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While conceptually straightforward, comprehensive AttnRes elevates computational demands. Per token operations require O(L²d) mathematical computations and O(Ld) memory for layer output retention. During standard training, this storage largely overlaps with existing backpropagation activations, though activation recomputation and pipeline parallelism magnify overheads due to cross-stage transmission requirements for earlier outputs.
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