Parametrize some TestAutogradFunctional tests to use logging_tensor #73854

soulitzer · 2022-03-07T17:03:12Z

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Remaining tests can be parametrized as well once this PR is finalized

Differential Revision: D34703584

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…ng_tensor" [ghstack-poisoned]

…ng_tensor" Remaining tests can be parametrized as well once this PR is finalized [ghstack-poisoned]

ghstack-source-id: 6281c56 Pull Request resolved: #73854

soulitzer · 2022-03-08T00:35:40Z

@albanD this might be ready for review. It's actually a bit tricky to convert to using make_tensor because there are some use cases that aren't covered: mainly torch.ones, torch.zeros, torch.tensor. If we simply parametrize over an argument of make_tensor, we're ignoring calls into torch.ones etc. (but maybe that is okay because most of the autograd stuff happens on the main input which is usually torch.rand). I'd like to avoid the hack of setting low/high to ones though on make_tensor to simulate torch.ones or torch.zeros because that would decrease readability (more).

It seems like if I wanted to modify the behavior of calls into torch.ones AND use make_tensor, but I only have a single argument to parametrize over, I'd use the namespace approach that I use now. That seems to defeat the purpose of using make_tensor at all though since I think the main utility of make_tensor currently is setting different low/high ranges and for different dtypes.

make_tensor seems only useful to introduce if we eventually plan to parametrize over dtype (not too likely anyway since we mostly care about float32/float64 for autograd).

…ng_tensor" Remaining tests can be parametrized as well once this PR is finalized [ghstack-poisoned]

ghstack-source-id: 1ed6372 Pull Request resolved: #73854

soulitzer · 2022-03-08T00:44:00Z

@soulitzer has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.

albanD

Ok

albanD · 2022-03-08T14:25:55Z

test/autograd/test_functional.py

+    "rand": torch.rand,
+    "tensor": torch.tensor,
+    # TODO: maybe move somewhere so other tests can also use
+    # TODO: according to https://pytorch.org/cppdocs/notes/tensor_creation.html


Do we need these other functions? Is that really a TODO?

Hmm let me address this in the follow up where I update the other tests, torch.eye is used once in this file so I might add that.

I'm fine with adding them as needed. And it is not really a TODO to be exhaustive here.

albanD · 2022-03-08T14:39:34Z

test/autograd/test_functional.py

+    TestCase, run_tests, subtest, gradcheck, gradgradcheck, parametrize, instantiate_parametrized_tests)
+from torch.testing._internal.logging_tensor import LoggingTensor
+
+# Utilities for parametrizing the tensor constructors used in autograd tests


I feel like these could go into a separate file.
But ok for now indeed.

…73854) Summary: Pull Request resolved: #73854 Remaining tests can be parametrized as well once this PR is finalized Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D34703584 Pulled By: soulitzer fbshipit-source-id: 83d90748c87cc8a18a7bb034e6402dae3d8c23a1

github-actions · 2022-03-08T23:46:07Z

Hey @soulitzer.
You've committed this PR, but it does not have both a 'release notes: ...' and 'topics: ...' label. Please add one of each to the PR. The 'release notes: ...' label should represent the part of PyTorch that this PR changes (fx, autograd, distributed, etc) and the 'topics: ...' label should represent the kind of PR it is (not user facing, new feature, bug fix, perf improvement, etc). The list of valid labels can be found here for the 'release notes: ...' and here for the 'topics: ...'.
For changes that are 'topic: not user facing' there is no need for a release notes label.

…#73854) Summary: Pull Request resolved: pytorch/pytorch#73854 Remaining tests can be parametrized as well once this PR is finalized Test Plan: Imported from OSS Reviewed By: albanD Differential Revision: D34703584 Pulled By: soulitzer fbshipit-source-id: 83d90748c87cc8a18a7bb034e6402dae3d8c23a1 (cherry picked from commit 427aace4800ea37b847353a76df546aadc66642e)

Parametrize some TestAutogradFunctional tests to use logging_tensor

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This was referenced Mar 7, 2022

[TESTING ONLY] Use redispatch mechanism to redispatch for make_dual and fw_primal #73705

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Move autograd functional tests to separate file #73852

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soulitzer mentioned this pull request Mar 7, 2022

Clean up some tests to use common_utils.parametrize #73853

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Update on "Parametrize some TestAutogradFunctional tests to use loggi…

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…ng_tensor" [ghstack-poisoned]

soulitzer requested a review from albanD March 7, 2022 17:12

Update on "Parametrize some TestAutogradFunctional tests to use loggi…

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…ng_tensor" Remaining tests can be parametrized as well once this PR is finalized [ghstack-poisoned]

Update on "Parametrize some TestAutogradFunctional tests to use loggi…

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…ng_tensor" Remaining tests can be parametrized as well once this PR is finalized [ghstack-poisoned]

soulitzer added a commit that referenced this pull request Mar 7, 2022

Parametrize some TestAutogradFunctional tests to use logging_tensor

b089b2a

ghstack-source-id: 6281c56 Pull Request resolved: #73854

Update on "Parametrize some TestAutogradFunctional tests to use loggi…

de5136c

…ng_tensor" Remaining tests can be parametrized as well once this PR is finalized [ghstack-poisoned]

soulitzer added a commit that referenced this pull request Mar 8, 2022

Parametrize some TestAutogradFunctional tests to use logging_tensor

e1e155b

ghstack-source-id: 1ed6372 Pull Request resolved: #73854

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Add ROCm5.2.3/AMDGPU support for PyTorch WBobby/pytorch#2

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