triton-ascend-reduce

Optimize reduce ops on Ascend by using in-kernel multi-dimensional indexing to operate directly on non-last-dimension tensors, bypassing costly host-side permute or reshape operations.

Frequent tl.sum calls slow down large-tensor normalization by triggering per-iteration reductions. Implement a delayed reduction pattern to accumulate per-element operations and perform a single final reduction.

The best way to handle multi-axis reductions for mixed-precision pooling is a two-stage reduction workflow, where stage 1 computes intermediate statistics and stage 2 applies final normalization.

Yes, you can satisfy PyTorch normalization semantics for multi-axis reductions in Ascend kernels by combining delayed accumulation with a two-stage reduction workflow across B, F, D1, D2 shapes.

Yes, the delayed reduction pattern works for softmax and layernorm operators on Ascend by accumulating per-element operations inside the loop and executing a single final reduction to improve throughput.