Limitations of the method about infinitransformer HOT 2 OPEN

Since infini attention uses segmentation which is mainly focused on reducing memory usage and computational cost into O(N) so if you use very long seq such as seq len = 1M, then you have to wait 5min per batch on 1x H100 gpu.

In this implementation I used for loop to make it work so you'll get exactly N times more time to inference or to train.

e.g., segment 2048 & block 2048 ==> 1 segments, just 1 attention computation segment 2048 & block 32K ==> 16 segments, slower 16 times than block 2048

Model performance check was done at the paper with PPL but we may need more tests on very long sequence based tests.

I agree with you that more tests on very long sequence input are needed. It's not unique to think about using the segmentation to break down the long sequence input by for loop, e.g. RingAttention. Just curious about the impact of compressive memory, how does it influence the computation of attention when dealing with the long input?

from infinitransformer.

Since infini attention uses segmentation which is mainly focused on reducing memory usage and computational cost into O(N) so if you use very long seq such as seq len = 1M, then you have to wait 5min per batch on 1x H100 gpu.

In this implementation I used for loop to make it work so you'll get exactly N times more time to inference or to train.

e.g.,
segment 2048 & block 2048 ==> 1 segments, just 1 attention computation
segment 2048 & block 32K ==> 16 segments, slower 16 times than block 2048

Model performance check was done at the paper with PPL but we may need more tests on very long sequence based tests.

from infinitransformer.