This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This is a new version of #15648 based on the latest master branch.
Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR.
In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.)
Fixes https://github.com/pytorch/pytorch/issues/71105
@ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797
Approved by: https://github.com/ezyang
Summary:
Sobol was modfied to not drop the first point. This update reflects this behavior in the docstring.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62548
Reviewed By: qingfeng10
Differential Revision: D30035627
Pulled By: Balandat
fbshipit-source-id: 64c659ea30c0c929778da3b58041875834e25e9a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51578https://github.com/pytorch/pytorch/pull/49710 introduced an edge case in which
drawing a single sample resulted in ignoring the `dtype` arg to `draw`. This
fixes this and adds a unit test to cover this behavior.
Test Plan: Unit tests
Reviewed By: danielrjiang
Differential Revision: D26204393
fbshipit-source-id: 441a44dc035002e7bbe6b662bf6d1af0e2cd88f4
Summary:
Performs the update that was suggested in https://github.com/pytorch/pytorch/issues/41489
Adjust the functionality to largely match that pf the scipy companion PR https://github.com/scipy/scipy/pull/10844/, including
- a new `draw_base2` method
- include zero as the first point in the (unscrambled) Sobol sequence
The scipy PR is also quite opinionated if the `draw` method doesn't get called with a base 2 number (for which the resulting sequence has nice properties, see the scipy PR for a comprehensive discussion of this).
Note that this update is a **breaking change** in the sense that sequences generated with the same parameters after as before will not be identical! They will have the same (better, arguably) distributional properties, but calling the engine with the same seed will result in different numbers in the sequence.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49710
Test Plan:
```
from torch.quasirandom import SobolEngine
sobol = SobolEngine(3)
sobol.draw(4)
sobol = SobolEngine(4, scramble=True)
sobol.draw(5)
sobol = SobolEngine(4, scramble=True)
sobol.draw_base2(2)
```
Reviewed By: malfet
Differential Revision: D25657233
Pulled By: Balandat
fbshipit-source-id: 9df50a14631092b176cc692b6024aa62a639ef61
Summary:
I ran `make linkcheck` using `sphinx.builders.linkcheck` on the documentation and noticed a few links weren't using HTTPS so I quickly updated them all.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40878
Differential Revision: D22404647
Pulled By: ngimel
fbshipit-source-id: 9c9756db59197304023fddc28f252314f6cf4af3
Summary:
`SobolEngine` is a quasi-random sampler used to sample points evenly between [0,1]. Here we use direction numbers to generate these samples. The maximum supported dimension for the sampler is 1111.
Documentation has been added, tests have been added based on Balandat 's references. The implementation is an optimized / tensor-ized implementation of Balandat 's implementation in Cython as provided in #9332.
This closes#9332 .
cc: soumith Balandat
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10505
Reviewed By: zou3519
Differential Revision: D9330179
Pulled By: ezyang
fbshipit-source-id: 01d5588e765b33b06febe99348f14d1e7fe8e55d
