Transformer Architecture and Attention Questions

Comprehensive understanding of Transformer architecture and attention mechanisms including the principles of self attention where queries keys and values are used to compute attention weights with appropriate scaling. Understand scaled dot product attention and multi head attention and why parallel attention heads improve representational capacity. Know positional encoding schemes including absolute positional encodings relative positional encodings rotary position encodings and alternative methods for injecting order information. Be able to explain encoder and decoder components feed forward networks residual connections and layer normalization and their role in training stability and optimization. Discuss attention variants and efficiency improvements such as sparse attention local windowed attention linear attention kernel based approximations and other methods to reduce memory and compute cost along with their trade offs. At senior and staff levels be prepared to reason about scaling Transformers to very large parameter counts including distributed training strategies parameter and data parallelism memory management and attention pattern design for long sequences and efficient inference. Be ready to apply this knowledge to sequence modeling language modeling and sequence transduction tasks and to justify architectural and implementation trade offs.