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Rechunking arrays with empty chunks #5256

merged 1 commit into from Aug 12, 2019


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commented Aug 9, 2019

It looks like our current rechunk implementation can fail when there are empty chunks present. For example:

import dask.array as da

# Create array with empty chunks
x = da.zeros((7, 24), chunks=((7,), (10, 0, 0, 9, 0, 5)))
# Rechunk array
y = x.rechunk((-1, -1))
print(f'x.shape, x.compute().shape = {x.shape}, {x.compute().shape}')
print(f'y.shape, y.compute().shape = {y.shape}, {y.compute().shape}')

Rechunking shouldn't impact the output shape of y. However, running this example on the current master branch gives:

x.shape, x.compute().shape = (7, 24), (7, 24)
y.shape, y.compute().shape = (7, 24), (7, 10)

I was able to track down the problem to _intersect_1d (in dask/array/ not properly incrementing the chunk index for the "old" (pre-rechunking) chunking scheme in the case where empty chunks are present. This PR adds logic to ensure the chunk index is always incremented, even when there are empty chunks.

  • Tests added / passed
  • Passes black dask / flake8 dask

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commented Aug 9, 2019

I found visualizing the task graph for y in the above example to be useful. With the changes in this PR, y includes all the non-empty chunks from x (i.e. the (0,0), (0,3), and (0,5) x chunks):


On master, rechunk isn't keeping track of the correct chunk index in x which correspond to non-empty chunks. In this case, y doesn't include the (0,0), (0,3), and (0,5) x chunks, but instead includes the first three chunks (two of which have zero size):


hence why the output shape of y.compute() on master is (7, 10) instead of (7, 24).

@TomAugspurger TomAugspurger added the array label Aug 12, 2019


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commented Aug 12, 2019

thanks @jrbourbeau

@TomAugspurger TomAugspurger merged commit 618f5df into dask:master Aug 12, 2019

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@jrbourbeau jrbourbeau deleted the Quansight-Labs:rechunk-0-size branch Aug 12, 2019

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