Decoder : Transformer Architecture (during Training)

About this lesson

Unlock the secrets of the Transformer Decoder architecture! In this deep dive, we explore the critical mechanics of Masked Self-Attention and Masked Multi-Head Attention—the components that allow models like GPT to generate text autoregressively while training at lightning speeds. What You Will Learn: • The Masking Mechanism: How a masking matrix of negative infinity (−∞) prevents "future peeking" or data leakage during the softmax operation. • Parallelism vs. Autoregression: Why the decoder processes the entire target sequence non-autoregressively during training for parallel efficiency, while remaining strictly sequential during inference. • The Sub-Block Structure: A breakdown of how Masked Multi-Head Self-Attention builds internal context before passing queries to the Cross-Attention layer. • Teacher Forcing: How right-shifted target sequences and start tokens signal the beginning of the decoding process. • Advanced Refinements: An introduction to StableMask, a parameter-free method that replaces traditional causal masks to stabilize attention distributions and encode absolute positional information. Whether you are a researcher or an AI enthusiast, this guide simplifies the complex linear transformations (Key, Query, Value) and residual connections that make modern NLP possible.

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