Recent discussions in the machine learning community have highlighted the advancements and applications of FlexAttention, a technique designed to enhance the efficiency of attention mechanisms in neural networks. Notably, one user reported a 64-fold speedup in creating a block mask for a sequence of 65,000 tokens when compiling the 'create_block_mask' function alongside FlexAttention. Another user noted that enabling FlexAttention on the Mistral 7B model doubled the end-to-end token throughput compared to using the cuDNN SDPA with SWA mask. Additionally, the Lingua codebase has been cited as an effective implementation of FlexAttention, demonstrating batched and sequence-stacked attention masking for within-document attention. Furthermore, a new development indicates that LE ATTENTION is now compatible with FlexAttention, allowing for intuitive construction of block-structured attention matrices while optimizing execution through sparsity manipulation. Overall, these innovations reflect significant progress in the optimization of attention mechanisms in modern machine learning frameworks.