wehub-resource-sync
150 lines
5.1 KiB
Python
150 lines
5.1 KiB
Python
import warnings
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import dask
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from dask import core
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from dask.dataframe.core import _concat
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from dask.highlevelgraph import HighLevelGraph
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from .scheduler import MultipleReturnFunc, multiple_return_get
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try:
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from dask.dataframe.optimize import optimize
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from dask.dataframe.shuffle import SimpleShuffleLayer, shuffle_group
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except ImportError:
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# SimpleShuffleLayer doesn't exist in this version of Dask.
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# This is the case for dask>=2025.1.0.
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SimpleShuffleLayer = None
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try:
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import dask_expr # noqa: F401
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SimpleShuffleLayer = None
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except ImportError:
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pass
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if SimpleShuffleLayer is not None:
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class MultipleReturnSimpleShuffleLayer(SimpleShuffleLayer):
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@classmethod
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def clone(cls, layer: SimpleShuffleLayer):
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# TODO(Clark): Probably don't need this since SimpleShuffleLayer
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# implements __copy__() and the shallow clone should be enough?
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return cls(
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name=layer.name,
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column=layer.column,
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npartitions=layer.npartitions,
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npartitions_input=layer.npartitions_input,
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ignore_index=layer.ignore_index,
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name_input=layer.name_input,
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meta_input=layer.meta_input,
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parts_out=layer.parts_out,
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annotations=layer.annotations,
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)
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def __repr__(self):
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return (
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f"MultipleReturnSimpleShuffleLayer<name='{self.name}', "
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f"npartitions={self.npartitions}>"
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)
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def __reduce__(self):
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attrs = [
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"name",
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"column",
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"npartitions",
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"npartitions_input",
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"ignore_index",
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"name_input",
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"meta_input",
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"parts_out",
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"annotations",
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]
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return (
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MultipleReturnSimpleShuffleLayer,
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tuple(getattr(self, attr) for attr in attrs),
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)
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def _cull(self, parts_out):
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return MultipleReturnSimpleShuffleLayer(
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self.name,
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self.column,
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self.npartitions,
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self.npartitions_input,
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self.ignore_index,
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self.name_input,
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self.meta_input,
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parts_out=parts_out,
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)
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def _construct_graph(self):
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"""Construct graph for a simple shuffle operation."""
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shuffle_group_name = "group-" + self.name
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shuffle_split_name = "split-" + self.name
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dsk = {}
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n_parts_out = len(self.parts_out)
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for part_out in self.parts_out:
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# TODO(Clark): Find better pattern than in-scheduler concat.
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_concat_list = [
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(shuffle_split_name, part_out, part_in)
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for part_in in range(self.npartitions_input)
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]
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dsk[(self.name, part_out)] = (_concat, _concat_list, self.ignore_index)
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for _, _part_out, _part_in in _concat_list:
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dsk[(shuffle_split_name, _part_out, _part_in)] = (
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multiple_return_get,
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(shuffle_group_name, _part_in),
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_part_out,
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)
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if (shuffle_group_name, _part_in) not in dsk:
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dsk[(shuffle_group_name, _part_in)] = (
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MultipleReturnFunc(
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shuffle_group,
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n_parts_out,
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),
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(self.name_input, _part_in),
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self.column,
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0,
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self.npartitions,
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self.npartitions,
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self.ignore_index,
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self.npartitions,
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)
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return dsk
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def rewrite_simple_shuffle_layer(dsk, keys):
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if not isinstance(dsk, HighLevelGraph):
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dsk = HighLevelGraph.from_collections(id(dsk), dsk, dependencies=())
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else:
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dsk = dsk.copy()
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layers = dsk.layers.copy()
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for key, layer in layers.items():
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if type(layer) is SimpleShuffleLayer:
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dsk.layers[key] = MultipleReturnSimpleShuffleLayer.clone(layer)
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return dsk
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def dataframe_optimize(dsk, keys, **kwargs):
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if not isinstance(keys, (list, set)):
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keys = [keys]
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keys = list(core.flatten(keys))
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if not isinstance(dsk, HighLevelGraph):
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dsk = HighLevelGraph.from_collections(id(dsk), dsk, dependencies=())
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dsk = rewrite_simple_shuffle_layer(dsk, keys=keys)
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return optimize(dsk, keys, **kwargs)
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else:
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def dataframe_optimize(dsk, keys, **kwargs):
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warnings.warn(
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"Custom dataframe shuffle optimization only works on "
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"dask>=2024.11.0,<2025.1.0, you are on version "
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f"{dask.__version__}."
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"Doing no additional optimization aside from the default one."
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)
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return None
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