Flash-attn performance: remove cuda sync during inference #33570
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What does this PR do?
#31629 & #32241 introduced a functionality in FA2 intended for training efficiency. However, it adds unnecessary cuda synchronization at inference time in every forward pass due to always checking
(torch.diff(position_ids, dim=-1) >= 0).all()
in theelif
condition. This PR fixes the performance issue by simply switching the order of the different checks in theelif
condition, to make good use of Python's default short-circuit evaluation. Indeed, at inference time,query_length
will always be 1 except during prefill, thus we will short-circuit torch synchronization all the time.Performance degradation was not so significant, but this PR allows to win back around 5-10% speed at inference time from the quick tests I ran.