task: add broadcast class implementation#2901
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jharlow-intel
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vlad-perevezentsev
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View rendered docs @ https://intelpython.github.io/dpnp/pull/2901/index.html |
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Array API standard conformance tests for dpnp=0.21.0dev0=py313h509198e_51 ran successfully. |
| if len(dpnp_arrays) > 1: | ||
| exec_q = dpt.get_execution_queue( | ||
| tuple(array.sycl_queue for array in dpnp_arrays) | ||
| ) | ||
| if exec_q is None: | ||
| raise dpt.ExecutionPlacementError( | ||
| "Execution placement can not be unambiguously inferred " | ||
| "from input arguments." | ||
| ) |
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do we need to check for compute follows data here? The arrays can be broadcast even if they aren't, it just means device routines can't be run with both as inputs
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if we do keep this check, we would need to check if they are dpnp.tensor.usm_ndarray as well
| The number of iterators. | ||
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| """ | ||
| return len(self._arrays) |
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primarily a question for @antonwolfy and @vlad-perevezentsev since it's design-related, but NumPy and CuPy differ drastically in this class implementation
https://numpy.org/doc/2.1/reference/generated/numpy.broadcast.html
https://docs.cupy.dev/en/latest/reference/generated/cupy.broadcast.html
do we want more of the CuPy or NumPy behavior? What is the intended use-case of this class to users?
3 participants
Adds a
broadcastclass implementation