distill_roberta_weights.py

from transformers.models.roberta.modeling_roberta import RobertaEncoder, RobertaModel
from torch.nn import Module

def distill_roberta_weights(
    teacher : Module,
    student : Module,
) -> None:
    """
    Recursively copies the weights of the (teacher) to the (student).
    This function is meant to be first called on a RobertaFor... model, but is then called on every children of that model recursively.
    The only part that's not fully copied is the encoder, of which only half is copied.
    """
    # If the part is an entire RoBERTa model or a RobertaFor..., unpack and iterate
    if isinstance(teacher, RobertaModel) or type(teacher).__name__.startswith('RobertaFor'):
        for teacher_part, student_part in zip(teacher.children(), student.children()):
            distill_roberta_weights(teacher_part, student_part)
    # Else if the part is an encoder, copy one out of every layer
    elif isinstance(teacher, RobertaEncoder):
            teacher_encoding_layers = [layer for layer in next(teacher.children())]
            student_encoding_layers = [layer for layer in next(student.children())]
            for i in range(len(student_encoding_layers)):
                student_encoding_layers[i].load_state_dict(teacher_encoding_layers[2*i].state_dict())
    # Else the part is a head or something else, copy the state_dict
    else:
        student.load_state_dict(teacher.state_dict())