Fix row corruption in batched multi-level cumulative scan

[antonwolfy]

Summary

dpnp.cumsum, dpnp.cumprod, and their nan/cumulative_* variants (including dpnp.tensor.cumulative_sum/cumulative_prod) could silently return incorrect results when accumulating along an axis of a multi-row array, with no error raised. The corruption is triggered when the accumulation axis is long enough to require three or more reduction levels in the internal block-scan (axis length greater than chunk_size ** 2, where chunk_size = wg_size * n_wi, i.e. roughly a million elements on GPU and about four million on CPU) and the array has more than one batch row. The elementwise total is conserved but redistributed across rows.

Root cause

In the batched scan driver inclusive_scan_iter (accumulators.hpp), the intermediate block-scan fixup update_local_chunks passed a NoOpIndexer for the per-row (iter) offset into the src buffer. The intermediate block-scan buffers, however, are laid out strided per row with a stride of src_size. The local_scans read already used the correct iter_gid * local_stride offset, but the src write offset was unstrided, so rows overwrote each other. This cancels out only when iter_nelems == 1 (a single row) or when fewer than three levels are needed, which is why the bug went unnoticed.

Fix

Give update_local_chunks a Strided1DIndexer{iter_nelems, src_size} for the row offset instead of the NoOpIndexer (the within-row out_indexer stays NoOp, since the level buffers are contiguous within a row).

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[github-actions]

View rendered docs @ https://intelpython.github.io/dpnp/index.html

[github-actions]

Array API standard conformance tests for dpnp=0.21.0dev8=py314ha0e2e8e_8 ran successfully.
Passed: 1376
Failed: 0
Skipped: 6

[coveralls]

coverage: 78.495%. remained the same — fix-cumulative-batched-multilevel-scan into master

[ndgrigorian]

LGTM other than minor nit