KD-Lib

A PyTorch model compression library containing easy-to-use methods for knowledge distillation, pruning, and quantization

Installation

From source (recommended)

https://github.com/SforAiDl/KD_Lib.git
cd KD_Lib
python setup.py install

From PyPI

pip install KD-Lib

Example usage

To implement the most basic version of knowledge distillation from Distilling the Knowledge in a Neural Network and plot loss curves:

import torch
import torch.optim as optim
from torchvision import datasets, transforms
from KD_Lib.KD import VanillaKD

# This part is where you define your datasets, dataloaders, models and optimizers

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=True,
        download=True,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=False,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

teacher_model = <your model>
student_model = <your model>

teacher_optimizer = optim.SGD(teacher_model.parameters(), 0.01)
student_optimizer = optim.SGD(student_model.parameters(), 0.01)

# Now, this is where KD_Lib comes into the picture

distiller = VanillaKD(teacher_model, student_model, train_loader, test_loader, 
                      teacher_optimizer, student_optimizer)  
distiller.train_teacher(epochs=5, plot_losses=True, save_model=True)    # Train the teacher network
distiller.train_student(epochs=5, plot_losses=True, save_model=True)    # Train the student network
distiller.evaluate(teacher=False)                                       # Evaluate the student network
distiller.get_parameters()                                              # A utility function to get the number of 
                                                                        # parameters in the  teacher and the student network

To train a collection of 3 models in an online fashion using the framework in Deep Mutual Learning and log training details to Tensorboard:

import torch
import torch.optim as optim
from torchvision import datasets, transforms
from KD_Lib.KD import DML
from KD_Lib.models import ResNet18, ResNet50          # To use models packaged in KD_Lib

# Define your datasets, dataloaders, models and optimizers

train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=True,
        download=True,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

test_loader = torch.utils.data.DataLoader(
    datasets.MNIST(
        "mnist_data",
        train=False,
        transform=transforms.Compose(
            [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
        ),
    ),
    batch_size=32,
    shuffle=True,
)

student_params = [4, 4, 4, 4, 4]
student_model_1 = ResNet50(student_params, 1, 10)
student_model_2 = ResNet18(student_params, 1, 10)

student_cohort = [student_model_1, student_model_2]

student_optimizer_1 = optim.SGD(student_model_1.parameters(), 0.01)
student_optimizer_2 = optim.SGD(student_model_2.parameters(), 0.01)

student_optimizers = [student_optimizer_1, student_optimizer_2]

# Now, this is where KD_Lib comes into the picture 

distiller = DML(student_cohort, train_loader, test_loader, student_optimizers, log=True, logdir="./logs")

distiller.train_students(epochs=5)
distiller.evaluate()
distiller.get_parameters()

Methods Implemented

Some benchmark results can be found in the logs file.

Paper / Method	Link	Repository (KD_Lib/)
Distilling the Knowledge in a Neural Network	https://arxiv.org/abs/1503.02531	KD/vision/vanilla
Improved Knowledge Distillation via Teacher Assistant	https://arxiv.org/abs/1902.03393	KD/vision/TAKD
Relational Knowledge Distillation	https://arxiv.org/abs/1904.05068	KD/vision/RKD
Distilling Knowledge from Noisy Teachers	https://arxiv.org/abs/1610.09650	KD/vision/noisy
Paying More Attention To The Attention	https://arxiv.org/abs/1612.03928	KD/vision/attention
Revisit Knowledge Distillation: a Teacher-free Framework	https://arxiv.org/abs/1909.11723	KD/vision/teacher_free
Mean Teachers are Better Role Models	https://arxiv.org/abs/1703.01780	KD/vision/mean_teacher
Knowledge Distillation via Route Constrained Optimization	https://arxiv.org/abs/1904.09149	KD/vision/RCO
Born Again Neural Networks	https://arxiv.org/abs/1805.04770	KD/vision/BANN
Preparing Lessons: Improve Knowledge Distillation with Better Supervision	https://arxiv.org/abs/1911.07471	KD/vision/KA
Improving Generalization Robustness with Noisy Collaboration in Knowledge Distillation	https://arxiv.org/abs/1910.05057	KD/vision/noisy
Distilling Task-Specific Knowledge from BERT into Simple Neural Networks	https://arxiv.org/abs/1903.12136	KD/text/BERT2LSTM
Deep Mutual Learning	https://arxiv.org/abs/1706.00384	KD/vision/DML
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks	https://arxiv.org/abs/1803.03635	Pruning/lottery_tickets
Regularizing Class-wise Predictions via Self-knowledge Distillation	https://arxiv.org/abs/2003.13964	KD/vision/CSDK

Please cite our pre-print if you find KD-Lib useful in any way :)

@misc{shah2020kdlib,
  title={KD-Lib: A PyTorch library for Knowledge Distillation, Pruning and Quantization}, 
  author={Het Shah and Avishree Khare and Neelay Shah and Khizir Siddiqui},
  year={2020},
  eprint={2011.14691},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}