Provide a mathematical deconstruction of the multi-head self-attention mechanism used in Transformer models. Specifically, derive the computational complexity reduction achieved by Flash Attention compared to standard attention, and analyze the impact of key-value cache size on inference memory bandwidth during auto-regressive decoding. Include pseudo-code for a kernel-efficient implementation of scaled dot-product attention.