transformer
How Transformers Work: A Detailed Exploration of Transformer Architecture
Source: DataCamp (Jan 9, 2024)
In Brief
- Article Type: Deep learning architecture guide
- Topic: How Transformer models work
- Audience: Data scientists, ML engineers, NLP practitioners
- Includes: Self-attention, encoder–decoder design, positional encoding, multi-head attention
- Models Discussed: BERT, GPT, LaMDA, and other Transformer-based models
What Are Transformers?
Transformers are neural network architectures designed for sequence-to-sequence tasks. They rely almost entirely on attention mechanisms instead of recurrence (as in RNNs).
Transformer as a Black Box
Image by the author. Transformer architecture as a black box.
Historical Context
- Introduced in the 2017 paper "Attention Is All You Need".
- Popularized via TensorFlow Tensor2Tensor and Harvard NLP annotated guides.
- Enabled models such as BERT and GPT-3.
The Shift from RNNs to Transformers
Limitations of RNNs
- Sequential processing (slow, poor GPU utilization)
- Long-range dependency issues (vanishing gradients)
Transformer Advantages
- Parallel computation
- Long-range attention via self-attention
The Transformer Architecture
High-Level Overview
Image by the author. Transformer as encoder-decoder black box.
Components:
- Encoder: Encodes input sequence
- Decoder: Generates output sequence
Image by the author. Global structure of Encoder-Decoder.
Image by the author. Encoder-Decoder repeated N times.
The Encoder Workflow
Image by the author. Encoder structure.
Step 1: Input Embeddings
- Tokens converted into fixed-size vectors (e.g., 512 dimensions)
Encoder workflow: Input Embedding
Step 2: Positional Encoding
- Adds position information using sine/cosine functions
Encoder workflow: Positional Encoding
Step 3: Stack of Encoder Layers
Each layer contains:
- Multi-head self-attention
- Feed-forward neural network
- Residual connections + Layer normalization
Encoder workflow: Stack of encoder layers
Step 3.1: Multi-Head Self-Attention
- Uses Query, Key, Value (QKV) vectors
- Multiple heads attend to different representation subspaces
Encoder workflow: Multi-Head Attention
Attention Computation
Matrix multiplication of Query and Key
Scaling attention scores
Softmax on attention scores
Combining softmax results with Value vectors
Step 3.2: Residual Connections + Normalization
Residual connections and normalization
Step 3.3: Feed-Forward Neural Network
- Two linear layers with ReLU in between
Encoder workflow: Feed-Forward Neural Network
Step 4: Encoder Output
- Produces contextualized vector representations
The Decoder Workflow
Image by the author. Decoder structure.
Step 1: Output Embeddings
- Similar to encoder embeddings
Step 2: Positional Encoding
- Adds positional information
Step 3: Stack of Decoder Layers
Each layer contains:
- Masked self-attention
- Encoder-decoder (cross) attention
- Feed-forward neural network
Step 3.1: Masked Self-Attention
Decoder workflow: Masked self-attention
Step 3.2: Encoder-Decoder (Cross) Attention
Decoder workflow: Encoder-Decoder attention
Step 3.3: Feed-Forward Neural Network
- Same structure as encoder FFN
Summary
Transformers replace recurrence with attention, enabling:
- Parallelism
- Long-range dependency modeling
- Scalable architectures for large language models
This architecture underpins modern models such as GPT, BERT, and other foundation models.
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