Knowledge Distillation as Semiparametric Inference

This code replicates the experiments of

Knowledge Distillation as Semiparametric Inference.
Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, and Lester Mackey.
International Conference on Learning Representations (ICLR). May 2021.

@inproceedings{
  dao2021knowledge,
  title={Knowledge Distillation as Semiparametric Inference},
  author={Tri Dao and Govinda M Kamath and Vasilis Syrgkanis and Lester Mackey},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

Required packages

Python >= 3.7, Pytorch >= 1.7. More details in requirements.txt.

We use Pytorch-lightning to organize training code, Hydra to organize configurations.

[Optional] We use Ray for distributed training.

[Optional] We use Wandb for logging.

Code structure

├─ cfg               # Configuration files, for Hydra
├─ datamodules        # Code for datasets
├─ models            # Model implementations
├─ results           # json files containing raw results, and pdf files containing plots
├─ scripts           # Scripts to tune hyperparameters
├─ distill_train.py  # Train student model, distilled from teacher model
├─ kd.py            # Implementation of knowledge distillation losses
├─ ray_runner.py      # Distributed training with Ray [optional]
├─ train.py          # Train a model (e.g. teacher or student) from scratch
├─ utils.py          # Utility functions

Training

To train a ResNet18 on CIFAR10, and save model to disk:

python train.py train.batch_size=512 train.optimizer.lr=4e-1 model=resnet18 +save_checkpoint_path=checkpoints/resnet18/final.ckpt runner=pl

To train a CNN5 student:

python distill_train.py train.batch_size=512 train.optimizer.lr=4e-1 train.kd.class=KDOrthoLoss train.gradient_clip_val=0.1 train.kd.temperature=2.0 runner=pl

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