Convert cudf.Scalar usage to pylibcudf and pyarrow usage #17686
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mroeschke
added
Python
Matt711
approved these changes
Jan 8, 2025
| @@ -36,7 +37,7 @@ def gather( | |||
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| @acquire_spill_lock() | |||
| def scatter( | |||
| sources: list[ColumnBase | cudf.Scalar], | |||
| sources: list[ColumnBase | ScalarLike], | |||
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Why this change? I think ScalarLike is an alias for Any.
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If it remains ScalarLike, should we handle the different scalars we can get?
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Thanks. I changed this when trying to going down the rabbit hole of using pylibcudf.Scalar here but it became more involved that I had thought. This was a leftover I forgot to revert
| @@ -3255,7 +3255,7 @@ def duplicated( | |||
| ) | |||
| distinct = libcudf.column.Column.from_pylibcudf(plc_column) | |||
| result = copying.scatter( | |||
| [cudf.Scalar(False, dtype=bool)], | |||
| [cudf.Scalar(False)], | |||
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I think you can make this plc.Scalar. And then fix scatter's implementation.
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Yup, I'll plan to tackle this in a follow up
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/merge |
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Description
A lot of
cudf.Scalarusage is to eventually end up with a device scalar object (pylibcudf.Scalar) to pass to a pylibcudf routine. The conversion logic to get there can be achieved by converting to apyarrow.Scalarand usingpylibcudf.interop.from_arrow. This way we offload a lot of scalar-conversion-logic incudf.Scalartopyarrow.Scalarwhich can further be converted using the interop method.This PR just tackles some straightforward cases of the above. Another PR will tackle the more involved cases
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