Summary:
This PR:
- adds a `.expand` method for `TransformedDistribution` along the lines of #11341.
- uses this method to simplify `.expand` in distribution classes that subclass off of `TransformedDistribution`.
- restores testing of `TransformedDistribution` fixtures.
- fixes some bugs wherein we were not setting certain attributes in the expanded instances, and adds tests for `.mean` and `.variance` which use these attributes.
There are many cases where users directly use `TransformedDistribution` rather than subclassing off it. In such cases, it seems rather inconvenient to have to write a separate class just to define a `.expand` method. The default implementation should suffice in these cases.
cc. fritzo, vishwakftw, alicanb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11607
Differential Revision: D9818225
Pulled By: soumith
fbshipit-source-id: 2c4b3812b9a03e6985278cfce0f9a127ce536f23
Summary:
Currently our `skipIfLapack` has uses a try-catch block and regex match the error message. It is highly unreliable. This PR adds `hasLAPACK` and `hasMAGMA` on ATen context, and expose the flags to python.
Also fixes refcounting bug with `PyModule_AddObject`. The method steals reference, but we didn't `Py_INCREF` in some places before calling it with `Py_True` or `Py_False`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11024
Differential Revision: D9564898
Pulled By: SsnL
fbshipit-source-id: f46862ec3558d7e0058ef48991cd9c720cb317e2
Summary:
The PR includes:
(1) torch.distributed.c10d, which now includes the complete backward compatible frontend API for `torch.distributed`
(2) `env://` init method functionality
(3) Minor change to `test_distributed.py`, which is now a test for `torch.distributed.c10d`.
(4) The old `test_distributed.py' is now moved to `test_distributed_thd`
(5) Miscellaneous bug fixes.
(6) DDP CPU test is removed since c10d doesn't have this support yet, but this is a very easy test after moving DDP CPU's dependency to torch.distributed.c10d.
(7) CI config to test MPI, NCCL, and Gloo backend of c10d
**Now all the distributed test including c10d DDP can pass with the c10d frontend API**
TODO: (in a separate PR)
MPI subgroup support, once this is added, CI group test will be enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10871
Differential Revision: D9554514
Pulled By: teng-li
fbshipit-source-id: fb686ad42258526c8b4372148e82969fac4f42dd
Summary:
Running `--accept` on a test doesn't tell you explicitly which sub-test is being updated, this PR fixes that
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10559
Differential Revision: D9353977
Pulled By: driazati
fbshipit-source-id: a9d4014386ff0fe388a092f3dcf50f157e460f04
Summary:
This PR for the ROCm target does the following:
* enable some unit tests on ROCm
* fix a missing static_cast that breaks BatchNorm call on ROCm
* fix BatchNorm to work on ROCm w/ ROCm warp sizes etc
* improve the pyhipify script by introducing kernel scope to some transpilations and other improvements
* fix a linking issue on ROCm
* for more unit test sets: mark currently broken tests broken (to be fixed)
* enable THINLTO (phase one) to parallelize linking
* address the first failing of the elementwise kernel by removing non-working ROCm specialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10266
Differential Revision: D9184178
Pulled By: ezyang
fbshipit-source-id: 03bcd1fe4ca4dd3241f09634dbd42b6a4c350297
Summary:
In this changeset:
* improvements to `hipify-python.py`
* marking unit tests broken for ROCm
* reducing the number of jobs for the built to avoid out of memory issues
* switch to Thrust/cub-hip master for the CI
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9653
Differential Revision: D9117791
Pulled By: ezyang
fbshipit-source-id: a6c3c7b81f2bda9825974bf9bf89a97767244352
Summary:
These could use some autograd tests, which are coming in a later PR, but using them in autograd is probably pretty rare.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9947
Reviewed By: ezyang
Differential Revision: D9032778
Pulled By: gchanan
fbshipit-source-id: fa5a6509d3bac31ea4fae25143e82de62daabfbd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9497Fixes#7883 by using `rfft`.
It's worth noting that this is BC breaking. And it's impossible to detect the change because the two signatures before and after this change supports a common subset of calling patterns, e.g., `stft(Tensor, int, int)`. (some other calling patterns will raise error).
soumith and I plan to change the current `stft` interface because it is a bit messy and non-standard. rafaelvalle suggested us that `librosa` is a good reference API to align with. After discussing with soumith and ezyang , and given that `stft` is only out for 1 release, I decide to go with directly changing the signature. Also, my understanding is that most researchers in this field will welcome this change as `librosa` seems to be the golden-standard here. (it doesn't yet support all `pad_mode` but those will become available if added to `F.pad`.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9308
Reviewed By: ezyang
Differential Revision: D8806148
Pulled By: SsnL
fbshipit-source-id: f6e8777d0c34d4a4d7024e638dc9c63242e8bb58
Summary:
Similar to https://github.com/pytorch/pytorch/pull/9187, This PR makes setting the `PYTORCH_TEST_WITH_ASAN` and `PYTORCH_TEST_WITH_UBSAN` flags easier internally, by allowing the flags to be set to `0`.
Closes https://github.com/pytorch/pytorch/pull/9202
Differential Revision: D8745533
Pulled By: yf225
fbshipit-source-id: 6293f52f2e8b1c3ef150becfdc2dd7ded56d5d80
Summary:
This PR makes setting the `NO_MULTIPROCESSING_SPAWN` easier internally, by allowing the flag to be set to `0`.
Closes https://github.com/pytorch/pytorch/pull/9187
Differential Revision: D8736206
Pulled By: yf225
fbshipit-source-id: b8a34cb9a747b13bc9428777a3ed766ce441cfe1
Summary:
This will resolve some of the timeout issues in CPU and GPU tests internally.
Closes https://github.com/pytorch/pytorch/pull/9061
Reviewed By: ezyang
Differential Revision: D8707471
Pulled By: yf225
fbshipit-source-id: 9dc82a2c9da0c540ae015442f74b9b2b1a67a246
* Some 0-sized dimension support, port catArray away from resizeLegacy.
The goal of this PR is to port catArray away from resizeLegacy (so we can delete the legacy resize calls), but since catArray has some weird behavior because
we don't have arbitrary 0-sized dimension support, I made some effort to fix these both in one pass.
The major changes here are:
1) catArray uses the new resize API, no longer the old resizeLegacy API.
2) As 1) is the last usage of resizeLegacy, it is deleted.
3) If compiled with USE_TH_SIZE_ZERO_DIM, catArray will work and properly check shapes for n-dimensional empty tensors.
4) However, we retain the old behavior of "ignoring" size [0] tensors in catArray. We previously allowed this because we didn't have n-dimensional empty tensors.
5) To get the above to work, we also add support for n-dimensional empty tensors for narrow and slice (ifdef USE_TH_SIZE_ZERO_DIM).
6) We change the stride formula for empty tensors to match NumPy; basically, we never multiply by 0 as the size, always at least 1, so the
strides are monotonically increasing in the empty tensor case.
7) We print the size of empty tensors if size != [0]; this matches NumPy behavior (even in cases where the size could be inferred from the brackets.
8) For test purposes, we add torch._C._use_zero_size_dim() to add tests for the above.
* Fix flake8.
* Address review comments.
* Add memory leak check in CUDA tests
* Tracking multi-GPU too
* fix run_test.py not running __name__ == '__main__' content; add test for make_cuda_memory_checked_test
* add a comment
* skip if cuda
* 1. Change the wrapper to a method in common.py:TestCase
2. Refactor common constants/method that initialize CUDA context into common_cuda.py
3. Update some test files to use TEST_CUDA and TEST_MULTIGPU
* Fix MaxUnpool3d forward memory leak
* Fix MultiLabelMarginCriterion forward memory leak
* Fix MultiMarginLoss backward memory leak
* default doCUDAMemoryCheck to False
* make the wrapper skip-able
* use TEST_MULTIGPU
* add align_corners=True/False tests for Upsample; fix TEST_CUDNN
* finalize interface
* VolumetricMaxUnpooling_updateOutput
* fix test_nccl
* rename THC caching allocator methods to be clearer
* make the wrapped function a method
* address comments; revert changes to aten/src/THC/THCCachingAllocator.cpp
* fix renamed var
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
This is the first of three PRs that #5537 will be split into.
This PR adds mkl headers to included files, and provides helper functions for MKL fft and cuFFT.
In particular, on POSIX, headers are using mkl-include from conda, and on Windows, it is from a new file @yf225 and I made and uploaded to s3.
* add mkl-include to required packages
* include MKL headers; add AT_MKL_ENABLED flag; add a method to query MKL availability
* Add MKL and CUFFT helpers
This replaces the torch.Tensor constructors with factories that produce
Variables. Similarly, functions on the torch module (e.g. torch.randn)
now return Variables.
To keep the PR to a reasonable size, I've left most of the unused tensor
code. Subsequent PRs will remove the dead code, clean-up calls to
torch.autograd.Variable, and rename Variable to Tensor everywhere.
There are some breaking changes because Variable and Tensors had
slightly different semantics. There's a list of those changes here:
https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge
* at::maybe_data_ptr and Check.h => TensorUtils.h
* THNN support for optional BN running_*
* ATen support for optional BN running_*
* Python nn.* support for optional BN running_*; Improve IN and BN doc
* Add tests for IN and BN new option
* Layer Norm
* Fix LRN doc
* functional interface for LN and IN
* Layer norm tests
* fix BN double backward returning undefined tensors
* fix jit test using wrong dim inputs for BN
* add/improve BN, IN and LN GPU tests with half type
* Udpate docs to be consistent with Conv notation
Fix onnx
Clarified onnx symbokic wrapper
* fix typo
* Address comments
Currently, a Variable can only be compared with a Variable, but a Tensor
can be compared with Tensors or numbers. Relax this constraint so Variables
behave identically to Tensors.
Three stage plan to no more stupidly weird "why isn't cuDNN enabled"
bugs:
- Add torch.backends.cudnn.disable_global_flags(), which as its name suggests,
disables global flag setting in cuDNN, so that you are not allowed to
make changes to this state. However, the flags() context
manager continues to work (since they are non-global changes).
- Call disable_global_flags() in test/common.py
- Switch all of the manual flag setting/unsetting in test/test_nn.py
to use the context manager.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Previously, we assumed that __main__ was the test file
being run, which is not true if you are using pytest. New
algorithm uses __module__ of the test class, which is a bit
more robust.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
This adds more heavy sanity checking when we run to_dense(); in particular,
we make sure that if it claims to be coalesced, it truly is coalesced, and if
it is not, that the coalesced version also to_dense() to the same thing.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Some tests, such as test_autograd.py, include random generation at the
top-level. It's going to be tough to police these files to ensure that
all randomness only happens within a test, so just set the seed as soon
as args are parsed (as well as before each test).
torch.manual_seed_all is no longer needed since torch.manual_seed also
seeds the CUDA random number generator.
