This is a PyTorch implementation/tutorial of the paper Distilling the Knowledge in a Neural Network.
It's a way of training a small network using the knowledge in a trained larger network; i.e. distilling the knowledge from the large network.
A large model with regularization or an ensemble of models (using dropout) generalizes better than a small model when trained directly on the data and labels. However, a small model can be trained to generalize better with help of a large model. Smaller models are better in production: faster, less compute, less memory.
The output probabilities of a trained model give more information than the labels because it assigns non-zero probabilities to incorrect classes as well. These probabilities tell us that a sample has a chance of belonging to certain classes. For instance, when classifying digits, when given an image of digit 7, a generalized model will give a high probability to 7 and a small but non-zero probability to 2, while assigning almost zero probability to other digits. Distillation uses this information to train a small model better.
The probabilities are usually computed with a softmax operation,
where is the probability for class and is the logit.
We train the small model to minimize the Cross entropy or KL Divergence between its output probability distribution and the large network's output probability distribution (soft targets).
One of the problems here is that the probabilities assigned to incorrect classes by the large network are often very small and don't contribute to the loss. So they soften the probabilities by applying a temperature ,
where higher values for will produce softer probabilities.
Paper suggests adding a second loss term for predicting the actual labels when training the small model. We calculate the composite loss as the weighted sum of the two loss terms: soft targets and actual labels.
The dataset for distillation is called the transfer set, and the paper suggests using the same training data.
We train on CIFAR-10 dataset. We train a large model that has parameters with dropout and it gives an accuracy of 85% on the validation set. A small model with parameters gives an accuracy of 80%.
We then train the small model with distillation from the large model, and it gives an accuracy of 82%; a 2% increase in the accuracy.
74import torch
75import torch.nn.functional
76from torch import nn
77
78from labml import experiment, tracker
79from labml.configs import option
80from labml_helpers.train_valid import BatchIndex
81from labml_nn.distillation.large import LargeModel
82from labml_nn.distillation.small import SmallModel
83from labml_nn.experiments.cifar10 import CIFAR10Configs
This extends from CIFAR10Configs
which defines all the dataset related configurations, optimizer, and a training loop.
86class Configs(CIFAR10Configs):
The small model
94 model: SmallModel
The large model
96 large: LargeModel
KL Divergence loss for soft targets
98 kl_div_loss = nn.KLDivLoss(log_target=True)
Cross entropy loss for true label loss
100 loss_func = nn.CrossEntropyLoss()
Temperature,
102 temperature: float = 5.
Weight for soft targets loss.
The gradients produced by soft targets get scaled by . To compensate for this the paper suggests scaling the soft targets loss by a factor of
108 soft_targets_weight: float = 100.
Weight for true label cross entropy loss
110 label_loss_weight: float = 0.5
112 def step(self, batch: any, batch_idx: BatchIndex):
Training/Evaluation mode for the small model
120 self.model.train(self.mode.is_train)
Large model in evaluation mode
122 self.large.eval()
Move data to the device
125 data, target = batch[0].to(self.device), batch[1].to(self.device)
Update global step (number of samples processed) when in training mode
128 if self.mode.is_train:
129 tracker.add_global_step(len(data))
Get the output logits, , from the large model
132 with torch.no_grad():
133 large_logits = self.large(data)
Get the output logits, , from the small model
136 output = self.model(data)
Soft targets
140 soft_targets = nn.functional.log_softmax(large_logits / self.temperature, dim=-1)
Temperature adjusted probabilities of the small model
143 soft_prob = nn.functional.log_softmax(output / self.temperature, dim=-1)
Calculate the soft targets loss
146 soft_targets_loss = self.kl_div_loss(soft_prob, soft_targets)
Calculate the true label loss
148 label_loss = self.loss_func(output, target)
Weighted sum of the two losses
150 loss = self.soft_targets_weight * soft_targets_loss + self.label_loss_weight * label_loss
Log the losses
152 tracker.add({"loss.kl_div.": soft_targets_loss,
153 "loss.nll": label_loss,
154 "loss.": loss})
Calculate and log accuracy
157 self.accuracy(output, target)
158 self.accuracy.track()
Train the model
161 if self.mode.is_train:
Calculate gradients
163 loss.backward()
Take optimizer step
165 self.optimizer.step()
Log the model parameters and gradients on last batch of every epoch
167 if batch_idx.is_last:
168 tracker.add('model', self.model)
Clear the gradients
170 self.optimizer.zero_grad()
Save the tracked metrics
173 tracker.save()
176@option(Configs.large)
177def _large_model(c: Configs):
181 return LargeModel().to(c.device)
184@option(Configs.model)
185def _small_student_model(c: Configs):
189 return SmallModel().to(c.device)
192def get_saved_model(run_uuid: str, checkpoint: int):
197 from labml_nn.distillation.large import Configs as LargeConfigs
In evaluation mode (no recording)
200 experiment.evaluate()
Initialize configs of the large model training experiment
202 conf = LargeConfigs()
Load saved configs
204 experiment.configs(conf, experiment.load_configs(run_uuid))
Set models for saving/loading
206 experiment.add_pytorch_models({'model': conf.model})
Set which run and checkpoint to load
208 experiment.load(run_uuid, checkpoint)
Start the experiment - this will load the model, and prepare everything
210 experiment.start()
Return the model
213 return conf.model
Train a small model with distillation
216def main(run_uuid: str, checkpoint: int):
Load saved model
221 large_model = get_saved_model(run_uuid, checkpoint)
Create experiment
223 experiment.create(name='distillation', comment='cifar10')
Create configurations
225 conf = Configs()
Set the loaded large model
227 conf.large = large_model
Load configurations
229 experiment.configs(conf, {
230 'optimizer.optimizer': 'Adam',
231 'optimizer.learning_rate': 2.5e-4,
232 'model': '_small_student_model',
233 })
Set model for saving/loading
235 experiment.add_pytorch_models({'model': conf.model})
Start experiment from scratch
237 experiment.load(None, None)
Start the experiment and run the training loop
239 with experiment.start():
240 conf.run()
244if __name__ == '__main__':
245 main('d46cd53edaec11eb93c38d6538aee7d6', 1_000_000)