset	PyTorch FP32 baseline	PyTorch compressed accuracy
SSD300-MobileNet	INT8 + Sparsity 70% (Magnitude)	VOC12+07 train, VOC07 eval	62.23	62.94
SSD300-VGG-BN	INT8	VOC12+07 train, VOC07 eval	78.28	77.96
SSD300-VGG-BN	INT8 + Sparsity 70% (Magnitude)	VOC12+07 train, VOC07 eval	78.28	77.59
SSD300-VGG-BN	Filter pruning, 40%, geometric median criterion	VOC12+07 train, VOC07 eval	78.28	77.72
SSD512-VGG-BN	INT8	VOC12+07 train, VOC07 eval	80.26	80.12
SSD512-VGG-BN	INT8 + Sparsity 70% (Magnitude)	VOC12+07 train, VOC07 eval	80.26	79.67

Semantic segmentation

Model	Compression algorithm	Dataset	PyTorch FP32 baseline	PyTorch compressed accuracy
UNet	INT8	CamVid	71.95	71.8
UNet	INT8 + Sparsity 60% (Magnitude)	CamVid	71.95	72.03
ICNet	INT8	CamVid	67.89	67.86
ICNet	INT8 + Sparsity 60% (Magnitude)	CamVid	67.89	67.18
UNet	INT8	Mapillary	56.23	55.87
UNet	INT8 + Sparsity 60% (Magnitude)	Mapillary	56.23	55.65
UNet	Filter pruning, 25%, geometric median criterion	Mapillary	56.23	55.62

NLP

Model	Compression algorithm	Dataset	PyTorch FP32 baseline	PyTorch compressed accuracy
BERT-base-chinese	INT8	XNLI	77.68	77.22
BERT-large (Whole Word Masking)	INT8	SQuAD v1.1	93.21 (F1)	92.68 (F1)
RoBERTa-large	INT8	MNLI	90.6 (matched)	89.25 (matched)
DistilBERT-base	INT8	SST-2	91.1	90.3
MobileBERT	INT8	SQuAD v1.1	89.98 (F1)	89.4 (F1)
GPT-2	INT8	WikiText-2 (raw)	19.73 (perplexity)	20.9 (perplexity)

Object detection (MMDetection)

Model	Compression algorithm	Dataset	PyTorch FP32 baseline	PyTorch compressed accuracy
RetinaNet-ResNet50-FPN	INT8	COCO2017	35.6 (avg bbox mAP)	35.3 (avg bbox mAP)
RetinaNet-ResNet50-FPN	INT8 + Sparsity 50%	COCO2017	35.6 (avg bbox mAP)	34.7 (avg bbox mAP)
RetinaNet-ResNeXt101-64x4d-FPN	INT8	COCO2017	39.6 (avg bbox mAP)	39.1 (avg bbox mAP)

Instance Segmentation (MMDetection)

Model	Compression algorithm	Dataset	PyTorch FP32 baseline	PyTorch compressed accuracy
Mask-RCNN-ResNet50-FPN	INT8	COCO2017	40.8 (avg bbox mAP), 37.0 (avg segm mAP)	40.6 (avg bbox mAP), 36.5 (avg segm mAP)

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}
}

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