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Model Distillation

You can distill large, complex teacher models into smaller student models without significant loss in performance with the help of Haystack. Read this guide to learn more about model distillation, different approaches, and student-teacher combinations.

Why Model Distillation?

A larger model size leads to more accurate model predictions. But the more parameters a model has, the more resource-intensive it is. This makes deployment more difficult and increases latency. Choosing the right reader is always a trade-off between the quality of results and the cost of deployment. Model distillation is an approach that we use to bridge the accuracy gap between a large language model and a smaller one. With this approach, you can preserve some of the accuracy of a large teacher model when training a smaller student model.

How Does It Work?

The goal of model distillation is for the student model to behave similarly to the teacher model. When training the student, you can use a range of approaches with different sets of loss functions to enforce this similarity. The two approaches we focused on are the approach proposed by Hinton et al. of distilling the outputs of the prediction layers and the approach used by TinyBERT (Jiao et al.) of also distilling intermediate layers.

Hinton's Approach

Hinton's prediction layer distillation approach introduces a loss function to minimise the difference between the logits (outputs of the prediction layer) of the student model and the teacher model. These logits contain the probabilities that the teacher model assigns to all possible answer spans in the document. This makes the training loss more expressive and allows for higher accuracy with the same model size. An advantage of this method is that it is very quick and does not have any requirements for the student architecture.

TinyBERT Approach

The TinyBERT approach adds an additional step before performing prediction layer distillation. This step is called intermediate layer distillation and its goal is to minimize the differences between the hidden states and the attentions of the student and teacher. Intermediate layer distillation increases performance because it forces the student model to behave similarly to the teacher model. In this approach, you can only use student models pretrained using the teacher model. It also increases the computation time by about 35x the time of performing prediction layer distillation.

Student-Teacher Combinations

The student-teacher combinations available depend on whether you are using intermediate layer distillaton, as it restricts the models that can be used with it.

Possible Student-Teacher Combinations for Prediction Layer Distillation

When only using prediction layer distillation, the only thing that needs to be the same is the tokenizer. This table contains a few examples of models with the same tokenizer:



Bert uncased tokenizer

Bert cased tokenizer

Possible Student-Teacher Combinations for Intermediate Layer Distillation

Intermediate layer distillation places strict restrictions on the kinds of models you can use. It requires that the student model was distilled during pretraining with the same teacher model. This means that for each student model there is only one teacher model you can use for fine tuning.

This table will be growing as we are actively working on training more student models compatible with other teachers.

Distilling a Model in Haystack

When using model distillation, you need to have both a teacher and a student model. The teacher model must be trained on the task. This means that you can use a fine-tuned teacher from the Hugging Face hub or you can fine-tune the teacher on your own data beforehand.



To learn how to fine-tune your teacher model on your own data, check out our Fine-tuning a Model on Your Own Data tutorial.

The following paragraphs are only relevant for intermediate layer distillation. To use prediction layer distillation, jump directly to "Prediction layer distillation". Otherwise, read on to learn how to use intermediate layer distillation.

Data Augmentation for Intermediate Layer Distillation

The optional intermediate layer distillation step requires a lot of data. Because acquiring a lot of training data is expensive, the TinyBERT paper suggests a data augmentation approach in which you multiply existing datasets. For data augmentation, you can use the script from Haystack.

This command creates an augmented dataset with 20 times the samples from the dataset.json data set and places it in the augmented_dataset.json file. In this example, dataset.json is in the same folder as

python --squad_path dataset.json --output_path augmented_dataset.json --multiplication_factor 20

Loading Student and Teacher Models

Both student and teacher models are standard FARMReaders so you can load them as you are used to:

student = FARMReader(...)
teacher = FARMReader(...)

Ensure that the student and teacher models use the same tokenizer. Otherwise, model distillation fails.

Distilling a Teacher Model to a Student Model

Intermediate Layer Distillation (optional)

To use intermediate layer distillation in haystack, call the distil_intermediate_layers_from method on the student passing the teacher model:

student.distil_intermediate_layers_from(teacher, data_dir=data_dir, train_filename=train_filename)

Ensure that the training dataset specified with data_dir and train_filename has been augmented using the script. distil_intermediate_layers_from accepts all the parameters you can use for training. You must provide all the necessary parameters in this method. The method also accepts additional parameters like temperature and distillation_loss. However, for intermediate layer distillation, you can leave them at their default values. Intermediate layer distillation is optional but if you decide to use it, you must perform prediction layer distillation after it to achieve good performance.

Prediction Layer Distillation

To use prediction layer distillation in Haystack, call the distil_prediction_layer_from method on the student and pass the teacher model:

student.distil_prediction_layer_from(teacher, temperature=5, distillation_loss_weight=0.5,
                                     data_dir=data_dir, train_filename=train_filename, ...)

Like distil_intermediate_layers_from, distil_prediction_layer_from accepts all parameters that you can use for training, but also accepts additional parameters. Everything you need to provide for training such as train_filename (name of file with training data set) and data_dir (folder of training data set file and optionally test and eval data set) is also necessary to provide in distil_from. You also need to provide the teacher model. All other parameters are optional, but we also recommend adjusting temperature and distillation_loss_weight.

What Parameters Should I Use?

Intermediate Layer Distillation

When using intermediate layer distillation, we recommend using the default parameters. You can try tuning the distillation-specific parameter temperature and adjusting all the parameters that are relevant for training (for example, learning_rate, n_epochs). The only exception to this is the parameter batch_size. Specify student_batch_size instead.

Prediction Layer Distillation

Prediction layer distillation is controlled by two parameters: temperature and distillation_loss_weight.

The temperature parameter specifies the certainty of the teacher model. Usually language models are very certain that one answer span is correct and assign all other answer spans a very low probability. We don't want this for distillation as it would be similar to having a label. You can correct this by using a higher temperature. We also don't want the probabilities to be too similar as this doesn't create a meaningful training signal. For this, we can decrease the temperature. In most experiments, we found a temperature between 1 and 5 to be most useful.

The distillation_loss_weight parameter specifies the weight given to the distillation loss in relation to the loss based on the label. For example, setting this to 0 effectively disables model distillation and setting it to 1 only uses model distillation making the labels unnecessary. Most times, you should set this to a relatively high value (for example, 0.75).

Here are the parameters that worked best in our experiments:

What Accuracy Can I Expect?

These tables show experiment results of the current version of model distillation in Haystack.

Only using prediction layer distillation

This table shows the results of the experiments where we applied prediction layer to distill a fine-tuned large model (~350M parameters) into a base model (~115M parameters and ~2x the speed of a large model).

Intermediate Layer Distillation and Prediction Layer Distillation

In this experiment, we used intermediate layer distillation. We distilled a base model (~115M parameters) into a TinyBERT-6L-768d model (~67M parameters and ~2x the speed of a base model).

The baseline is the student model fine-tuned without distillation.