Summary:
In one of my previous PRs that rewrite `tensordot` implementation, I mistakenly take empty value of `dims_a` and `dims_b` as illegal values. This turns out to be not true. Empty `dims_a` and `dims_b` are supported, in fact common when `dims` is passed as an integer. This PR removes the unnecessary check.
Fixes https://github.com/pytorch/pytorch/issues/61096
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61331
Reviewed By: eellison
Differential Revision: D29578910
Pulled By: gmagogsfm
fbshipit-source-id: 96e58164491a077ddc7a1d6aa6ccef8c0c9efda2
Summary:
There is a very common error when writing docs: One forgets to write a matching `` ` ``, and something like ``:attr:`x`` is rendered in the docs. This PR fixes most (all?) of these errors (and a few others).
I found these running ``grep -r ">[^#<][^<]*\`"`` on the `docs/build/html/generated` folder. The regex finds an HTML tag that does not start with `#` (as python comments in example code may contain backticks) and that contains a backtick in the rendered HTML.
This regex has not given any false positive in the current codebase, so I am inclined to suggest that we should add this check to the CI. Would this be possible / reasonable / easy to do malfet ?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60474
Reviewed By: mrshenli
Differential Revision: D29309633
Pulled By: albanD
fbshipit-source-id: 9621e0e9f87590cea060dd084fa367442b6bd046
Summary:
Improved torch.einsum testing and fixed a bug where lower case letters appeared before upper case letters in the sorted order which is inconsistent with NumPy.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/59731
Reviewed By: SplitInfinity, ansley
Differential Revision: D29183078
Pulled By: heitorschueroff
fbshipit-source-id: a33980d273707da2d60a387a2af2fa41527ddb68
Summary:
This PR adds an alternative way of calling `torch.einsum`. Instead of specifying the subscripts as letters in the `equation` parameter, one can now specify the subscripts as a list of integers as in `torch.einsum(operand1, subscripts1, operand2, subscripts2, ..., [subscripts_out])`. This would be equivalent to `torch.einsum('<subscripts1>,<subscripts2>,...,->[<subscript_out>]', operand1, operand2, ...)`
TODO
- [x] Update documentation
- [x] Add more error checking
- [x] Update tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56625
Reviewed By: zou3519
Differential Revision: D28062616
Pulled By: heitorschueroff
fbshipit-source-id: ec50ad34f127210696e7c545e4c0675166f127dc
Summary:
Backward methods for `torch.lu` and `torch.lu_solve` require the `torch.lu_unpack` method.
However, while `torch.lu` is a Python wrapper over a native function, so its gradient is implemented via `autograd.Function`,
`torch.lu_solve` is a native function, so it cannot access `torch.lu_unpack` as it is implemented in Python.
Hence this PR presents a native (ATen) `lu_unpack` version. It is also possible to update the gradients for `torch.lu` so that backward+JIT is supported (no JIT for `autograd.Function`) with this function.
~~The interface for this method is different from the original `torch.lu_unpack`, so it is decided to keep it hidden.~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46913
Reviewed By: albanD
Differential Revision: D28355725
Pulled By: mruberry
fbshipit-source-id: 281260f3b6e93c15b08b2ba66d5a221314b00e78
Summary:
**BC-breaking note**
This PR updates the deprecation notice for torch.norm to point users to the new torch.linalg.vector_norm and torch.linalg.matrix_norm functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57986
Reviewed By: nikithamalgifb
Differential Revision: D28353625
Pulled By: heitorschueroff
fbshipit-source-id: 5de77d89f0e84945baa5fea91f73918dc7eeafd4
Summary:
This one's easy. I also included a bugfix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57735
Reviewed By: bdhirsh
Differential Revision: D28318277
Pulled By: mruberry
fbshipit-source-id: c3c4546a11ba5b555b99ee79b1ce6c0649fa7323
Summary:
Backward methods for `torch.lu` and `torch.lu_solve` require the `torch.lu_unpack` method.
However, while `torch.lu` is a Python wrapper over a native function, so its gradient is implemented via `autograd.Function`,
`torch.lu_solve` is a native function, so it cannot access `torch.lu_unpack` as it is implemented in Python.
Hence this PR presents a native (ATen) `lu_unpack` version. It is also possible to update the gradients for `torch.lu` so that backward+JIT is supported (no JIT for `autograd.Function`) with this function.
~~The interface for this method is different from the original `torch.lu_unpack`, so it is decided to keep it hidden.~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46913
Reviewed By: astaff
Differential Revision: D28117714
Pulled By: mruberry
fbshipit-source-id: befd33db12ecc147afacac792418b6f4948fa4a4
Summary:
As this diff shows, currently there are a couple hundred instances of raw `noqa` in the codebase, which just ignore all errors on a given line. That isn't great, so this PR changes all existing instances of that antipattern to qualify the `noqa` with respect to a specific error code, and adds a lint to prevent more of this from happening in the future.
Interestingly, some of the examples the `noqa` lint catches are genuine attempts to qualify the `noqa` with a specific error code, such as these two:
```
test/jit/test_misc.py:27: print(f"{hello + ' ' + test}, I'm a {test}") # noqa E999
test/jit/test_misc.py:28: print(f"format blank") # noqa F541
```
However, those are still wrong because they are [missing a colon](https://flake8.pycqa.org/en/3.9.1/user/violations.html#in-line-ignoring-errors), which actually causes the error code to be completely ignored:
- If you change them to anything else, the warnings will still be suppressed.
- If you add the necessary colons then it is revealed that `E261` was also being suppressed, unintentionally:
```
test/jit/test_misc.py:27:57: E261 at least two spaces before inline comment
test/jit/test_misc.py:28:35: E261 at least two spaces before inline comment
```
I did try using [flake8-noqa](https://pypi.org/project/flake8-noqa/) instead of a custom `git grep` lint, but it didn't seem to work. This PR is definitely missing some of the functionality that flake8-noqa is supposed to provide, though, so if someone can figure out how to use it, we should do that instead.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56272
Test Plan:
CI should pass on the tip of this PR, and we know that the lint works because the following CI run (before this PR was finished) failed:
- https://github.com/pytorch/pytorch/runs/2365189927
Reviewed By: janeyx99
Differential Revision: D27830127
Pulled By: samestep
fbshipit-source-id: d6dcf4f945ebd18cd76c46a07f3b408296864fcb
Summary:
Fixes https://github.com/pytorch/pytorch/issues/54631
I removed the phrase "When `onesided` is the default value `True`". It's not always the default and it's also confusing because it doesn't seem to relate to the bullet points it's introducing. It makes more sense in the sentence before, i.e. these frequencies are included "when the output is onesided". So, I've rewritten it as that meaning and included the correct formula for frequencies.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54877
Reviewed By: ngimel
Differential Revision: D27562785
Pulled By: mruberry
fbshipit-source-id: d7f36382611e8e176e3370393d1b371d577d46bb
Summary:
As per title.
Numerical stability increased by replacing inverses with solutions to systems of linear triangular equations.
Unblocks computing `torch.det` for FULL-rank inputs of complex dtypes via the LU decomposition once https://github.com/pytorch/pytorch/pull/48125/files is merged:
```
LU, pivots = input.lu()
P, L, U = torch.lu_unpack(LU, pivots)
det_input = P.det() * torch.prod(U.diagonal(0, -1, -2), dim=-1) # P is not differentiable, so we are fine even if it is complex.
```
Unfortunately, since `lu_backward` is implemented as `autograd.Function`, we cannot support both autograd and scripting at the moment.
The solution would be to move all the lu-related methods to ATen, see https://github.com/pytorch/pytorch/issues/53364.
Resolves https://github.com/pytorch/pytorch/issues/52891
TODOs:
* extend lu_backward for tall/wide matrices of full rank.
* move lu-related functionality to ATen and make it differentiable.
* handle rank-deficient inputs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53994
Reviewed By: pbelevich
Differential Revision: D27188529
Pulled By: anjali411
fbshipit-source-id: 8e053b240413dbf074904dce01cd564583d1f064
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53424
Fixes https://github.com/pytorch/pytorch/issues/24807 and supersedes the stale https://github.com/pytorch/pytorch/issues/25093 (Cc Microsheep). If you now run the reproduction
```python
import torch
if __name__ == "__main__":
t = torch.tensor([1, 2, 3], dtype=torch.float64)
```
with `pylint==2.6.0`, you get the following output
```
test_pylint.py:1:0: C0114: Missing module docstring (missing-module-docstring)
test_pylint.py:4:8: E1101: Module 'torch' has no 'tensor' member; maybe 'Tensor'? (no-
member)
test_pylint.py:4:38: E1101: Module 'torch' has no 'float64' member (no-member)
```
Now `pylint` doesn't recognize `torch.tensor` at all, but it is promoted in the stub. Given that it also doesn't recognize `torch.float64`, I think fixing this is out of scope of this PR.
---
## TL;DR
This BC-breaking only for users that rely on unintended behavior. Since `torch/__init__.py` loaded `torch/tensor.py` it was populated in `sys.modules`. `torch/__init__.py` then overwrote `torch.tensor` with the actual function. With this `import torch.tensor as tensor` does not fail, but returns the function rather than the module. Users that rely on this import need to change it to `from torch import tensor`.
Reviewed By: zou3519
Differential Revision: D26223815
Pulled By: bdhirsh
fbshipit-source-id: 125b9ff3d276e84a645cd7521e8d6160b1ca1c21
Summary:
This PR addresses [a two-year-old TODO in `test/test_type_hints.py`](12942ea52b/test/test_type_hints.py (L21-L22)) by replacing most of the body of our custom `get_examples_from_docstring` function with [a function from Python's built-in `doctest.DocTestParser` class](https://docs.python.org/3/library/doctest.html#doctest.DocTestParser.get_examples). This mostly made the parser more strict, catching a few errors in existing doctests:
- missing `...` in multiline statements
- missing space after `>>>`
- unmatched closing parenthesis
Also, as shown by [the resulting diff of the untracked `test/generated_type_hints_smoketest.py` file](https://pastebin.com/vC5Wz6M0) (also linked from the test plan below), this introduces a few incidental changes as well:
- standalone comments are no longer preserved
- indentation is now visually correct
- [`example_torch_promote_types`](4da9ceb743/torch/_torch_docs.py (L6753-L6772)) is now present
- an example called `example_torch_tensor___array_priority__` is added, although I can't tell where it comes from
- the last nine lines of code from [`example_torch_tensor_align_as`](5d45140d68/torch/_tensor_docs.py (L386-L431)) are now present
- the previously-misformatted third line from [`example_torch_tensor_stride`](5d45140d68/torch/_tensor_docs.py (L3508-L3532)) is now present
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50596
Test Plan:
Checkout the base commit, typecheck the doctests, and save the generated file:
```
$ python test/test_type_hints.py TestTypeHints.test_doc_examples
$ cp test/generated_type_hints_smoketest.py /tmp
```
Then checkout this PR, do the same thing, and compare:
```
$ python test/test_type_hints.py TestTypeHints.test_doc_examples
$ git diff --no-index {/tmp,test}/generated_type_hints_smoketest.py
```
The test should succeed, and the diff should match [this paste](https://pastebin.com/vC5Wz6M0).
Reviewed By: walterddr
Differential Revision: D25926245
Pulled By: samestep
fbshipit-source-id: 23bc379ff438420e556263c19582dba06d8e42ec
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48965
This PR pulls `__torch_function__` checking entirely into C++, and adds a special `object_has_torch_function` method for ops which only have one arg as this lets us skip tuple construction and unpacking. We can now also do away with the Python side fast bailout for `Tensor` (e.g. `if any(type(t) is not Tensor for t in tensors) and has_torch_function(tensors)`) because they're actually slower than checking with the Python C API.
Test Plan: Existing unit tests. Benchmarks are in #48966
Reviewed By: ezyang
Differential Revision: D25590732
Pulled By: robieta
fbshipit-source-id: 6bd74788f06cdd673f3a2db898143d18c577eb42
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49022
**BC-breaking note**:
Previously torch.stft took an optional `return_complex` parameter that indicated whether the output would be a floating point tensor or a complex tensor. By default `return_complex` was False to be consistent with the previous behavior of torch.stft. This PR changes this behavior so `return_complex` is a required argument.
**PR Summary**:
* **#49022 stft: Change require_complex warning to an error**
Test Plan: Imported from OSS
Reviewed By: ngimel
Differential Revision: D25658906
Pulled By: mruberry
fbshipit-source-id: 11932d1102e93f8c7bd3d2d0b2a607fd5036ec5e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49189
This reverts commit d307601365 and fixes the bug with diagonals and ellipsis combined.
Test Plan: Imported from OSS
Reviewed By: glaringlee
Differential Revision: D25540722
Pulled By: heitorschueroff
fbshipit-source-id: 86d0c9a7dcfda600b546457dad102af2ff33e353
Summary:
**BC-breaking note:**
Previously, when given a complex input, `torch.linalg.norm` and `torch.norm` would return a complex output. `torch.linalg.cond` would sometimes return a complex output and sometimes return a real output when given a complex input, depending on its `p` argument. This PR changes this behavior to match `numpy.linalg.norm` and `numpy.linalg.cond`, so that a complex input will result in the downgraded real number type, consistent with NumPy.
**PR Summary:**
The following cases were previously unsupported for complex inputs, and this commit adds support:
- Frobenius norm
- Norm order 2 (vector and matrix)
- CUDA vector norm
Part of https://github.com/pytorch/pytorch/issues/47833
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48284
Reviewed By: H-Huang
Differential Revision: D25420880
Pulled By: mruberry
fbshipit-source-id: 11f6a2f3cad57d66476d30921c3f6ab8f3cd4017
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47860
This PR makes torch.einsum compatible with numpy.einsum except for the sublist input option as requested here https://github.com/pytorch/pytorch/issues/21412. It also fixed 2 performance issues linked below and adds a check for reducing to torch.dot instead of torch.bmm which is faster in some cases.
fixes#45854, #37628, #30194, #15671fixes#41467 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer
a = torch.randn(10000, 100, 101, device='cuda')
b = torch.randn(10000, 101, 3, device='cuda')
c = torch.randn(10000, 100, 1, device='cuda')
d = torch.randn(10000, 100, 1, 3, device='cuda')
print(Timer(
stmt='torch.einsum("bij,bjf->bif", a, b)',
globals={'a': a, 'b': b}
).blocked_autorange())
print()
print(Timer(
stmt='torch.einsum("bic,bicf->bif", c, d)',
globals={'c': c, 'd': d}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413850>
torch.einsum("bij,bjf->bif", a, b)
Median: 4.53 ms
IQR: 0.00 ms (4.53 to 4.53)
45 measurements, 1 runs per measurement, 1 thread
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413700>
torch.einsum("bic,bicf->bif", c, d)
Median: 63.86 us
IQR: 1.52 us (63.22 to 64.73)
4 measurements, 1000 runs per measurement, 1 thread
```
fixes#32591 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer
a = torch.rand(1, 1, 16, 2, 16, 2, 16, 2, 2, 2, 2, device="cuda")
b = torch.rand(729, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, device="cuda")
print(Timer(
stmt='(a * b).sum(dim = (-3, -2, -1))',
globals={'a': a, 'b': b}
).blocked_autorange())
print()
print(Timer(
stmt='torch.einsum("...ijk, ...ijk -> ...", a, b)',
globals={'a': a, 'b': b}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de28850>
(a * b).sum(dim = (-3, -2, -1))
Median: 17.86 ms
2 measurements, 10 runs per measurement, 1 thread
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de286a0>
torch.einsum("...ijk, ...ijk -> ...", a, b)
Median: 296.11 us
IQR: 1.38 us (295.42 to 296.81)
662 measurements, 1 runs per measurement, 1 thread
```
TODO
- [x] add support for ellipsis broadcasting
- [x] fix corner case issues with sumproduct_pair
- [x] update docs and add more comments
- [x] add tests for error cases
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D24923679
Pulled By: heitorschueroff
fbshipit-source-id: 47e48822cd67bbcdadbdfc5ffa25ee8ba4c9620a
Summary:
Fixes https://github.com/pytorch/pytorch/issues/43837
This adds a `torch.broadcast_shapes()` function similar to Pyro's [broadcast_shape()](7c2c22c10d/pyro/distributions/util.py (L151)) and JAX's [lax.broadcast_shapes()](https://jax.readthedocs.io/en/test-docs/_modules/jax/lax/lax.html). This helper is useful e.g. in multivariate distributions that are parameterized by multiple tensors and we want to `torch.broadcast_tensors()` but the parameter tensors have different "event shape" (e.g. mean vectors and covariance matrices). This helper is already heavily used in Pyro's distribution codebase, and we would like to start using it in `torch.distributions`.
- [x] refactor `MultivariateNormal`'s expansion logic to use `torch.broadcast_shapes()`
- [x] add unit tests for `torch.broadcast_shapes()`
- [x] add docs
cc neerajprad
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43935
Reviewed By: bdhirsh
Differential Revision: D25275213
Pulled By: neerajprad
fbshipit-source-id: 1011fdd597d0a7a4ef744ebc359bbb3c3be2aadc
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46398
This PR makes torch.einsum compatible with numpy.einsum except for the sublist input option as requested here https://github.com/pytorch/pytorch/issues/21412. It also fixed 2 performance issues linked below and adds a check for reducing to torch.dot instead of torch.bmm which is faster in some cases.
fixes#45854, #37628, #30194, #15671fixes#41467 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer
a = torch.randn(10000, 100, 101, device='cuda')
b = torch.randn(10000, 101, 3, device='cuda')
c = torch.randn(10000, 100, 1, device='cuda')
d = torch.randn(10000, 100, 1, 3, device='cuda')
print(Timer(
stmt='torch.einsum("bij,bjf->bif", a, b)',
globals={'a': a, 'b': b}
).blocked_autorange())
print()
print(Timer(
stmt='torch.einsum("bic,bicf->bif", c, d)',
globals={'c': c, 'd': d}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413850>
torch.einsum("bij,bjf->bif", a, b)
Median: 4.53 ms
IQR: 0.00 ms (4.53 to 4.53)
45 measurements, 1 runs per measurement, 1 thread
<torch.utils.benchmark.utils.common.Measurement object at 0x7fa37c413700>
torch.einsum("bic,bicf->bif", c, d)
Median: 63.86 us
IQR: 1.52 us (63.22 to 64.73)
4 measurements, 1000 runs per measurement, 1 thread
```
fixes#32591 with benchmark below
```python
import torch
from torch.utils.benchmark import Timer
a = torch.rand(1, 1, 16, 2, 16, 2, 16, 2, 2, 2, 2, device="cuda")
b = torch.rand(729, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, device="cuda")
print(Timer(
stmt='(a * b).sum(dim = (-3, -2, -1))',
globals={'a': a, 'b': b}
).blocked_autorange())
print()
print(Timer(
stmt='torch.einsum("...ijk, ...ijk -> ...", a, b)',
globals={'a': a, 'b': b}
).blocked_autorange())
```
```
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de28850>
(a * b).sum(dim = (-3, -2, -1))
Median: 17.86 ms
2 measurements, 10 runs per measurement, 1 thread
<torch.utils.benchmark.utils.common.Measurement object at 0x7efe0de286a0>
torch.einsum("...ijk, ...ijk -> ...", a, b)
Median: 296.11 us
IQR: 1.38 us (295.42 to 296.81)
662 measurements, 1 runs per measurement, 1 thread
```
TODO
- [x] add support for ellipsis broadcasting
- [x] fix corner case issues with sumproduct_pair
- [x] update docs and add more comments
- [x] add tests for error cases
Test Plan: Imported from OSS
Reviewed By: malfet
Differential Revision: D24860367
Pulled By: heitorschueroff
fbshipit-source-id: 31110ee598fd598a43acccf07929b67daee160f9
Summary:
Fixes https://github.com/pytorch/pytorch/issues/40869
Resubmit of https://github.com/pytorch/pytorch/pull/33818.
Adds support for `list()` by desugaring it to a list comprehension.
Last time I landed this it made one of the tests slow, and got unlanded. I think that's bc the previous PR changed the emission of `list()` on a list input or a str input to a list comprehension, which is the more general way of emitting `list()`, but also a little bit slower. I updated this version to emit to the builtin operators for these two case. Hopefully it can land without being reverted this time...
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42382
Reviewed By: navahgar
Differential Revision: D24767674
Pulled By: eellison
fbshipit-source-id: a1aa3d104499226b28f47c3698386d365809c23c
Summary:
As per title. Limitations: only for batches of squared full-rank matrices.
CC albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46284
Reviewed By: zou3519
Differential Revision: D24448266
Pulled By: albanD
fbshipit-source-id: d98215166268553a648af6bdec5a32ad601b7814
Summary:
* Removes incorrect statement that "the vector norm will be applied to the last dimension".
* More clearly describe each different combination of `p`, `ord`, and input size.
* Moves norm tests from `test/test_torch.py` to `test/test_linalg.py`
* Adds test ensuring that `p='fro'` and `p=2` give same results for mutually valid inputs
Fixes https://github.com/pytorch/pytorch/issues/41388
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42696
Reviewed By: bwasti
Differential Revision: D23876862
Pulled By: mruberry
fbshipit-source-id: 36f33ccb6706d5fe13f6acf3de8ae14d7fbdff85
Summary:
This PR makes the deprecation warnings for existing fft functions more prominent and makes the torch.stft deprecation warning consistent with our current deprecation planning.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45409
Reviewed By: ngimel
Differential Revision: D23974975
Pulled By: mruberry
fbshipit-source-id: b90d8276095122ac3542ab625cb49b991379c1f8
Summary:
Changes the deprecation of norm to a docs deprecation, since PyTorch components still rely on norm and some behavior, like automatically flattening tensors, may need to be ported to torch.linalg.norm. The documentation is also updated to clarify that torch.norm and torch.linalg.norm are distinct.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45415
Reviewed By: ngimel
Differential Revision: D23958252
Pulled By: mruberry
fbshipit-source-id: fd54e807c59a2655453a6bcd9f4073cb2c12e8ac
Summary:
Ref https://github.com/pytorch/pytorch/issues/42175, fixes https://github.com/pytorch/pytorch/issues/34797
This adds complex support to `torch.stft` and `torch.istft`. Note that there are really two issues with complex here: complex signals, and returning complex tensors.
## Complex signals and windows
`stft` currently assumes all signals are real and uses `rfft` with `onesided=True` by default. Similarly, `istft` always takes a complex fourier series and uses `irfft` to return real signals.
For `stft`, I now allow complex inputs and windows by calling the full `fft` if either are complex. If the user gives `onesided=True` and the signal is complex, then this doesn't work and raises an error instead. For `istft`, there's no way to automatically know what to do when `onesided=False` because that could either be a redundant representation of a real signal or a complex signal. So there, the user needs to pass the argument `return_complex=True` in order to use `ifft` and get a complex result back.
## stft returning complex tensors
The other issue is that `stft` returns a complex result, represented as a `(... X 2)` real tensor. I think ideally we want this to return proper complex tensors but to preserver BC I've had to add a `return_complex` argument to manage this transition. `return_complex` defaults to false for real inputs to preserve BC but defaults to True for complex inputs where there is no BC to consider.
In order to `return_complex` by default everywhere without a sudden BC-breaking change, a simple transition plan could be:
1. introduce `return_complex`, defaulted to false when BC is an issue but giving a warning. (this PR)
2. raise an error in cases where `return_complex` defaults to false, making it a required argument.
3. change `return_complex` default to true in all cases.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43886
Reviewed By: glaringlee
Differential Revision: D23760174
Pulled By: mruberry
fbshipit-source-id: 2fec4404f5d980ddd6bdd941a63852a555eb9147
Summary:
- `torch._VF` is a hack to work around the lack of support for `torch.functional` in the JIT
- that hack hides `torch._VF` functions from Mypy
- could be worked around by re-introducing a stub file for `torch.functional`, but that's undesirable
- so instead try to make both happy at the same time: the type ignore comments are needed for Mypy, and don't seem to affect the JIT after excluding them from the `get_type_line()` logic
Encountered this issue while trying to make `mypy` run on `torch/functional.py` in gh-43446.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43454
Reviewed By: glaringlee
Differential Revision: D23305579
Pulled By: malfet
fbshipit-source-id: 50e490693c1e53054927b57fd9acc7dca57e88ca
Summary:
According to pytorch/rfcs#3
From the goals in the RFC:
1. Support subclassing `torch.Tensor` in Python (done here)
2. Preserve `torch.Tensor` subclasses when calling `torch` functions on them (done here)
3. Use the PyTorch API with `torch.Tensor`-like objects that are _not_ `torch.Tensor`
subclasses (done in https://github.com/pytorch/pytorch/issues/30730)
4. Preserve `torch.Tensor` subclasses when calling `torch.Tensor` methods. (done here)
5. Propagating subclass instances correctly also with operators, using
views/slices/indexing/etc. (done here)
6. Preserve subclass attributes when using methods or views/slices/indexing. (done here)
7. A way to insert code that operates on both functions and methods uniformly
(so we can write a single function that overrides all operators). (done here)
8. The ability to give external libraries a way to also define
functions/methods that follow the `__torch_function__` protocol. (will be addressed in a separate PR)
This PR makes the following changes:
1. Adds the `self` argument to the arg parser.
2. Dispatches on `self` as well if `self` is not `nullptr`.
3. Adds a `torch._C.DisableTorchFunction` context manager to disable `__torch_function__`.
4. Adds a `torch::torch_function_enabled()` and `torch._C._torch_function_enabled()` to check the state of `__torch_function__`.
5. Dispatches all `torch._C.TensorBase` and `torch.Tensor` methods via `__torch_function__`.
TODO:
- [x] Sequence Methods
- [x] Docs
- [x] Tests
Closes https://github.com/pytorch/pytorch/issues/28361
Benchmarks in https://github.com/pytorch/pytorch/pull/37091#issuecomment-633657778
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37091
Reviewed By: ngimel
Differential Revision: D22765678
Pulled By: ezyang
fbshipit-source-id: 53f8aa17ddb8b1108c0997f6a7aa13cb5be73de0
Summary:
**BC-Breaking Note:**
BC breaking changes in the case where keepdim=True. Before this change, when calling `torch.norm` with keepdim=True and p='fro' or p=number, leaving all other optional arguments as their default values, the keepdim argument would be ignored. Also, any time `torch.norm` was called with p='nuc', the result would have one fewer dimension than the input, and the dimensions could be out of order depending on which dimensions were being reduced. After the change, for each of these cases, the result has the same number and order of dimensions as the input.
**PR Summary:**
* Fix keepdim behavior
* Throw descriptive errors for unsupported sparse norm args
* Increase unit test coverage for these cases and for complex inputs
These changes were taken from part of PR https://github.com/pytorch/pytorch/issues/40924. That PR is not going to be merged because it overrides `torch.norm`'s interface, which we want to avoid. But these improvements are still useful.
Issue https://github.com/pytorch/pytorch/issues/24802
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41956
Reviewed By: albanD
Differential Revision: D22837455
Pulled By: mruberry
fbshipit-source-id: 509ecabfa63b93737996f48a58c7188b005b7217
Summary:
I added the following to the docs:
1. `torch.save`.
1. Added doc for `_use_new_zipfile_serialization` argument.
2. Added a note telling that extension does not matter while saving.
3. Added an example showing the use of above argument along with `pickle_protocol=5`.
2. `torch.split`
1. Added an example showing the use of the function.
3. `torch.squeeze`
1. Added a warning for batch_size=1 case.
4. `torch.set_printoptions`
1. Changed the docs of `sci_mode` argument from
```
sci_mode: Enable (True) or disable (False) scientific notation. If
None (default) is specified, the value is defined by `_Formatter`
```
to
```
sci_mode: Enable (True) or disable (False) scientific notation. If
None (default=False) is specified, the value is defined by
`torch._tensor_str._Formatter`.
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39303
Differential Revision: D21904504
Pulled By: zou3519
fbshipit-source-id: 92a324257d09d6bcfa0b410d4578859782b94488
Summary:
Fix for https://github.com/pytorch/pytorch/issues/37986
Follows the stack in https://github.com/pytorch/pytorch/pull/33783 stack to make functions in `torch/functional.py` resolve to their python implementations. Because the return type of `torch.unique` depends on `return_inverse` and `return_counts` I had to refactor the implementation to use our boolean_dispatch mechanism.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38156
Differential Revision: D21504449
Pulled By: eellison
fbshipit-source-id: 7efb1dff3b5c00655da10168403ac4817286ff59
Summary:
This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages.
As a result, running `mypy` in the root dir of the repo now runs on:
- `torch/`
- `aten/src/ATen/function_wrapper.py` (the only file already covered in CI)
In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`.
Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through):
<details>
```
torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx'
torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined
torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch"
torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2'
torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2'
torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2'
torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2'
torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2'
torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx'
torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util'
torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework'
torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer'
torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...")
torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items"
torch/nn/_reduction.py:27: error: Name 'Optional' is not defined
torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/_reduction.py:47: error: Name 'Optional' is not defined
torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs
torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs
torch/nn/modules/utils.py:27: error: Name 'List' is not defined
torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment"
torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction'
caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor"
caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor"
caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"?
caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor"
caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor"
caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs
torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed)
caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs
caffe2/python/__init__.py:7: error: Module has no attribute "CPU"
caffe2/python/__init__.py:8: error: Module has no attribute "CUDA"
caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN"
caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL"
caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL"
caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP"
caffe2/python/__init__.py:13: error: Module has no attribute "HIP"
caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"?
caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"?
caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module"
caffe2/python/__init__.py:35: error: Module has no attribute "cuda"
caffe2/python/__init__.py:37: error: Module has no attribute "cuda"
caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory"
torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs
torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs
torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import)
torch/_tensor_str.py:164: error: Not all arguments converted during string formatting
torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/_linalg_utils.py:26: error: Name 'Optional' is not defined
torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:63: error: Name 'Optional' is not defined
torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Optional' is not defined
torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined
torch/_linalg_utils.py:88: error: Name 'Optional' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined
torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched'
torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...")
torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm")
torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined
torch/sparse/__init__.py:71: error: Name 'Optional' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined
torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/nn/init.py:109: error: Name 'Tensor' is not defined
torch/nn/init.py:126: error: Name 'Tensor' is not defined
torch/nn/init.py:142: error: Name 'Tensor' is not defined
torch/nn/init.py:165: error: Name 'Tensor' is not defined
torch/nn/init.py:180: error: Name 'Tensor' is not defined
torch/nn/init.py:194: error: Name 'Tensor' is not defined
torch/nn/init.py:287: error: Name 'Tensor' is not defined
torch/nn/init.py:315: error: Name 'Tensor' is not defined
torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util'
torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction'
torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer'
torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_"
torch/jit/_builtins.py:80: error: Module has no attribute "stft"
torch/jit/_builtins.py:81: error: Module has no attribute "cdist"
torch/jit/_builtins.py:82: error: Module has no attribute "norm"
torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm"
torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm"
torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C'
torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...")
torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import)
torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C'
torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports
torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass"
torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache"
torch/_lowrank.py:11: error: Name 'Tensor' is not defined
torch/_lowrank.py:13: error: Name 'Optional' is not defined
torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Optional' is not defined
torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:14: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Tensor' is not defined
torch/_lowrank.py:82: error: Name 'Optional' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:82: error: Name 'Tuple' is not defined
torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:130: error: Name 'Tensor' is not defined
torch/_lowrank.py:130: error: Name 'Optional' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:130: error: Name 'Tuple' is not defined
torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/_lowrank.py:167: error: Name 'Tensor' is not defined
torch/_lowrank.py:167: error: Name 'Optional' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/_lowrank.py:167: error: Name 'Tuple' is not defined
torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type
torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases
torch/quantization/observer.py:45: error: Invalid base class "ABC"
torch/quantization/observer.py:127: error: Name 'Tensor' is not defined
torch/quantization/observer.py:127: error: Name 'Tuple' is not defined
torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:192: error: Name 'Tensor' is not defined
torch/quantization/observer.py:192: error: Name 'Tuple' is not defined
torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric"
torch/quantization/observer.py:534: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tensor' is not defined
torch/quantization/observer.py:885: error: Name 'Tuple' is not defined
torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/quantization/observer.py:894: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:894: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:899: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:902: error: Name 'Tensor' is not defined
torch/quantization/observer.py:925: error: Name 'Tensor' is not defined
torch/quantization/observer.py:928: error: Cannot determine type of 'min_val'
torch/quantization/observer.py:929: error: Cannot determine type of 'max_val'
torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]"
torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric"
torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:289: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:290: error: Module has no attribute "ops"
torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined
torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined
torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops"
torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops"
torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined
torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined
torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined
torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig'
torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__"
torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views'
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"?
torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16
torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15
torch/functional.py:114: error: Name 'Tuple' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:114: error: Name 'Optional' is not defined
torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]"
torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:204: note: Possible overload variants:
torch/functional.py:204: note: def __setitem__(self, int, int) -> None
torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:204: note: def __getitem__(self, int) -> int
torch/functional.py:204: note: def __getitem__(self, slice) -> List[int]
torch/functional.py:207: error: "Tensor" has no attribute "copy_"
torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int"
torch/functional.py:212: note: Possible overload variants:
torch/functional.py:212: note: def __setitem__(self, int, int) -> None
torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None
torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor"
torch/functional.py:212: note: def __getitem__(self, int) -> int
torch/functional.py:212: note: def __getitem__(self, slice) -> List[int]
torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor")
torch/functional.py:334: error: Name 'Optional' is not defined
torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]"
torch/functional.py:431: error: Module has no attribute "stft"
torch/functional.py:766: error: Module has no attribute "cdist"
torch/functional.py:768: error: Module has no attribute "cdist"
torch/functional.py:770: error: Module has no attribute "cdist"
torch/functional.py:775: error: Name 'Optional' is not defined
torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'Optional' is not defined
torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:780: error: Name 'number' is not defined
torch/functional.py:780: error: Name 'norm' already defined on line 775
torch/functional.py:785: error: Name 'Optional' is not defined
torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:785: error: Name 'number' is not defined
torch/functional.py:785: error: Name 'norm' already defined on line 775
torch/functional.py:790: error: Name 'Optional' is not defined
torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:790: error: Name 'norm' already defined on line 775
torch/functional.py:795: error: Name 'norm' already defined on line 775
torch/functional.py:960: error: Name 'Any' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any")
torch/functional.py:960: error: Name 'Tuple' is not defined
torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized"
torch/functional.py:1041: error: Name 'Optional' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1041: error: Name 'Tuple' is not defined
torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/functional.py:1056: error: Name 'Optional' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/functional.py:1056: error: Name 'Tuple' is not defined
torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple")
torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]")
torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]")
torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm"
torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor")
torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]")
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase"
torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor"
torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]")
torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None")
torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric"
torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric"
torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C'
torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module'
torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68
torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass"
torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524
torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer'
torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching'
torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None")
torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None")
torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined
torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional")
torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops"
torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined
torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined
torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List")
torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights'
torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]")
torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]")
torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None")
torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...")
torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None")
torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...")
torch/distributions/__init__.py:158: error: Name 'transforms' is not defined
torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C'
torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse'
torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import)
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore'
torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore'
torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions'
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"?
torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or
Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584
Reviewed By: seemethere, ailzhang
Differential Revision: D21155985
Pulled By: ezyang
fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1
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:
This PR implements the following linear algebra algorithms for low-rank matrices:
- [x] Approximate `A` as `Q Q^H A` - using Algorithm 4.4 from [Halko et al, 2009](http://arxiv.org/abs/0909.4061).
+ exposed as `torch.lowrank.get_approximate_basis(A, q, niter=2, M=None) -> Q`
+ [x] dense matrices
+ [x] batches of dense matrices
+ [x] sparse matrices
+ [x] documentation
- [x] SVD - using Algorithm 5.1 from [Halko et al, 2009](http://arxiv.org/abs/0909.4061).
+ uses `torch.lowrank.get_approximate_basis`
+ exposed as `torch.svd_lowrank(A, q=6, niter=2, M=None) -> (U, S, V)`
+ [x] dense matrices
+ [x] batches of dense matrices
+ [x] sparse matrices
+ [x] documentation
- [x] PCA - using `torch.svd_lowrank`
+ uses `torch.svd_lowrank`
+ exposed as `torch.pca_lowrank(A, center=True, q=None, niter=2) -> (U, S, V)`
+ [x] dense matrices
+ [x] batches of dense matrices
+ [x] sparse matrices, uses non-centered sparse matrix algorithm
+ [x] documentation
- [x] generalized eigenvalue solver using the original LOBPCG algorithm [Knyazev, 2001](https://epubs.siam.org/doi/abs/10.1137/S1064827500366124)
+ exposed as `torch.lobpcg(A, B=None, k=1, method="basic", ...)`
+ [x] dense matrices
+ [x] batches of dense matrices
+ [x] sparse matrices
+ [x] documentation
- [x] generalized eigenvalue solver using robust LOBPCG with orthogonal basis selection [Stathopoulos, 2002](https://epubs.siam.org/doi/10.1137/S1064827500370883)
+ exposed as `torch.lobpcg(A, B=None, k=1, method="ortho", ...)`
+ [x] dense matrices
+ [x] batches of dense matrices
+ [x] sparse matrices
+ [x] documentation
- [x] generalized eigenvalue solver using the robust and efficient LOBPCG Algorithm 8 from [Duersch et al, 2018](https://epubs.siam.org/doi/abs/10.1137/17M1129830) that switches to orthogonal basis selection automatically
+ the "ortho" method improves iterations so rapidly that in the current test cases it does not make sense to use the basic iterations at all. If users will have matrices for which basic iterations could improve convergence then the `tracker` argument allows breaking the iteration process at user choice so that the user can switch to the orthogonal basis selection if needed. In conclusion, there is no need to implement Algorithm 8 at this point.
- [x] benchmarks
+ [x] `torch.svd` vs `torch.svd_lowrank`, see notebook [Low-rank SVD](https://github.com/Quansight/pearu-sandbox/blob/master/pytorch/Low-rank%20SVD.ipynb). In conclusion, the low-rank SVD is going to be useful only for large sparse matrices where the full-rank SVD will fail due to memory limitations.
+ [x] `torch.lobpcg` vs `scipy.sparse.linalg.lobpcg`, see notebook [LOBPCG - pytorch vs scipy](https://github.com/Quansight/pearu-sandbox/blob/master/pytorch/LOBPCG%20-%20pytorch%20vs%20scipy.ipynb). In conculsion, both implementations give the same results (up to numerical errors from different methods), scipy lobpcg implementation is generally faster.
+ [x] On very small tolerance cases, `torch.lobpcg` is more robust than `scipy.sparse.linalg.lobpcg` (see `test_lobpcg_scipy` results)
Resolves https://github.com/pytorch/pytorch/issues/8049.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29488
Differential Revision: D20193196
Pulled By: vincentqb
fbshipit-source-id: 78a4879912424595e6ea95a95e483a37487a907e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/33504
Fix resolution fo functions that are bound onto torch in torch/functional.py. This does not fix compilation of all of those functions, those will be done in follow ups. Does torch.stft as a start.
Fixes#21478
Test Plan: Imported from OSS
Differential Revision: D20014591
Pulled By: eellison
fbshipit-source-id: bb362f1b5479adbb890e72a54111ef716679d127
Summary:
This adds `__torch_function__` support for all functions in `torch.functional` and `torch.nn.functional`.
The changes to C++ code and codegen scripts are to facilitate adding `__torch_function__` support for the native functions in `torch._C._nn`. Note that I moved the `handle_torch_function` C++ function to a header that both `python_torch_functions.cpp` and `python_nn_functions.cpp` include. The changes to `python_nn_functions.cpp` mirror the changes I made to `python_torch_functions.cpp` when `__torch_function__` support was first added in https://github.com/pytorch/pytorch/issues/27064. Due to the somewhat different way the `torch._C` and `torch._C._nn` namespaces are initialized I needed to create a new static reference to the `torch._C._nn` namespace (`THPNNVariableFunctions`). I'm not sure if that is the best way to do this. In principle I could import these namespaces in each kernel and avoid the global variable but that would have a runtime cost.
I added `__torch_function__` support to the Python functions in `torch.nn.functional` following the approach in https://github.com/pytorch/pytorch/issues/32194.
I re-enabled the test that checks if all functions in the `torch` namespace are explicitly tested for `__torch_function__` support. I also generalized the check to work for `torch.functional` and `torch.nn.functional` as well. This test was explicitly disabled in https://github.com/pytorch/pytorch/issues/30730 and I'm happy to disable it again if you think that's appropriate. I figured now was as good a time as any to try to re-enable it.
Finally I adjusted the existing torch API tests to suppress deprecation warnings and add keyword arguments used by some of the code in `torch.nn.functional` that were missed when I originally added the tests in https://github.com/pytorch/pytorch/issues/27064.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32799
Differential Revision: D19956809
Pulled By: ezyang
fbshipit-source-id: 40d34e0109cc4b9f3ef62f409d2d35a1d84e3d22
Summary:
Understanding which ops return views and which return tensors with new storage is a common user issue, and an issue for developers connecting accelerators to PyTorch, too. This generic test suite verifies that ops which should return views do (and a few ops that shouldn't don't). The documentation has also been updated for .t(), permute(), unfold(), and select() to clarify they return views.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32512
Differential Revision: D19659454
Pulled By: mruberry
fbshipit-source-id: b4334be9b698253a979e1bb8746fdb3ca24aa4e3
Summary:
For now I'm just removing the decorators from all of the currently overridable functions in `torch.functional`. This means they are no longer overridable, however this should fix the benchmark regressions reported in https://github.com/pytorch/pytorch/issues/30831. Moving forward we'll be looking at reducing the overhead of the python-level override mechanism and failing that, re-implementing all of these operators in C++.
cc hl475
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30839
Differential Revision: D18838848
Pulled By: ezyang
fbshipit-source-id: 22b8015d7b2f7a947f1ebc9632c998e081b48ad8
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/30892
Fixes all outstanding lints and actually installs a properly configured
flake8
Test Plan: Imported from OSS
Differential Revision: D18862825
Pulled By: suo
fbshipit-source-id: 08e9083338a7309272e17bb803feaa42e348aa85
Summary:
This is a re-do of https://github.com/pytorch/pytorch/issues/27064, which was reverted (b8792c0438). This was landed at the same time as other work that added new operators to the `torch` namespace so the check for whether the `torch` namespace is exhaustively checked for overridability was triggering test failures.
I've temporarily disabled that check and added an explanatory comment that the check will be re-enabled in a future PR that will be merged during a time when the commit velocity on PyTorch is lower.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30730
Differential Revision: D18813270
Pulled By: ezyang
fbshipit-source-id: 70477c4656dca8fea6e7bc59259555041fcfbf68
Summary:
Changelog:
- Remove `torch.gels` which was deprecated in v1.2.0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26480
Test Plan: - No tests were changed and all callsites for `torch.gels` where modified to `torch.lstsq` when `torch.lstsq` was introduced
Differential Revision: D17527207
Pulled By: zou3519
fbshipit-source-id: 28e2fa3a3bf30eb6b9029bb5aab198c4d570a950
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23804
`output = tensor.align_to(names)` returns a view of `tensor` such that
`output.names = names`. Dimensions with the same names in `tensor` and
`output` have the same sizes; dimensions with new names have size 1.
The following must be true for this operation to succeed:
1) tensor.names must be a subsequence (not necessarily contiguous) of `names`
2) Aligning tensor.names to names must not change the absolute position from the
right of any unnamed dimension.
In practice, these constraints mean that aligning cannot transpose
names.
Some examples:
- Tensor[C].align_to(C) -> Tensor[C]
- Tensor[N].align_to([N, C]) -> Tensor[N, C]
- Tensor[H, W].align_to([N, H, W, C]) -> Tensor[N, H, W, C]
- Tensor[None].align_to([N, None]) -> Tensor[N, None]
- Tensor[N].align_to([N, None None]) -> Tensor[N, None, None]
Examples of error cases:
- Tensor[W, H].align_to([N, H, W, C]) -> Error (not a subsequence)
- Tensor[None, H].align_to([None, H, W]) -> Error (would change the
absolute position from the right of a None dimension)
`torch.align_tensors(*tensors)` aligns the named dimensions of each
tensor according to the alignment rules so that they can be used in an
operation. More concretely, it aligns each tensor to the
longest names among the names of the tensors in `tensors`.
This allows users to emulate "broadcasting by names", which is one of
the things named tensors tries to enable. Here is an example:
```
imgs: Tensor[N, C, H, W]
scale: Tensor[N]
// Doesn't work because we do broadcasting by alignment by default
imgs * scale
// Does work
imgs, scale = torch.align_tensors(imgs, scale)
imas * scale
```
Future:
- Consider allowing broadcasting by names by default.
Test Plan:
- The diff looks pretty large but more than half of it is testing.
- new tests [namedtensor ci]
Differential Revision: D16657927
Pulled By: zou3519
fbshipit-source-id: e2f958bf5146c8ee3b694aba57d21b08e928a4e6
Summary:
Changelog:
- Rename `gels` to `lstsq`
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lstsq` under the name `gels` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23460
Test Plan: - All tests should pass to confirm that the patch is correct
Differential Revision: D16547834
Pulled By: colesbury
fbshipit-source-id: b3bdb8f4c5d14c7716c3d9528e40324cc544e496
Summary:
We want to be able to call stft from a torchscript which requires that stft have a type annotation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21302
Differential Revision: D15607973
Pulled By: cpuhrsch
fbshipit-source-id: c4a5c09cdaafe7e81cf487a3ad216d1b03464a21
Summary:
Currently when the argument to isinf and isfinite is not tensor, a ValueError is raised. This, however, should be a TypeError, because the error is a type mismatch.
In the error message, "str(tensor)" is replaced by "repr(tensor)" because, when an error occurs, a printable representation of the object is likely more useful than the "informal" string version of the object.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20817
Differential Revision: D15495624
Pulled By: ezyang
fbshipit-source-id: 514198dcd723a7031818e50a87e187b22d51af73
Summary:
"then the output would also has k tensors" -> "then the output would also have k tensors"
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20425
Differential Revision: D15320152
Pulled By: zou3519
fbshipit-source-id: b04e2ccd29c6a3e33ad1040d0ea975a01a7bd9b5
Summary:
Changelog:
- Rename `potri` to `cholesky_inverse` to remain consistent with names of `cholesky` methods (`cholesky`, `cholesky_solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `cholesky_inverse` under the name `potri` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19498
Differential Revision: D15029901
Pulled By: ezyang
fbshipit-source-id: 2074286dc93d8744cdc9a45d54644fe57df3a57a
Summary:
Changelog:
- Rename `btrisolve` to `lu_solve` to remain consistent with names of solve methods (`cholesky_solve`, `triangular_solve`, `solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lu_solve` under the name `btrisolve` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18726
Differential Revision: D14726237
Pulled By: zou3519
fbshipit-source-id: bf25f6c79062183a4153015e0ec7ebab2c8b986b
Summary:
Changelog:
- Renames `btriunpack` to `lu_unpack` to remain consistent with the `lu` function interface.
- Rename all relevant tests, fix callsites
- Create a tentative alias for `lu_unpack` under the name `btriunpack` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18529
Differential Revision: D14683161
Pulled By: soumith
fbshipit-source-id: 994287eaa15c50fd74c2f1c7646edfc61e8099b1
Summary:
Changelog:
- Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`).
- Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple.
- Rename all tests, fix callsites
- Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage.
- Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435
Differential Revision: D14680352
Pulled By: soumith
fbshipit-source-id: af58dfc11fa53d9e8e0318c720beaf5502978cd8
Summary:
Fixes: https://github.com/pytorch/pytorch/issues/12598
This PR was originally authorized by ptrblck at https://github.com/pytorch/pytorch/pull/15495, but since there was no update for months after the request change, I clone that branch and resolve the code reviews here. Hope everything is good now. Especially, the implementation of count is changed from ptrblck's original algorithm to the one ngimel suggest, i.e. using `unique_by_key` and `adjacent_difference`.
The currently implementation of `_unique_dim` is VERY slow for computing inverse index and counts, see https://github.com/pytorch/pytorch/issues/18405. I will refactor `_unique_dim` in a later PR. For this PR, please allow me to keep the implementation as is.
cc: ptrblck ezyang ngimel colesbury
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18391
Reviewed By: soumith
Differential Revision: D14605905
Pulled By: VitalyFedyunin
fbshipit-source-id: 555f5a12a8e28c38b10dfccf1b6bb16c030bfdce
Summary:
Changelog:
- Renames `trtrs` to `triangular_solve` to remain consistent with `cholesky_solve` and `solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `triangular_solve` under the name `trtrs`, and add a deprecation warning to not promote usage.
- Move `isnan` to _torch_docs.py
- Remove unnecessary imports
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18213
Differential Revision: D14566902
Pulled By: ezyang
fbshipit-source-id: 544f57c29477df391bacd5de700bed1add456d3f
Summary:
Why do we need this workaround? `PythonArgParser` handles these two cases well.
The discussion started at https://github.com/pytorch/pytorch/pull/6201#issuecomment-378724406. The conclusion at that time by goldsborough was:
> Because we wanted to allow `dim=None` in Python and route to a different function. Essentially the problem was wanting to wrap the C++ function in Python. AFAIK there is no way of translating `dim=None` behavior into C++? So Richard and I came up with this strategy
Maybe at that time `PythonArgParser` was not powerful enough to handle the routing of two function with same name but different C++ signature.
Will keep an eye on the CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17103
Differential Revision: D14523503
Pulled By: VitalyFedyunin
fbshipit-source-id: cae3e2678062da2eccd93b51d4050578c7a9ab80
Summary:
Changelog:
- Renames `gesv` to `solve` to remain consistent with `cholesky_solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `solve` under the name `gesv`, and add a deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18060
Differential Revision: D14503117
Pulled By: zou3519
fbshipit-source-id: 99c16d94e5970a19d7584b5915f051c030d49ff5
Summary:
Eventually we should remove these when we're certain that all our ops
handle memory overlaps correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17576
Differential Revision: D14349990
Pulled By: zou3519
fbshipit-source-id: c3a09f6113b9b1bf93e7f13c0b426c45b2cdf21f
Summary:
This PR is a simple fix for the mistake in the "tensor" and "torch.Tensor"doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16842
Differential Revision: D14020300
Pulled By: ezyang
fbshipit-source-id: 3ab04f1223d6e60f8da578d04d759e385d23acbb
Summary:
We have:
- This is an initial stab at creating a type stub `torch/__init__.pyi` .
- This is only tested on Python 3, since that's the only Python version mypy
works on.
- So far, we only aim at doing this for torch functions and torch.Tensor.
- Quite a few methods and functions have to be typed manually. These are
done in `torch/__init__.pyi.in`
For me, PyCharm (the non-paid one) didn't seem to indicate errors in the .pyi when opening and seemed to be able to get the type hint for the few functions I tried, but I don't use PyCharm for my usual PyTorch activities, so I didn't extensively try this out.
An example of a generated PYI is at [this gist](https://gist.github.com/ezyang/bf9b6a5fa8827c52152858169bcb61b1).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12500
Differential Revision: D13695553
Pulled By: ezyang
fbshipit-source-id: 4566c71913ede4e4c23ebc4a72c17151f94e8e21
Summary:
The documentation stated that operands to einsum should be a list of Tensors, not individual arguments. The function, however, now accepts individual arguments for each Tensor operand *and* a single argument consisting of a list of Tensors. The documentation was updated to reflect this change.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16323
Differential Revision: D13832647
Pulled By: soumith
fbshipit-source-id: c01c2b350f47674d3170337f493b0ee2ea381b3f
Summary:
This PR does three things:
~~Allow `int64_t?` in function schema, which provide an elegant way of implementing null-able int arguments, as discussed in https://github.com/pytorch/pytorch/pull/15208#pullrequestreview-185230081~~
~~Originally implemented in https://github.com/pytorch/pytorch/pull/15235~~
~~Example:~~
```yaml
- func: myop(Tensor self, int64_t? dim=None) -> Tensor
variants: function
```
~~cc: zou3519~~
Edit: implemented in https://github.com/pytorch/pytorch/pull/15234
Previously tried in https://github.com/pytorch/pytorch/pull/12064. There was a problem that C++ does not have kwarg support, which makes it confusing to know whether `unique(t, 1)` actually means `unique(t, dim=1)` or `unique(t, sorted=1)`.
Now I think I have a better idea on how to implement this: there are two ATen operators: `unique` and `unique_dim`. `unique` has the same signature as in python, and exported to both python and C++. `unique_dim` has signature `unique_dim(tensor, dim, sorted=False, return_inverse=False)`, and only exported to C++, which could be used more naturally for a C++ user.
Differential Revision: D13540278
Pulled By: wanchaol
fbshipit-source-id: 3768c76a90b0881f565a1f890459ebccbdfe6ecd
Summary:
Adding supports for torch.nomr:
i. multi dimensions for dim
ii. dtype that specifies math/output tensor type
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15414
Differential Revision: D13702022
Pulled By: ezyang
fbshipit-source-id: da2676f2b6aff988889b1539d0de8ecd4946823a
Summary:
Wanted to use `Tensor.isnan` in C++, figured it'd be nice to have, so I made it into a tiny native function.
gchanan ezyang apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15722
Differential Revision: D13591315
Pulled By: goldsborough
fbshipit-source-id: a78bd22101fde87a0257f759b9bfcf3b4208f5fa
Summary:
Changelog:
- Optimize btriunpack by using `torch.where` instead of indexing, inplace operations instead of out place operations and avoiding costly permutations by computing the final permutation over a list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15286
Differential Revision: D13562038
Pulled By: soumith
fbshipit-source-id: e2c94cfab5322bf1d24bf56d7b056619f553acc6
Summary:
Currently torch.isinf on integral tensor will raise RuntimeError: value cannot be converted to type int16_t without overflow: inf.
This pr will suppress the error and return false(0) for all integral tensors. The behavior will also be consistent with np.isinf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15489
Reviewed By: zou3519
Differential Revision: D13540786
Pulled By: flashhack
fbshipit-source-id: e730dea849da6a59f3752d347bcfbadfd12c6483
Summary:
Changelog:
- Renames `potrs` to `cholesky_solve` to remain consistent with Tensorflow and Scipy (not really, they call their function chol_solve)
- Default argument for upper in cholesky_solve is False. This will allow a seamless interface between `cholesky` and `cholesky_solve`, since the `upper` argument in both function are the same.
- Rename all tests
- Create a tentative alias for `cholesky_solve` under the name `potrs`, and add deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15334
Differential Revision: D13507724
Pulled By: soumith
fbshipit-source-id: b826996541e49d2e2bcd061b72a38c39450c76d0
Summary:
This PR performs a renaming of the function `potrf` responsible for the Cholesky
decomposition on positive definite matrices to `cholesky` as NumPy and TF do.
Billing of changes
- make potrf cname for cholesky in Declarations.cwrap
- modify the function names in ATen/core
- modify the function names in Python frontend
- issue warnings when potrf is called to notify users of the change
Reviewed By: soumith
Differential Revision: D10528361
Pulled By: zou3519
fbshipit-source-id: 19d9bcf8ffb38def698ae5acf30743884dda0d88
Summary:
`torch.isfinite()` used to crash on int inputs.
```
>>> import torch
>>> a = torch.tensor([1, 2])
>>> torch.isfinite(a)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/scratch/pytorch/torch/functional.py", line 262, in isfinite
return (tensor == tensor) & (tensor.abs() != inf)
RuntimeError: value cannot be converted to type int64_t without overflow: inf
```
But this is a easy special case and numpy also supports it.
```
>>> import numpy as np
>>> a = np.array([1, 2])
>>> a.dtype
dtype('int64')
>>> np.isfinite(a)
array([ True, True], dtype=bool)
```
So added a hacky line to handle non-floating-point input. Since pytorch raises exception when overflow, we can safely assume all valid int tensors are infinite numbers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12750
Differential Revision: D10428204
Pulled By: ailzhang
fbshipit-source-id: f39b2d0975762c91cdea23c766ff1e21d85d57a5
Summary:
- Fix broken sparse_coo_examples, update output
- Tensor(...) to tensor(...)
- Fix arguments to math.log to be floats
While the last might be debateable, mypy currently complains when passing an int to math.log. As it is not essential for our examples, let's be clean w.r.t. other people's expectations.
These popped up while checking examples in the context of #12500 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12707
Differential Revision: D10415256
Pulled By: SsnL
fbshipit-source-id: c907b576b02cb0f89d8f261173dbf4b3175b4b8d
Summary:
- This was one of the few functions left out from the list of functions in
NumPy's `linalg` module
- `multi_mm` is particularly useful for DL research, for quick analysis of
deep linear networks
- Added tests and doc string
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12380
Differential Revision: D10357136
Pulled By: SsnL
fbshipit-source-id: 52b44fa18d6409bdeb76cbbb164fe4e88224458e
