We find that an attention mask is rarely used in real-world applications, except for the causal mask during training.  Consequently, we reduce kernel complexity and compute cost by building in the option to use a causal mask as attention mask, and select this new capability with the is_causal parameter introduced in conjunction with the new SDPA operator.  
Providing the is_causal Boolean flag for the frequently used causal mask also obviates the expensive and memory-intensive allocation of a causal mask, increasing training memory efficiency by allowing more memory to be used for large batch sizes, and reduce memory bandwidth and cache contention – which are both at a premium in GPU accelerators – by not needing to load an attention mask tensor.