This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/31116
Changelist:
- remove BUILD_NAMEDTENSOR macro
- remove torch._C._BUILD_NAMEDTENSOR
- remove all python behavior that relies on torch._C._BUILD_NAMEDTENSOR
Future:
- In the next diff, I will remove all usages of
ATen/core/EnableNamedTensor.h since that header doesn't do anything
anymore
- After that, we'll be done with the BUILD_NAMEDTENSOR removal.
Test Plan: - run CI
Differential Revision: D18934951
Pulled By: zou3519
fbshipit-source-id: 0a0df0f1f0470d0a01c495579333a2835aac9f5d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27308
Currently, `tensor.align_to(*names)` has the restriction that the
`tensor` must be fully named. This doesn't need to be the case, when
using Ellipsis, we "expand the ellipsis to all unmentioned dimensions,
in the order which they appear in the original tensor".
For example, consider `tensor: Tensor[None, None, C]`.
`tensor.align_to(C, None, None)` is ambiguous because the user might
have wanted to switch the order of the None dimensions and there is no
way to specify that using this API. However, `tensor.align_to('C', ...)`
isn't ambiguous: we can select the two unnamed dimensions in the order
in which they appear.
To actually implement this, we write a brand-new `align_to(names,
ellipsis_idx)` function in c++ that is separate from the regular
`align_to(names)` implementation. Ideally we would support "..." as a
special name in c++ and combine the two implementations; we'll need to
support "..." in c++ in the future but that requires a bit of extra work.
In this PR, Python processees the ellipsis and then calls the correct
overload.
Test Plan: - run tests
Differential Revision: D17745179
Pulled By: zou3519
fbshipit-source-id: 9fed06d224215cfb7efecd8c002604baab3c45e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26648
Previously:
- `Tensor.align_to(*names)` only works on fully named tensors. In addition, the
desired ordering `names` must not have any None-names.
- `Tensor.align_to(*names)` accepted `...`, but expanded it based on
position. i.e., in `tensor.align_to('N', ..., 'C', 'H')`, `...` expand
to `*tensor.names[1:-2]`. This is wildly incorrect: see the following
concrete example.
```
tensor = tensor.refine_names('N', 'C', 'H, 'W')
tensor.align_to('W', ...) # ... expands to 'C', 'H, 'W'
```
This PR changes it so that `...` in `tensor.align_to` grabs all
unmentioned dimensions from `tensor`, in the order that they appear.
`align_to` is the only function that takes ellipsis that requires this
change. This is because all other functions (`refine_to`) require their
list of names to work in a positional manner, but `align_to` lets the
user reorder dimensions.
This does not add very much overhead to `align_to`, as shown in the
following benchmark. However, in the future, we should resolve to make
these operations faster; align_to should be as fast as view but isn't
most likely due to Python overhead.
```
[ins] In [2]: import torch
...: named = torch.randn(3, 3, 3, 3, names=('N', 'C', 'H', 'W'))
...: unnamed = torch.randn(3, 3, 3, 3)
...: %timeit unnamed[:]
...: %timeit unnamed.view(-1)
...: %timeit named.align_to(...)
...: %timeit named.align_to('N', 'C', 'H', 'W')
31 µs ± 126 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
43.8 µs ± 146 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
69.6 µs ± 142 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
66.1 µs ± 1.13 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
```
Test Plan:
- new tests [namedtensor ci]
allows the user to transpose and permute dimensions.
Differential Revision: D17528207
Pulled By: zou3519
fbshipit-source-id: 4efc70329f84058c245202d0b267d0bc5ce42069
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26548
This makes the naming more consistent with PyTorch's API. The original
concern was that `tensor.rename` might make the operation seem like it
is in-place. However, we have many "verb" APIs: `tensor.add(other)`, for
example, doesn't add other to tensor in-place, but `tensor.add_(other)`
does.
`tensor.rename_` does exactly the same place as `tensor.rename`, but
in-place.
Test Plan: - [namedtensor ci]
Differential Revision: D17502021
Pulled By: zou3519
fbshipit-source-id: 6a5b93136a820075013cd1e30fb8fc6b9d77d7d9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26350
Python 3 lets us use `...` to perform indexing. Semantically, `...`
means "the rest of the unspecified dimensions". For example, while
indexing, one can do (for 5D `tensor`) `tensor[0, 0, ..., 0]` and
the `...` is expanded into `tensor[0, 0, :, :, 0]`.
Previously, we were using '*' to represent a similar behavior in names.
For example, `tensor.refine_names` supports things like the following:
```
x = torch.randn(2, 3, 4, 5, 6)
x_out = x.refine_names('*', 'H', 'W') # refine only the last two
dimensions
```
This PR changes it so that named tensor API functions recognize `'...'`
(in Python 2 and Python 3) and `...` (in Python 3 exclusively) instead
of `'*'`.
Test Plan: - [namedtensor ci]
Differential Revision: D17424666
Pulled By: zou3519
fbshipit-source-id: 003182879fd38ced3fea051217572a457cdaf7cf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26349
The directory holds a lot of private helper functions that help
implement named tensor functionality. Instead of naming each helper
function with a leading underscore, I change the name of the import to
`_namedtensor_internals` to signal it should not be used directly.
Test Plan: - [namedtensor ci]
Differential Revision: D17424178
Pulled By: zou3519
fbshipit-source-id: 8f7b74346765759303480e581038a661021acf53
