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Reduce memory usage of as_categorical_column #14138
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rapids-bot
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Reduce memory usage of as_categorical_column #14138
rapids-bot
merged 1 commit into
rapidsai:branch-23.10
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wence-:wence/fix/categorical-mem-usage
Sep 20, 2023
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The main culprit is in the way the codes returned from _label_encoding were being ordered. We were generating an int64 column for the order, gathering through the left gather map, and then argsorting, before using that ordering as a gather map for the codes. We note that gather(y, with=argsort(x)) is equivalent to sort_by_key(y, with=x) so use that instead (avoiding an unnecessary gather). Furthermore we also note that gather([0..n), with=x) is just equivalent to x, so we can avoid a gather too. This reduces the peak memory footprint of categorifying a random column of 500_000_000 int32 values where there are 100 unique values from 24.75 GiB to 11.67 GiB.
galipremsagar
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Sep 20, 2023
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Sep 20, 2023
bdice
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Sep 20, 2023
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Great! Note that this is an example of the performance antipattern discussed in #13557.
shwina
approved these changes
Sep 20, 2023
/merge |
Is performance affected? |
Yes, but positively, I run: import time
import cupy as cp
import cudf
import rmm
rmm.reinitialize(pool_allocator=True)
rng = cp.random._generator.RandomState(seed=108)
for K in [2**4, 2**10, 2**12, 2**14, 2**16]:
for N in [1_000_000, 10_000_000, 100_000_000, 250_000_000]:
col = cudf.core.column.as_column(rng.choice(cp.arange(K, dtype="uint32"), size=N, replace=True))
start = time.time()
for _ in range((reps := 1_000_000_000 // N)):
y = col.astype("category", ordered=False)
del y
end = time.time()
del col Across column sizes and number of unique values, the new code is between 25 and 30% faster. |
Excellent! |
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improvement
Improvement / enhancement to an existing function
non-breaking
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Performance
Performance related issue
Python
Affects Python cuDF API.
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Description
The main culprit is in the way the codes returned from _label_encoding were being ordered. We were generating an int64 column for the order, gathering through the left gather map, and then argsorting, before using that ordering as a gather map for the codes.
We note that gather(y, with=argsort(x)) is equivalent to sort_by_key(y, with=x) so use that instead (avoiding an unnecessary gather). Furthermore we also note that gather([0..n), with=x) is just equivalent to x, so we can avoid a gather too.
This reduces the peak memory footprint of categorifying a random column of 500_000_000 int32 values where there are 100 unique values from 24.75 GiB to 11.67 GiB.
Test code
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