IntelPython / dpctl / 8510870928

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Implements accumulation functions in dpctl.tensor (#1602)

* Use `shT` instead of `std::vector<py::ssize_t>` in `repeat`

* Add missing host task to `host_tasks_list` in _reduction.py

* Implements `dpt.cumulative_logsumexp`, `dpt.cumulative_prod`, and `dpt.cumulative_sum`

The Python bindings for these functions are implemented in a new submodule `_tensor_accumulation_impl`

* Adds the first tests for `dpt.cumulative_sum`

* Pass host task vector to accumulator kernel calls

This resolves hangs in unique functions

* Implements `out` keyword for accumulators

* Fixes cumulative_logsumexp when both an intermediate input and result temporary are needed

* Only permute dims of allocated outputs if accumulated axis is not the trailing axis

Fixes a bug where in some cases output axes were not being permuted

* Enable scalar inputs to accumulation functions

* Adds test for scalar inputs to cumulative_sum

* Adds docstrings for cumulative_sum. cumulative_prod, and cumulative_logsumexp

* Removed redundant dtype kind check in _default_accumulation_dtype

* Reduce repetition of code allocation out array in _accumulate_common

* Adds tests for accumulation function identities, `include_initial` keyword

* Adds more tests for cumulative_sum

* Correct typo in kernels/accumulators.hpp

constexpr nwiT variables rather than nwiT constexpr variables

* Increase work per work item in inclusive_scan_iter_1d update step

* Removes a dead branch from _accumulate_common

As `out` and the input would have to have the same data type to overlap, the second branch is never reached if `out` is the same array as the input

* More accumulator tests

* Removes dead branch from _accumulators.py

A second out temporary does not need to be made in either branch when input and requested dtype are not implemented, as temporaries are always made

Also removes part of a test intended to reach this branch

* Adds tests for `cumula... (continued)

3436 of 3923 branches covered (87.59%)

Branch coverage included in aggregate %.

131 of 138 new or added lines in 3 files covered. (94.93%)

10520 of 11942 relevant lines covered (88.09%)

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