What is Transformer Architecture

How It Works

The transformer consists of encoder and decoder stacks, each containing layers of multi-head self-attention and feed-forward networks with residual connections and layer normalization. The encoder processes the full input in parallel using bidirectional self-attention, while the decoder generates output tokens autoregressively using masked self-attention and cross-attention to the encoder output.

Technical Details

The original transformer uses learned positional embeddings, 6 encoder/decoder layers, 8 attention heads, and 512-dimensional representations. Modern variants include encoder-only (BERT), decoder-only (GPT), and encoder-decoder (T5) architectures scaled to billions of parameters. Key innovations include pre-norm layer ordering, RMSNorm, grouped query attention, and flash attention for efficient computation on modern hardware.

Best Practices

• Use decoder-only transformers for generative tasks and encoder-only for embedding tasks
• Apply proper learning rate warmup and decay schedules for stable training
• Use mixed-precision (bf16/fp16) training and inference for memory and speed efficiency
• Implement gradient checkpointing for training large models on limited GPU memory

Common Pitfalls

• Underestimating the memory and compute requirements of self-attention at long sequence lengths
• Not applying proper initialization, leading to training instability at large scale
• Training from scratch when fine-tuning a pretrained transformer would be far more effective
• Ignoring positional encoding limitations that affect generalization to unseen sequence lengths

Advanced Tips

• Use mixture-of-experts (MoE) layers to scale model capacity without proportional compute increase
• Implement ring attention or sequence parallelism for processing very long contexts
• Apply LoRA or QLoRA for parameter-efficient fine-tuning of large transformers
• Use multimodal transformers that process interleaved image, text, and audio tokens in a unified architecture