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 PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
Not sure, how it worked before, but if arguments must be annotated is optional if they are defaulted to None
Towards enabling mypy-1.4.1 in lintrunner
<!--
copilot:poem
-->
### <samp>🤖 Generated by Copilot at 5e1b9f4</samp>
> _We annotate the arguments of doom_
> _To show the `None` values of gloom_
> _We improve the type checking and readability_
> _With `Optional` annotations of metal-ity_
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105022
Approved by: https://github.com/izaitsevfb, https://github.com/huydhn, https://github.com/Skylion007
`Dirichlet.log_prob()` incorrectly returns NaN in the case where $x_i=0$ and $\alpha_i=1$. The Dirichlet PDF is given by:
$$\frac{1}{B(\alpha)} \prod_{i=1}^{K} x_i^{\alpha_i - 1}$$
So this corresponds to the case where one of the terms has the form $0^0=1$. The logarithm of such a term should be 0, but you get NaN if you try to calculate it as `0 * log(0)`.
This PR implements the same algorithm that `scipy.stats.dirichlet` uses to avoid this behavior, namely `xlogy(alpha - 1, x)` instead of `(alpha - 1) * log(x)`. It also adds a test case comparing the pytorch and scipy implementations for this specific case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103605
Approved by: https://github.com/albanD
This PR fixes the `torch.distributions.wishart.Wishart` example.
Running the current example
```python
m = Wishart(torch.eye(2), torch.Tensor([2]))
m.sample() # Wishart distributed with mean=`df * I` and
# variance(x_ij)=`df` for i != j and variance(x_ij)=`2 * df` for i == j
```
fails with
```
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Untitled-1 in
[321](untitled:Untitled-1?line=320) # %%
----> [322](untitled:Untitled-1?line=321) m = Wishart(torch.eye(2), torch.Tensor([2]))
[323](untitled:Untitled-1?line=322) m.sample() # Wishart distributed with mean=`df * I` and
[324](untitled:Untitled-1?line=323) # variance(x_ij)=`df` for i != j and variance(x_ij)=`2 * df` for i == j
Untitled-1 in __init__(self, df, covariance_matrix, precision_matrix, scale_tril, validate_args)
[83](untitled:Untitled-1?line=82)
[84](untitled:Untitled-1?line=83) if param.dim() < 2:
---> [85](untitled:Untitled-1?line=84) raise ValueError("scale_tril must be at least two-dimensional, with optional leading batch dimensions")
[86](untitled:Untitled-1?line=85)
[87](untitled:Untitled-1?line=86) if isinstance(df, Number):
ValueError: scale_tril must be at least two-dimensional, with optional leading batch dimensions
```
Is seems that the parameters of `Wishart.__init__()` were re-ordered, but the documentation was not updated.
This PR fixes it. Here is the updated behaviour:
```python
m = Wishart(torch.Tensor([2]), covariance_matrix=torch.eye(2))
m.sample()
```
```
Untitled-1:255: UserWarning: Singular sample detected.
tensor([[[6.6366, 0.7796],
[0.7796, 0.2136]]])
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95816
Approved by: https://github.com/ngimel, https://github.com/kit1980
Enables PyLint error codes implemented in ruff. These are un-opinionated static analysis checks on Python code that finds common bugs. After running all the PLE error codes that are implemented in ruff, I fixed the bugs, added a few ignores for malformed Python code that is part of our JIT test script, and finally added a few ignores for a false positive on PLE0605 and submitted an issue upstream to fix in ruff https://github.com/charliermarsh/ruff/issues/4345 .
Common bugs found here include analysis for malformed logging format calls, bad string format calls, invalid escape sequences, and more.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101079
Approved by: https://github.com/malfet
This is a suggestion for a minor modification.
The line `log_normalization[self.total_count + value == 0.] = 0.` prevents Jit compilation when the condition occurs, with the error message
`RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.`
I propose an alternative that does not involve in-place operations. It uses the function `nan_to_num()` to replace infinite values by 0 where `self.total_count + value == 0.` while leaving `nan` and `-inf` as they are. Readability is suboptimal because the code does not replace nan with numbers, but I could not find a function that only replaces infinite values.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96748
Approved by: https://github.com/fritzo, https://github.com/soulitzer
Fix `Gumbel.cdf` function.
**Description**
When transformed parameters is outside of the support of underlying Uniform distribution. This makes behavior of `Gumbel.cdf` consistent with other `TransformedDistribution` that pass value of validate_args to the base distribution.
**Issue**
running `Gumbel(0.0,1.0,validate_args=False).cdf(20.0)` would cause `ValueError` exception from `_validate_sample`
**Testing**
Test was added to the `test_distributions.py` to check if `Gumbel(0.0,1.0,validate_args=False).cdf(20.0)` successfully returns `1.0`
This is a second attempt to push changes , after https://github.com/pytorch/pytorch/pull/82488
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91698
Approved by: https://github.com/fritzo, https://github.com/zou3519
`log1p` offers better precision near zero since `(1 + x) - 1` truncates any
values less than the float epsilon to zero. For `soft_margin_loss` this also
requires one fewer kernel invocation which for numel=1e7 gives me a 1.2x speedup
on CUDA and a 1.1x speedup on CPU.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/92114
Approved by: https://github.com/ngimel, https://github.com/lezcano
The `PositiveDefiniteTransform` is required to transform from an unconstrained space to positive definite matrices, e.g. to support testing the Wishart mode in #76690. It is a simple extension of the `LowerCholeskyTransform`.
I've also added a small test that ensures the generated data belong to the domain of the associated transform. Previously, the data generated for the inverse transform of the `LowerCholeskyTransform` wasn't part of the domain, and the test only passed because the comparison uses `equal_nan=True`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76777
Approved by: https://github.com/lezcano, https://github.com/fritzo, https://github.com/soumith
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
### Description
Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function.
### Testing
There shouldn't be any testing required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487
Approved by: https://github.com/albanD
`Transform` is not currently pickleable if the inverse transform cache `_inv` is not `None` because `_inv` is a `weakref` which cannot be serialized by `pickle`.
The following succeeds.
```python
>>> import torch as th
>>> import pickle
>>> dist = th.distributions.TransformedDistribution(
... th.distributions.Normal(0, 1),
... [th.distributions.AffineTransform(2, 3)]
... )
>>> th.save(dist, "some-file.pt")
```
But the transformed distribution can no longer be pickled after evaluating `log_prob` (which implicitly creates `_inv`).
```python
>>> dist.log_prob(th.linspace(0, 1, 10))
>>> th.save(dist, "some-file.pt")
TypeError: cannot pickle 'weakref' object
```
This PR fixes the issue by setting `_inv` to `None` in `__getstate__`. cc @fritzo, @neerajprad
Pull Request resolved: https://github.com/pytorch/pytorch/pull/81707
Approved by: https://github.com/fritzo
