Per documentation, one should be able to explicitly pass dim argument as None to get tensor size across all dimentions/strides, but before this change it was incorrectly interpreted as named tensor call.
Modify `size` and `stride` signatures generated by `gen_pyi.py` to highlight that overload with `None` will return a Tuple, but one with `dim: _int` returns `int`.
Add regression test to validate the behavior, and remove the check for asserts from two named tensors tests (NamedTensors are dead, aren't they?)
Fixes https://github.com/pytorch/pytorch/issues/111944
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111991
Approved by: https://github.com/zou3519
Preparation for the next PR in this stack: #89559.
I replaced
- `self.assertTrue(torch.equal(...))` with `self.assertEqual(..., rtol=0, atol=0, exact_device=True)`,
- the same for `self.assertFalse(...)` with `self.assertNotEqual(...)`, and
- `assert torch.equal(...)` with `torch.testing.assert_close(..., rtol=0, atol=0)` (note that we don't need to set `check_device=True` here since that is the default).
There were a few instances where the result of `torch.equal` is used directly. In that cases I've replaced with `(... == ...).all().item()` while sometimes also dropping the `.item()` depending on the context.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89527
Approved by: https://github.com/mruberry
Part of #29137
**BC Breaking Note**
This PR breaks C++ API backward compatibility for `at::std`. A call that has argument types `at::std(Tensor, OptionalIntArrayRef, int64_t, bool)` used to resolve to the `std.correction` overload, but now it resolves to the `std.dim` overload. In order to call the `std.correction` overload, the `int64_t` argument can be wrapped in a `c10::optional`, so that the call has the form `at::std(Tensor, OptionalIntArrayRef, optional<int64_t>, bool)`. The same is true for the corresponding arguments of the `std.out` and `std.correction_out` overloads of `at::std_out`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81845
Approved by: https://github.com/albanD
unflatten now has a free function version in torch.flatten in addition to
the method in torch.Tensor.flatten.
Updated docs to reflect this and polished them a little.
For consistency, changed the signature of the int version of unflatten in
native_functions.yaml.
Some override tests were failing because unflatten has unusual
characteristics in terms of the .int and .Dimname versions having
different number of arguments so this required some changes
to test/test_override.py
Removed support for using mix of integer and string arguments
when specifying dimensions in unflatten.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81399
Approved by: https://github.com/Lezcano, https://github.com/ngimel
Summary:
Fixes https://github.com/pytorch/pytorch/issues/61137 by raising an error if an empty tuple is passed in for the names:
```
>>> torch.empty((2, 3), names=['a', 'b']).flatten((), 'abc')
RuntimeError: flatten(tensor, dims, out_dim): dims cannot be empty
```
or from the original issue:
```
>>> torch.empty((2, 3)).flatten((), 'abc')
RuntimeError: flatten(tensor, dims, out_dim): dims cannot be empty
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61953
Reviewed By: iramazanli
Differential Revision: D30574571
Pulled By: malfet
fbshipit-source-id: e606e84458a8dd66e5da6d0eb1a260f37b4ce91b
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60059Fixes#60053.
The problem is that `.names()` always triggers the named tensor warning.
To not trigger it, one has to guard it with has_names:
`x.has_names() ? x.names() : DimnameList{}`
This is not the first time this has happened; we should probably
make it so that .names() doesn't raise a warning unless it is actually
populated with names. That's a little tricky to implement so I'm leaving
it for the future.
Test Plan:
- New test, also run `python test/test_nn.py -v -k "max_pool"` and
confirm there are no warnings.
Reviewed By: gchanan
Differential Revision: D29152737
Pulled By: zou3519
fbshipit-source-id: 89a2fdbe6a6064a7044b5b75f7d0c58e51e57509
Summary:
Fixes https://github.com/pytorch/pytorch/issues/51719, https://github.com/pytorch/pytorch/issues/28142
**Change**
- Update `torch.Tensor.unflatten` to support users pass`-1` as the inferred size for both tensors and named tensors.
- Examples of using `-1` in the `unflatten` function are added to the docs.
- Fix the rendered issue of original `unflatten` docs by removing a blank line between its example section.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51955
Reviewed By: agolynski
Differential Revision: D26467198
Pulled By: zou3519
fbshipit-source-id: 6a3ede25561223187273796427ad0cb63f125364
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45847
Original PR here https://github.com/pytorch/pytorch/pull/45084. Created this one because I was having problems with ghstack.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D24136629
Pulled By: heitorschueroff
fbshipit-source-id: dd7c7540a33f6a19e1ad70ba2479d5de44abbdf9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42563
Moved logic for non-named unflatten from python nn module to aten/native to be reused by the nn module later. Fixed some inconsistencies with doc and code logic.
Test Plan: Imported from OSS
Reviewed By: zou3519
Differential Revision: D23030301
Pulled By: heitorschueroff
fbshipit-source-id: 7c804ed0baa5fca960a990211b8994b3efa7c415
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38739
Instead of codegenning the named tensor support checks into
CPUType/CUDAType, we instead add a new dispatch key that is put
into tensor whenever it has names. By default, the fallback
implementation says that named tensors are not supported, but
if they are supported, we register a fallthrough which lets
us through to the true backend implementation.
There are a bunch of small pieces which are necessary to make this
happen:
- NameMode now also excludes DispatchKey::Named from the dispatch set
- To avoid bad error messages, we add a teensy special case to
the dispatcher for named_not_supported_kernel: if we see that
the boxed kernel we need to invoke from unboxed is this kernel,
but we don't support boxing, but it's a kernel which is known
to not need boxing, we just pass in nullptr for the stack.
The special case here is very nice: it doesn't affect the fast
path and only gets exercised when things are not supported.
- I need to add support for per operator fallthrough registration.
This is done similarly to how we support fallthrough fallback,
by just keeping track if the registered kernel for an operator
is a fallthrough.
It is possible we could go even further down this path, and move
the named tensor logic itself into this key. I leave this
up to future work.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Test Plan: Imported from OSS
Differential Revision: D21662643
Pulled By: ezyang
fbshipit-source-id: 5bc6ae14a1f600189bd8bf865f74dd1700d932f7
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35624
Python 2 has reached end-of-life and is no longer supported by PyTorch.
This test case is valid syntax in Python 3.
Test Plan: CI
Differential Revision: D20842877
Pulled By: dreiss
fbshipit-source-id: 856e72171496aa1d517f2f27a8a5066462cf4f76
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/35615
Python 2 has reached end-of-life and is no longer supported by PyTorch.
Now we can clean up a lot of cruft that we put in place to support it.
These changes were all done manually, and I skipped anything that seemed
like it would take more than a few seconds, so I think it makes sense to
review it manually as well (though using side-by-side view and ignoring
whitespace change might be helpful).
Test Plan: CI
Differential Revision: D20842886
Pulled By: dreiss
fbshipit-source-id: 8cad4e87c45895e7ce3938a88e61157a79504aed
Summary:
See NumPy's division documentation here: https://numpy.org/doc/1.18/reference/generated/numpy.divide.html#numpy.divide.
True division is the same as PyTorch's default division except when both inputs are integer or bool tensors. In the latter case the inputs are (conceptually) cast to the default floating type before the division is performed.
The function is implemented for dense and sparse tensors and supports exporting to ONNX from PyTorch's eager mode or JIT traces. The function is inherently incompatible with exporting to ONNX via JIT script, and is another datapoint suggesting we should deprecate exporting scripted graphs to ONNX.
Tests are added for the type promotion, named tensor, and ONNX export behavior.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/34236
Reviewed By: houseroad
Differential Revision: D20334087
Pulled By: mruberry
fbshipit-source-id: 83d00d886f46f713215d7d9e02ffd043164c57f1
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
