Summary:
I think these can be safely removed since the min version of supported Python is now 3.6
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47822
Reviewed By: smessmer
Differential Revision: D24954936
Pulled By: ezyang
fbshipit-source-id: 5d4b2aeb78fc97d7ee4abaf5fb2aae21bf765e8b
Summary:
checks sizes of sparse tensors when comparing them in assertEqual.
Removes additional checks in safeCoalesce, safeCoalesce should not be a test for `.coalesce()` function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47148
Reviewed By: mruberry
Differential Revision: D24823127
Pulled By: ngimel
fbshipit-source-id: 9303a6ff74aa3c9d9207803d05c0be2325fe392a
Summary:
The `IN_CIRCLECI` variable is a misnomer since the flag really indicates when we enable XML reporting because we want to run the test in CI. Since this doesn't necessarily mean CircleCI in particular, IN_CI is more accurate and general.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46567
Reviewed By: walterddr
Differential Revision: D24407642
Pulled By: janeyx99
fbshipit-source-id: 5e141a0571b914310a174a58ac0fde58e9521c6b
Summary:
According to https://app.circleci.com/pipelines/github/pytorch/pytorch/228154/workflows/31951076-b633-4391-bd0d-b2953c940876/jobs/8290059
TestFFTCUDA.test_fftn_backward_cuda_complex128 takes 242 seconds to finish, where most of the time spent checking 2nd gradient
Refactor common part of test_fft_backward and test_fftn_backward into _fft_grad_check_helper
Introduce `slowAwareTest` decorator
Split test into fast and slow parts by checking 2nd degree gradient only during the slow part
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46509
Reviewed By: walterddr
Differential Revision: D24378901
Pulled By: malfet
fbshipit-source-id: 606670c2078480219905f63b9b278b835e760a66
Summary:
This PR adds support for complex-valued input for `torch.pinverse`.
Fixed cuda SVD implementation to return singular values with real dtype.
Fixes https://github.com/pytorch/pytorch/issues/45385.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45819
Reviewed By: heitorschueroff
Differential Revision: D24306539
Pulled By: anjali411
fbshipit-source-id: 2fe19bc630de528e0643132689e1bc5ffeaa162a
Summary:
Rename jobs for testing GraphExecutor configurations to something a little bit more sensical.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45715
Reviewed By: ezyang, anjali411
Differential Revision: D24114344
Pulled By: Krovatkin
fbshipit-source-id: 89e5f54aaebd88f8c5878e060e983c6f1f41b9bb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45776
Splitting out backend and custom class registration into their own library is
not currently implemented in fbcode, so detect that we are running tests in
fbcode and disable those tests.
Test Plan: buck test mode/no-gpu mode/dev caffe2/test:jit
Reviewed By: smessmer
Differential Revision: D24085871
fbshipit-source-id: 1fcc0547880bc4be59428e2810b6a7f6e50ef798
Summary:
This PR adds support for complex-valued input for `torch.symeig`.
TODO:
- [ ] complex cuda tests raise `RuntimeError: _th_bmm_out not supported on CUDAType for ComplexFloat`
Update: Added xfailing tests for complex dtypes on CUDA. Once support for complex `bmm` is added these tests will work.
Fixes https://github.com/pytorch/pytorch/issues/45061.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45121
Reviewed By: mrshenli
Differential Revision: D24049649
Pulled By: anjali411
fbshipit-source-id: 2cd11f0e47d37c6ad96ec786762f2da57f25dac5
Summary:
This PR fixes three OpInfo-related bugs and moves some functions from TestTorchMathOps to be tested using the OpInfo pattern. The bugs are:
- A skip test path in test_ops.py incorrectly formatted its string argument
- Decorating the tests in common_device_type.py was incorrectly always applying decorators to the original test, not the op-specific variant of the test. This could cause the same decorator to be applied multiple times, overriding past applications.
- make_tensor was incorrectly constructing tensors in some cases
The functions moved are:
- asin
- asinh
- sinh
- acosh
- tan
- atan
- atanh
- tanh
- log
- log10
- log1p
- log2
In a follow-up PR more or all of the remaining functions in TestTorchMathOps will be refactored as OpInfo-based tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44277
Reviewed By: mrshenli, ngimel
Differential Revision: D23617361
Pulled By: mruberry
fbshipit-source-id: edb292947769967de9383f6a84eb327f027509e0
Summary:
This PR fixes three OpInfo-related bugs and moves some functions from TestTorchMathOps to be tested using the OpInfo pattern. The bugs are:
- A skip test path in test_ops.py incorrectly formatted its string argument
- Decorating the tests in common_device_type.py was incorrectly always applying decorators to the original test, not the op-specific variant of the test. This could cause the same decorator to be applied multiple times, overriding past applications.
- make_tensor was incorrectly constructing tensors in some cases
The functions moved are:
- asin
- asinh
- sinh
- acosh
- tan
- atan
- atanh
- tanh
- log
- log10
- log1p
- log2
In a follow-up PR more or all of the remaining functions in TestTorchMathOps will be refactored as OpInfo-based tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44277
Reviewed By: ngimel
Differential Revision: D23568330
Pulled By: mruberry
fbshipit-source-id: 03e69fccdbfd560217c34ce4e9a5f20e10d05a5e
Summary:
This PR adds a new test suite, test_ops.py, designed for generic tests across all operators with OpInfos. It currently has two kinds of tests:
- it validates that the OpInfo has the correct supported dtypes by verifying that unsupported dtypes throw an error and supported dtypes do not
- it runs grad and gradgrad checks on each op and its variants (method and inplace) that has an OpInfo
This is a significant expansion and simplification of the current autogenerated autograd tests, which spend considerable processing their inputs. As an alternative, this PR extends OpInfos with "SampleInputs" that are much easier to use. These sample inputs are analogous to the existing tuples in`method_tests()`.
Future PRs will extend OpInfo-based testing to other uses of `method_tests()`, like test_jit.py, to ensure that new operator tests can be implemented entirely using an OpInfo.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43451
Reviewed By: albanD
Differential Revision: D23481723
Pulled By: mruberry
fbshipit-source-id: 0c2cdeacc1fdaaf8c69bcd060d623fa3db3d6459
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43631
I added a new test for just profiler stuff - I don't think the test should go in test_jit.py. Maybe this should just go in test_tensorexpr_fuser, but I'm not really testing tensorexpr stuff either... LMK
Test Plan: Imported from OSS
Reviewed By: bertmaher
Differential Revision: D23358810
Pulled By: eellison
fbshipit-source-id: 074238e1b60e4c4a919a052b7a5312b790ad5d82
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43591
100 randomized inputs vs 50 doesn't change the balance that much but speed up test runtime
Test Plan: CI
Reviewed By: orionr, seemethere
Differential Revision: D23332393
fbshipit-source-id: 7a8ff9127ee3e045a83658a7a670a844f3862987
Summary:
This PR creates a new namespace, torch.fft (torch::fft) and puts a single function, fft, in it. This function is analogous to is a simplified version of NumPy's [numpy.fft.fft](https://numpy.org/doc/1.18/reference/generated/numpy.fft.fft.html?highlight=fft#numpy.fft.fft) that accepts no optional arguments. It is intended to demonstrate how to add and document functions in the namespace, and is not intended to deprecate the existing torch.fft function.
Adding this namespace was complicated by the existence of the torch.fft function in Python. Creating a torch.fft Python module makes this name ambiguous: does it refer to a function or module? If the JIT didn't exist, a solution to this problem would have been to make torch.fft refer to a callable class that mimicked both the function and module. The JIT, however, cannot understand this pattern. As a workaround it's required to explicitly `import torch.fft` to access the torch.fft.fft function in Python:
```
import torch.fft
t = torch.randn(128, dtype=torch.cdouble)
torch.fft.fft(t)
```
See https://github.com/pytorch/pytorch/issues/42175 for future work. Another possible future PR is to get the JIT to understand torch.fft as a callable class so it need not be imported explicitly to be used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41911
Reviewed By: glaringlee
Differential Revision: D22941894
Pulled By: mruberry
fbshipit-source-id: c8e0b44cbe90d21e998ca3832cf3a533f28dbe8d
Summary:
For CUDA >= 10.2, the `CUBLAS_WORKSPACE_CONFIG` environment variable must be set to either `:4096:8` or `:16:8` to ensure deterministic CUDA stream usage. This PR adds some logic inside `torch.set_deterministic()` to raise an error if this environment variable is not set properly and CUDA >= 10.2.
Issue https://github.com/pytorch/pytorch/issues/15359
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41377
Reviewed By: malfet
Differential Revision: D22758459
Pulled By: ezyang
fbshipit-source-id: 4b96f1e9abf85d94ba79140fd927bbd0c05c4522
Summary:
xref gh-39002 which handled the reading but not the writing of the onnx expect files, and the last comment in that PR which points out `XXX` was suboptimal.
xref [this comment](https://github.com/pytorch/pytorch/pull/37091#discussion_r456460168) which pointed out the problem.
This PR:
- replaces `XXX` with `CURRENT_VERSION` in the stored files
- ensures that updating the results with the `--accept` flag will maintain the change
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41910
Reviewed By: pbelevich
Differential Revision: D22758671
Pulled By: ezyang
fbshipit-source-id: 47c345c66740edfc8f0fb9ff358047a41e19b554
Summary:
This test function is confusing since our `assertEqual` behavior allows for tolerance to be specified, and this is a redundant mechanism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41514
Reviewed By: ngimel
Differential Revision: D22569348
Pulled By: mruberry
fbshipit-source-id: 2b2ff8aaa9625a51207941dfee8e07786181fe9f
Summary:
BC-breaking NOTE:
In PyTorch 1.6 bool and integral fill values given to torch.full must set the dtype our out keyword arguments. In prior versions of PyTorch these fill values would return float tensors by default, but in PyTorch 1.7 they will return a bool or long tensor, respectively. The documentation for torch.full has been updated to reflect this.
PR NOTE:
This PR causes torch.full to throw a runtime error when it would have inferred a float dtype by being given a boolean or integer value. A versioned symbol for torch.full is added to preserve the behavior of already serialized Torchscript programs. Existing tests for this behavior being deprecated have been updated to reflect it now being unsupported, and a couple new tests have been added to validate the versioned symbol behavior. The documentation of torch.full has also been updated to reflect this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40364
Differential Revision: D22176640
Pulled By: mruberry
fbshipit-source-id: b20158ebbcb4f6bf269d05a688bcf4f6c853a965
Summary:
Make `common_utils.TestCase.precision` a property, because it is overriden as such in `common_device_type`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40057
Differential Revision: D22138385
Pulled By: malfet
fbshipit-source-id: 0e7c14654bf60f18f585efc61f96fdd0af23346f
Summary:
Currently compare_with_numpy requires a device and dtype, but these arguments are ignored if a tensor is provided. This PR updates the function to only take device and dtype if a tensor-like object is given. This should prevent confusion that you could, for example, pass a CPU float tensor but provided a CUDA device and integer dtype.
Several tests are updated to reflect this behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40064
Differential Revision: D22058072
Pulled By: mruberry
fbshipit-source-id: b494bb759855977ce45b79ed3ffb0319a21c324c
Summary:
Remove PY3 and PY34 checks from `torch/testing/_internal/common_utils.py`
Remove PY35 global var from `torch.jit.annotations`
Always call `try_get_real_signature` in `torch/jit/annotations.py`
Use `map` instead of `imap`, since Python-2 is no longer support, so map is always lazy.
Remove all pre Python-3.6 checks from `torch/_six.py` and `torch/_appdirs.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39879
Differential Revision: D22037811
Pulled By: malfet
fbshipit-source-id: af0c79f976569c2059d39ecb49c6b8285161734f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38211
Just because the annotations are inline doesn't mean the files type
check; most of the newly annotated files have type errors and I
added exclusions for them in mypy.ini. The payoff of moving
all of these modules inline is I can delete the relevant code
generation logic for the pyi files (which was added ignore
annotations that weren't actually relevant anymore.)
For the most part the translation was completely mechanical, but there
were two hairy issues. First, I needed to work around a Python 3.6 and
earlier bug where Generic has a nontrivial metaclass. This fix is in
torch/jit/__init__.py. Second, module.py, we need to apply the same
fix for avoiding contravariance checks that the pyi file used to have;
this is done by declaring forward as a variable (rather than a
function), which appears to be sufficient enough to get mypy to not
contravariantly check input arguments.
Because we aren't actually typechecking these modules in most
cases, it is inevitable that some of these type annotations are wrong.
I slavishly copied the old annotations from the pyi files unless there
was an obvious correction I could make. These annotations will probably
need fixing up later.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21497397
Pulled By: ezyang
fbshipit-source-id: 2b08bacc152c48f074e7edc4ee5dce1b77d83702
Summary:
**BC breaking note:**
In PyTorch 1.5 passing the out= kwarg to some functions, like torch.add, could affect the computation. That is,
```
out = torch.add(a, b)
```
could produce a different tensor than
```
torch.add(a, b, out=out)
```
This is because previously the out argument participated in the type promotion rules. For greater consistency with NumPy, Python, and C++, in PyTorch 1.6 the out argument no longer participates in type promotion, and has no effect on the computation performed.
**ORIGINAL PR NOTE**
This PR effectively rewrites Tensor Iterator's "compute_types" function to both clarify its behavior and change how our type promotion works to never consider the out argument when determining the iterator's "common dtype," AKA its "computation type." That is,
```
a = op(b, c)
```
should always produce the same result as
```
op(b, c, out=a)
```
This is consistent with NumPy and programming languages like Python and C++.
The conceptual model for this change is that a TensorIterator may have a "common computation type" that all inputs are cast to and its computation performed in. This common computation type, if it exists, is determined by applying our type promotion rules to the inputs.
A common computation type is natural for some classes of functions, like many binary elementwise functions (e.g. add, sub, mul, div...). (NumPy describes these as "universal functions.") Many functions, however, like indexing operations, don't have a natural common computation type. In the future we'll likely want to support setting the TensorIterator's common computation type explicitly to enable "floating ufuncs" like the sin function that promote integer types to the default scalar type. Logic like that is beyond the type promotion system, which can only review inputs.
Implementing this change in a readable and maintainable manner was challenging because compute_types() has had many small modifications from many authors over ~2 year period, and the existing logic was in some places outdated and in other places unnecessarily complicated. The existing "strategies" approach also painted with a broad brush, and two of them no longer made conceptual sense after this change. As a result, the new version of this function has a small set of flags to control its behavior. This has the positive effect of disentangling checks like all operands having the same device and their having the same dtype.
Additional changes in this PR:
- Unary operations now support out arguments with different dtypes. Like binary ops they check canCast(computation type, out dtype).
- The dtype checking for lerp was outdated and its error message included the wrong variable. It has been fixed.
- The check for whether all tensors are on the same device has been separated from other checks. TensorIterators used by copy disable this check.
- As a result of this change, the output dtype can be computed if only the input types are available.
- The "fast path" for checking if a common dtype computation is necessary has been updated and simplified to also handle zero-dim tensors.
- A couple helper functions for compute_types() have been inlined to improve readability.
- The confusingly named and no longer used promote_gpu_output_dtypes_ has been removed. This variable was intended to support casting fp16 reductions on GPU, but it has become a nullop. That logic is now implemented here: 856215509d/aten/src/ATen/native/ReduceOpsUtils.h (L207).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39655
Differential Revision: D21970878
Pulled By: mruberry
fbshipit-source-id: 5e6354c78240877ab5d6b1f7cfb351bd89049012
Summary:
When debugging it is sometimes useful to call test code manually. This change makes that easier.
Before this change, one would get the following error:
```
$ python -c "from torch.testing._internal.jit_utils import JitTestCase; JitTestCase()"
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/jansel/pytorch/torch/testing/_internal/common_utils.py", line 740, in __init__
test_method = getattr(self, method_name)
AttributeError: 'JitTestCase' object has no attribute 'runTest'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39695
Test Plan: `python -c "from torch.testing._internal.jit_utils import JitTestCase; JitTestCase()"`
Differential Revision: D21959249
Pulled By: jansel
fbshipit-source-id: 8435249f102338c957c3a7a7aad48d21d372a8cf
Summary:
Cut from https://github.com/pytorch/pytorch/pull/38994.
This is a helper function for comparing torch and NumPy behavior. It updates the existing and increasingly popular _np_compare function and moves it to be a method on TestCase.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39179
Differential Revision: D21855082
Pulled By: mruberry
fbshipit-source-id: edca3b78ae392d32243b02bf61960898b6ba590f
Summary:
`msg` argument must be passed to `assertRaises`, because its exception is passed upstream (with custom error message) if `assertEquals` succeedes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39217
Differential Revision: D21786141
Pulled By: malfet
fbshipit-source-id: f8c3d4f30f474fe269e50252a06eade76d575a68
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37181
Now that assertEquals considers dtypes in determining tolerance, most
tests don't need explicitly set precision.
Those that do are a few half precision tests on cuda. In this PR, those
are broken out to be handled explicitly, though we may also want to
consider further loosening the tolerance on half-precision.
Test Plan: Imported from OSS
Differential Revision: D21728402
Pulled By: nairbv
fbshipit-source-id: 85f3daf63f1bdbb5101e8dea8c125f13448ca228
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:
closes gh-32561 closes gh-38545. As part of the fallout from gh-36797, this PR
- replaces the producer_version: "1.6" in onnx expect tests with `producer_version: "XXX"
- adapts `testing/_internal/common_utils.py` with a regex to change the onnx producer_version so tests still pass
The consistency of the torch version and the onnx `producer_version` is tested in gh-36797, so there is no reason to test it again in the expect tests.
xref gh-38629 which documented how to run the onnx tests and at the same time refactored the Community documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39002
Differential Revision: D21723062
Pulled By: ezyang
fbshipit-source-id: 1bd6a8ed37d5383e69d017226dc09c0645a69aff
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:
Edit: this has been updated to reflect the PR's current status, which has changed after review.
This PR updates the behavior of the assertEqual, assertNotEqual, and assert_allclose to be consistent with each other and torch.isclose. It corrects several additional bugs in the current implementations and adds extensive testing and comments, too.
These updates follow from changes to assertEqual like https://github.com/pytorch/pytorch/pull/34258 and https://github.com/pytorch/pytorch/pull/37069, and from our discussion of torch.isclose for complex tensors (see https://github.com/pytorch/pytorch/issues/36462), where we decided to implement a NumPy-compatible mathematical notion of "closeness" for complex tensors that is not a great fit for our testing framework.
The detailed changelist is:
- New test framework functions for comparing tensors and scalars
- Tensors are compared using isclose; the real and imaginary parts of complex tensors are compared independently
- Scalars are compared using the same algorithm
- assertEqual and assert_allclose now use this common comparison function, instead of each implementing their own with divergent behavior
- assertEqual-like debug messages are now available for all tensor and scalar comparisons, with additional context when comparing the components of sparse, quantized, and complex tensors
- Extensive testing of the comparison behavior and debug messages
- Small Updates
- assertEqual now takes an "exact_device" argument, analogous to "exact_dtype", which should be useful in multidevice tests
- assertEqual now takes an "equal_nan" argument for argument consistency with torch.isclose
- assertEqual no longer takes the "allow_inf" keyword, which misleadingly only applied to scalar comparisons, was only ever set (rarely) to true, and is not supported by torch.isclose
- Bug fixes:
- the exact_dtype attribute has been removed (no longer needed after https://github.com/pytorch/pytorch/pull/38103)
- message arguments passed to assertEqual are now handled correctly
- bool x other dtype comparisons are now supported
- uint8 and int8 tensor comparisons now function properly
- rtol for integer comparisons is now supported (default is zero)
- rtol and atol for scalar comparisons are now supported
- complex scalar comparisons are now supported, analogous to complex tensor comparisons
- assertNotEqual is now equivalent to the logical negation of assertEqual
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37294
Differential Revision: D21596830
Pulled By: mruberry
fbshipit-source-id: f2576669f7113a06f82581fc71883e6b772de19b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38485
Python 2 has reached end-of-life and is no longer supported by PyTorch.
This class does nothing in Python 3.
Test Plan: CI
Reviewed By: ailzhang
Differential Revision: D21575260
Pulled By: dreiss
fbshipit-source-id: 184696c9fa501e8d2517950b47cdbc90b2ae8053
