Bumps [setuptools](https://github.com/pypa/setuptools) from 70.0.0 to 78.1.1.
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a href="https://github.com/pypa/setuptools/blob/main/NEWS.rst">setuptools's changelog</a>.</em></p>
<blockquote>
<h1>v78.1.1</h1>
<h2>Bugfixes</h2>
<ul>
<li>More fully sanitized the filename in PackageIndex._download. (<a href="https://redirect.github.com/pypa/setuptools/issues/4946">#4946</a>)</li>
</ul>
<h1>v78.1.0</h1>
<h2>Features</h2>
<ul>
<li>Restore access to _get_vc_env with a warning. (<a href="https://redirect.github.com/pypa/setuptools/issues/4874">#4874</a>)</li>
</ul>
<h1>v78.0.2</h1>
<h2>Bugfixes</h2>
<ul>
<li>Postponed removals of deprecated dash-separated and uppercase fields in <code>setup.cfg</code>.
All packages with deprecated configurations are advised to move before 2026. (<a href="https://redirect.github.com/pypa/setuptools/issues/4911">#4911</a>)</li>
</ul>
<h1>v78.0.1</h1>
<h2>Misc</h2>
<ul>
<li><a href="https://redirect.github.com/pypa/setuptools/issues/4909">#4909</a></li>
</ul>
<h1>v78.0.0</h1>
<h2>Bugfixes</h2>
<ul>
<li>Reverted distutils changes that broke the monkey patching of command classes. (<a href="https://redirect.github.com/pypa/setuptools/issues/4902">#4902</a>)</li>
</ul>
<h2>Deprecations and Removals</h2>
<ul>
<li>Setuptools no longer accepts options containing uppercase or dash characters in <code>setup.cfg</code>.</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="8e4868a036"><code>8e4868a</code></a> Bump version: 78.1.0 → 78.1.1</li>
<li><a href="100e9a61ad"><code>100e9a6</code></a> Merge pull request <a href="https://redirect.github.com/pypa/setuptools/issues/4951">#4951</a></li>
<li><a href="8faf1d7e0c"><code>8faf1d7</code></a> Add news fragment.</li>
<li><a href="2ca4a9fe47"><code>2ca4a9f</code></a> Rely on re.sub to perform the decision in one expression.</li>
<li><a href="e409e80029"><code>e409e80</code></a> Extract _sanitize method for sanitizing the filename.</li>
<li><a href="250a6d1797"><code>250a6d1</code></a> Add a check to ensure the name resolves relative to the tmpdir.</li>
<li><a href="d8390feaa9"><code>d8390fe</code></a> Extract _resolve_download_filename with test.</li>
<li><a href="4e1e89392d"><code>4e1e893</code></a> Merge <a href="https://github.com/jaraco/skeleton">https://github.com/jaraco/skeleton</a></li>
<li><a href="3a3144f0d2"><code>3a3144f</code></a> Fix typo: <code>pyproject.license</code> -> <code>project.license</code> (<a href="https://redirect.github.com/pypa/setuptools/issues/4931">#4931</a>)</li>
<li><a href="d751068fd2"><code>d751068</code></a> Fix typo: pyproject.license -> project.license</li>
<li>Additional commits viewable in <a href="https://github.com/pypa/setuptools/compare/v70.0.0...v78.1.1">compare view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting `@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
- `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it
- `@dependabot merge` will merge this PR after your CI passes on it
- `@dependabot squash and merge` will squash and merge this PR after your CI passes on it
- `@dependabot cancel merge` will cancel a previously requested merge and block automerging
- `@dependabot reopen` will reopen this PR if it is closed
- `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
- `@dependabot show <dependency name> ignore conditions` will show all of the ignore conditions of the specified dependency
- `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
- `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
- `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)
You can disable automated security fix PRs for this repo from the [Security Alerts page](https://github.com/pytorch/pytorch/network/alerts).
</details>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154075
Approved by: https://github.com/Skylion007
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
This PR adds enforcement of testing header only APIs.
The benefit of torch/header_only_apis.txt is twofold:
1) this gives us a clear view of what we expect to be header only
2) this allows us to enforce testing
The enforcement added in this PR is very basic--we literally string match that a symbol in `torch/header_only_apis.txt` is in a cpp test. This is meant to be a first step in verifying our APIs are properly tested and can get fancier over time. For now, I've added myself as a codeowner to learn what to look out for in terms of proper tests. Over time, I anticipate we can automate more steps, but right now let's just get something out the door.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153635
Approved by: https://github.com/albanD
ghstack dependencies: #153965
Change logging.error to logging.exception to log additional information when relevant. A few places have slipped in logging.errors in try except since I last did a clean up here and the rule is stabilized so I am enabling it codebase wide. I have NOQA'd much of our custom exception stack trace handling for RPC calls and distributed and tried to a fix a few errors based on whether we immediately reraised it or if we didn't print any exception handling where it could be useful.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153473
Approved by: https://github.com/albanD, https://github.com/cyyever
https://github.com/pytorch/pytorch/pull/129001#discussion_r1645126801 is the motivation for the whole stack of PRs. In `torch/__init__.py`, `torch._C.Type` shadows `from typing import Type`, and there is no type stub for `torch._C.Type` in `torch/_C/__init__.pyi`. So we need to use `from typing import Type as _Type`. After enabling [Generic TypeAlias (PEP 585)](https://peps.python.org/pep-0585) in the `.pyi` type stub files, we can use `type` instead of `typing.Type` or `from typing import Type as _Type`.
------
- [Generic TypeAlias (PEP 585)](https://peps.python.org/pep-0585): e.g. `typing.List[T] -> list[T]`, `typing.Dict[KT, VT] -> dict[KT, VT]`, `typing.Type[T] -> type[T]`.
- [Union Type (PEP 604)](https://peps.python.org/pep-0604): e.g. `Union[X, Y] -> X | Y`, `Optional[X] -> X | None`, `Optional[Union[X, Y]] -> X | Y | None`.
Note that in `.pyi` stub files, we do not need `from __future__ import annotations`. So this PR does not violate issue #117449:
- #117449
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150727
Approved by: https://github.com/aorenste
ghstack dependencies: #150726
### Changes
- Detect NVSHMEM install location via `sysconfig.get_path("purelib")`, which typically resolves to `<conda_env>/lib/python/site-packages`, and NVSHMEM include and lib live under `nvidia/nvshmem`
- Added link dir via `target_link_directories`
- Removed direct dependency on mlx5
- Added preload rule (following other other NVIDIA libs)
### Plan of Record
1. End user experience: link against NVSHMEM dynamically (NVSHMEM lib size is 100M, similar to NCCL, thus we'd like users to `pip install nvshmem` than torch carrying the bits)
2. Developer experience: at compile time, prefers wheel dependency than using Git submodule
General rule: submodule for small lib that torch can statically link with
If user pip install a lib, our CI build process should do the same, rather than building from Git submodule (just for its header, for example)
3. Keep `USE_NVSHMEM` to gate non-Linux platforms, like Windows, Mac
4. At configuration time, we should be able to detect whether nvshmem is available, if not, we don't build `NVSHMEMSymmetricMemory` at all.
For now, we have symbol dependency on two particular libs from NVSHMEM:
- libnvshmem_host.so: contains host side APIs;
- libnvshmem_device.a: contains device-side global variables AND device function impls.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153010
Approved by: https://github.com/ngimel, https://github.com/fduwjj, https://github.com/Skylion007
Add additional conditions to `build_pytorch_libs.py` to avoid fetching NCCL when `USE_CUDA` or `USE_NCCL` are disabled. While at it, adjust the existing condition for `USE_SYSTEM_NCCL` to use the utility function.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152533
Approved by: https://github.com/albanD
Summary: Further testing the script, we found that we shouldn't always assume rank 0 is the first rank, so we need to check all entries and see if it P2P op for this coalesced group.
Test Plan: Directly test with corner case.
Differential Revision: D73266257
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151683
Approved by: https://github.com/fegin
We revisited how coalesced collective is working in https://github.com/pytorch/pytorch/pull/151243 and we now want to enable the script to work for slow path. The change is indeed bc-breaking but this is needed to make it work and the API is an internal use API. It is not user facing. For slow path the individual has input-sizes and output sizes recorded but no state. The final one has the state ready. We check the correctness of each individual collective one by one but we don't check the state match for these collectives, we can only check the state match for the last one which is the work item with coalesced label.
Added more unit test for slow path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151247
Approved by: https://github.com/d4l3k, https://github.com/XilunWu
This PR is to enable FR for all coalesce ops for fast path. (batch p2p is enabled in the current script, so we will mainly focus on non-P2P ops). To explain what is fast path, let's revisit how coalesced collective is working today:
For non-P2P coalesced ops, there are are several ways to call it (due to legendary reasons):
- Way one: Directly call python api like all_reduce_coalesced in python, this will be deprecated soon.
- Way two: Directly call api inside PGNCCL like allreduce_coalesced. The way case 1 will eventually call into this. This is not deprecated and will not be deprecated, IIUC.
- Way three: Using _coalescing_manager in python, like:
```
with _coalescing_manager():
for i in range(num_colls):
dist.all_reduce(tensors[i])
```
This way has two path:
- Fast path: when users call all-reduce, all-gather-into-tensor or reduce-scatter, we will only launch one big collective by calling the api from case 1.
- Slow path: we call startCoalescing() in the beginning and then a bunch of collectives (each one will generate a FR entry) and then endCoalescing(). Inside startCoalescing(), groupStart() is called and inside endCoalescing(), groupEnd() is then called. So although this is going to be one collective, we call into PGNCCL for each collective coalesced in the slow path case.
- For uneven all-gather (allgather_v) and reduce-scatter, it follows the pattern mention in slow path. It directly call cpp api inside PGNCCL.
This PR addressed the fast path because this is just an easy case, we store the collectives info on the python side, and we will only call into PGNCCL once so there will only be one work and one FR entry. We can just treat them as regular coalesced collective.
We add some e2e unit test for build_db function so that the change to FR is more thoroughly tested.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151243
Approved by: https://github.com/d4l3k, https://github.com/wz337
## Improvements to `docstring_linter`
* Add a "grandfather list" of existing undocumented classes and functions (`--grandfather`, `--grandfather-tolerance`, `--no-grandfather`, `--write-grandfather`)
* In classes, now just one of the class itself or its `__init__()` method needs to be documented (`--lint-init` turns the old behavior back on)
* Now classes and functions defined local to other functions do not need to be documented (`--lint-local` turns the old behavior back on)
* New `--report` flag produces a compact report of long, undocumented classes or function definitions: see attached example run over all pytorch: [pytorch-docs.json](https://github.com/user-attachments/files/18455981/pytorch-docs.json)
## Help text
```
$ python tools/linter/adapters/docstring_linter.py --help
usage: docstring_linter.py [-h] [-l] [-v] [--grandfather GRANDFATHER] [--grandfather-tolerance GRANDFATHER_TOLERANCE] [--lint-init]
[--lint-local] [--lint-protected] [--max-class MAX_CLASS] [--max-def MAX_DEF]
[--min-docstring MIN_DOCSTRING] [--no-grandfather] [--report] [--write-grandfather]
[files ...]
`docstring_linter` reports on long functions, methods or classes without docstrings
positional arguments:
files A list of files or directories to lint
optional arguments:
-h, --help show this help message and exit
-l, --lintrunner Run for lintrunner and print LintMessages which aren't edits
-v, --verbose Print more debug info
--grandfather GRANDFATHER, -g GRANDFATHER
Set the grandfather list
--grandfather-tolerance GRANDFATHER_TOLERANCE, -t GRANDFATHER_TOLERANCE
Tolerance for grandfather sizes, in percent
--lint-init, -i Lint __init__ and class separately
--lint-local, -o Lint definitions inside other functions
--lint-protected, -p Lint functions, methods and classes that start with _
--max-class MAX_CLASS, -c MAX_CLASS
Maximum number of lines for an undocumented class
--max-def MAX_DEF, -d MAX_DEF
Maximum number of lines for an undocumented function
--min-docstring MIN_DOCSTRING, -s MIN_DOCSTRING
Minimum number of characters for a docstring
--no-grandfather, -n Disable the grandfather list
--report, -r Print a report on all classes and defs
--write-grandfather, -w
Rewrite the grandfather list
```
---
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145834
Approved by: https://github.com/amjames, https://github.com/eellison
Enabled bf16 grouped gemm with an API similar to _scaled_group_gemm, except without scale and fast accum arguments. All transpose variants are enabled, unlike scaled gemm. Ideally we'd factor out a lot more code from scaled gemm, currently there's a lot of repetition between scaled and non-scaled versions. I factored out only a helper kernel that prepares arguments.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150374
Approved by: https://github.com/drisspg
Before this change:
```console
$ make setup-env-cuda PYTHON="${HOMEBREW_PREFIX}/bin/python3.12"
$ source venv/bin/activate
$ python3 -c 'import torch'
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/home/PanXuehai/Projects/pytorch/torch/__init__.py", line 379, in <module>
from torch._C import * # noqa: F403
^^^^^^^^^^^^^^^^^^^^^^
ImportError: libcudnn.so.9: cannot open shared object file: No such file or directory
```
This PR adds `site-packages/nvidia/**/lib` to `LD_LIBRARY_PATH` in `venv/bin/activate` script to let NVIDIA PyPI packages can be loaded correctly.
See also:
- #141837
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143262
Approved by: https://github.com/malfet
