Implement torch.broadcast_tensors by zou3519 · Pull Request #10075 · pytorch/pytorch

zou3519 · 2018-07-31T15:36:58Z

This exposes expand_outplace to python. Fixes #8076. Fixes #10041.

I didn't name it torch.broadcast because numpy.broadcast does something
slightly different (it returns an object with the correct shape
information).

Test Plan: new test_torch, test_autograd tests.

cc @soumith @fritzo

fmassa · 2018-07-31T16:04:44Z

Nice!
I believe the equivalent numpy function is numpy.broadcast_arrays, should we aim at maintaining the same name?

soumith · 2018-07-31T16:12:39Z

yes let's match numpy and do broadcast_arrays

zou3519 · 2018-07-31T16:20:36Z

Should it be named "broadcast_arrays" or "broadcast_tensors"? @fmassa and I were thinking broadcast_tensors because we call our data "tensors" but numpy calls their data "arrays".

I'll also try to implement varargs for this via a python wrapper function, it shouldn't be too bad.

fritzo · 2018-07-31T16:30:57Z

Nice! The additional behavior of torch.distributions.utils.broadcast_all() is to handle python floats. I believe we can update broadcast_all() to use your broadcast_arrays() as follows (in either this PR or a follow-up PR):

# in torch/distributions/utils.py
def broadcast_all(*values):
    """docstring"""
    if not all(map(torch.is_tensor, values)):
        # promote floats to tensors
        new_tensor = torch.tensor
        for value in values:
            if torch.is_tensor(value):
                new_tensor = value.new_tensor
                break
        values = [v if torch.is_tensor(v) else new_tensor(v) for v in values]
    return torch.broadcast_arrays(*values)

This would also be a great test to see that distributions are compatible with the new version 😄

vadimkantorov · 2018-07-31T16:47:15Z

Or even put the scalar broadcasting in torch.broadcast_all/arrays? :) If torch.where uses it, it would automatically solve the torch.where not accepting scalars.

fmassa · 2018-07-31T17:36:37Z

@vadimkantorov I think there was a discussion around using torch.as_tensor (or something equivalent) in the beginning of every PyTorch function, so that it also works for numbers and numpy arrays.
Not sure what's the status of that. @gchanan did we decide on something about that?

This exposes expand_outplace to python. Fixes pytorch#8076. Fixes pytorch#10041. I didn't name it torch.broadcast because numpy.broadcast does something slightly different (it returns an object with the correct shape information). Test Plan: new test_torch, test_autograd tests.

- s/broadcast_all/broadcast_tensors/ - broadcast_tensors now takes varargs

zou3519 · 2018-08-01T18:58:03Z

This should be good for review, despite the hanging tests.

I updated the following:

renamed torch.broadcast_all to torch.broadcast_tensors (@fmassa)
Changed torch.distributions.utils.broadcast_all() to use torch.broadcast_tensors (@fritzo). All the distribution tests pass so it looks like it's good to go :)

torch/distributions/utils.py

-    return values
+    if not all(map(torch.is_tensor, values)):
+        # promote numbers to tensors of dtype torch.get_default_dtype()
+        def default_promotion(v):


torch/distributions/utils.py

-    scalar_idxs = [i for i in range(len(values)) if isinstance(values[i], Number)]
-    tensor_idxs = [i for i in range(len(values)) if values[i].__class__.__name__ == 'Tensor']
-    if len(scalar_idxs) + len(tensor_idxs) != len(values):
+    if not all(map(lambda v: torch.is_tensor(v) or isinstance(v, Number), values)):


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Summary: This exposes expand_outplace to python. Fixes #8076. Fixes #10041. I didn't name it torch.broadcast because numpy.broadcast does something slightly different (it returns an object with the correct shape information). Pull Request resolved: pytorch/pytorch#10075 Differential Revision: D9125816 Pulled By: zou3519 fbshipit-source-id: ebe17c8bb54a73ec84b8f76ce14aff3e9c56f4d1

fritzo · 2018-08-07T17:10:38Z

This change is great. How can I start to use it? It does not appear to have landed in master yet.

EDIT Sorry, I was pointing to a fork 😊

zou3519 · 2018-08-07T17:34:43Z

It should be on master, the following works for me on a latest checkout:

import torch
x = torch.randn(2, 2)
y = torch.randn(1)
torch.broadcast_tensors(x, y)

Summary: This exposes expand_outplace to python. Fixes pytorch#8076. Fixes pytorch#10041. I didn't name it torch.broadcast because numpy.broadcast does something slightly different (it returns an object with the correct shape information). Pull Request resolved: pytorch#10075 Differential Revision: D9125816 Pulled By: zou3519 fbshipit-source-id: ebe17c8bb54a73ec84b8f76ce14aff3e9c56f4d1

Summary: This uses zou3519's new `torch.broadcast_tensors()` #10075 to make `Categorical.log_prob()` and the `*Normal.__init__()` methods jittable. Previously `.log_prob()` was failing due to calls to `torch._C.infer_size()` with errors like ``` def log_prob(self, value): if self._validate_args: self._validate_sample(value) > value_shape = torch._C._infer_size(value.size(), self.batch_shape) if self.batch_shape else value.size() E RuntimeError: expected int at position 0, but got: Tensor ``` After this change I'm able to jit many more of Pyro's tests. Reviewed By: ezyang Differential Revision: D9477487 Pulled By: apaszke fbshipit-source-id: 5f39b29c6b8fa606ad30b02fefe2dfb618e883d6

Summary: This uses zou3519's new `torch.broadcast_tensors()` pytorch#10075 to make `Categorical.log_prob()` and the `*Normal.__init__()` methods jittable. Previously `.log_prob()` was failing due to calls to `torch._C.infer_size()` with errors like ``` def log_prob(self, value): if self._validate_args: self._validate_sample(value) > value_shape = torch._C._infer_size(value.size(), self.batch_shape) if self.batch_shape else value.size() E RuntimeError: expected int at position 0, but got: Tensor ``` After this change I'm able to jit many more of Pyro's tests. Reviewed By: ezyang Differential Revision: D9477487 Pulled By: apaszke fbshipit-source-id: 5f39b29c6b8fa606ad30b02fefe2dfb618e883d6

zou3519 requested review from apaszke, colesbury, ezyang, gchanan, soumith and zdevito as code owners July 31, 2018 15:36

zou3519 added 2 commits July 31, 2018 11:14

Some changes:

c5e418b

- s/broadcast_all/broadcast_tensors/ - broadcast_tensors now takes varargs

zou3519 force-pushed the pytorch-broadcast branch from 2909f12 to c5e418b Compare July 31, 2018 18:14

zou3519 changed the title ~~Implement torch.broadcast_all~~ Implement torch.broadcast_tensors Jul 31, 2018

zou3519 added 2 commits July 31, 2018 12:04

Reimplement broadcast_all in torch/distributions/utils.

fb6b76d

Python 2 compatibility

f28068f

fritzo reviewed Aug 1, 2018

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torch/distributions/utils.py Outdated

return values

if not all(map(torch.is_tensor, values)):

# promote numbers to tensors of dtype torch.get_default_dtype()

def default_promotion(v):

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facebook-github-bot closed this in 6b338c8 Aug 2, 2018

zou3519 deleted the pytorch-broadcast branch August 2, 2018 02:33

soumith mentioned this pull request Aug 2, 2018

[JIT]torch._C._infer_size throws an exception when traced #7996

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fritzo mentioned this pull request Aug 7, 2018

[distributions] Make more distributions jittable #10321

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zou3519 mentioned this pull request Aug 23, 2018

Add itertools.{prod, combinations, combinations_with_replacement} like op to pytorch #9393

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ezyang added the merged label Jun 26, 2019

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