Weight Tying In Transformers: Learning With Shared Weights

Central to the transformer architecture is its capacity for handling large datasets and its attention mechanisms, allowing for contextualized representation learning. However, as the complexity of these models grows, so does the challenge of parameter efficiency, leading researchers to explore methods such as weight tying.

Weight tying refers to the practice of sharing weights between different layers or components of a network, particularly the embedding and output layers. This technique not only reduces the overall parameter count but has also been shown to enhance learning efficiency by enforcing tighter constraints on representational capacity.

Understanding Weight Tying

The Concept of Weight Tying

Weight tying involves sharing parameters between different components of a model to optimize the learning process. In transformer architectures, this typically refers to linking the weights of the input embedding layer with the weights of the output layer (softmax layer).

Rationale Behind Weight Tying

The primary motivation for weight tying can be summarized as follows:

• Parameter Efficiency: By sharing the weights, the model significantly reduces its number of trainable parameters, alleviating the risk of overfitting, particularly in scenarios where labeled data is scarce.
• Regularization Effects: The implicit regularization introduced through weight tying encourages the model to learn more generalized representations, as the shared weights must cater to both embedding and output tasks. This can enhance generalization across languages or domains.
• Faster Convergence: Reduced parameter space often leads to quicker convergence during training, as the optimizer can more effectively navigate the loss landscape.

Implementation

In the implementations, weight tying typically involves defining the embedding layer’s weights as a shared matrix with the output layer:

# Example in PyTorch
class TransformerModel(nn.Module):
    def __init__(self, vocab_size, embed_dim):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embed_dim)
        self.fc_out = nn.Linear(embed_dim, vocab_size, bias=False)
        
        # Weight tying
        self.fc_out.weight = self.embedding.weight

    # Forward method and other layers here...

In this code snippet, the output layer (fc_out) shares the weights with the embedding layer (embedding), ensuring that the same embedding matrix is utilized for both input representation and output prediction.

Reference:
1. Using the Output Embedding to Improve Language Models