Detailed Explanation of the Encoder-Decoder Structure in the Transformer Model

Detailed Explanation of the Encoder-Decoder Structure in the Transformer Model

Topic Description: Please explain in detail the encoder-decoder structure in the Transformer model, including the constituent modules of the encoder and decoder, data flow, connection methods between layers, and the interaction mechanism between the encoder and decoder.

Key Knowledge Points:

  1. Overview of the Overall Architecture
  2. Detailed Explanation of the Encoder Structure
  3. Detailed Explanation of the Decoder Structure
  4. Encoder-Decoder Attention Mechanism
  5. Differences Between Training and Inference Processes

Step-by-Step Explanation:

Step 1: Overall Architecture Overview
The Transformer adopts the classic encoder-decoder architecture, designed specifically for sequence-to-sequence tasks:

  • Encoder: Maps the input sequence (e.g., source language sentence) to an intermediate representation.
  • Decoder: Autoregressively generates the output sequence based on the encoder's output and the already generated parts.
  • Core Feature: Completely based on self-attention mechanisms, abandoning RNNs and CNNs.

Step 2: In-Depth Analysis of the Encoder Structure
Each encoder layer contains two core sub-layers:

  1. Multi-Head Self-Attention Sub-layer

    • Function: Allows each position to attend to all positions in the input sequence.
    • Computation: Uses Query, Key, and Value matrices to calculate attention weights.
    • Output: Context-aware representations after weighted summation.

  2. Feed-Forward Neural Network Sub-layer

    • Structure: Two linear transformations with a ReLU activation function in between.
    • Formula: FFN(x) = max(0, xW₁ + b₁)W₂ + b₂
    • Function: Applies independent non-linear transformations to each position.

  3. Residual Connections and Layer Normalization

    • Each sub-layer has a residual connection: LayerOutput = LayerNorm(x + Sublayer(x))
    • Layer normalization is performed after the residual addition to stabilize the training process.

Step 3: Special Design of the Decoder Structure
The decoder incorporates key modifications based on the encoder:

  1. Masked Multi-Head Self-Attention Sub-layer

    • Masking Mechanism: Prevents the current position from attending to subsequent positions (maintaining autoregressive properties).
    • Implementation: Sets future positions in the attention weight matrix to negative infinity.
    • Function: Ensures that during decoding, only tokens generated so far can be relied upon.

  2. Encoder-Decoder Attention Sub-layer

    • Queries (Q) come from the output of the decoder's previous layer.
    • Keys (K) and Values (V) come from the final output of the encoder.
    • Allows the decoder to focus on relevant parts of the input sequence.

  3. Layer Stacking and Output Layer

    • The decoder is also stacked with N identical layers.
    • Finally, a linear layer and Softmax are applied to generate the output probability distribution.

Step 4: Interaction Mechanism Between Encoder and Decoder
Key interaction occurs in the decoder's second sub-layer:

  1. Attention Matrix Calculation

    • Q = Output from the decoder's masked self-attention.
    • K, V = Final layer output from the encoder.
    • Calculates cross-sequence attention weights.

  2. Information Flow

    • The encoder output serves as "memory" for the decoder to query.
    • Each decoding step can access the complete encoded information.
    • Achieves alignment between the source and target sequences.

Step 5: Differences Between Training and Inference

  1. Training Phase (Parallel Processing)

    • The decoder uses teacher forcing, with the entire target sequence input simultaneously.
    • Autoregressive properties are ensured through masking, but computation can be parallelized.
    • Outputs for all positions are calculated in one forward pass.

  2. Inference Phase (Sequence Generation)

    • Autoregressive generation: Each step predicts the next token based on already generated tokens.
    • Encoder outputs can be precomputed and cached.
    • The decoder generates step by step, adding only one new token at a time.

Summary: The encoder-decoder structure of the Transformer, through the ingenious combination of self-attention, masked attention, and cross-attention, achieves powerful sequence transformation capabilities while maintaining training efficiency and inference accuracy.