8000 [Needs someone to complete] Reduce sum on many axes by vlasenkov · Pull Request #2116 · pytorch/pytorch · GitHub
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[Needs someone to complete] Reduce sum on many axes #2116

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vlasenkov
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@vlasenkov vlasenkov commented Jul 16, 2017 •
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Resolves #2006

  • arrange algorithm
  • arrange places to insert the feature (as separate function or as Tensor/Variable method)
  • implement the feature
  • rename all keepdim to keepdims
  • write docs
  • write tests

@vlasenkov vlasenkov mentioned this pull request Jul 16, 2017
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fmassa
fmassa reviewed Jul 16, 2017
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Thanks for the PR!
I wonder if it would be better to remove the implementation from cwrap, to avoid conflicts? I think that it's better than implementing these ops in cwrap, because we automatically have support for autograd.
Also, what is the behavior of numpy for operations like median when multiple axis are passed? Does it perform multiple kernel calls, or does it transpose+view+kernel call? For sum it moght not matter, but for other ops that might make a difference.

torch/functional.py
@@ -2,6 +2,7 @@
from ._utils import _range
from operator import mul
from functools import reduce
import collections

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torch/functional.py Outdated
input = input.sum(ax, keepdims=True)
else:
for ax in sorted(axes, reverse=True):
input = input.sum(ax)

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torch/functional.py Outdated
def sum(input, axes, keepdims=False, out=None):
if isinstance(axes, collections.Iterable)
if a.dim() > 3:
if keepdims:

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torch/functional.py
# permute
# reduce single dim
else:
return torch._C.sum(input, axes, keepdims, out)

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8000
@vlasenkov vlasenkov changed the title [WIP] Reduce sum on many axes [Needs someone to complete] Reduce sum on many axes Aug 10, 2017
@fmassa fmassa mentioned this pull request Jan 22, 2018
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@bernardohenz
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I am trying to perform std on many axes, pretty similar to what you are doing with sum. Is this problem solved?

@t-vi t-vi mentioned this pull request Mar 30, 2018
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zou3519 pushed a commit to zou3519/pytorch that referenced this pull request Mar 30, 2018
@pjh5
Fail fast in pytest (pytorch#2116)
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@karandwivedi42
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@zou3519 closed in #6152?

@fmassa fmassa closed this Jun 27, 2018
@tstandley
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I also want to do var over many axes. is this solved? (same question as @bernardohenz except with var).

I should note that numpy supports this, and the only way to do this in pytorch currently is to compute the mean, subtract (using expand), square, and then take the mean. Basically manually.

@soumith
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soumith commented Aug 15, 2018

@tstandley we are working on mean, variance and stdv on multiple axes. @colesbury should put up a PR soon for it.

@anilkeshwani
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anilkeshwani commented Nov 1, 2021 •
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For signposting:
Users / developers who are looking for up-to-date information on reduction operators in general (e.g. sum, min, max, var etc.) should see the Reductions tracking issue #61417 which lists all of these proposed modifications and ongoing work.

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Tensor.sum() over multiple axes
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@vlasenkov @bernardohenz @karandwivedi42 @tstandley @soumith @anilkeshwani @fmassa @ezyang
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