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Implement kernels for median and paritition functions - #3138
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View rendered docs @ https://intelpython.github.io/dpnp/pull/3138/index.html |
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Array API standard conformance tests for dpnp=0.22.0dev0=np2py314h8d9cdd5_27 ran successfully. |
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coverage: 84.44% (+0.06%) from 84.376% — feature/implement-median-partition-fns into master |
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partition.cpp is a dpnp source, built with the coverage flags, but gen_coverage.py treated _tensor_sorting_impl as libtensor-only and left it out of the default report. List it first in the module like the other dpnp sources, so the linker keeps its instrumented copies of shared inline functions.
The device coverage counters of partition.cpp and median.cpp reach the sort and top_k kernels through the shared inline helpers, about 500 device globals per kernel image. Their init events make the OpenCL CPU runtime use a freed command, so the coverage tests segfaulted at random in top_k, sort and unique_all.
With n=1 the signed-zero row was all NaN, so NumPy and dpnp warned. test_all_nan_rows covers that case.
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This PR proposes the addition of updates to radix selection algorithms to generalize for cases of
medianandpartitionrelated functions, which allows the removal ofdpnp_partition_cfrom the DPNP backendThe median and partition functions can pass through either a sorting or a radix selection path, based on input types (and in the case of partition, always when
kthis multiple elements)Supersedes #2423