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I was wondering whether matching all kinds of input types is really necessary. What NumPy does is it casts the input array to the best supported type, instead of defining kernels for all the types.
This is currently only barely manageable with the current setup... Adding an out= or dtype= may make it exponentially worse, as we have to consider that it may not be the type we expected. Not to mention that mixed types might or might not kill vectorization instructions.
I can't help but wonder if there's a better way to do this, if we can somehow define a set of rules and have the type signatures generated, or if supporting different inputs/outputs is really necessary, or if we can cast to the output dtype as NumPy does.
The text was updated successfully, but these errors were encountered:
I was wondering whether matching all kinds of input types is really necessary. What NumPy does is it casts the input array to the best supported type, instead of defining kernels for all the types.
This is currently only barely manageable with the current setup... Adding an
out=
ordtype=
may make it exponentially worse, as we have to consider that it may not be the type we expected. Not to mention that mixed types might or might not kill vectorization instructions.I can't help but wonder if there's a better way to do this, if we can somehow define a set of rules and have the type signatures generated, or if supporting different inputs/outputs is really necessary, or if we can cast to the output dtype as NumPy does.
The text was updated successfully, but these errors were encountered: