The Principle and Role of Feedforward Neural Networks (FFN) in the Transformer Model

The Principle and Role of Feedforward Neural Networks (FFN) in the Transformer Model

Problem Description
In the Transformer model, each encoder and decoder layer contains a Feedforward Neural Network (FFN), which follows the self-attention mechanism. Please explain the structure and role of the FFN, and analyze its design characteristics (such as dimension expansion, nonlinear transformation, etc.).

Detailed Explanation of Knowledge Points

  1. Position and Role of the FFN

    • The FFN is located after the self-attention layer and the residual connection/layer normalization, and is one of the core components of the Transformer block.
    • Its function is to perform nonlinear transformation and feature enhancement on the attention output, compensating for the limitations of linear projection in the self-attention mechanism.

  2. Structural Breakdown of the FFN
    The FFN consists of two linear transformations and an activation function, with the following specific structure:

    • Input: The output of the self-attention layer (with a dimension of d_model, e.g., 512 or 768).
    • First Linear Transformation: Expands the input dimension from d_model to d_ff (typically d_ff = 4 * d_model).
    • Activation Function: Commonly ReLU or GELU, introducing nonlinearity.
    • Second Linear Transformation: Reduces the dimension from d_ff back to d_model to match the input dimension.
    • Mathematical Formula:

\[ \text{FFN}(x) = \text{Linear}_2(\text{Activation}(\text{Linear}_1(x))) \]

  1. Analysis of Design Motivation

    • Dimension Expansion: By increasing the intermediate layer dimension (e.g., 4 times), the model can learn more complex feature interactions and enhance its expressive power.
    • Nonlinear Activation: Breaks the limitations of linear operations in self-attention, helping the model fit complex functions.
    • Parameter Independence: The FFN parameters are shared across positions, but transformations are performed independently at each position, balancing efficiency and flexibility.

  2. Differences from Fully Connected Networks

    • Traditional fully connected layers typically reduce dimensions layer by layer, whereas the FFN expands first and then compresses, forming a "bottleneck structure" to enhance feature extraction capability.
    • The FFN operates only on data at each position independently and does not interact across positions (this is handled by the self-attention mechanism).

  3. Practical Application Example
    Assuming an input vector dimension d_model=512 and an FFN intermediate layer dimension d_ff=2048:

    • Step 1: Linear transformation 1 maps the 512-dimensional input to 2048 dimensions.
    • Step 2: The ReLU activation function filters negative values (e.g., input [-1, 2] → output [0, 2]).
    • Step 3: Linear transformation 2 compresses the 2048-dimensional result back to 512 dimensions, matching the input dimension for residual connections.

  4. Why is the FFN Effective?

    • The self-attention mechanism excels at capturing global dependencies but is essentially a weighted sum of linear transformations. The FFN injects the ability to process local patterns through nonlinear transformations, complementing each other.
    • Experiments show that removing the FFN leads to a significant performance drop in the model, especially in complex language tasks.

Summary
The FFN is the "feature processing factory" in the Transformer. Through dimension expansion and nonlinear activation, it transforms the global information output by self-attention into richer representations. Its design balances model capacity and computational efficiency, making it one of the key components of the Transformer's success.