In the field of deep learning, the Transformer architecture has become a core technology for natural language processing (NLP) and many other tasks. Layer Normalization (LN), as an important component of the Transformer architecture, plays an indispensable role in the stability and performance of the model. This article will provide a detailed introduction to the location and function of LN in the Transformer architecture, as well as comparisons with other related technologies, to help you better understand and apply this technique.
1. The location and function of LN in the Transformer architecture
Location of LN:
In the classic Transformer architecture, LN is typically located in the following positions:
- (1) Before each sub - layer (sub - layers refer to Multi - Head Attention or Feedforward Network):
There is an LN layer before entering the self - attention mechanism (Multi - Head Attention) and the feedforward neural network (Feedforward Network, abbreviated as FNN).
- (2) Before the output layer:
After going through all the encoder or decoder layers, and before making the final prediction (such as Softmax), there is generally an LN layer.
Function of LN:
- Stabilizing the training process: LN can normalize the activation values of each sample within a layer, helping to alleviate the problems of vanishing and exploding gradients and improve the stability of training.
- Accelerating convergence: By normalizing the activations, the model's training becomes more efficient, speeding up the convergence rate.
- Improving the model's generalization ability: LN can reduce over - fitting and enhance the model's performance on unseen data.
2. Why there is no LN layer in the hidden layers of FNN?
- Consistent dimensionality: Although there are changes in dimensionality within the FNN (usually first expanding and then reducing dimensions), LN is generally not applied to the hidden layers of the FNN.
- The role of non - linear activation functions: The FNN contains non - linear activation functions (such as ReLU or GELU), whose non - linear properties help the model learn complex patterns without the need for further normalization through LN.
- Simplified design: In practice, LN layers are often applied at the input and output of the FNN, rather than in its internal hidden layers. This simplifies the model and improves computational efficiency.
3. What is the core function of LN?
Core function:
- Normalizing activation values: The main function of LN is to normalize the activation values of each sample, making their mean 0 and variance 1.
- Reducing internal covariate shift: By normalizing the activations, the distribution of inputs received by subsequent layers is made more stable, improving the training effect.
- Suitable for variable - length sequences and small - batch data: Unlike Batch Normalization (BN), LN does not rely on batch - level statistics and is more suitable for handling variable - length sequences or small - batch sizes.
4. Does LN perform dimensionality transformation for Tokens?
- Clarifying misunderstandings:
- LN does not change the dimensionality of Tokens: The function of LN is to normalize activation values, not to change the dimensionality of Tokens. It does not perform dimensionality expansion or reduction on the data.
- Location of dimensionality transformation:
- Dimensionality changes in the FNN: In the FNN, the input dimension is usually first expanded from \(d_{\text{model}}\) to \(d_{\text{ff}}\) (typically 4 times \(d_{\text{model}}\)), and then reduced back to \(d_{\text{model}}\).
- Purpose: This dimensionality expansion allows the model to learn richer features in a higher - dimensional space, which are then mapped back to the original embedding dimension through dimensionality reduction.
5. Why is the Token dimension in Transformer not directly consistent with the hidden layer dimension of FNN?
- Design reasons:
- Feature richness: Dimensionality expansion allows the FNN to capture more complex features in a larger - dimensional space.
- Computational efficiency and balance: If all layers of the Transformer used high - dimensional representations, the computational cost would significantly increase. By expanding dimensions only where necessary, a balance between performance and computational cost can be achieved.
- The role of LN:
- During the dimensionality expansion and reduction process, LN can stabilize the activation values, ensuring the stability of the model during training.
6. Why is the usage frequency of LN in Transformer lower than that in CNN?
- Different model characteristics:
- Batch Normalization (BN) in CNN: CNNs typically use BN because they process fixed - size images, and BN works well with batch - level statistics.
- Layer Normalization (LN) in Transformer: Transformers process variable - length sequence data, and LN is more suitable for this scenario. However, compared to using BN after every convolutional layer in CNNs, LN is used more cautiously in Transformers, typically only at key locations.
- Computational considerations:
- Computational overhead: Over - using LN can increase computational costs. Using LN only at necessary locations in the Transformer can reduce unnecessary overhead.
7. Why is LN not used after every weight in Transformer?
- The difference between weights and Tokens:
- Weights: Refers to the parameters in the model.
- Tokens: Refers to the elements in a sequence, such as words in a sentence.
- LN usage strategy:
- Applied at key locations: LN is typically applied between stacked layers, at the inputs of Self - Attention and FNN, to stabilize the inputs to these modules.
- Avoiding over - regularization: Not all layers need LN, and over - using it may affect the model's performance.
8. Comparison of LN application in CNN and Transformer
| Comparison Item | Regularization in CNN | Regularization in Transformer |
|----------------|-----------------------|------------------------|
| Common Regularization Method | Batch Normalization (BN) | Layer Normalization (LN) |
| Application Frequency | After almost every convolutional layer | Only at key locations |
| Data Type Handled | Fixed - size images | Variable - length sequence data |
| Statistics Relied On | Batch dimension (Batch Size) | Feature dimension (Hidden Size) |
| Suitability for Small - Batch and Variable - Length Sequences | Not suitable | Suitable |
| Main Function | Accelerating training and stability | Stabilizing training and accelerating convergence |
9. Case Analysis: The Role of LN in Transformer
Assumption:
- We have an input sequence of length \(T\), with each Token having an embedding dimension of \(d_{\text{model}}\).
Computation process of the Transformer:
1. Input embedding:
- Convert the input Tokens into \(d_{\text{model}}\) - dimensional vector representations.
2. Positional encoding:
- Add positional encoding to each Token to preserve sequence information.
3. Multi - Head Attention:
- LN operation: Normalize the input before entering the self - attention mechanism to stabilize the input distribution.
- Compute self - attention to obtain contextual information.
4. Residual connection:
- Add the output of self - attention to the original input.
5. Feedforward Network (FNN):
- LN operation: Normalize again before entering the FNN.
- Dimensionality changes:
- Expansion: From \(d_{\text{model}}\) to \(d_{\text{ff}}\) (e.g., \(4d_{\text{model}}\)).
- Non - linear activation: Through ReLU or GELU activation functions.
- Reduction: From \(d_{\text{ff}}\) back to \(d_{\text{model}}\).
- Residual connection: Add the output of the FNN to the input.
6. Output layer:
- LN operation: Normalize the entire sequence before the final output.
Analysis:
- Why is LN used before self - attention and FNN?
- Stabilizing input distribution: LN makes the mean of the input 0 and the variance 1, stabilizing the input distribution and aiding model training.
- Accelerating training efficiency: Normalized inputs speed up model convergence.
- Why is LN not used within the FNN?
- Not necessary: The FNN already contains non - linear activation functions and has a clear dimensionality change design, so the effect of LN is not significant.
- Avoiding excessive computation: Adding LN to each hidden layer of the FNN would increase the computational load and affect model efficiency.
- Purpose of dimensionality changes:
- Expansion (from \(d_{\text{model}}\) to \(d_{\text{ff}}\)): Capture higher - dimensional features to enhance the model's expressive power.
- Reduction (from \(d_{\text{ff}}\) to \(d_{\text{model}}\)): Map high - dimensional features back to the original dimension to ensure consistency in subsequent calculations.
10. Summary
- Core function of LN: Normalize activation values to stabilize training and improve model performance.
- Location of LN in Transformer: Mainly before self - attention mechanisms and FNN, and before the final output layer.
- LN does not perform dimensionality transformation: LN does not change the dimensionality of Tokens but normalizes activation values.
- Dimensionality design in Transformer: Dimensionality expansion and reduction in the FNN enhance the model's expressive power and computational efficiency.
- LN usage strategy: In the Transformer, LN is used only at key locations to balance model performance and computational cost.
- Differences