Faster Transformers for Longer Context with FlashAttention-2

AI summary: Stanford University researchers have developed FlashAttention-2, a technique that accelerates the training of large Transformer models on extended sequences. The method optimizes memory access and parallelism, reducing slower operations and improving GPU utilization. FlashAttention-2 achieves up to 2x speedup over its predecessor and 10x over standard PyTorch implementations. This advancement makes it economically viable to train models on longer sequences, potentially enabling Transformers to comprehend entire books or videos. The team aims to further optimize FlashAttention-2 for new hardware and remove the context length bottleneck for Transformers entirely.
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