Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44060
Right now it skips grad checks as well.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23484018
Pulled By: gchanan
fbshipit-source-id: 24a8f1af41f9918aaa62bc3cd78b139b2f8de1e1
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44055
There is no functional change here. Another patch will rename NewCriterionTest to CriterionTest.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23482572
Pulled By: gchanan
fbshipit-source-id: de364579067e2cc9de7df6767491f8fa3a685de2
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44050
We don't actually turn on the CTCLoss tests since they fail, but this allows you to toggle check_forward_only and for the code to actually run.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23481091
Pulled By: gchanan
fbshipit-source-id: f2a3b0a2dee27341933c5d25f1e37a878b04b9f6
Summary:
Reland PR https://github.com/pytorch/pytorch/issues/40056
A new overload of upsample_linear1d_backward_cuda was added in a recent commit, so I had to add the nondeterministic alert to it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41538
Reviewed By: zou3519
Differential Revision: D22608376
Pulled By: ezyang
fbshipit-source-id: 54a2aa127e069197471f1feede6ad8f8dc6a2f82
Summary:
Most time-consuming tests in test_nn (taking about half the time) were gradgradchecks on Conv3d. Reduce their sizes, and, most importantly, run gradgradcheck single-threaded, because that cuts the time of conv3d tests by an order of magnitude, and barely affects other tests.
These changes bring test_nn time down from 1200 s to ~550 s on my machine.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40999
Differential Revision: D22396896
Pulled By: ngimel
fbshipit-source-id: 3b247caceb65d64be54499de1a55de377fdf9506
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21740237
Pulled By: mruberry
fbshipit-source-id: acbc027aa1d7877a49664d94db9a5fff91a07042
Summary:
This updates assertEqual and assertEqual-like functions to either require both or neither of atol and rtol be specified. This should improve clarity around handling precision in the test suite, and it allows us to remove the legacy positional atol argument from assertEqual. In addition, the "message" kwarg is replace with a kwarg-only "msg" argument whose name is consistent with unittest's assertEqual argument.
In the future we could make "msg" an optional third positional argument to be more consistent with unittest's assertEqual, but requiring it be specified should be clear, and we can easily update the signature to make "msg" an optional positional argument in the future, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38872
Differential Revision: D21717199
Pulled By: mruberry
fbshipit-source-id: 9feb856f94eee911b44f6c7140a1d07c1b026d3a
Summary:
Our test suite used to set double as its default scalar type, and when it was switched to not do so (to be more consistent with how users experience PyTorch), a few tests had to still set the default scalar type to double to function properly. Now that the jit no longer creates double tensors so frequently, it appears that test_jit no longer needs to set double as its default scalar type, too.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36982
Differential Revision: D21152120
Pulled By: mruberry
fbshipit-source-id: ea6d3c1ad55552dc5affa1fe1bd0e5189849e6d7
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35190
The following are the main changes:
- The main logic of C++ API parity test mechanism is moved from `test/test_cpp_api_parity.py` to `test/cpp_api_parity/module_impl_check.py` and `test/cpp_api_parity/functional_impl_check.py`, so that there is a clear separation between module tests and functional tests, although they still share a lot of common utility functions which are all in `test/cpp_api_parity/utils.py`.
- Module init tests (i.e. testing whether C++ module accepts the same constructor options as the corresponding Python module) is removed and will be added again in the future.
- `cpp_constructor_args` / `cpp_options_args` / `cpp_function_call` are added as appropriate to all test params dict in `torch/testing/_internal/common_nn.py`, to indicate how to run C++ API parity test for this test params dict.
Test Plan: Imported from OSS
Differential Revision: D20588198
Pulled By: yf225
fbshipit-source-id: 11238c560c8247129584b9b49df73fff40c4d81d
Summary:
This PR enables bfloat16 type for loss criterion ops(and the ops they depend on) and few miscellaneous ops required to train resnet50.
iotamudelta ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34469
Differential Revision: D20348856
Pulled By: ezyang
fbshipit-source-id: 0a8f06c2169cfa3c9cf319120e27150170095f6c
Summary:
This PR fixed documentation for `torch.add` with alpha. It also fixed these deprecated python calls `torch.add` and `torch.addmm` in tests, which may affect performance in *test/test_sparse.py* and *test/test_nn.py*.
cc csarofeen ptrblck
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33935
Differential Revision: D20313320
Pulled By: ngimel
fbshipit-source-id: fb08413d7e244865952e3fc0e1be7f1794ce4e9a
Summary:
The `BatchNorm*` part of the issue (see gh-12013) seems to have been fixed in the master branch and these tests would make it concrete.
However I would appreciate comments on https://github.com/pytorch/pytorch/issues/12013#issuecomment-575871264 on whether the current behaviour is satisfactory.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32384
Differential Revision: D19704154
Pulled By: ngimel
fbshipit-source-id: 1bbbbf1ae1215a460b22cf26e6b263e518ecf60b
Summary:
This PR adds support for 0-dim batch size input for `torch.nn.functional.interpolate` for various modes of interpolation.
Fixes part of gh-12013
CC: rgommers ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32400
Differential Revision: D19557090
Pulled By: ezyang
fbshipit-source-id: 6822f148bb47bfbcacb5e03798bf2744f24a2a32
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30445
Create distributed and rpc directories under caffe/test for better management
of unit tests.
Differential Revision: D18702786
fbshipit-source-id: e9daeed0cfb846ef68806f6decfcb57c0e0e3606
