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Author SHA1 Message Date
0fabc3ba44 CUDA aarch64 12.6 and 12.8 builds fix triton constraints (#165022)
CUDA aarch64 12.6 and 12.8 builds fix triton constraints (#165013)

Since we have introduced CUDA aarch64 builds for all cuda versions we need to remove this constraint.
This was missed by https://github.com/pytorch/pytorch/pull/162364

Proper constraint on triton should be:
```
Requires-Dist: triton==3.5.0; platform_system == "Linux"
```

not:
```
Requires-Dist: triton==3.5.0; platform_system == "Linux" and platform_machine == "x86_64"
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165013
Approved by: https://github.com/Camyll, https://github.com/nWEIdia, https://github.com/tinglvv

(cherry picked from commit 81dbeb06f4b3eb6c56625ec25d377eb7c7c6c573)

Co-authored-by: atalman <atalman@fb.com>
2025-10-08 21:09:57 -04:00
26e023a973 [MPS] Update OS version in error message (#164949)
[MPS] Update OS version in error message (#164946)

Followup after https://github.com/pytorch/pytorch/pull/159912
Fixes https://github.com/pytorch/pytorch/issues/164943

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164946
Approved by: https://github.com/Camyll

(cherry picked from commit 01f3a43462da594b65a6c9e8b46c132cd360cea9)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-10-08 11:11:48 -07:00
6f12be2770 CUDA 13.0 builds fix on Amazon Linux 2023 (#164893)
CUDA 13.0 builds fix on Amazon Linux 2023 (#164870)

During 2.9 rc testing I am seeing an issue on Amazon Linux 2023 with CUDA 13.0 builds

This is related to:
 https://github.com/pytorch/pytorch/issues/152756

Workflow: https://github.com/pytorch/test-infra/actions/runs/18324074610/job/52184079262

Error:
```
WARNING: There was an error checking the latest version of pip.
+ python3.11 .ci/pytorch/smoke_test/smoke_test.py --package torchonly
Traceback (most recent call last):
  File "/usr/local/lib64/python3.11/site-packages/torch/__init__.py", line 333, in _load_global_deps
    ctypes.CDLL(global_deps_lib_path, mode=ctypes.RTLD_GLOBAL)
  File "/usr/lib64/python3.11/ctypes/__init__.py", line 376, in __init__
    self._handle = _dlopen(self._name, mode)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^
OSError: libcudart.so.13: cannot open shared object file: No such file or directory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/pytorch/pytorch/.ci/pytorch/smoke_test/smoke_test.py", line 12, in <module>
    import torch
  File "/usr/local/lib64/python3.11/site-packages/torch/__init__.py", line 425, in <module>
    _load_global_deps()
  File "/usr/local/lib64/python3.11/site-packages/torch/__init__.py", line 383, in _load_global_deps
    _preload_cuda_deps(lib_folder, lib_name)
  File "/usr/local/lib64/python3.11/site-packages/torch/__init__.py", line 317, in _preload_cuda_deps
    raise ValueError(f"{lib_name} not found in the system path {sys.path}")
Traceback (most recent call last):
ValueError: libnvToolsExt.so.*[0-9] not found in the system path ['/pytorch/pytorch/.ci/pytorch/smoke_test', '/usr/lib64/python311.zip', '/usr/lib64/python3.11', '/usr/lib64/python3.11/lib-dynload', '/usr/local/lib64/python3.11/site-packages', '/usr/local/lib/python3.11/site-packages', '/usr/lib64/python3.11/site-packages', '/usr/lib/python3.11/site-packages']
  File "/home/ec2-user/actions-runner/_work/test-infra/test-infra/test-infra/.github/scripts/run_with_env_secrets.py", line 102, in <module>
    main()
  File "/home/ec2-user/actions-runner/_work/test-infra/test-infra/test-infra/.github/scripts/run_with_env_secrets.py", line 98, in main
    run_cmd_or_die(f"docker exec -t {container_name} /exec")
  File "/home/ec2-user/actions-runner/_work/test-infra/test-infra/test-infra/.github/scripts/run_with_env_secrets.py", line 39, in run_cmd_or_die
    raise RuntimeError(f"Command {cmd} failed with exit code {exit_code}")
RuntimeError: Command docker exec -t 7d9c5bd403cac9a9ee824d63a1d6f6057ecce89a7daa94a81617dbf8eff0ff2e /exec failed with exit code 1
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164870
Approved by: https://github.com/Camyll


(cherry picked from commit 483f4e0db91166128ad8922d86dc7222338d4ecc)

Co-authored-by: atalman <atalman@fb.com>
Co-authored-by: Eli Uriegas <1700823+seemethere@users.noreply.github.com>
2025-10-07 19:33:08 -07:00
42f0c2c970 update the baseline data for the operator benchmark (#164789)
update the baseline data for the operator benchmark (#162693)

According to the results of the last four operator benchmark runs, we found that five models achieved more than a 30% improvement compared to the baseline. Therefore, we will update the operator benchmark baseline data.
We use the average results from the four runs as the new baseline for the five models.

And add a pull request trigger for the operator benchmark workflow

Benchmarking   Framework | Benchmarking   Module Name | Case Name | tag | run_backward | baseline   old | r1 | r2 | r3 | r4 | avg | speedup
-- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | --
PyTorch | add | add_M1_N1_K1_cpu | short | FALSE | 3.9497 | 2.57 | 2.54 | 2.38 | 2.31 | 2.45 | 1.61
PyTorch | functional.hardtanh | functional.hardtanh_dims(512	512)_contigFalse_inplaceFalse_dtypetorch.quint8 | short | FALSE | 67.118 | 50.02 | 49.80 | 46.78 | 48.94 | 48.88 | 1.37
PyTorch | relu6 | relu6_dims(512	512)_contigFalse_inplaceFalse_dtypetorch.quint8 | short | FALSE | 68.739 | 51.17 | 51.19 | 48.07 | 50.42 | 50.21 | 1.37
PyTorch | relu6 | relu6_dims(256	1024)_contigFalse_inplaceFalse_dtypetorch.quint8 | short | FALSE | 69.1875 | 51.97 | 52.77 | 50.00 | 51.24 | 51.50 | 1.34
PyTorch | functional.hardtanh | functional.hardtanh_dims(256	1024)_contigFalse_inplaceFalse_dtypetorch.quint8 | short | FALSE | 67.436 | 50.98 | 51.69 | 49.06 | 49.87 | 50.40 | 1.34

@chuanqi129 @huydhn @desertfire @jainapurva

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162693
Approved by: https://github.com/huydhn

(cherry picked from commit f7ea4975abb0aeb0224894f0b54b1f8fd1fa70e3)

Co-authored-by: LifengWang <lifeng.a.wang@intel.com>
2025-10-07 07:10:51 -07:00
b015422da1 fix cpp extension distributed warning spew (#164785)
fix cpp extension distributed warning spew (#162764)

With the new change we only log the warning if we're running non distributed code or if we're in rank 0. Unit testing that certain messages get printed on certain ranks only feels kinda jank so test plan is below instead

Test plan

```python
# torchrun --nproc_per_node=2 demo_fix.py

import os
import logging

logging.getLogger('torch.utils.cpp_extension').setLevel(logging.DEBUG)

import torch
if 'RANK' in os.environ:
    torch.distributed.init_process_group('nccl')

from torch.utils.cpp_extension import _get_cuda_arch_flags
_get_cuda_arch_flags()

print(f"Rank {os.environ.get('RANK', '0')} done")
```

Logs showing how how `TORCH_CUDA_ARCH_LIST`only shows up once if we explicitly set the the logging level to `logging.DEBUG`. It also improves the debug message to explain what the actual behavior will be

```
(source) [marksaroufim@devgpu005]~% torchrun --nproc_per_node=2 demo_fix.py

W0911 18:30:16.594000 1315439 /home/marksaroufim/pytorch/torch/distributed/run.py:814]
W0911 18:30:16.594000 1315439 /home/marksaroufim/pytorch/torch/distributed/run.py:814] *****************************************
W0911 18:30:16.594000 1315439 /home/marksaroufim/pytorch/torch/distributed/run.py:814] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0911 18:30:16.594000 1315439 /home/marksaroufim/pytorch/torch/distributed/run.py:814] *****************************************
[rank0]:V0911 18:30:18.921000 1316753 pytorch/torch/utils/cpp_extension.py:2444] TORCH_CUDA_ARCH_LIST is not set, using TORCH_CUDA_ARCH_LIST='10.0+PTX' for visible GPU architectures. Set os.environ['TORCH_CUDA_ARCH_LIST'] to override.
Rank 0 done
Rank 1 done
```

But if we just use the default and comment out `logging.getLogger('torch.utils.cpp_extension').setLevel(logging.DEBUG)`

Then we get

```
(source) [marksaroufim@devgpu005]~% torchrun --nproc_per_node=2 demo_fix.py
W0911 18:14:33.926000 690759 /home/marksaroufim/pytorch/torch/distributed/run.py:814]
W0911 18:14:33.926000 690759 /home/marksaroufim/pytorch/torch/distributed/run.py:814] *****************************************
W0911 18:14:33.926000 690759 /home/marksaroufim/pytorch/torch/distributed/run.py:814] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0911 18:14:33.926000 690759 /home/marksaroufim/pytorch/torch/distributed/run.py:814] *****************************************
Rank 0 done
Rank 1 done
(source) [marksaroufim@devgpu005]~%
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162764
Approved by: https://github.com/ezyang, https://github.com/zou3519

(cherry picked from commit f7e83219619a05934a344ca699c33ee69d5a3642)

Co-authored-by: Mark Saroufim <marksaroufim@meta.com>
2025-10-06 16:58:36 -07:00
d4c4307032 Fix docker build issue after 164575 (#164779)
Fix docker build issue after 164575 (#164774)

Looks like https://github.com/pytorch/pytorch/pull/164575 introduced an issue.
The command is wrong:
```
conda install -c "whl/nightly" -y python=3.11 conda=25.7.0
```
Should be just using default conda channel:
```
conda install  -y python=3.11 conda=25.7.0
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164774
Approved by: https://github.com/Camyll

(cherry picked from commit c1f40d33c89b361a1edad17aa25cfff1ab4014fd)

Co-authored-by: atalman <atalman@fb.com>
2025-10-06 16:56:06 -04:00
3b57315b1b [ROCm] Increase binary build timeout to 5 hours (300 minutes) (#164770)
[ROCm] Increase binary build timeout to 5 hours (300 minutes) (#163776)

Despite narrowing down the [FBGEMM_GENAI build to gfx942](https://github.com/pytorch/pytorch/pull/162648), the nightly builds still timed out because they [didn't get enough time to finish the post-PyTorch-build steps](https://github.com/pytorch/pytorch/actions/runs/17969771026/job/51109432897).

This PR increases timeout for ROCm builds for both [libtorch ](https://github.com/pytorch/pytorch/actions/runs/17969771026)and [manywheel](https://github.com/pytorch/pytorch/actions/runs/17969771041), because both of those are close to the 4hr mark currently.

This PR is a more ROCm-targeted version of https://github.com/pytorch/pytorch/pull/162880 (which is for release/2.9 branch).

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163776
Approved by: https://github.com/jeffdaily


(cherry picked from commit 0ec946a0522748332f42675a4d690ff32d773d42)

Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
2025-10-06 16:08:40 -04:00
c74f05797d Pin conda version for Docker builds (#164579)
Pin conda version for Docker builds (#164575)

Mitigates https://github.com/pytorch/pytorch/issues/164574
Remove unused CUDA_CHANNEL var - this was used before when we had  pytorch install via conda.

Please note: CUDA 13.0 failures are expected since the CI tries to build against prod and CUDA 13.0 is not available in prod yet.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164575
Approved by: https://github.com/malfet, https://github.com/Camyll

(cherry picked from commit e40fe634b1a7aa33e278b1404ee02dea12277080)

Co-authored-by: atalman <atalman@fb.com>
2025-10-03 11:44:46 -04:00
fd364580a9 [Cherry-Pick] Work Around exposing statically linked libstdc++ CXX11 ABI strong symbols (#163980) (#164508)
* Work Around exposing statically linked libstdc++ CXX11 ABI strong symbols (#163980)

Work Around for: https://github.com/pytorch/pytorch/issues/133437

Test plan:
1. Build whl in CI
2. Download
3. Run ``nm -D libtorch_cpu.so | grep "recursive_directory_iterator"``

Test with check_binary_symbols.py:

Success:
```
num_cxx11_symbols: 2326
num_pre_cxx11_symbols: 0
lib: /home/ec2-user/github/variant-repack/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so
num_statically_linked_symbols (T): 0
```

Fail when using "W" instead of "T" as type calling ``cxx11_statically_linked_symbols = grep_symbols(
        lib, STATICALLY_LINKED_CXX11_ABI, symbol_type="W"
    )`` :
```
num_cxx11_symbols: 2326
num_pre_cxx11_symbols: 0
lib: /home/ec2-user/github/variant-repack/.venv/lib/python3.10/site-packages/torch/lib/libtorch_cpu.so
num_statically_linked_symbols (T): 20
Traceback (most recent call last):
  File "/home/ec2-user/github/variant-repack/test/pytorch/.ci/pytorch/smoke_test/check_binary_symbolsc.py", line 130, in <module>
    main()
  File "/home/ec2-user/github/variant-repack/test/pytorch/.ci/pytorch/smoke_test/check_binary_symbolsc.py", line 126, in main
    check_lib_statically_linked_libstdc_cxx_abi_symbols(libtorch_cpu_path)
  File "/home/ec2-user/github/variant-repack/test/pytorch/.ci/pytorch/smoke_test/check_binary_symbolsc.py", line 95, in check_lib_statically_linked_libstdc_cxx_abi_symbols
    raise RuntimeError(
RuntimeError: Found statically linked libstdc++ symbols (recursive_directory_iterator), but there shouldn't be any, see: ['std::filesystem::__cxx11::recursive_directory_iterator::recursion_pending() const', 'std::filesystem::__cxx11::recursive_directory_iterator::depth() const', 'std::filesystem::__cxx11::recursive_directory_iterator::options() const', 'std::filesystem::__cxx11::recursive_directory_iterator::operator*() const', 'std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>::operator bool() const', 'std::filesystem::__cxx11::recursive_directory_iterator::disable_recursion_pending()', 'std::filesystem::__cxx11::recursive_directory_iterator::pop(std::error_code&)', 'std::filesystem::__cxx11::recursive_directory_iterator::pop()', 'std::filesystem::__cxx11::recursive_directory_iterator::increment(std::error_code&)', 'std::filesystem::__cxx11::recursive_directory_iterator::recursive_directory_iterator(std::filesystem::__cxx11::path const&, std::filesystem::directory_options, std::error_code*)', 'std::filesystem::__cxx11::recursive_directory_iterator::recursive_directory_iterator(std::filesystem::__cxx11::path const&, std::filesystem::directory_options, std::error_code*)', 'std::filesystem::__cxx11::recursive_directory_iterator::~recursive_directory_iterator()', 'std::filesystem::__cxx11::recursive_directory_iterator::~recursive_directory_iterator()', 'std::filesystem::__cxx11::recursive_directory_iterator::operator=(std::filesystem::__cxx11::recursive_directory_iterator&&)', 'std::filesystem::__cxx11::recursive_directory_iterator::operator=(std::filesystem::__cxx11::recursive_directory_iterator const&)', 'std::filesystem::__cxx11::recursive_directory_iterator::operator++()', 'std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>::__shared_ptr(std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>&&)', 'std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>::__shared_ptr()', 'std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>::__shared_ptr(std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>&&)', 'std::__shared_ptr<std::filesystem::__cxx11::recursive_directory_iterator::_Dir_stack, (__gnu_cxx::_Lock_policy)2>::__shared_ptr()']
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163980
Approved by: https://github.com/isuruf, https://github.com/malfet

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>

* fix

---------

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-10-02 17:49:44 -04:00
2f6387e9a1 [CherrryPick][2.9] Cherry pick request for Reapply "Make functionalization ViewMeta serializable with pickle #163769 (#163873)
Reapply "Make functionalization `ViewMeta` serializable with pickle. (#143712)"  (#163769)

NOTE: This is a re-export of https://github.com/pytorch/pytorch/pull/161994 ; the changes between these two PRs is exclusively to the buck/build files

(Summary from #161994 )
Attempted rebase of https://github.com/pytorch/pytorch/pull/143712.

This reverts commit 6c713ccb5e0df227dd5b630057cbccd373cbe7d6.

cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx chenyang78 kadeng chauhang amjames Lucaskabela

imported-using-ghimport

Test Plan: Imported from OSS

Differential Revision: D81524507

Pulled By: Lucaskabela

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163769
Approved by: https://github.com/dolpm


(cherry picked from commit 7d710403b003e44bf31d367673a05468e49df75d)

Co-authored-by: Brian Hirsh <hirsheybar@fb.com>
2025-10-02 16:07:51 -04:00
017d857f5f fix pickling for BitwiseFn (#163861)
* fix pickling for BitwiseFn (#163571)

Summary:
ran into AttributeError: Can't get local object 'make_opaque_bitwise_fn.<locals>.BitwiseFn'

looks like it was fixed for UnaryFn but not BitwiseFn in https://github.com/pytorch/pytorch/pull/138395

Fixes #147841

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163571
Approved by: https://github.com/jamesjwu

(cherry picked from commit cde5c9aebd7a2eda0c935de1ab7a40b6453c5813)

* Fix lintrunner with -a

---------

Co-authored-by: dolpm <34420038+dolpm@users.noreply.github.com>
Co-authored-by: Lucas Kabela <lucaskabela@meta.com>
2025-10-02 15:35:40 -04:00
d6e8411889 Make sure Windows CUDA 12.8 build follow same arches as Linux builds (#164477)
Make sure Windows CUDA 12.8 build follow same arches as Linux builds (#164470)

I believe ``set TORCH_CUDA_ARCH_LIST=7.0;7.5;8.0;8.6;9.0;10.0;12.0`` is the one thats actually used. Hence remove 6.1  to align the support with Linux support.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164470
Approved by: https://github.com/tinglvv, https://github.com/nWEIdia, https://github.com/Camyll

(cherry picked from commit 235b995ce18de632ab816940319fcd66b46039b8)

Co-authored-by: Andrey Talman <atalman@fb.com>
2025-10-02 14:33:06 -04:00
10b501fde9 [Flex] Fix silent correctness w/ backpropping grads (#164366)
[Flex] Fix silent correctness w/ backpropping grads (#163677)

Fixes #https://github.com/pytorch/pytorch/issues/162228

# Summary

Majority of our tests are only compiling flex-attention in isolation. This means that for fake tensor propagation the input primals and all captured buffers dont do any intermediate computation below autograd.  As a result result the by happen chance match the `require_grad`ness of the eager implementation and this check  will pass. However if score_mod is a the result of some other intermediate fake tensor prop then it is not guaranteed to have accurate req_gradness, which was happening here.

TLDR is that this was a boot and suspenders that was actually harmful and we should just let the joint graph handle creating the correct joint graph

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163677
Approved by: https://github.com/ydwu4

(cherry picked from commit e2ce79e4cce5327b71fcf366fad1133030563285)

Co-authored-by: drisspg <drisspguessous@gmail.com>
2025-10-01 14:43:28 -07:00
31c72b8a96 [a2av] Separate in/out splits into two tensors (#164028)
[a2av] Separate in/out splits into two tensors (#163837)

Old signature:
`all_to_all_vdev(Tensor input, Tensor(a!) out, Tensor(a!) in_out_splits, str group_name)`
New signature:
`all_to_all_vdev(Tensor input, Tensor(a!) out, Tensor in_splits, Tensor(a!) out_splits_offsets, str group_name)`

i.e. split `in_out_splits` into IN tensor and OUT tensor so that we can define the TORCH_LIBRARY signature better.
Also to be in line with the 2D version.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163837
Approved by: https://github.com/fduwjj
ghstack dependencies: #163886

(cherry picked from commit bbf8aa43efe755b9c310347b3780962fca85bf9c)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-10-01 14:43:19 -07:00
1cd83de315 [Flex attention] Fix flex attention head broadcast (#164368)
[Flex attention] Fix flex attention head broadcast (#163426)

Fixes part of #163314

In particular bug: **Bug 1: H=None Broadcasting Produces Incorrect Results**

This fixes a shape bug when slicing BlockMask on the Q-tile axis with an int (**mask[:, :, i]**). That form of indexing collapses the Q dimension, so kv_num_blocks/kv_indices lose their expected [B, H, Q_tiles, …] shape. Due to them losing shape, even though the mask_mod remains "interpretable", the kernel’s stride math then reads wrong offsets. Due to this we get silent numerical mismatches compared to regular SDPA, especially when single position decoding/H broadcasting.

The B=None, H=None works case is accidental: with singleton batch/head the kernel maps to index 0 via `sparse_idx_z = off_zq % 1` and `sparse_idx_hq = off_hq % 1` and with a single Q tile `q_start // SPARSE_Q_MULTIPLE = 0`. The missing Q-tiles stride is multiplied by 0, so the bad offset from the collapsed Q axis doesn’t move the pointer and it happens to read the first tile correctly. Once H > 1 or there are multiple Q tiles, those terms become nonzero and the kernel indexes with wrong strides which causes silent error

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163426
Approved by: https://github.com/drisspg

(cherry picked from commit 1a42656d6c43a9bb7eb90c511884ce451d29422f)

Co-authored-by: Isalia20 <irakli.salia854@gmail.com>
2025-10-01 13:48:10 -07:00
881c2ccae9 Update Gloo submodule (#164371)
Update Gloo submodule (#163112)

Which makes PyTorch buildable with gcc-15, tested by running the build inside `fedora:44` docker
```
docker run --rm -it fedora:44 bash -c "yum install -y g++ python3-devel git; git clone https://github.com/pytorch/pytorch; cd pytorch; git checkout 8f710acce8332979c9a7bf97e72666dfd35c43e6; python3 -mpip install -r requirements.txt; python3 setup.py bdist_wheel"
```

Fixes https://github.com/pytorch/pytorch/issues/156595
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163112
Approved by: https://github.com/huydhn

(cherry picked from commit 65845d72917fc27cd89a88b067e7c8f44bc0c987)

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2025-10-01 12:00:18 -07:00
764f65584a [MPS] Chunk fillBuffer into 4Gb slices (#164370)
[MPS] Chunk fillBuffer into 4Gb slices (#164108)

To avoid regression on MacOS 26, which one could observe by running the following script
```swift
import Metal

let bufferSize = 1<<32 + 4

guard let device = MTLCreateSystemDefaultDevice() else { fatalError("No Metal device found") }
guard let buffer = device.makeBuffer(length: bufferSize, options: .storageModeShared) else { fatalError("Failed to create buffer") }

guard let cmdQueue = device.makeCommandQueue() else { fatalError("Failed to create command queue") }
guard let cmdBuffer = cmdQueue.makeCommandBuffer() else { fatalError("Failed to create command buffer") }
guard let blitEncoder = cmdBuffer.makeBlitCommandEncoder() else { fatalError("Failed to create blit encoder") }

blitEncoder.fill(buffer: buffer, range: 0..<bufferSize, value: 0x42)
blitEncoder.endEncoding()

cmdBuffer.commit()
cmdBuffer.waitUntilCompleted()

let tailOffs = 8
let hostPtr = buffer.contents().bindMemory(to: UInt8.self, capacity: bufferSize)
let tail = Array(UnsafeBufferPointer(start: hostPtr + (bufferSize - tailOffs), count: tailOffs))

for (idx, val) in tail.enumerated() {
    print("Offs 0x\(String(bufferSize - tailOffs + idx, radix: 16)): 0x\(String(val, radix: 16))")
}
```

Test plan: run `test_indexing.py` on MacOS-26

Fixes https://github.com/pytorch/pytorch/issues/161265
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164108
Approved by: https://github.com/Skylion007

(cherry picked from commit 6db1b9dd217501e0b3171d96335bed7b2bb53c36)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-10-01 11:59:56 -07:00
3e8a062385 Update Microsoft C++ Redistributable to the latest version (#164369)
Update Microsoft C++ Redistributable to the latest version (#161430)

Update Microsoft C++ Redistributable link to the latest version as one of the libraries used by AMD currently has a dependency on that.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/161430
Approved by: https://github.com/malfet

(cherry picked from commit 1330c638bef7fac64a42935b5a46ee32637ddd4d)

Co-authored-by: Saman Khatir <saman.khatir@amd.com>
2025-10-01 11:57:53 -07:00
3abee625e1 Fix warn message (#164367)
Fix warn message (#163578)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163578
Approved by: https://github.com/albanD, https://github.com/Skylion007, https://github.com/atalman, https://github.com/v0i0

(cherry picked from commit f3f67ff43a014b75b804d5ded0c7de3d8e0be65f)

Co-authored-by: drisspg <drisspguessous@gmail.com>
2025-10-01 11:57:16 -07:00
f227c883f9 [MPSHooks] Release pending command encoder (#164365)
[MPSHooks] Release pending command encoder (#164093)

Before returning a comand buffer, as subsequent calle are very likely to allocate their own encoder, which results in the following runtime error
```
 tryCoalescingPreviousComputeCommandEncoderWithConfig:nextEncoderClass:]:1090: failed assertion `A command encoder is already encoding to this command buffer'
```

Added regression test to `test_mps_extension`

Please note, that `torch::mps::get_command_buffer()` should be called with dispatch_queue held, both before and after this change, but many implementations skip that

Fixes https://github.com/pytorch/pytorch/issues/163721
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164093
Approved by: https://github.com/atalman, https://github.com/Skylion007

(cherry picked from commit 8f32adc90a7fee83583c9ba89dbdfabb317e0452)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-10-01 11:56:42 -07:00
a5feacb14b [SDPA] [MPS] Fixes regression in 2.8.0 for scaled_dot_product_attention using mps (#164364)
[SDPA] [MPS] Fixes regression in 2.8.0 for scaled_dot_product_attention using mps (#163598)

Fixes #163597

- Updates fast SDPA implementations to take in query tensor stride info similar to key and value instead of assuming stride.
- Updated tests with additional transpose/permutation layouts. New tests catch the regression.

### Benchmarking with script found in [implementation PR](https://github.com/pytorch/pytorch/pull/152781#:~:text=19.8%25%20speed%20improvement-,Script%20to%20get%20perf%3A,-import%20torch%0Aimport)

Times are averaged over 100000 iterations. This change should not have any significant performance difference. Tested on an M3 Pro

### Vector Fast Path (q_len=1, k_len=256)

- Before: 0.160 ms
- After: 0.157 ms

### Vector 2-pass (q_len=1, k_len=4096)

- Before: 0.342 ms
- After: 0.339 ms

### Vector Fast Path (q_len=8, k_len=256)

- Before: 0.228 ms
- After: 0.231 ms

### Vector 2-pass (q_len=8, k_len=4096)

- Before: 0.432 ms
- After:  0.436 ms

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163598
Approved by: https://github.com/malfet

(cherry picked from commit 1c12d7416bc4f1cf0bc8a229e64169fc361b688e)

Co-authored-by: Vismai Khanderao <59114226+Vismai-Khanderao@users.noreply.github.com>
2025-10-01 11:37:14 -07:00
71282c8364 Update Sphinx theme (#164147) (#164254)
Fix links in the top nav bar: 71e55749be

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164147
Approved by: https://github.com/albanD

(cherry picked from commit e88cca069171ceb117dd1ceb73e8bf3e54aa83cf)
2025-10-01 09:59:45 -07:00
e70d9f5322 [vllm hash update] update the pinned vllm hash (#164190) (#164312)
* [vllm hash update] update the pinned vllm hash (#164190)

This PR is auto-generated nightly by [this action](https://github.com/pytorch/pytorch/blob/main/.github/workflows/nightly.yml).
Update the pinned vllm hash.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164190
Approved by: https://github.com/pytorchbot

* Cherry pick b7125b3c456d48445ab0b84fab28702577cd9557

Signed-off-by: Huy Do <huydhn@gmail.com>

---------

Signed-off-by: Huy Do <huydhn@gmail.com>
Co-authored-by: PyTorch UpdateBot <pytorchupdatebot@users.noreply.github.com>
2025-10-01 06:43:17 -07:00
005e3e8d78 Clean up obsoleted vLLM tests (#164282)
Clean up obsoleted vLLM tests (#163383)

They have been removed in https://github.com/vllm-project/vllm/pull/25117 and https://github.com/vllm-project/vllm/pull/22772, thus failing in trunk at the moment after the latest pin commit update

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163383
Approved by: https://github.com/wdvr, https://github.com/seemethere, https://github.com/malfet

(cherry picked from commit a31acf32bd18e115df910002aef42baf7a9b4a33)

Co-authored-by: Huy Do <huydhn@gmail.com>
2025-09-30 14:40:57 -07:00
72cf48ea43 [AARCH64][CD][CUDA13][Triton][PTXAS] Turn on BUILD_BUNDLE_PTXAS=1 (#164236)
[AARCH64][CD][CUDA13][Triton][PTXAS] Turn on BUILD_BUNDLE_PTXAS=1   (#163988)

See also #163972, which was intended to be this PR.

Triton (release/3.5.x) by default ships CUDA12.8 ptxas.
This PR tries to bundle a ptxas version for cuda13, so that it can help https://github.com/pytorch/pytorch/issues/163801 when users run on new devices like THOR and Spark.

Fixes https://github.com/pytorch/pytorch/issues/163801

Test Plan:

Check binary size increase against nightly or v2.9RC
Install the binary from into a working THOR and GB200/GH100 machine (reproduce the original issue first on THOR), then install the binary built from this PR and we expect the issue to be gone without any additional user setting. Testing on GB200 is to ensure no regression.
Reference: https://github.com/pytorch/pytorch/pull/119750 and 5c814e2527

Note: with this PR, the pytorch world's torch.compile is supposed to find ptxas via "torch/_inductor/runtime/compile_tasks.py" and "_set_triton_ptxas_path". Use cases that do not go through "_set_triton_ptxas_path" may not be able to use the cuda13 ptxas binary.
However, as is, the triton world does not know the existence of this new cuda13 ptxas. So IF a users thinks there is already pytorch/bin/ptxas and delete the ptxas from triton, then  c6ad34f7eb/python/triton/knobs.py (L216) would still complain ptxas not found (if removed - it won't know this new one available)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163988
Approved by: https://github.com/atalman

(cherry picked from commit 3b4ad4a17d69e2db495ecaf3bae8916282a4eb0d)

Co-authored-by: Wei Wang <weiwan@nvidia.com>
2025-09-30 13:53:56 -04:00
a21a4bf11a [CI] Move libtorch-cpu-shared-with-deps-release-build to python 3.10 (#164182)
[CI] Move libtorch-cpu-shared-with-deps-release-build to python 3.10 (#162877)

Related to https://github.com/pytorch/pytorch/pull/162862

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162877
Approved by: https://github.com/malfet

(cherry picked from commit c9e57d7e9f326e427fc4ae5c318fd017cd4b75a9)

Co-authored-by: atalman <atalman@fb.com>
2025-09-29 15:52:16 -07:00
21fec65781 Use linux.g4dn.4xlarge.nvidia.gpu for cuda 12.4 legacy driver tests (#164172)
Use linux.g4dn.4xlarge.nvidia.gpu for cuda 12.4 legacy driver tests (#163956)

Workaround for https://github.com/pytorch/pytorch/issues/163658

Looks like the workflow passes on 12.8 build that use inux.g4dn.4xlarge.nvidia.gpu but its failing on 12.6 builds that use linux.4xlarge.nvidia.gpu: https://github.com/pytorch/pytorch/actions/runs/17953843505/job/51080623612#step:13:470

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163956
Approved by: https://github.com/malfet


(cherry picked from commit 349c960970f4e29eff0d37a9b3c1ca5ed86a121a)

Co-authored-by: atalman <atalman@fb.com>
Co-authored-by: Mark Saroufim <marksaroufim@meta.com>
2025-09-29 16:14:37 -04:00
22d46b50ec [CUDA] revert PR 130472 (#163379)
[CUDA] revert PR 130472 (#162950)

This change may also resolve https://github.com/pytorch/pytorch/issues/161789, though verification is still needed.

PR #130472 would introduced the problem of  freeing the same address without clean metadata. according to the below discussion, reverted it.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162950
Approved by: https://github.com/ngimel, https://github.com/eqy, https://github.com/syed-ahmed

(cherry picked from commit 4a160dae3cabaff358a6bb2490d0160dd1bf2cdf)

Co-authored-by: thenumberouscode <dream20151224@163.com>
2025-09-29 16:05:26 -04:00
d1b63e2b4a Skip test_conv3d_cudnn_broken on ROCM (#164163)
Skip test_conv3d_cudnn_broken on ROCM (#164138)

Followup after https://github.com/pytorch/pytorch/pull/163903  Fixes https://github.com/pytorch/pytorch/issues/164137

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164138
Approved by: https://github.com/Camyll

(cherry picked from commit 95be302889b8683b7ec7793a69ffa8891b6b5af8)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-09-29 11:41:18 -07:00
20100b7210 [c10d] P2P tensors must be dense (#163981)
[c10d] P2P tensors must be dense (#163719)

Fixes #161324
by adding `is_non_overlapping_and_dense` check.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163719
Approved by: https://github.com/ngimel

(cherry picked from commit 11a231ef52841a549913b7a6d423cc9004b6b58b)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-09-29 11:27:24 -07:00
a2c77043ee Add operator benchmarking run to CI nightly (#164151)
Add operator benchmarking run to CI nightly (#162530)

This PR introduces a new "operator microbenchmark" CI workflow and GitHub Actions for operator microbenchmarks, updating test scripts and job matrices to support new parameters, and broadening the operator benchmark tests to include more data types, larger shapes, and gradient tests. The benchmark configurations now focus more on different cuda hardware and multiple dtypes (bf16, fp16, fp32), for both compile and eager mode.

**Benchmark Configuration and Coverage:**

* Expanded operator benchmark configurations in `addmm_test.py`, `bmm_test.py`, `matmul_test.py`, and `mm_test.py` to benchmark multiple dtypes on CUDA devices, in eager and compile mode, for forward and backward run. The configs with tag "long" for the above mentioned files are being run in CI.
* The CI benchmarking is running on various hardwares: H100, A100.
* The CI job also uploads the microbenchmarking outputs to a [HUD](https://hud.pytorch.org/benchmark/llms?repoName=pytorch%2Fpytorch&benchmarkName=PyTorch+operator+microbenchmark) dashboard.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162530
Approved by: https://github.com/huydhn


(cherry picked from commit 54b38f3b46c33a1cc4e8f7894619358afcbd7c89)

Co-authored-by: jainapurva <apurvajain.kota@gmail.com>
Co-authored-by: Huy Do <huydhn@gmail.com>
2025-09-29 11:21:19 -07:00
b64fc8e41e Fix operator benchmark issue#162708 (#164140)
Fix operator benchmark issue#162708 (#162744)

This PR skips memory metric calculation for ops which don't take tensor input, fixing the operator_benchmark bug

Fixes https://github.com/pytorch/pytorch/issues/162708

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162744
Approved by: https://github.com/huydhn

(cherry picked from commit 5f66902ecfb9cb4f7b9c50cb86307217cec1dbe9)

Co-authored-by: jainapurva <apurvajain.kota@gmail.com>
2025-09-29 09:34:26 -07:00
709f4f62a0 [cuDNN][Convolution] Disable cuDNN for 3D convolutions with kernel size != 1 for cuDNN 9.8+ (#164027)
[cuDNN][Convolution] Disable cuDNN for 3D convolutions with kernel size != 1 for cuDNN 9.8+ (#163581)

To workaround #163539

Still confirming whether 9.10 is affected. The original test states that the convolution is "large," but note that the input size does not apepar to require 64-bit indexing.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163581
Approved by: https://github.com/ngimel, https://github.com/malfet


(cherry picked from commit e2817ac20426356278502db3b1614ea87cb7cff7)

Co-authored-by: Eddie Yan <eddiey@nvidia.com>
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-09-29 09:07:14 -07:00
11f776c8ee [cuDNN][SDPA] Disable dropout for cuDNN SDPA on 9.11 - 9.13 (#164026)
[cuDNN][SDPA] Disable dropout for cuDNN SDPA on 9.11 - 9.13 (#163903)

cuDNN introduced some broken heuristics for these cases so we need to disable dropout to avoid unexpected crashes due to heuristics refusing to proceed

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163903
Approved by: https://github.com/ngimel, https://github.com/malfet, https://github.com/atalman

(cherry picked from commit ed3085814a870f7a07b7f9c696999a47d4f85376)

Co-authored-by: Eddie Yan <eddiey@nvidia.com>
2025-09-29 09:06:23 -07:00
45e257f046 [cuDNN][conv][64-bit] Disable cuDNN for 64-bit depthwise convs again (#164023)
[cuDNN][conv][64-bit] Disable cuDNN for 64-bit depthwise convs again (#163171)

test is breaking, will check if there's an older version that we can enable on to avoid completely dropping support

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163171
Approved by: https://github.com/ngimel, https://github.com/malfet

(cherry picked from commit 0ea10f9912a9ec7c6d606bc71e3ec91f20372212)

Co-authored-by: eqy <eddiey@nvidia.com>
2025-09-29 09:03:36 -07:00
37e2626639 Update the operator benchmarking, to benchmark using torch.compile (#164101)
Update the operator benchmarking, to benchmark using torch.compile (#161394)

This pull request enhances the PyTorch operator benchmarking suite by introducing support for benchmarking with `torch.compile` mode, in addition to existing Eager and JIT. It also adds peak memory measurement (fwd/bwd pass); improves the output format in JSON to be used by dashboard for reporting; and introduce some more CLI options. The new CLI flags introduced are:

- Added `--use-compile` CLI argument and corresponding logic to run benchmarks using `torch.compile`, including mutual exclusivity with `--use-jit`
- Added `--benchmark-name` argument for customizing the benchmark name in output
- Updated default value for `--output-json-for-dashboard` to `benchmark-results.json` for more predictable output file name

Sample command to run a single operator:
`python -m pt.mm_test --use-compile`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161394
Approved by: https://github.com/jbschlosser

(cherry picked from commit af60398c3a057506363e028bf328843a755b4f24)

Co-authored-by: jainapurva <apurvajain.kota@gmail.com>
2025-09-29 07:49:05 -07:00
d7a703ea92 [SymmMem] Barrier on team instead of world (#163376)
[SymmMem] Barrier on team instead of world (#163298)

As titled. Avoiding a potential hang when running dispatch and combine in subgroups.

The rest is just re-arrange of the tests to create a sub-group test class. (no substantial change)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163298
Approved by: https://github.com/fegin

(cherry picked from commit f8fb437197033c33ecc435cd5e1e6a5b2bc5bf69)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-09-26 16:41:18 -07:00
daa3d04325 [SymmMem] Fix memory allocation hold-up (#163375)
[SymmMem] Fix memory allocation hold-up (#162680)

Problem:
Without MemPool it looks like nvshmem backend never deallocates memory.

Cause:
Handles in `symm_mems_` (a map) keeps reference to memory allocations.

Solution:
- Remove reference to allocation from handles -- the reference is never used anyway.
- Use `unique_ptr` instead of `shared_ptr` to wrap allocation to ensure single ownership.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162680
Approved by: https://github.com/ezyang
ghstack dependencies: #163298

(cherry picked from commit 7130b174e07dbc1a708934b18dede3d88e8f779f)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-09-26 16:35:56 -07:00
999304396f [dist] handle discontiguous allgather/reducescatter inputs (#163987)
[dist] handle discontiguous allgather/reducescatter inputs (#163712)

Fixes #163483

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163712
Approved by: https://github.com/ezyang, https://github.com/kwen2501

(cherry picked from commit 71eec6a0bf69f712f4b9279fdc8d1459be0426e6)

Co-authored-by: Natalia Gimelshein <ngimel@meta.com>
2025-09-26 16:21:08 -07:00
5340e741df [Reland][163423] Promote @requires_nvshmem instead of enable_triton (#163916)
[Reland][163423] Promote `@requires_nvshmem` instead of `enable_triton` (#163549)

#163423 was approved but reverted due to a revert of base.
Relanding without base.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163549
Approved by: https://github.com/wdvr


(cherry picked from commit 6e6c899347db952f6a691feb4e8610fe9cca0279)

Co-authored-by: Ke Wen <kw2501@fb.com>
Co-authored-by: Wouter Devriendt <wouterdevriendt@meta.com>
2025-09-26 15:58:30 -07:00
7cadf8ac04 [Inductor][Intel GPU] Save threads_per_warp from tirton compiled kernel for launching kernel correctly in cpp wrapper. (#163388)
[Inductor][Intel GPU] Save `threads_per_warp` from tirton compiled kernel for launching kernel correctly in cpp wrapper. (#163315)

On the Inductor XPU backend, `threads_per_warp` is not always 32. For Intel GEMM Triton kernels, it can be 16. This information must be preserved for XPU so that the Cpp wrapper can launch the kernel with the correct configuration.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163315
Approved by: https://github.com/EikanWang, https://github.com/desertfire

(cherry picked from commit 9f8a311af09586ac4026d6a56fc7c4ac7acc62ed)

Co-authored-by: xinan.lin <xinan.lin@intel.com>
2025-09-26 14:42:09 -04:00
f9e495fe8e Move inductor jobs 3.9->3.10 (#163954)
Move inductor jobs 3.9->3.10 (#162323)

Related to: https://github.com/pytorch/pytorch/issues/161167

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162323
Approved by: https://github.com/huydhn, https://github.com/Skylion007


(cherry picked from commit e8eeb060348f250975124abb957b1d7d9c4af9a0)

Co-authored-by: atalman <atalman@fb.com>
Co-authored-by: Huy Do <huydhn@gmail.com>
2025-09-26 12:37:50 -04:00
57dc68844d [CI] Fix test_triton_wait_until hang (#163914)
[CI] Fix test_triton_wait_until hang (#163886)

I don't know why `nvshmem_barrier_all_kernel`  leads the test to hang. Will investigate.
But since it is an unnecessary call here, I am removing it to unblock other PRs.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163886
Approved by: https://github.com/fegin

(cherry picked from commit 96275dbf88372bb32a123c4ea918498128fbecb9)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-09-26 12:16:00 -04:00
63da9d2730 [Release 2.9] Update torch-xpu-ops commit pin (#163622)
Update commit pin to 789f59
2025-09-26 09:46:02 -04:00
824d59fbf6 [CI] Install libuv for Win testing (#163907)
[CI] Install libuv for Win testing (#163797)

Current working theory why f0078941cf caused a regression, are because Windows CI no longer could be build with distributed, as it could not find libuv
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163797
Approved by: https://github.com/wdvr

(cherry picked from commit cc660d38ac533b92f3ad4cb1105f7a16f74b9f09)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-09-26 00:03:22 -07:00
fc8bf12b38 Fix cpp build (#163887)
Fix cpp build (#162774)

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162774
Approved by: https://github.com/malfet, https://github.com/atalman

(cherry picked from commit b61bdc7cc4c841bf7574bc993f3fd445682f0997)

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
2025-09-25 14:50:59 -07:00
49dab18ecf [CD] Add statically linked windows libraries to exclude list (#163862)
[CD] Add statically linked windows libraries to exclude list (#163768)

Fixes: https://github.com/pytorch/pytorch/issues/159514

Seeing following in the Wheel build logs:
```
Linking CXX static library lib\kineto.lib
Linking CXX static library lib\dnnl.lib
....
```

These files are around 800MB uncompressed and 109MB compressed, hence provide ~50% size reduction for Windows CPU builds.

Test Plan: Build Pytorch Windows binary. Build vision, audio and torchcodec with this binary. Smoke test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163768
Approved by: https://github.com/albanD, https://github.com/malfet

(cherry picked from commit 98c4e35f14601909c113b4fd2857b6f0fb525316)

Co-authored-by: atalman <atalman@fb.com>
2025-09-25 14:46:56 -07:00
0154ca1d3d [BE] Update Python min version to 3.10 (#162310) (#163885)
* [BE] Update Python min version to 3.10 (#162310)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162310
Approved by: https://github.com/atalman, https://github.com/Skylion007, https://github.com/ZainRizvi

* comment out executorch

---------

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-09-25 14:44:48 -07:00
132d9fac3b Revert "[BE] Update Python min version to 3.10 (#162310)" (#163882)
Revert "[BE] Update Python min version to 3.10 (#162310) (#163802)"

This reverts commit 7d024a6e299eee2830e9fbdae1913e432160bb23.
2025-09-25 10:54:12 -07:00
87c5d4a858 [cherrypick] [CI] Move Windows build/tests to Python-3.10 #162862 (#163800)
[CI] Move Windows build/tests to Python-3.10 (#162862)

What supposed to be a very simple change end up being quite involved, as current Windows CI framework is quite inflexible, i.e. it takes a lots of argument, but later on ignores them, namely:
 - `PYTHON_VERSION` used to be a no-op that is simply ignored by the scripts
 - With this change, `setup-win` action will create an environment called `py_tmp` with specific python version + intel-openmp (that is hard runtime requirement, but for some reason not packaged into the wheel nor marked as such)
 - Copied test type dependencies from be01a40157/aws/ami/windows/scripts/Installers/Install-Pip-Dependencies.ps1 (L16) into `win-test.sh`, but made some adjustments to be compatible with 3.10 runtime (scipy version update) and just make rerun-tests compatible with the rest of the deps

I think in the long run, one needs to update 4432e2cacd/aws/ami/windows/scripts/Installers/Install-Miniconda3.ps1 that currently pins Miniconda python to 3.9, but also figure out how CI can still create a new environment without having to download all the dependencies all the time
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162862
Approved by: https://github.com/wdvr, https://github.com/huydhn
ghstack dependencies: #163339, #163341

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-09-25 09:06:52 -07:00
b0dc90881c [CD] Simplify NVIDIA driver installation step (#163349) (#163790)
Undo changes introduced in https://github.com/pytorch/pytorch/pull/160956 as driver has been updated to 580 for both fleets

Fixes https://github.com/pytorch/pytorch/issues/163342
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163349
Approved by: https://github.com/seemethere

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-09-25 10:40:57 -04:00
c0577aad39 Use cuda nvrtc so file based on cuda version used by torch (#163642) (#163788)
Fixes https://github.com/pytorch/pytorch/issues/162367

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163642
Approved by: https://github.com/msaroufim
2025-09-25 10:40:09 -04:00
9952b87600 [CD] CUDA 13.0 fix preload logic to include nvidia/cu13/lib/ (#163766)
[CD] CUDA 13.0 fix preload logic to include nvidia/cu13/lib/ (#163661)

Preload logic no longer works with CUDA 13.0
See the installation path:
```
ls /home/ubuntu/.venv/lib/python3.10/site-packages/nvidia/cu13/lib/
libcheckpoint.so   libcudadevrt.a      libcufft.so.12   libcufile_rdma.so.1  libcusolver.so.12    libnvJitLink.so.13  libnvperf_target.so            libnvrtc.alt.so.13    libpcsamplingutil.so
libcublas.so.13    libcudart.so.13     libcufftw.so.12  libcupti.so.13       libcusolverMg.so.12  libnvblas.so.13     libnvrtc-builtins.alt.so.13.0  libnvrtc.so.13
libcublasLt.so.13  libcudart_static.a  libcufile.so.0   libcurand.so.10      libcusparse.so.12    libnvperf_host.so   libnvrtc-builtins.so.13.0      libnvtx3interop.so.1

ls /home/ubuntu/.venv/lib/python3.10/site-packages/nvidia/
cu13  cudnn  cusparselt  nccl  nvshmem
```

Test using script from : https://github.com/pytorch/pytorch/issues/162367
```
Kernel test passed!
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163661
Approved by: https://github.com/nWEIdia, https://github.com/tinglvv, https://github.com/Camyll

(cherry picked from commit 141fc7276ebc722b6076cc3afe4fbc6307a1b775)

Co-authored-by: atalman <atalman@fb.com>
2025-09-25 10:38:16 -04:00
300bade202 [Cherry-Pick] [CD] CUDA 13 specific followup changes. Remove sm50-70 From CUDA 12.6 and CUDA 12.8 builds (#162455) (#163764)
* [CD] CUDA 13 specific followup changes (#162455)

Follow up for CUDA 13 bring up https://github.com/pytorch/pytorch/issues/159779
sm50-70 should not be added to sbsa build arch list, as previous archs had no support for arm.
remove platform_machine from PYTORCH_EXTRA_INSTALL_REQUIREMENTS

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162455
Approved by: https://github.com/atalman

* update

---------

Co-authored-by: Ting Lu <tingl@nvidia.com>
2025-09-25 10:37:52 -04:00
96f0c0fa07 Fix some edge cases (#163106)
Fix some edge cases (#162295)

``` Summary
🔝 Top 5 Performance Differences (by absolute %):
shape: (5, 7)
┌────────────────┬────────────────┬─────────────────────────────┬───────────────────┬──────────────────────┬───────────────────────────┬───────────┐
│ attn_type      ┆ dtype          ┆ shape(B,Hq,M,Hkv,N,D)       ┆ TFlops BWD (base) ┆ TFlops BWD (no_peel) ┆ no_peel_speedup_over_base ┆ pct_delta │
│ ---            ┆ ---            ┆ ---                         ┆ ---               ┆ ---                  ┆ ---                       ┆ ---       │
│ str            ┆ str            ┆ str                         ┆ f64               ┆ f64                  ┆ f64                       ┆ f64       │
╞════════════════╪════════════════╪═════════════════════════════╪═══════════════════╪══════════════════════╪═══════════════════════════╪═══════════╡
│ sliding_window ┆ torch.bfloat16 ┆ (2, 16, 1024, 4, 1024, 64)  ┆ 56.937931         ┆ 58.960459            ┆ 1.035522                  ┆ 3.552163  │
│ noop           ┆ torch.bfloat16 ┆ (2, 16, 1024, 4, 1024, 128) ┆ 89.221306         ┆ 86.295642            ┆ 0.967209                  ┆ -3.27911  │
│ causal         ┆ torch.bfloat16 ┆ (2, 16, 4096, 4, 4096, 128) ┆ 111.552594        ┆ 114.380841           ┆ 1.025353                  ┆ 2.535349  │
│ alibi          ┆ torch.bfloat16 ┆ (2, 16, 1024, 16, 1024, 64) ┆ 74.830149         ┆ 76.685445            ┆ 1.024793                  ┆ 2.479344  │
│ alibi          ┆ torch.bfloat16 ┆ (2, 16, 1024, 4, 1024, 64)  ┆ 55.279932         ┆ 56.369312            ┆ 1.019707                  ┆ 1.97066   │
└────────────────┴────────────────┴─────────────────────────────┴───────────────────┴──────────────────────┴───────────────────────────┴───────────┘

🔺 Top 5 Cases Where no_peel (change) is Faster than base (baseline):
shape: (5, 7)
┌────────────────┬────────────────┬─────────────────────────────┬───────────────────┬──────────────────────┬───────────────────────────┬───────────┐
│ attn_type      ┆ dtype          ┆ shape(B,Hq,M,Hkv,N,D)       ┆ TFlops BWD (base) ┆ TFlops BWD (no_peel) ┆ no_peel_speedup_over_base ┆ pct_delta │
│ ---            ┆ ---            ┆ ---                         ┆ ---               ┆ ---                  ┆ ---                       ┆ ---       │
│ str            ┆ str            ┆ str                         ┆ f64               ┆ f64                  ┆ f64                       ┆ f64       │
╞════════════════╪════════════════╪═════════════════════════════╪═══════════════════╪══════════════════════╪═══════════════════════════╪═══════════╡
│ sliding_window ┆ torch.bfloat16 ┆ (2, 16, 1024, 4, 1024, 64)  ┆ 56.937931         ┆ 58.960459            ┆ 1.035522                  ┆ 3.552163  │
│ causal         ┆ torch.bfloat16 ┆ (2, 16, 4096, 4, 4096, 128) ┆ 111.552594        ┆ 114.380841           ┆ 1.025353                  ┆ 2.535349  │
│ alibi          ┆ torch.bfloat16 ┆ (2, 16, 1024, 16, 1024, 64) ┆ 74.830149         ┆ 76.685445            ┆ 1.024793                  ┆ 2.479344  │
│ alibi          ┆ torch.bfloat16 ┆ (2, 16, 1024, 4, 1024, 64)  ┆ 55.279932         ┆ 56.369312            ┆ 1.019707                  ┆ 1.97066   │
│ causal         ┆ torch.bfloat16 ┆ (4, 16, 4096, 4, 4096, 64)  ┆ 111.08814         ┆ 112.447047           ┆ 1.012233                  ┆ 1.22327   │
└────────────────┴────────────────┴─────────────────────────────┴───────────────────┴──────────────────────┴───────────────────────────┴───────────┘

🔻 Top 5 Cases Where no_peel (change) is Slower than base (baseline):
shape: (5, 7)
┌────────────────┬────────────────┬─────────────────────────────┬───────────────────┬──────────────────────┬───────────────────────────┬───────────┐
│ attn_type      ┆ dtype          ┆ shape(B,Hq,M,Hkv,N,D)       ┆ TFlops BWD (base) ┆ TFlops BWD (no_peel) ┆ no_peel_speedup_over_base ┆ pct_delta │
│ ---            ┆ ---            ┆ ---                         ┆ ---               ┆ ---                  ┆ ---                       ┆ ---       │
│ str            ┆ str            ┆ str                         ┆ f64               ┆ f64                  ┆ f64                       ┆ f64       │
╞════════════════╪════════════════╪═════════════════════════════╪═══════════════════╪══════════════════════╪═══════════════════════════╪═══════════╡
│ noop           ┆ torch.bfloat16 ┆ (2, 16, 1024, 4, 1024, 128) ┆ 89.221306         ┆ 86.295642            ┆ 0.967209                  ┆ -3.27911  │
│ causal         ┆ torch.bfloat16 ┆ (4, 16, 1024, 4, 1024, 64)  ┆ 78.23082          ┆ 76.693169            ┆ 0.980345                  ┆ -1.965531 │
│ sliding_window ┆ torch.bfloat16 ┆ (2, 16, 2048, 4, 2048, 128) ┆ 96.95663          ┆ 95.573333            ┆ 0.985733                  ┆ -1.426717 │
│ alibi          ┆ torch.bfloat16 ┆ (4, 16, 2048, 4, 2048, 64)  ┆ 93.373473         ┆ 92.294147            ┆ 0.988441                  ┆ -1.155924 │
│ alibi          ┆ torch.bfloat16 ┆ (2, 16, 2048, 4, 2048, 128) ┆ 96.95147          ┆ 96.105389            ┆ 0.991273                  ┆ -0.872685 │
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162295
Approved by: https://github.com/mlazos, https://github.com/v0i0

(cherry picked from commit 864ffe12d737403230e8257b9bce0a830bd590c1)

Co-authored-by: drisspg <drisspguessous@gmail.com>
2025-09-25 10:29:39 -04:00
7d024a6e29 [BE] Update Python min version to 3.10 (#162310) (#163802)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162310
Approved by: https://github.com/atalman, https://github.com/Skylion007, https://github.com/ZainRizvi
ghstack dependencies: #162862

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-09-24 15:48:19 -07:00
be29c5b207 Add analytics ID to cpp docs (#163695)
Add analytics ID to cpp docs (#163370)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163370
Approved by: https://github.com/albanD

(cherry picked from commit e6a9db58d71e474deac28276de1f611638c32eeb)

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
2025-09-24 15:45:17 -07:00
5322dab793 Update pytorch.org links in docs/conf.py (#163703)
Update pytorch.org links in docs/conf.py (#163682)

Update links in conf.py to docs.pytorch.org

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163682
Approved by: https://github.com/sekyondaMeta, https://github.com/albanD

(cherry picked from commit 8c8416b021e59a5ec58aceb38eeffc63885a28bc)

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
2025-09-24 15:44:43 -07:00
1dadb6196b [BE] Introduce CONDA_ROOT_DIR (#163805)
[BE] Introduce `CONDA_ROOT_DIR` (#163341)

Which equal to `%CONDA_PARENT_DIR%/Miniconda3`, and replace this pattern with `%CONDA_ROOT_DIR%` throughout the codebase
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163341
Approved by: https://github.com/clee2000
ghstack dependencies: #163339

(cherry picked from commit a273475b01e912f402378a522bb9c4ed37e8413a)

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2025-09-24 15:42:16 -07:00
6c058c1262 Move ROCM trunk wheel builds to 3.10 (#163804)
Move ROCM trunk wheel builds to 3.10 (#163339)

This code is a delicious spaghetti: Sometimes python version is defined in jinja template (see https://github.com/pytorch/pytorch/pull/162297 ) sometimes in shell script (see https://github.com/pytorch/pytorch/pull/162877 ), but this time around it's in a python file (and there is another one called `generate_binary_build_matrix.py` that defines `FULL_PYTHON_VERSIONS`)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163339
Approved by: https://github.com/clee2000

(cherry picked from commit 52dd7a898c117305b4407c7f26bbcc7b39f20aaa)

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2025-09-24 15:41:55 -07:00
715dca6725 [export] Remove .contiguous() when saving weights to raw bytes (#163662)
[export] Remove .contiguous() when saving weights to raw bytes (#163587)

Summary: `.contiguous()` will discard the original storage size of the tensor, and could lead to issues during loading.

Test Plan:
buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_1D_tensor_slicing
buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_2D_tensor_slicing

Differential Revision: D83016250

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163587
Approved by: https://github.com/angelayi

(cherry picked from commit 720a7b2887ca4efc8d63b32373182bc97918c76e)

Co-authored-by: Yiming Zhou <yimingzhou@meta.com>
2025-09-23 10:15:06 -07:00
47cb45e4f6 Update pytorch_sphinx_theme2 to latest hash (#163655)
Update pytorch_sphinx_theme2 to latest hash (#163269)

The updated theme:
- Fixes articleBody in the json+ld that caused previous Google Search issues
- Other minor fixes
- 404.html fixes
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163269
Approved by: https://github.com/albanD

(cherry picked from commit 68e75be86ab618bb6b1dc32b603a780ff6046262)

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
2025-09-23 10:13:51 -07:00
4966d058f2 CUDA 13.0 Warning update for supported architectures (#163633)
CUDA 13.0 Warning update for supported architectures (#163585)

Please see build script: 8da008678f/.ci/manywheel/build_cuda.sh (L69-L71)

This should display correct warning:
``
Please install PyTorch with a following CUDA
configurations: 12.6 12.8 13.0 following instructions at
https://pytorch.org/get-started/locally/
``
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163585
Approved by: https://github.com/malfet

(cherry picked from commit 3c64b2abab5a23809140da5bd6272307b776e459)

Co-authored-by: atalman <atalman@fb.com>
2025-09-23 10:13:06 -07:00
579794ed7b [SymmMem] Fix put_signal + wait_until hang (#163458)
[SymmMem] Fix put_signal + wait_until hang (#163194)

The test used a wrong ptr to refer to remote address:
```
            dst_ptr = out_hdl.buffer_ptrs[peer]
            src_ptr = inp_hdl.buffer_ptrs[rank]
            sig_ptr = out_hdl.signal_pad_ptrs[peer]
```
All three indices should be `rank` instead of `peer` because NVSHMEM APIs accept local address as input and perform translation internally. Without correct signal address, the peer would be waiting, thus hang.

Also adjusted the signature of `nvshmem.putmem_signal_block` to accept tensor instead of pointer.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163194
Approved by: https://github.com/ngimel
ghstack dependencies: #163025, #163152

(cherry picked from commit 80f8be9840c20c3efe1274266b52ab098f4d1030)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-09-23 10:10:02 -07:00
7cf37ae3cb [2.9 cherry pick][triton] update 3.5 pin to bbb06c0334a6772b92d24bde54956e675c8c6604 (#163382) (#163583)
Includes:
* https://github.com/triton-lang/triton/pull/8211 to work around a PTXAS bug that was causing 03-matrix-multiplication tutorial matmuls to underperform due to excessive WGMMA waits
* https://github.com/triton-lang/triton/pull/8157 to fix a convert_layout bug

Verified that this passes Triton CI in https://github.com/pytorch/pytorch/pull/159158 and improves gemm perf (see https://github.com/pytorch/pytorch/issues/159704)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163382
Approved by: https://github.com/Camyll, https://github.com/atalman
2025-09-22 18:20:20 -07:00
f83cf0714e [graph partition] Add way to register custom rule (#163310) (#163395)
This PR adds an experimental way to register a custom rule for if
inductor should partition the graph around an operator.

Test Plan:
- new test

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163310
Approved by: https://github.com/ProExpertProg, https://github.com/BoyuanFeng, https://github.com/eellison
ghstack dependencies: #162117, #162307, #162651
2025-09-22 18:18:07 -07:00
ddd5074afc [CI] Update NVIDIA driver to 580.82.07 (#163522)
[CI] Update NVIDIA driver to `580.82.07` (#163111)

To make CI machines capable of running CUDA-13 tests. Unfortunately, this upgrade regresses NUMBA integration, so live patch it with 6e08c9d08e

This fix was suggested in https://github.com/pytorch/pytorch/issues/162878#issuecomment-3288635745

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163111
Approved by: https://github.com/huydhn

(cherry picked from commit 8dbac62edb48815dfca84dfdcca40d6a24d0652b)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-09-22 11:45:48 -04:00
35c55da805 [Graph Partition] improve custom op output alias (#163380)
[Graph Partition] improve custom op output alias (#163227)

For a custom op with multiple outputs, we will see the following generated code:
```
buf1 = op1(arg0)
buf3 = buf0[0]
buf4 = buf0[1]
del buf1 # <--- if buf1 is not accessed in the future
```

If `buf1` is not accessed in the future, it's good to deallocate early. So we don't delay `del` until both buf3 and buf4 are not used anymore. Note that buf3 and buf4 hold reference to the data such that `del buf1` does not prevent their usage.

However, when there are mutating args, we don't see `del buf1` immediately.

```python
@torch.library.custom_op(
    "mylib::op1",
    mutates_args=["x"],
    schema="(Tensor(a!)?  x) -> (Tensor, Tensor)",
    device_types="cuda",
)
def op1(x) -> tuple[torch.Tensor, torch.Tensor]:
    x = x + 1
    return (x + 1, x + 2)
```

<img width="661" height="821" alt="image" src="https://github.com/user-attachments/assets/3d1d1f5a-9749-4652-bb02-da593c78702d" />

Why? Because `buf3` is a MultiOutput with `buf1` as input and believes `buf1` (an output of FallbackKernel op1) has inputs that alias output.
72fedf0575/torch/_inductor/ir.py (L7976-L7982)

According to `[NOTE: FallbackKernel supported operators]`, as a mutating op that are auto-functionalizable, buf1's output should NOT alias any of the inputs. This PR improves get_inputs_that_alias_output of Fallback Kernel.

Use case: [moe custom op in vllm](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/layer.py#L2057-L2064)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163227
Approved by: https://github.com/zou3519

(cherry picked from commit 4967ad8baa724b8b1acc123698bb1265723feb87)

Co-authored-by: Boyuan Feng <boyuan@meta.com>
2025-09-19 16:36:03 -07:00
a576d48637 Skip test_ind_worker_queue on Windows and macOS (flaky) (#163363)
Skip test_ind_worker_queue on Windows and macOS (flaky) (#162555)

Fixes https://github.com/pytorch/pytorch/issues/68643

It was closed by the bot yesterday and the issue was still there https://github.com/pytorch/pytorch/actions/runs/17595694816/job/49989589647.  It's better to just skip it directly in the code as this test has been disabled on Windows and MacOS since 2021 O_o
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162555
Approved by: https://github.com/clee2000

(cherry picked from commit 98e22c8a693644c6d235d7a858dc411b1aefafa7)

Co-authored-by: Huy Do <huydhn@gmail.com>
2025-09-19 13:07:00 -07:00
25d8c0be68 Add decomp rule to assert_tensor_metadata for BatchedTensors (#163361)
Add decomp rule to assert_tensor_metadata for BatchedTensors  (#163008)

Whenever there is device move, export introduces assert_tensor_metadata aten operator to make sure to guard for device specialization. This aten op didn't work with Vmap because we didn't register explicit decomp rule saying we just skip BatchedTensor and call it on underlying tensor

Differential Revision: [D82483979](https://our.internmc.facebook.com/intern/diff/D82483979)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163008
Approved by: https://github.com/huydhn

(cherry picked from commit e28983be76aa4651e3cb69dc3a4234d75038d938)

Co-authored-by: Tugsbayasgalan Manlaibaatar <tmanlaibaatar@fb.com>
2025-09-19 13:00:57 -07:00
b1aae80953 [Cherry Pick][Graph Partition] allow sharing default device context (#163097)
cherry pick PR 162873
2025-09-19 11:10:29 -07:00
eqy
76bebf38de [Release 2.9] [cuDNN][SDPA][submodule] Roll-back cuDNN frontend upgrade, update Met… (#163265)
[cuDNN][SDPA][submodule] Roll-back cuDNN frontend upgrade, update Meta registration (#163104)

For https://github.com/pytorch/torchtitan/issues/1713

Also note that we will need to rollback the cuDNN frontend upgrade in 2.9 as it currently introduces a segmentation fault by assuming tensors have their strides and sizes populated at graph creation time 1a7b4b78db/include/cudnn_frontend/node/sdpa_support_surface.h (L447%C2%A0)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163104
Approved by: https://github.com/drisspg
2025-09-19 10:53:04 -07:00
bc158ebdc7 [SymmMem] Fix NVSHMEM plugin + Triton 3.5 (#163262)
[SymmMem] Fix NVSHMEM plugin + Triton 3.5 (#163152)

1. The dispatch signatures defined in `core.extern_elementwise` call must match the C signature of the NVSHMEM functions, in particular the dtypes. Otherwise, there would be weird errors, such as IMA or hang. When matched, most of time the NVSHMEM device function will be inlined into the generated PTX. When not matched, it is represented as a function call in the PTX (not sure if it is the function call that goes wrong).

2. When calling the `core.extern` wrappers from the `triton.jit` kernels, the input must be cast to match the signatures defined in 1, e.g. via `nbytes.to(tl.int64)`. Otherwise, Triton will report a key error when searching for such kernel.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163152
Approved by: https://github.com/ngimel
ghstack dependencies: #163025

(cherry picked from commit 57a54a04b6eb78e0aa7d13b48e25fb8c0c49fd60)

Co-authored-by: Ke Wen <kw2501@meta.com>
2025-09-19 10:51:02 -07:00
ffa6f63fe2 Revert "Make distributed modules importable even when backend not bui… (#163024)
Revert "Make distributed modules importable even when backend not built (#159889)" (#162568)

This reverts commit a0d026688cd69583d5a4e0c6f3e5fda141a7f4a9.

Revert "Always build USE_DISTRIBUTED. (#160449)"

This reverts commit d80297a6846f1f2c36fd4f19e22919f2abe8fcea.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162568
Approved by: https://github.com/huydhn

Co-authored-by: Edward Yang <ezyang@meta.com>
2025-09-19 10:34:55 -07:00
baab5c6c8b [ONNX] Update export docstring & Set fallback=False by default (#162637)
* [ONNX] Update export docstring (#162622)

Update export docstring to reflect the latest configuration.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162622
Approved by: https://github.com/titaiwangms

(cherry picked from commit 7e2e83cdbe532b230dee40cfe0454116c9b64710)

* Change fallback option to False in ONNX export

* Change fallback parameter default to False

---------

Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
2025-09-16 17:23:47 -07:00
9718af107e Support vmap + custom autograd function/improve DTensor constructor inefficiency (#162738)
Support vmap + custom autograd function/improve DTensor constructor inefficiency (#162240)

This makes gemma3 exportable on transformers=4.55.4

In HF, there is a torch funciton mode called TransformGetItemToIndex which internally calls custom autograd function. When this custom autograd function is called under vmap, It triggers CustomFunctionHigherOrderOP which error-ed because there was no pre-dispatch proxy mode implementation.

Since there are number of requests lately to add various operators in pre-dispatch IR, I introduce a decorator in export that works similar to `allow_in_graph`. Basically:
1) We intercept custom_autograd_function.apply at pre-dispatch mode when this decorator is applied
2) We apply `flat_apply` HOP to hide the pytree spec for this autograd function. Note that this adds restriction that this custom autograd function needs to take in fx-able types.
3) subclass constructor decorator is implemented similarly, so we just refactor it to use similar implementation as this new decorator. eventually we should delete the subclass constructor decorator.
4) Move some code in subclass constructor decorator to exit early in non-export environment which should shave off some inefficiency (around 1% according to @swolchok 's benchmark)

Fixes: https://github.com/pytorch/pytorch/issues/161563#issuecomment-3246309758

Differential Revision: [D82141316](https://our.internmc.facebook.com/intern/diff/D82141316)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162240
Approved by: https://github.com/ydwu4

(cherry picked from commit 463fbc8ca0537e5635236190d2ca38ce6fcef831)

Co-authored-by: Tugsbayasgalan Manlaibaatar <tmanlaibaatar@fb.com>
2025-09-16 17:22:16 -07:00
7f8ba48c2a Fix the regression issue caused by non-arrch64 platforms not hitting the MKLDNN path. (#162778)
Fix the regression issue caused by non-arrch64 platforms not hitting the MKLDNN path. (#162168)

This issue was introduced by the commit in issue #161065. Added an extra check to provide a proper path for other platforms.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162168
Approved by: https://github.com/mingfeima, https://github.com/malfet


(cherry picked from commit 563921619b3e820b170475b9278ff94ee6e1a32c)

Co-authored-by: Yuxingwang-intel <yuxing.wang@intel.com>
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-09-16 17:21:10 -07:00
aebf427c53 [Release 2.9] Update torch-xpu-ops commit pin (#162935)
Update commit pin to f8408a
2025-09-16 17:19:31 -07:00
44baf2ff8d fix deterministic scatter_add path for multi-d tensors (#162977)
fix deterministic scatter_add path for multi-d tensors (#162866)

PReviously for more than 2d tensor `select` didn't work correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162866
Approved by: https://github.com/valentinandrei

(cherry picked from commit bf6b40da3e3be7718b8ddc94eed2da8cabaa5e86)

Co-authored-by: Natalia Gimelshein <ngimel@meta.com>
2025-09-16 17:17:36 -07:00
1076941ff7 [ONNX] Fix rotary_embedding_23 implementation (#163041)
[ONNX] Fix rotary_embedding_23 implementation (#162865)

The implementation of rotary_embedding_23 when input is 3D was incorrect.

## Tested

Locally with

```py
import onnx_ir as ir
import onnx
import torch
import os
import numpy as np

base_path = "/home/justinchu/dev/onnx/onnx/backend/test/data/node"
test_names = [
    "test_rotary_embedding",
    "test_rotary_embedding_3d_input",
    "test_rotary_embedding_interleaved",
    "test_rotary_embedding_no_position_ids",
    "test_rotary_embedding_no_position_ids_interleaved",
    "test_rotary_embedding_no_position_ids_rotary_dim",
    "test_rotary_embedding_with_interleaved_rotary_dim",
    "test_rotary_embedding_with_rotary_dim",
]
model_paths = [os.path.join(base_path, name) for name in test_names]

for path in model_paths:
    print(f"Checking {path} for issues...")

    model = onnx.load(os.path.join(path, "model.onnx"))
    input0 = ir.from_proto(
        onnx.load_tensor(os.path.join(path, "test_data_set_0", "input_0.pb"))
    ).numpy()
    input1 = ir.from_proto(
        onnx.load_tensor(os.path.join(path, "test_data_set_0", "input_1.pb"))
    ).numpy()
    input2 = ir.from_proto(
        onnx.load_tensor(os.path.join(path, "test_data_set_0", "input_2.pb"))
    ).numpy()
    if os.path.exists(os.path.join(path, "test_data_set_0", "input_3.pb")):
        input3 = ir.from_proto(
            onnx.load_tensor(os.path.join(path, "test_data_set_0", "input_3.pb"))
        ).numpy()
    else:
        input3 = None
    output0 = ir.from_proto(
        onnx.load_tensor(os.path.join(path, "test_data_set_0", "output_0.pb"))
    ).numpy()

    m = ir.from_proto(model)

    node = m.graph[-1]
    print(node)
    assert node.op_type == "RotaryEmbedding"

    interleaved = node.attributes.get_int("interleaved", 0)
    num_heads = node.attributes.get_int("num_heads", 0)
    rotary_embedding_dim = node.attributes.get_int("rotary_embedding_dim", 0)

    torch_out = torch.onnx.ops.rotary_embedding(
        torch.tensor(input0),
        torch.tensor(input1),
        torch.tensor(input2),
        position_ids=torch.tensor(input3) if input3 is not None else None,
        interleaved=bool(interleaved),
        num_heads=num_heads,
        rotary_embedding_dim=rotary_embedding_dim,
    )
    torch_out = torch_out.detach().cpu().numpy()
    np.testing.assert_allclose(torch_out, output0)
```

Fix https://github.com/pytorch/pytorch/issues/162848

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162865
Approved by: https://github.com/kunal-vaishnavi, https://github.com/titaiwangms

(cherry picked from commit fdf68fa5d70abebee1c5090a51ea30c7aa40b9b0)

Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
2025-09-16 17:16:23 -07:00
0ac9fa4413 [ez][CI] Fix docs push in nightly workflow (#163085)
[ez][CI] Fix docs push in nightly workflow (#162657)

HUD metrics page says docs push hasn't happened in 21 days
<img width="293" height="142" alt="image" src="https://github.com/user-attachments/assets/f930aab8-0503-4bf2-b962-8c375dec6b78" />

I guess main branch docs just haven't been updated?  Did anyone notice?  Do we care?

Either way I think this should fix it

Likely started after https://github.com/pytorch/pytorch/pull/161182
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162657
Approved by: https://github.com/huydhn

(cherry picked from commit 2f533959430c2a41fe16ef79fe4d680a5c4e0585)

Co-authored-by: Catherine Lee <csl@fb.com>
2025-09-16 12:04:17 -07:00
152383b745 fix typo: summit -> submit (#162597)
fix typo: summit -> submit (#162587)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162587
Approved by: https://github.com/justinchuby

(cherry picked from commit fefc406a3d0d90db0f808419fb88045f90b213cd)

Co-authored-by: Masaki Kozuki <mkozuki@nvidia.com>
2025-09-12 11:41:11 -04:00
c31a8186c1 [CD] Aarch64 Fix packaging `libarm_compute.so` and other libraries to the aarch64 CUDA wheels (#162596)
[CD] Aarch64 Fix packaging ``libarm_compute.so`` and other libraries to the aarch64 CUDA wheels (#162566)

Fixes aarch64 linux packaging, following error:
https://github.com/pytorch/vision/actions/runs/17612462583/job/50037380487#step:15:62
```
Traceback (most recent call last):
  File "/__w/vision/vision/pytorch/vision/setup.py", line 13, in <module>
    import torch
  File "/__w/_temp/conda_environment_17612462583/lib/python3.11/site-packages/torch/__init__.py", line 415, in <module>
    from torch._C import *  # noqa: F403
    ^^^^^^^^^^^^^^^^^^^^^^
ImportError: libarm_compute.so: cannot open shared object file: No such file or directory
```
Due to missing dependencies.

Current Error:
File torch-2.10.0.dev20250910+cu130-cp310-cp310-linux_aarch64.whl is extracted
File is repackaged as torch-2.10.0.dev20250910+cu130-cp310-cp310-manylinux_2_28_aarch64.whl
File torch-2.10.0.dev20250910+cu130-cp310-cp310-linux_aarch64.whl renamed as torch-2.10.0.dev20250910+cu130-cp310-cp310-manylinux_2_28_aarch64.whl
Hence the repackaging does not take any effect.

This PR does following
File torch-2.10.0.dev20250910+cu130-cp310-cp310-linux_aarch64.whl is extracted
File torch-2.10.0.dev20250910+cu130-cp310-cp310-linux_aarch64.whl  deleted
File is repackaged as torch-2.10.0.dev20250910+cu130-cp310-cp310-manylinux_2_28_aarch64.whl

Looks like after migrating from zipping the wheel to wheel pack renaming the wheel is no longer necessary. Hence removing renaming and deleting old file.
```
2025-09-10T10:10:05.9652454Z Using nvidia libs from pypi - skipping CUDA library bundling
2025-09-10T10:10:05.9656595Z Copying to /pytorch/dist/tmp/torch/lib/libgomp.so.1
2025-09-10T10:10:05.9873843Z Copying to /pytorch/dist/tmp/torch/lib/libgfortran.so.5
2025-09-10T10:10:06.0410041Z Copying to /pytorch/dist/tmp/torch/lib/libarm_compute.so
2025-09-10T10:10:06.2869242Z Copying to /pytorch/dist/tmp/torch/lib/libarm_compute_graph.so
2025-09-10T10:10:06.4385740Z Copying to /pytorch/dist/tmp/torch/lib/libnvpl_lapack_lp64_gomp.so.0
2025-09-10T10:10:06.5461372Z Copying to /pytorch/dist/tmp/torch/lib/libnvpl_blas_lp64_gomp.so.0
2025-09-10T10:10:06.5728970Z Copying to /pytorch/dist/tmp/torch/lib/libnvpl_lapack_core.so.0
2025-09-10T10:10:06.6231872Z Copying to /pytorch/dist/tmp/torch/lib/libnvpl_blas_core.so.0
2025-09-10T10:10:14.1503110Z Updated tag from Tag: cp310-cp310-linux_aarch64
2025-09-10T10:10:14.1503482Z  to Tag: cp310-cp310-manylinux_2_28_aarch64
2025-09-10T10:10:14.1503682Z
2025-09-10T10:10:41.6498892Z Repacking wheel as /pytorch/dist/torch-2.10.0.dev20250910+cu130-cp310-cp310-manylinux_2_28_aarch64.whl...OK
2025-09-10T10:10:41.9394460Z Renaming torch-2.10.0.dev20250910+cu130-cp310-cp310-linux_aarch64.whl wheel to torch-2.10.0.dev20250910+cu130-cp310-cp310-manylinux_2_28_aarch64.whl
```

Test Plan, Executed on local file:
```
  inflating: ubuntu/dist/tmp/torch-2.9.0.dev20250909+cu130.dist-info/WHEEL
  inflating: ubuntu/dist/tmp/torch-2.9.0.dev20250909+cu130.dist-info/entry_points.txt
  inflating: ubuntu/dist/tmp/torch-2.9.0.dev20250909+cu130.dist-info/top_level.txt
  inflating: ubuntu/dist/tmp/torch-2.9.0.dev20250909+cu130.dist-info/RECORD
Bundling CUDA libraries with wheel
Updated tag from Tag: cp310-cp310-manylinux_2_28_aarch64
 to Tag: cp310-cp310-manylinux_2_28_aarch64

Repacking wheel as ubuntu/dist/torch-2.9.0.dev20250909+cu130-cp310-cp310-manylinux_2_28_aarch64.whl...OK
Copying torch-2.9.0.dev20250909+cu130-cp310-cp310-manylinux_2_28_aarch64.whl to artifacts
Build Complete. Created torch-2.9.0.dev20250909+cu130-cp310-cp310-manylinux_2_28_aarch64.whl..
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162566
Approved by: https://github.com/jeanschmidt, https://github.com/NicolasHug

(cherry picked from commit 3d32bb114bf0d5bd0193dc40f20253635dddf080)

Co-authored-by: atalman <atalman@fb.com>
2025-09-10 12:22:02 -04:00
ce928e17c1 CUDA 13.0 Windows Nvidia Driver Update to 580.88 (#162501)
CUDA 13.0 Windows Nvidia Driver Update to 580.88 (#162425)

Related to https://github.com/pytorch/pytorch/issues/162333
https://github.com/pytorch/pytorch/issues/159779

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162425
Approved by: https://github.com/tinglvv, https://github.com/malfet

(cherry picked from commit e38e953432764e00f16999c8b7df6346ad357a16)

Co-authored-by: atalman <atalman@fb.com>
2025-09-09 14:27:57 -04:00
cd2c98a5b5 [Release 2.9] Release only changes (#162493) 2025-09-09 11:15:20 -07:00
1924 changed files with 25010 additions and 59204 deletions

View File

@ -33,7 +33,8 @@ pip install -r /pytorch/requirements.txt
pip install auditwheel==6.2.0 wheel
if [ "$DESIRED_CUDA" = "cpu" ]; then
echo "BASE_CUDA_VERSION is not set. Building cpu wheel."
python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn
#USE_PRIORITIZED_TEXT_FOR_LD for enable linker script optimization https://github.com/pytorch/pytorch/pull/121975/files
USE_PRIORITIZED_TEXT_FOR_LD=1 python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn
else
echo "BASE_CUDA_VERSION is set to: $DESIRED_CUDA"
export USE_SYSTEM_NCCL=1
@ -47,5 +48,6 @@ else
export USE_NVIDIA_PYPI_LIBS=1
fi
python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
#USE_PRIORITIZED_TEXT_FOR_LD for enable linker script optimization https://github.com/pytorch/pytorch/pull/121975/files
USE_PRIORITIZED_TEXT_FOR_LD=1 python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
fi

View File

@ -13,6 +13,49 @@ def list_dir(path: str) -> list[str]:
return check_output(["ls", "-1", path]).decode().split("\n")
def build_ArmComputeLibrary() -> None:
"""
Using ArmComputeLibrary for aarch64 PyTorch
"""
print("Building Arm Compute Library")
acl_build_flags = [
"debug=0",
"neon=1",
"opencl=0",
"os=linux",
"openmp=1",
"cppthreads=0",
"arch=armv8a",
"multi_isa=1",
"fixed_format_kernels=1",
"build=native",
]
acl_install_dir = "/acl"
acl_checkout_dir = os.getenv("ACL_SOURCE_DIR", "ComputeLibrary")
if os.path.isdir(acl_install_dir):
shutil.rmtree(acl_install_dir)
if not os.path.isdir(acl_checkout_dir) or not len(os.listdir(acl_checkout_dir)):
check_call(
[
"git",
"clone",
"https://github.com/ARM-software/ComputeLibrary.git",
"-b",
"v25.02",
"--depth",
"1",
"--shallow-submodules",
]
)
check_call(
["scons", "Werror=1", f"-j{os.cpu_count()}"] + acl_build_flags,
cwd=acl_checkout_dir,
)
for d in ["arm_compute", "include", "utils", "support", "src", "build"]:
shutil.copytree(f"{acl_checkout_dir}/{d}", f"{acl_install_dir}/{d}")
def replace_tag(filename) -> None:
with open(filename) as f:
lines = f.readlines()
@ -274,7 +317,7 @@ if __name__ == "__main__":
).decode()
print("Building PyTorch wheel")
build_vars = ""
build_vars = "CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000 "
# MAX_JOB=5 is not required for CPU backend (see commit 465d98b)
if enable_cuda:
build_vars += "MAX_JOBS=5 "
@ -313,17 +356,23 @@ if __name__ == "__main__":
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1 : branch.find('-')]} PYTORCH_BUILD_NUMBER=1 "
if enable_mkldnn:
build_ArmComputeLibrary()
print("build pytorch with mkldnn+acl backend")
build_vars += "USE_MKLDNN=ON USE_MKLDNN_ACL=ON "
build_vars += "ACL_ROOT_DIR=/acl "
build_vars += (
"USE_MKLDNN=ON USE_MKLDNN_ACL=ON "
"ACL_ROOT_DIR=/acl "
"LD_LIBRARY_PATH=/pytorch/build/lib:/acl/build:$LD_LIBRARY_PATH "
"ACL_INCLUDE_DIR=/acl/build "
"ACL_LIBRARY=/acl/build "
)
if enable_cuda:
build_vars += "BLAS=NVPL "
else:
build_vars += "BLAS=OpenBLAS OpenBLAS_HOME=/opt/OpenBLAS "
build_vars += "BLAS=OpenBLAS OpenBLAS_HOME=/OpenBLAS "
else:
print("build pytorch without mkldnn backend")
os.system(f"cd /pytorch; {build_vars} python3 -m build --wheel --no-isolation")
os.system(f"cd /pytorch; {build_vars} python3 setup.py bdist_wheel")
if enable_cuda:
print("Updating Cuda Dependency")
filename = os.listdir("/pytorch/dist/")

View File

@ -241,7 +241,7 @@ def wait_for_connection(addr, port, timeout=15, attempt_cnt=5):
try:
with socket.create_connection((addr, port), timeout=timeout):
return
except (ConnectionRefusedError, TimeoutError): # noqa: PERF203
except (ConnectionRefusedError, socket.timeout): # noqa: PERF203
if i == attempt_cnt - 1:
raise
time.sleep(timeout)
@ -299,6 +299,40 @@ def install_condaforge_python(host: RemoteHost, python_version="3.8") -> None:
)
def build_OpenBLAS(host: RemoteHost, git_clone_flags: str = "") -> None:
print("Building OpenBLAS")
host.run_cmd(
f"git clone https://github.com/xianyi/OpenBLAS -b v0.3.28 {git_clone_flags}"
)
make_flags = "NUM_THREADS=64 USE_OPENMP=1 NO_SHARED=1 DYNAMIC_ARCH=1 TARGET=ARMV8"
host.run_cmd(
f"pushd OpenBLAS && make {make_flags} -j8 && sudo make {make_flags} install && popd && rm -rf OpenBLAS"
)
def build_ArmComputeLibrary(host: RemoteHost, git_clone_flags: str = "") -> None:
print("Building Arm Compute Library")
acl_build_flags = " ".join(
[
"debug=0",
"neon=1",
"opencl=0",
"os=linux",
"openmp=1",
"cppthreads=0",
"arch=armv8a",
"multi_isa=1",
"fixed_format_kernels=1",
"build=native",
]
)
host.run_cmd(
f"git clone https://github.com/ARM-software/ComputeLibrary.git -b v25.02 {git_clone_flags}"
)
host.run_cmd(f"cd ComputeLibrary && scons Werror=1 -j8 {acl_build_flags}")
def embed_libgomp(host: RemoteHost, use_conda, wheel_name) -> None:
host.run_cmd("pip3 install auditwheel")
host.run_cmd(
@ -408,7 +442,7 @@ def build_torchvision(
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
host.run_cmd(f"cd vision && {build_vars} python3 -m build --wheel --no-isolation")
host.run_cmd(f"cd vision && {build_vars} python3 setup.py bdist_wheel")
vision_wheel_name = host.list_dir("vision/dist")[0]
embed_libgomp(host, use_conda, os.path.join("vision", "dist", vision_wheel_name))
@ -463,7 +497,7 @@ def build_torchdata(
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
host.run_cmd(f"cd data && {build_vars} python3 -m build --wheel --no-isolation")
host.run_cmd(f"cd data && {build_vars} python3 setup.py bdist_wheel")
wheel_name = host.list_dir("data/dist")[0]
embed_libgomp(host, use_conda, os.path.join("data", "dist", wheel_name))
@ -519,7 +553,7 @@ def build_torchtext(
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
host.run_cmd(f"cd text && {build_vars} python3 -m build --wheel --no-isolation")
host.run_cmd(f"cd text && {build_vars} python3 setup.py bdist_wheel")
wheel_name = host.list_dir("text/dist")[0]
embed_libgomp(host, use_conda, os.path.join("text", "dist", wheel_name))
@ -580,7 +614,7 @@ def build_torchaudio(
host.run_cmd(
f"cd audio && export FFMPEG_ROOT=$(pwd)/third_party/ffmpeg && export USE_FFMPEG=1 \
&& ./packaging/ffmpeg/build.sh \
&& {build_vars} python3 -m build --wheel --no-isolation"
&& {build_vars} python3 setup.py bdist_wheel"
)
wheel_name = host.list_dir("audio/dist")[0]
@ -666,6 +700,7 @@ def start_build(
configure_system(
host, compiler=compiler, use_conda=use_conda, python_version=python_version
)
build_OpenBLAS(host, git_clone_flags)
if host.using_docker():
print("Move libgfortant.a into a standard location")
@ -688,12 +723,10 @@ def start_build(
f"git clone --recurse-submodules -b {branch} https://github.com/pytorch/pytorch {git_clone_flags}"
)
host.run_cmd("pytorch/.ci/docker/common/install_openblas.sh")
print("Building PyTorch wheel")
build_opts = ""
if pytorch_build_number is not None:
build_opts += f" -C--build-option=--build-number={pytorch_build_number}"
build_opts += f" --build-number {pytorch_build_number}"
# Breakpad build fails on aarch64
build_vars = "USE_BREAKPAD=0 "
if branch == "nightly":
@ -710,18 +743,15 @@ def start_build(
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
if enable_mkldnn:
host.run_cmd("pytorch/.ci/docker/common/install_acl.sh")
build_ArmComputeLibrary(host, git_clone_flags)
print("build pytorch with mkldnn+acl backend")
build_vars += " USE_MKLDNN=ON USE_MKLDNN_ACL=ON"
build_vars += " BLAS=OpenBLAS"
build_vars += " OpenBLAS_HOME=/opt/OpenBLAS"
build_vars += " ACL_ROOT_DIR=/acl"
host.run_cmd(
f"cd $HOME/pytorch && {build_vars} python3 -m build --wheel --no-isolation{build_opts}"
f"cd $HOME/pytorch && export ACL_ROOT_DIR=$HOME/ComputeLibrary && {build_vars} python3 setup.py bdist_wheel{build_opts}"
)
print("Repair the wheel")
pytorch_wheel_name = host.list_dir("pytorch/dist")[0]
ld_library_path = "/acl/build:$HOME/pytorch/build/lib"
ld_library_path = "$HOME/acl/build:$HOME/pytorch/build/lib"
host.run_cmd(
f"export LD_LIBRARY_PATH={ld_library_path} && auditwheel repair $HOME/pytorch/dist/{pytorch_wheel_name}"
)
@ -733,7 +763,7 @@ def start_build(
else:
print("build pytorch without mkldnn backend")
host.run_cmd(
f"cd pytorch && {build_vars} python3 -m build --wheel --no-isolation{build_opts}"
f"cd pytorch && {build_vars} python3 setup.py bdist_wheel{build_opts}"
)
print("Deleting build folder")
@ -877,7 +907,7 @@ def terminate_instances(instance_type: str) -> None:
def parse_arguments():
from argparse import ArgumentParser
parser = ArgumentParser("Build and test AARCH64 wheels using EC2")
parser = ArgumentParser("Builid and test AARCH64 wheels using EC2")
parser.add_argument("--key-name", type=str)
parser.add_argument("--debug", action="store_true")
parser.add_argument("--build-only", action="store_true")
@ -974,7 +1004,7 @@ if __name__ == "__main__":
install_condaforge_python(host, args.python_version)
sys.exit(0)
python_version = args.python_version if args.python_version is not None else "3.10"
python_version = args.python_version if args.python_version is not None else "3.9"
if args.use_torch_from_pypi:
configure_system(host, compiler=args.compiler, python_version=python_version)

View File

@ -69,8 +69,7 @@ RUN bash ./install_cuda.sh 13.0
ENV DESIRED_CUDA=13.0
FROM ${ROCM_IMAGE} as rocm
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ENV PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
ENV MKLROOT /opt/intel

View File

@ -36,12 +36,6 @@ case ${DOCKER_TAG_PREFIX} in
;;
rocm*)
BASE_TARGET=rocm
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
# add gfx950 conditionally starting in ROCm 7.0
if [[ "$ROCM_VERSION" == *"7.0"* ]]; then
PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
fi
EXTRA_BUILD_ARGS="${EXTRA_BUILD_ARGS} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
;;
*)
echo "ERROR: Unknown docker tag ${DOCKER_TAG_PREFIX}"

View File

@ -84,8 +84,8 @@ fi
_UCX_COMMIT=7836b165abdbe468a2f607e7254011c07d788152
_UCC_COMMIT=430e241bf5d38cbc73fc7a6b89155397232e3f96
if [[ "$image" == *rocm* ]]; then
_UCX_COMMIT=29831d319e6be55cb8c768ca61de335c934ca39e
_UCC_COMMIT=9f4b242cbbd8b1462cbc732eb29316cdfa124b77
_UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
_UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
fi
tag=$(echo $image | awk -F':' '{print $2}')
@ -175,6 +175,20 @@ case "$tag" in
fi
GCC_VERSION=11
VISION=yes
ROCM_VERSION=6.4
NINJA_VERSION=1.9.0
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
if [[ $tag =~ "benchmarks" ]]; then
INDUCTOR_BENCHMARKS=yes
fi
;;
pytorch-linux-noble-rocm-alpha-py3)
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
VISION=yes
ROCM_VERSION=7.0
NINJA_VERSION=1.9.0
TRITON=yes
@ -182,9 +196,6 @@ case "$tag" in
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
PYTORCH_ROCM_ARCH="gfx90a;gfx942;gfx950"
if [[ $tag =~ "benchmarks" ]]; then
INDUCTOR_BENCHMARKS=yes
fi
;;
pytorch-linux-jammy-xpu-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
@ -441,3 +452,12 @@ elif [ "$HAS_TRITON" = "yes" ]; then
echo "expecting triton to not be installed, but it is"
exit 1
fi
# Sanity check cmake version. Executorch reinstalls cmake and I'm not sure if
# they support 4.0.0 yet, so exclude them from this check.
CMAKE_VERSION=$(drun cmake --version)
if [[ "$EXECUTORCH" != *yes* && "$CMAKE_VERSION" != *4.* ]]; then
echo "CMake version is not 4.0.0:"
drun cmake --version
exit 1
fi

View File

@ -56,13 +56,9 @@ ENV INSTALLED_VISION ${VISION}
# Install rocm
ARG ROCM_VERSION
RUN mkdir ci_commit_pins
COPY ./common/common_utils.sh common_utils.sh
COPY ./ci_commit_pins/rocm-composable-kernel.txt ci_commit_pins/rocm-composable-kernel.txt
COPY ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh common_utils.sh
RUN rm -r ci_commit_pins
RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
RUN rm install_rocm_magma.sh

View File

@ -1 +1 @@
e0dda9059d082537cee36be6c5e4fe3b18c880c0
56392aa978594cc155fa8af48cd949f5b5f1823a

View File

@ -1,2 +1,2 @@
transformers==4.56.0
transformers==4.54.0
soxr==0.5.0

View File

@ -1 +1 @@
v2.28.3-1
v2.27.5-1

View File

@ -1 +1 @@
v2.28.3-1
v2.27.7-1

View File

@ -1 +0,0 @@
7fe50dc3da2069d6645d9deb8c017a876472a977

View File

@ -1 +1 @@
27664085f804afc83df26f740bb46c365854f2c4
bbb06c0334a6772b92d24bde54956e675c8c6604

27
.ci/docker/common/install_acl.sh Executable file → Normal file
View File

@ -1,27 +1,16 @@
#!/bin/bash
# Script used only in CD pipeline
set -euo pipefail
set -eux
ACL_VERSION=${ACL_VERSION:-"v25.02"}
ACL_INSTALL_DIR="/acl"
readonly version=v25.02
readonly src_host=https://github.com/ARM-software
readonly src_repo=ComputeLibrary
# Clone ACL
git clone https://github.com/ARM-software/ComputeLibrary.git -b "${ACL_VERSION}" --depth 1 --shallow-submodules
[[ ! -d ${src_repo} ]] && git clone ${src_host}/${src_repo}.git
cd ${src_repo}
git checkout $version
ACL_CHECKOUT_DIR="ComputeLibrary"
# Build with scons
pushd $ACL_CHECKOUT_DIR
scons -j8 Werror=0 debug=0 neon=1 opencl=0 embed_kernels=0 \
os=linux arch=armv8a build=native multi_isa=1 \
fixed_format_kernels=1 openmp=1 cppthreads=0
popd
# Install ACL
sudo mkdir -p ${ACL_INSTALL_DIR}
for d in arm_compute include utils support src build
do
sudo cp -r ${ACL_CHECKOUT_DIR}/${d} ${ACL_INSTALL_DIR}/${d}
done
rm -rf $ACL_CHECKOUT_DIR

View File

@ -42,27 +42,22 @@ install_pip_dependencies() {
# A workaround, ExecuTorch has moved to numpy 2.0 which is not compatible with the current
# numba and scipy version used in PyTorch CI
conda_run pip uninstall -y numba scipy
# Yaspin is needed for running CI test (get_benchmark_analysis_data.py)
pip_install yaspin==3.1.0
popd
}
setup_executorch() {
pushd executorch
export PYTHON_EXECUTABLE=python
export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON -DEXECUTORCH_BUILD_TESTS=ON"
export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
as_jenkins .ci/scripts/setup-linux.sh --build-tool cmake || true
popd
}
if [ $# -eq 0 ]; then
clone_executorch
install_buck2
install_conda_dependencies
install_pip_dependencies
pushd executorch
setup_executorch
popd
else
"$@"
fi
clone_executorch
install_buck2
install_conda_dependencies
install_pip_dependencies
setup_executorch

12
.ci/docker/common/install_openblas.sh Executable file → Normal file
View File

@ -3,10 +3,8 @@
set -ex
OPENBLAS_VERSION=${OPENBLAS_VERSION:-"v0.3.30"}
# Clone OpenBLAS
git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION}" --depth 1 --shallow-submodules
cd /
git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION:-v0.3.30}" --depth 1 --shallow-submodules
OPENBLAS_CHECKOUT_DIR="OpenBLAS"
OPENBLAS_BUILD_FLAGS="
@ -19,7 +17,5 @@ CFLAGS=-O3
BUILD_BFLOAT16=1
"
make -j8 ${OPENBLAS_BUILD_FLAGS} -C $OPENBLAS_CHECKOUT_DIR
sudo make install -C $OPENBLAS_CHECKOUT_DIR
rm -rf $OPENBLAS_CHECKOUT_DIR
make -j8 ${OPENBLAS_BUILD_FLAGS} -C ${OPENBLAS_CHECKOUT_DIR}
make -j8 ${OPENBLAS_BUILD_FLAGS} install -C ${OPENBLAS_CHECKOUT_DIR}

View File

@ -2,11 +2,6 @@
set -ex
# for pip_install function
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
ROCM_COMPOSABLE_KERNEL_VERSION="$(cat $(dirname $0)/../ci_commit_pins/rocm-composable-kernel.txt)"
ver() {
printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
}
@ -42,6 +37,12 @@ EOF
rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
# Special case for ROCM_VERSION == 7.0
if [[ $(ver "$ROCM_VERSION") -eq $(ver 7.0) ]]; then
rocm_baseurl="https://repo.radeon.com/rocm/apt/7.0_alpha2"
amdgpu_baseurl="https://repo.radeon.com/amdgpu/30.10_alpha2/ubuntu"
fi
# Add amdgpu repository
UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
@ -112,8 +113,6 @@ EOF
rm -rf HIP clr
fi
pip_install "git+https://github.com/rocm/composable_kernel@$ROCM_COMPOSABLE_KERNEL_VERSION"
# Cleanup
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
@ -177,8 +176,6 @@ install_centos() {
sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
done
pip_install "git+https://github.com/rocm/composable_kernel@$ROCM_COMPOSABLE_KERNEL_VERSION"
# Cleanup
yum clean all
rm -rf /var/cache/yum

View File

@ -12,8 +12,8 @@ function do_install() {
rocm_version_nodot=${rocm_version//./}
# https://github.com/icl-utk-edu/magma/pull/65
MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
# Version 2.7.2 + ROCm related updates
MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
rocm_dir="/opt/rocm"

View File

@ -66,15 +66,15 @@ if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}"
# Triton needs at least gcc-9 to build
apt-get install -y g++-9
CXX=g++-9 conda_run python -m build --wheel --no-isolation
CXX=g++-9 conda_run python setup.py bdist_wheel
elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
# Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
add-apt-repository -y ppa:ubuntu-toolchain-r/test
apt-get install -y g++-9
CXX=g++-9 conda_run python -m build --wheel --no-isolation
CXX=g++-9 conda_run python setup.py bdist_wheel
else
conda_run python -m build --wheel --no-isolation
conda_run python setup.py bdist_wheel
fi
# Copy the wheel to /opt for multi stage docker builds

View File

@ -0,0 +1,9 @@
#!/bin/bash
set -xe
# Script used in Linux x86 and aarch64 CD pipeline
# Workaround for exposing statically linked libstdc++ CXX11 ABI symbols.
# see: https://github.com/pytorch/pytorch/issues/133437
LIBNONSHARED=$(gcc -print-file-name=libstdc++_nonshared.a)
nm -g $LIBNONSHARED | grep " T " | grep recursive_directory_iterator | cut -c 20- > weaken-symbols.txt
objcopy --weaken-symbols weaken-symbols.txt $LIBNONSHARED $LIBNONSHARED

View File

@ -40,16 +40,12 @@ case ${DOCKER_TAG_PREFIX} in
;;
rocm*)
# we want the patch version of 6.4 instead
if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
fi
BASE_TARGET=rocm
GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
# add gfx950 conditionally starting in ROCm 7.0
if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
fi
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
;;
*)

View File

@ -130,7 +130,8 @@ ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/op
RUN for cpython_version in "cp312-cp312" "cp313-cp313" "cp313-cp313t"; do \
/opt/python/${cpython_version}/bin/python -m pip install setuptools wheel; \
done;
ADD ./common/patch_libstdc.sh patch_libstdc.sh
RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
# cmake-3.18.4 from pip; force in case cmake3 already exists
RUN yum install -y python3-pip && \

View File

@ -62,13 +62,6 @@ ARG OPENBLAS_VERSION
ADD ./common/install_openblas.sh install_openblas.sh
RUN bash ./install_openblas.sh && rm install_openblas.sh
# Install Arm Compute Library
FROM base as arm_compute
# use python3.9 to install scons
RUN python3.9 -m pip install scons==4.7.0
RUN ln -sf /opt/python/cp39-cp39/bin/scons /usr/local/bin
COPY ./common/install_acl.sh install_acl.sh
RUN bash ./install_acl.sh && rm install_acl.sh
FROM base as final
# remove unnecessary python versions
@ -77,5 +70,6 @@ RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
COPY --from=openblas /opt/OpenBLAS/ /opt/OpenBLAS/
COPY --from=arm_compute /acl /acl
ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:/acl/build/:$LD_LIBRARY_PATH
ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:$LD_LIBRARY_PATH
ADD ./common/patch_libstdc.sh patch_libstdc.sh
RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh

View File

@ -86,15 +86,6 @@ FROM base as nvpl
ADD ./common/install_nvpl.sh install_nvpl.sh
RUN bash ./install_nvpl.sh && rm install_nvpl.sh
# Install Arm Compute Library
FROM base as arm_compute
# use python3.9 to install scons
RUN python3.9 -m pip install scons==4.7.0
RUN ln -sf /opt/python/cp39-cp39/bin/scons /usr/local/bin
COPY ./common/install_acl.sh install_acl.sh
RUN bash ./install_acl.sh && rm install_acl.sh
FROM base as final
FROM final as cuda_final
ARG BASE_CUDA_VERSION
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
@ -102,7 +93,7 @@ COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BAS
COPY --from=magma /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=nvpl /opt/nvpl/lib/ /usr/local/lib/
COPY --from=nvpl /opt/nvpl/include/ /usr/local/include/
COPY --from=arm_compute /acl /acl
RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
ENV PATH=/usr/local/cuda/bin:$PATH
ENV LD_LIBRARY_PATH=/acl/build/:$LD_LIBRARY_PATH
ADD ./common/patch_libstdc.sh patch_libstdc.sh
RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh

View File

@ -0,0 +1,71 @@
FROM centos:8 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ENV PATH /opt/rh/gcc-toolset-11/root/bin/:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
# change to a valid repo
RUN sed -i 's|#baseurl=http://mirror.centos.org|baseurl=http://vault.centos.org|g' /etc/yum.repos.d/CentOS-Linux-*.repo
# enable to install ninja-build
RUN sed -i 's|enabled=0|enabled=1|g' /etc/yum.repos.d/CentOS-Linux-PowerTools.repo
RUN yum -y update
RUN yum install -y wget curl perl util-linux xz bzip2 git patch which zlib-devel sudo
RUN yum install -y autoconf automake make cmake gdb gcc-toolset-11-gcc-c++
FROM base as openssl
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# Install python
FROM base as python
RUN yum install -y openssl-devel zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel libpcap-devel xz-devel libffi-devel
ADD common/install_cpython.sh install_cpython.sh
RUN bash ./install_cpython.sh && rm install_cpython.sh
FROM base as conda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
RUN /opt/conda/bin/conda install -y cmake
FROM base as intel
# Install MKL
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
COPY --from=conda /opt/conda /opt/conda
ENV PATH=/opt/conda/bin:$PATH
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM base as patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh
RUN cp $(which patchelf) /patchelf
FROM base as jni
ADD ./common/install_jni.sh install_jni.sh
ADD ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
FROM base as libpng
ADD ./common/install_libpng.sh install_libpng.sh
RUN bash ./install_libpng.sh && rm install_libpng.sh
FROM base as final
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
COPY --from=intel /opt/intel /opt/intel
COPY --from=conda /opt/conda /opt/conda
COPY --from=patchelf /usr/local/bin/patchelf /usr/local/bin/patchelf
COPY --from=jni /usr/local/include/jni.h /usr/local/include/jni.h
COPY --from=libpng /usr/local/bin/png* /usr/local/bin/
COPY --from=libpng /usr/local/bin/libpng* /usr/local/bin/
COPY --from=libpng /usr/local/include/png* /usr/local/include/
COPY --from=libpng /usr/local/include/libpng* /usr/local/include/
COPY --from=libpng /usr/local/lib/libpng* /usr/local/lib/
COPY --from=libpng /usr/local/lib/pkgconfig /usr/local/lib/pkgconfig
RUN yum install -y ninja-build

View File

@ -28,7 +28,6 @@ fi
MANY_LINUX_VERSION=${MANY_LINUX_VERSION:-}
DOCKERFILE_SUFFIX=${DOCKERFILE_SUFFIX:-}
OPENBLAS_VERSION=${OPENBLAS_VERSION:-}
ACL_VERSION=${ACL_VERSION:-}
case ${image} in
manylinux2_28-builder:cpu)
@ -42,6 +41,13 @@ case ${image} in
GPU_IMAGE=arm64v8/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13 --build-arg NINJA_VERSION=1.12.1"
MANY_LINUX_VERSION="2_28_aarch64"
OPENBLAS_VERSION="v0.3.30"
;;
manylinuxcxx11-abi-builder:cpu-cxx11-abi)
TARGET=final
GPU_IMAGE=""
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
MANY_LINUX_VERSION="cxx11-abi"
;;
manylinuxs390x-builder:cpu-s390x)
TARGET=final
@ -76,7 +82,7 @@ case ${image} in
;;
manylinux2_28-builder:rocm*)
# we want the patch version of 6.4 instead
if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
fi
TARGET=rocm_final
@ -84,10 +90,6 @@ case ${image} in
DEVTOOLSET_VERSION="11"
GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
# add gfx950 conditionally starting in ROCm 7.0
if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
PYTORCH_ROCM_ARCH="${PYTORCH_ROCM_ARCH};gfx950"
fi
DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
;;
manylinux2_28-builder:xpu)
@ -119,8 +121,7 @@ tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
DOCKER_BUILDKIT=1 docker build \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--build-arg "OPENBLAS_VERSION=${OPENBLAS_VERSION:-}" \
--build-arg "ACL_VERSION=${ACL_VERSION:-}" \
--build-arg "OPENBLAS_VERSION=${OPENBLAS_VERSION}" \
--target "${TARGET}" \
-t "${tmp_tag}" \
$@ \

View File

@ -10,11 +10,6 @@ boto3==1.35.42
#Pinned versions: 1.19.12, 1.16.34
#test that import:
build==1.3.0
#Description: A simple, correct Python build frontend.
#Pinned versions: 1.3.0
#test that import:
click
#Description: Command Line Interface Creation Kit
#Pinned versions:
@ -52,10 +47,10 @@ flatbuffers==24.12.23
#Pinned versions: 24.12.23
#test that import:
hypothesis==6.56.4
hypothesis==5.35.1
# Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
#Description: advanced library for generating parametrized tests
#Pinned versions: 6.56.4
#Pinned versions: 5.35.1
#test that import: test_xnnpack_integration.py, test_pruning_op.py, test_nn.py
junitparser==2.1.1
@ -98,7 +93,7 @@ librosa==0.10.2 ; python_version == "3.12" and platform_machine != "s390x"
#Pinned versions:
#test that import:
mypy==1.16.0 ; platform_system == "Linux"
mypy==1.16.0 ; platform_system != "Windows"
# Pin MyPy version because new errors are likely to appear with each release
# Skip on Windows as lots of type annotations are POSIX specific
#Description: linter
@ -111,12 +106,14 @@ networkx==2.8.8
#Pinned versions: 2.8.8
#test that import: functorch
ninja==1.11.1.4
ninja==1.11.1.3
#Description: build system. Used in some tests. Used in build to generate build
#time tracing information
#Pinned versions: 1.11.1.4
#Pinned versions: 1.11.1.3
#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
numba==0.49.0 ; python_version < "3.9" and platform_machine != "s390x"
numba==0.55.2 ; python_version == "3.9" and platform_machine != "s390x"
numba==0.55.2 ; python_version == "3.10" and platform_machine != "s390x"
numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
#Description: Just-In-Time Compiler for Numerical Functions
@ -137,7 +134,7 @@ numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
#test_nn.py, test_namedtensor.py, test_linalg.py, test_jit_cuda_fuser.py,
#test_jit.py, test_indexing.py, test_datapipe.py, test_dataloader.py,
#test_binary_ufuncs.py
numpy==1.22.4; python_version == "3.10"
numpy==1.22.4; python_version == "3.9" or python_version == "3.10"
numpy==1.26.2; python_version == "3.11" or python_version == "3.12"
numpy==2.1.2; python_version >= "3.13"
@ -169,12 +166,12 @@ optree==0.13.0
pillow==11.0.0
#Description: Python Imaging Library fork
#Pinned versions: 11.0.0
#Pinned versions: 10.3.0
#test that import:
protobuf==5.29.5
protobuf==5.29.4
#Description: Google's data interchange format
#Pinned versions: 5.29.5
#Pinned versions: 5.29.4
#test that import: test_tensorboard.py, test/onnx/*
psutil
@ -217,7 +214,7 @@ pytest-subtests==0.13.1
#Pinned versions:
#test that import:
xdoctest==1.3.0
xdoctest==1.1.0
#Description: runs doctests in pytest
#Pinned versions: 1.1.0
#test that import:
@ -268,7 +265,7 @@ scipy==1.14.1 ; python_version >= "3.12"
#test that import:
# needed by torchgen utils
typing-extensions==4.12.2
typing-extensions>=4.10.0
#Description: type hints for python
#Pinned versions:
#test that import:
@ -329,6 +326,8 @@ pywavelets==1.7.0 ; python_version >= "3.12"
lxml==5.3.0
#Description: This is a requirement of unittest-xml-reporting
# Python-3.9 binaries
PyGithub==2.3.0
sympy==1.13.3
@ -361,10 +360,9 @@ pwlf==2.2.1
#test that import: test_sac_estimator.py
# To build PyTorch itself
pyyaml==6.0.2
pyyaml
pyzstd
setuptools==78.1.1
packaging==23.1
setuptools>=70.1.0
six
scons==4.5.2 ; platform_machine == "aarch64"
@ -379,16 +377,13 @@ dataclasses_json==0.6.7
#Pinned versions: 0.6.7
#test that import:
cmake==3.31.6
cmake==4.0.0
#Description: required for building
tlparse==0.4.0
#Description: required for log parsing
filelock==3.18.0
#Description: required for inductor testing
cuda-bindings>=12.0,<13.0 ; platform_machine != "s390x" and platform_system != "Darwin"
cuda-bindings>=12.0,<13.0 ; platform_machine != "s390x"
#Description: required for testing CUDAGraph::raw_cuda_graph(). See https://nvidia.github.io/cuda-python/cuda-bindings/latest/support.html for how this version was chosen. Note "Any fix in the latest bindings would be backported to the prior major version" means that only the newest version of cuda-bindings will get fixes. Depending on the latest version of 12.x is okay because all 12.y versions will be supported via "CUDA minor version compatibility". Pytorch builds against 13.z versions of cuda toolkit work with 12.x versions of cuda-bindings as well because newer drivers work with old toolkits.
#test that import: test_cuda.py

View File

@ -1,15 +1,8 @@
sphinx==5.3.0
#Description: This is used to generate PyTorch docs
#Pinned versions: 5.3.0
standard-imghdr==3.13.0; python_version >= "3.13"
#Description: This is needed by Sphinx, so it needs to be added here.
# The reasons are as follows:
# 1) This module has been removed from the Python standard library since Python 3.13(https://peps.python.org/pep-0594/#imghdr);
# 2) The current version of Sphinx (5.3.0) is not compatible with Python 3.13.
# Once Sphinx is upgraded to a version compatible with Python 3.13 or later, we can remove this dependency.
-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@71e55749be14ceb56e7f8211a9fb649866b87ad4#egg=pytorch_sphinx_theme2
# TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
# but it doesn't seem to work and hangs around idly. The initial thought that it is probably
# something related to Docker setup. We can investigate this later.

View File

@ -52,13 +52,9 @@ ENV INSTALLED_VISION ${VISION}
# Install rocm
ARG ROCM_VERSION
RUN mkdir ci_commit_pins
COPY ./common/common_utils.sh common_utils.sh
COPY ./ci_commit_pins/rocm-composable-kernel.txt ci_commit_pins/rocm-composable-kernel.txt
COPY ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh common_utils.sh
RUN rm -r ci_commit_pins
RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
RUN rm install_rocm_magma.sh

View File

@ -66,11 +66,6 @@ class VllmBuildParameters:
"DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile.tmp_vllm"
)
# the cleaning script to remove torch dependencies from pip
cleaning_script: Path = env_path_field(
"cleaning_script", ".github/ci_configs/vllm/use_existing_torch.py"
)
# OUTPUT_DIR: where docker buildx (local exporter) will write artifacts
output_dir: Path = env_path_field("OUTPUT_DIR", "external/vllm")
@ -165,7 +160,6 @@ class VllmBuildRunner(BaseRunner):
logger.info("Running vllm build with inputs: %s", inputs)
vllm_commit = clone_vllm()
self.cp_torch_cleaning_script(inputs)
self.cp_dockerfile_if_exist(inputs)
# cp torch wheels from root direct to vllm workspace if exist
self.cp_torch_whls_if_exist(inputs)
@ -211,11 +205,6 @@ class VllmBuildRunner(BaseRunner):
copy(inputs.torch_whls_path, tmp_dir)
return tmp_dir
def cp_torch_cleaning_script(self, inputs: VllmBuildParameters):
script = get_path(inputs.cleaning_script, resolve=True)
vllm_script = Path(f"./{self.work_directory}/use_existing_torch.py")
copy(script, vllm_script)
def cp_dockerfile_if_exist(self, inputs: VllmBuildParameters):
if not inputs.use_local_dockerfile:
logger.info("using vllm default dockerfile.torch_nightly for build")

View File

@ -11,7 +11,7 @@ from typing import Any
from cli.lib.common.cli_helper import BaseRunner
from cli.lib.common.envs_helper import env_path_field, env_str_field, get_env
from cli.lib.common.path_helper import copy, get_path, remove_dir
from cli.lib.common.path_helper import copy, remove_dir
from cli.lib.common.pip_helper import (
pip_install_first_match,
pip_install_packages,
@ -43,10 +43,6 @@ class VllmTestParameters:
torch_cuda_arch_list: str = env_str_field("TORCH_CUDA_ARCH_LIST", "8.9")
cleaning_script: Path = env_path_field(
"cleaning_script", ".github/ci_configs/vllm/use_existing_torch.py"
)
def __post_init__(self):
if not self.torch_whls_path.exists():
raise ValueError("missing torch_whls_path")
@ -96,13 +92,11 @@ class VllmTestRunner(BaseRunner):
self._set_envs(params)
clone_vllm(dst=self.work_directory)
self.cp_torch_cleaning_script(params)
with working_directory(self.work_directory):
remove_dir(Path("vllm"))
self._install_wheels(params)
self._install_dependencies()
# verify the torches are not overridden by test dependencies
check_versions()
def run(self):
@ -131,11 +125,6 @@ class VllmTestRunner(BaseRunner):
# double check the torches are not overridden by other packages
check_versions()
def cp_torch_cleaning_script(self, params: VllmTestParameters):
script = get_path(params.cleaning_script, resolve=True)
vllm_script = Path(f"./{self.work_directory}/use_existing_torch.py")
copy(script, vllm_script)
def _install_wheels(self, params: VllmTestParameters):
logger.info("Running vllm test with inputs: %s", params)
if not pkg_exists("torch"):

View File

@ -1,11 +1,11 @@
SHELL=/usr/bin/env bash
DOCKER_CMD ?= docker
DESIRED_ROCM ?= 7.0
DESIRED_ROCM ?= 6.4
DESIRED_ROCM_SHORT = $(subst .,,$(DESIRED_ROCM))
PACKAGE_NAME = magma-rocm
# inherit this from underlying docker image, do not pass this env var to docker
#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201
#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201
DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
-v $(shell git rev-parse --show-toplevel)/.ci:/builder \
@ -16,7 +16,6 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
magma-rocm/build_magma.sh
.PHONY: all
all: magma-rocm70
all: magma-rocm64
all: magma-rocm63
@ -25,11 +24,6 @@ clean:
$(RM) -r magma-*
$(RM) -r output
.PHONY: magma-rocm70
magma-rocm70: DESIRED_ROCM := 7.0
magma-rocm70:
$(DOCKER_RUN)
.PHONY: magma-rocm64
magma-rocm64: DESIRED_ROCM := 6.4
magma-rocm64:

View File

@ -6,8 +6,8 @@ set -eou pipefail
# The script expects DESIRED_CUDA and PACKAGE_NAME to be set
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
# https://github.com/icl-utk-edu/magma/pull/65
MAGMA_VERSION=d6e4117bc88e73f06d26c6c2e14f064e8fc3d1ec
# Version 2.7.2 + ROCm related updates
MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
# Folders for the build
PACKAGE_FILES=${ROOT_DIR}/magma-rocm/package_files # metadata
@ -20,7 +20,7 @@ mkdir -p ${PACKAGE_DIR} ${PACKAGE_OUTPUT}/linux-64 ${PACKAGE_BUILD} ${PACKAGE_RE
# Fetch magma sources and verify checksum
pushd ${PACKAGE_DIR}
git clone https://github.com/jeffdaily/magma
git clone https://bitbucket.org/icl/magma.git
pushd magma
git checkout ${MAGMA_VERSION}
popd

View File

@ -142,7 +142,7 @@ time CMAKE_ARGS=${CMAKE_ARGS[@]} \
EXTRA_CAFFE2_CMAKE_FLAGS=${EXTRA_CAFFE2_CMAKE_FLAGS[@]} \
BUILD_LIBTORCH_CPU_WITH_DEBUG=$BUILD_DEBUG_INFO \
USE_NCCL=${USE_NCCL} USE_RCCL=${USE_RCCL} USE_KINETO=${USE_KINETO} \
python -m build --wheel --no-isolation --outdir /tmp/$WHEELHOUSE_DIR
python setup.py bdist_wheel -d /tmp/$WHEELHOUSE_DIR
echo "Finished setup.py bdist at $(date)"
# Build libtorch packages

View File

@ -104,7 +104,7 @@ if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
export ROCclr_DIR=/opt/rocm/rocclr/lib/cmake/rocclr
fi
echo "Calling -m pip install . -v --no-build-isolation at $(date)"
echo "Calling 'python -m pip install .' at $(date)"
if [[ $LIBTORCH_VARIANT = *"static"* ]]; then
STATIC_CMAKE_FLAG="-DTORCH_STATIC=1"

View File

@ -107,10 +107,6 @@ if [[ $ROCM_INT -ge 60200 ]]; then
ROCM_SO_FILES+=("librocm-core.so")
fi
if [[ $ROCM_INT -ge 70000 ]]; then
ROCM_SO_FILES+=("librocroller.so")
fi
OS_NAME=`awk -F= '/^NAME/{print $2}' /etc/os-release`
if [[ "$OS_NAME" == *"CentOS Linux"* || "$OS_NAME" == *"AlmaLinux"* ]]; then
LIBGOMP_PATH="/usr/lib64/libgomp.so.1"

View File

@ -89,7 +89,7 @@ fi
if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
export USE_MKLDNN=1
export USE_MKLDNN_ACL=1
export ACL_ROOT_DIR=/acl
export ACL_ROOT_DIR=/ComputeLibrary
fi
if [[ "$BUILD_ENVIRONMENT" == *riscv64* ]]; then
@ -290,13 +290,13 @@ else
WERROR=1 python setup.py clean
WERROR=1 python -m build --wheel --no-isolation
WERROR=1 python setup.py bdist_wheel
else
python setup.py clean
if [[ "$BUILD_ENVIRONMENT" == *xla* ]]; then
source .ci/pytorch/install_cache_xla.sh
fi
python -m build --wheel --no-isolation
python setup.py bdist_wheel
fi
pip_install_whl "$(echo dist/*.whl)"

View File

@ -67,7 +67,7 @@ fi
# wheels with cxx11-abi
echo "Checking that the gcc ABI is what we expect"
if [[ "$(uname)" != 'Darwin' ]]; then
if [[ "$(uname)" != 'Darwin' && "$(uname -m)" != "s390x" ]]; then
# We also check that there are cxx11 symbols in libtorch
#
echo "Checking that symbols in libtorch.so have the right gcc abi"

View File

@ -258,19 +258,11 @@ function install_torchrec_and_fbgemm() {
git clone --recursive https://github.com/pytorch/fbgemm
pushd fbgemm/fbgemm_gpu
git checkout "${fbgemm_commit}" --recurse-submodules
# until the fbgemm_commit includes the tbb patch
patch <<'EOF'
--- a/FbgemmGpu.cmake
+++ b/FbgemmGpu.cmake
@@ -184,5 +184,6 @@ gpu_cpp_library(
fbgemm_gpu_tbe_cache
fbgemm_gpu_tbe_optimizers
fbgemm_gpu_tbe_utils
+ tbb
DESTINATION
fbgemm_gpu)
EOF
python setup.py bdist_wheel --build-variant=rocm
python setup.py bdist_wheel \
--build-variant=rocm \
-DHIP_ROOT_DIR="${ROCM_PATH}" \
-DCMAKE_C_FLAGS="-DTORCH_USE_HIP_DSA" \
-DCMAKE_CXX_FLAGS="-DTORCH_USE_HIP_DSA"
popd
# Save the wheel before cleaning up
@ -292,7 +284,7 @@ EOF
function clone_pytorch_xla() {
if [[ ! -d ./xla ]]; then
git clone --recursive --quiet https://github.com/pytorch/xla.git
git clone --recursive -b r2.9 https://github.com/pytorch/xla.git
pushd xla
# pin the xla hash so that we don't get broken by changes to xla
git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"

View File

@ -0,0 +1,40 @@
#!/bin/bash
# This is where the local pytorch install in the docker image is located
pt_checkout="/var/lib/jenkins/workspace"
source "$pt_checkout/.ci/pytorch/common_utils.sh"
echo "functorch_doc_push_script.sh: Invoked with $*"
set -ex -o pipefail
version=${DOCS_VERSION:-nightly}
echo "version: $version"
# Build functorch docs
pushd $pt_checkout/functorch/docs
make html
popd
git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
pushd functorch_ghpages
if [ "$version" == "main" ]; then
version=nightly
fi
git rm -rf "$version" || true
mv "$pt_checkout/functorch/docs/build/html" "$version"
git add "$version" || true
git status
git config user.email "soumith+bot@pytorch.org"
git config user.name "pytorchbot"
# If there aren't changes, don't make a commit; push is no-op
git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
git status
if [[ "${WITH_PUSH:-}" == true ]]; then
git push -u origin gh-pages
fi
popd

View File

@ -36,11 +36,11 @@ fi
print_cmake_info
if [[ ${BUILD_ENVIRONMENT} == *"distributed"* ]]; then
# Needed for inductor benchmarks, as lots of HF networks make `torch.distribtued` calls
USE_DISTRIBUTED=1 USE_OPENMP=1 WERROR=1 python -m build --wheel --no-isolation
USE_DISTRIBUTED=1 USE_OPENMP=1 WERROR=1 python setup.py bdist_wheel
else
# Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
# that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python -m build --wheel --no-isolation -C--build-option=--plat-name=macosx_11_0_arm64
USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel --plat-name macosx_11_0_arm64
fi
if which sccache > /dev/null; then
print_sccache_stats

View File

@ -55,7 +55,7 @@ test_python_shard() {
setup_test_python
time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests --shard "$1" "$NUM_TEST_SHARDS"
time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --shard "$1" "$NUM_TEST_SHARDS"
assert_git_not_dirty
}

View File

@ -26,7 +26,6 @@ if [[ "${SHARD_NUMBER:-2}" == "2" ]]; then
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
time python test/run_test.py --verbose -i distributed/test_compute_comm_reordering
time python test/run_test.py --verbose -i distributed/test_aten_comm_compute_reordering
time python test/run_test.py --verbose -i distributed/test_store
time python test/run_test.py --verbose -i distributed/test_symmetric_memory
time python test/run_test.py --verbose -i distributed/test_pg_wrapper

View File

@ -32,6 +32,9 @@ LIBTORCH_NAMESPACE_LIST = (
"torch::",
)
# Patterns for detecting statically linked libstdc++ symbols
STATICALLY_LINKED_CXX11_ABI = [re.compile(r".*recursive_directory_iterator.*")]
def _apply_libtorch_symbols(symbols):
return [
@ -53,12 +56,17 @@ def get_symbols(lib: str) -> list[tuple[str, str, str]]:
return [x.split(" ", 2) for x in lines.decode("latin1").split("\n")[:-1]]
def grep_symbols(lib: str, patterns: list[Any]) -> list[str]:
def grep_symbols(
lib: str, patterns: list[Any], symbol_type: str | None = None
) -> list[str]:
def _grep_symbols(
symbols: list[tuple[str, str, str]], patterns: list[Any]
) -> list[str]:
rc = []
for _s_addr, _s_type, s_name in symbols:
# Filter by symbol type if specified
if symbol_type and _s_type != symbol_type:
continue
for pattern in patterns:
if pattern.match(s_name):
rc.append(s_name)
@ -80,6 +88,18 @@ def grep_symbols(lib: str, patterns: list[Any]) -> list[str]:
return functools.reduce(list.__add__, (x.result() for x in tasks), [])
def check_lib_statically_linked_libstdc_cxx_abi_symbols(lib: str) -> None:
cxx11_statically_linked_symbols = grep_symbols(
lib, STATICALLY_LINKED_CXX11_ABI, symbol_type="T"
)
num_statically_linked_symbols = len(cxx11_statically_linked_symbols)
print(f"num_statically_linked_symbols (T): {num_statically_linked_symbols}")
if num_statically_linked_symbols > 0:
raise RuntimeError(
f"Found statically linked libstdc++ symbols (recursive_directory_iterator): {cxx11_statically_linked_symbols[:100]}"
)
def check_lib_symbols_for_abi_correctness(lib: str) -> None:
print(f"lib: {lib}")
cxx11_symbols = grep_symbols(lib, LIBTORCH_CXX11_PATTERNS)
@ -107,6 +127,7 @@ def main() -> None:
libtorch_cpu_path = str(install_root / "lib" / "libtorch_cpu.so")
check_lib_symbols_for_abi_correctness(libtorch_cpu_path)
check_lib_statically_linked_libstdc_cxx_abi_symbols(libtorch_cpu_path)
if __name__ == "__main__":

View File

@ -386,8 +386,8 @@ def smoke_test_compile(device: str = "cpu") -> None:
def smoke_test_nvshmem() -> None:
if not torch.cuda.is_available() or target_os == "windows":
print("Windows platform or CUDA is not available, skipping NVSHMEM test")
if not torch.cuda.is_available():
print("CUDA is not available, skipping NVSHMEM test")
return
# Check if NVSHMEM is compiled in current build
@ -396,9 +396,7 @@ def smoke_test_nvshmem() -> None:
except ImportError:
# Not built with NVSHMEM support.
# torch is not compiled with NVSHMEM prior to 2.9
from torch.torch_version import TorchVersion
if TorchVersion(torch.__version__) < (2, 9):
if torch.__version__ < "2.9":
return
else:
# After 2.9: NVSHMEM is expected to be compiled in current build

View File

@ -322,29 +322,23 @@ test_python_shard() {
# modify LD_LIBRARY_PATH to ensure it has the conda env.
# This set of tests has been shown to be buggy without it for the split-build
time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
assert_git_not_dirty
}
test_python() {
# shellcheck disable=SC2086
time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests $INCLUDE_CLAUSE --verbose $PYTHON_TEST_EXTRA_OPTION
time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --verbose $PYTHON_TEST_EXTRA_OPTION
assert_git_not_dirty
}
test_python_smoke() {
# Smoke tests for H100/B200
# Smoke tests for H100
time python test/run_test.py --include test_matmul_cuda inductor/test_fp8 inductor/test_max_autotune $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
assert_git_not_dirty
}
test_python_smoke_b200() {
# Targeted smoke tests for B200 - staged approach to avoid too many failures
time python test/run_test.py --include test_matmul_cuda inductor/test_fp8 $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
assert_git_not_dirty
}
test_h100_distributed() {
# Distributed tests at H100
time python test/run_test.py --include distributed/_composable/test_composability/test_pp_composability.py $PYTHON_TEST_EXTRA_OPTION --upload-artifacts-while-running
@ -390,7 +384,6 @@ test_dynamo_wrapped_shard() {
--exclude-distributed-tests \
--exclude-torch-export-tests \
--exclude-aot-dispatch-tests \
--exclude-quantization-tests \
--shard "$1" "$NUM_TEST_SHARDS" \
--verbose \
--upload-artifacts-while-running
@ -435,7 +428,7 @@ test_inductor_distributed() {
# this runs on both single-gpu and multi-gpu instance. It should be smart about skipping tests that aren't supported
# with if required # gpus aren't available
python test/run_test.py --include distributed/test_dynamo_distributed distributed/test_inductor_collectives distributed/test_aten_comm_compute_reordering distributed/test_compute_comm_reordering --verbose
python test/run_test.py --include distributed/test_dynamo_distributed distributed/test_inductor_collectives distributed/test_compute_comm_reordering --verbose
assert_git_not_dirty
}
@ -1163,12 +1156,6 @@ test_distributed() {
fi
}
test_quantization() {
echo "Testing quantization"
python test/test_quantization.py
}
test_rpc() {
echo "Testing RPC C++ tests"
# NB: the ending test_rpc must match the current function name for the current
@ -1415,7 +1402,7 @@ EOF
pip3 install -r requirements.txt
# shellcheck source=./common-build.sh
source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
python -m build --wheel --no-isolation -C--build-option=--bdist-dir="base_bdist_tmp" --outdir "base_dist"
python setup.py bdist_wheel --bdist-dir="base_bdist_tmp" --dist-dir="base_dist"
python -mpip install base_dist/*.whl
echo "::endgroup::"
@ -1563,10 +1550,14 @@ test_executorch() {
install_torchvision
install_torchaudio
INSTALL_SCRIPT="$(pwd)/.ci/docker/common/install_executorch.sh"
pushd /executorch
"${INSTALL_SCRIPT}" setup_executorch
export PYTHON_EXECUTABLE=python
export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
# NB: We need to rebuild ExecuTorch runner here because it depends on PyTorch
# from the PR
bash .ci/scripts/setup-linux.sh --build-tool cmake
echo "Run ExecuTorch unit tests"
pytest -v -n auto
@ -1580,14 +1571,17 @@ test_executorch() {
popd
# Test torchgen generated code for Executorch.
echo "Testing ExecuTorch op registration"
"$BUILD_BIN_DIR"/test_edge_op_registration
assert_git_not_dirty
}
test_linux_aarch64() {
python test/run_test.py --include test_modules test_mkldnn test_mkldnn_fusion test_openmp test_torch test_dynamic_shapes \
test_transformers test_multiprocessing test_numpy_interop test_autograd test_binary_ufuncs test_complex test_spectral_ops \
test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops profiler/test_memory_profiler \
distributed/elastic/timer/api_test distributed/elastic/timer/local_timer_example distributed/elastic/timer/local_timer_test \
test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
# Dynamo tests
@ -1617,7 +1611,7 @@ test_operator_benchmark() {
test_inductor_set_cpu_affinity
cd benchmarks/operator_benchmark/pt_extension
python -m pip install . -v --no-build-isolation
python -m pip install .
cd "${TEST_DIR}"/benchmarks/operator_benchmark
$TASKSET python -m benchmark_all_test --device "$1" --tag-filter "$2" \
@ -1681,8 +1675,6 @@ elif [[ "${TEST_CONFIG}" == *executorch* ]]; then
test_executorch
elif [[ "$TEST_CONFIG" == 'jit_legacy' ]]; then
test_python_legacy_jit
elif [[ "$TEST_CONFIG" == 'quantization' ]]; then
test_quantization
elif [[ "${BUILD_ENVIRONMENT}" == *libtorch* ]]; then
# TODO: run some C++ tests
echo "no-op at the moment"
@ -1809,14 +1801,10 @@ elif [[ "${BUILD_ENVIRONMENT}" == *xpu* ]]; then
test_xpu_bin
elif [[ "${TEST_CONFIG}" == smoke ]]; then
test_python_smoke
elif [[ "${TEST_CONFIG}" == smoke_b200 ]]; then
test_python_smoke_b200
elif [[ "${TEST_CONFIG}" == h100_distributed ]]; then
test_h100_distributed
elif [[ "${TEST_CONFIG}" == "h100-symm-mem" ]]; then
test_h100_symm_mem
elif [[ "${TEST_CONFIG}" == "b200-symm-mem" ]]; then
test_h100_symm_mem
elif [[ "${TEST_CONFIG}" == h100_cutlass_backend ]]; then
test_h100_cutlass_backend
else

View File

@ -1,32 +0,0 @@
#!/bin/bash
set -ex -o pipefail
# Suppress ANSI color escape sequences
export TERM=vt100
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
# shellcheck source=./common-build.sh
source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
echo "Environment variables"
env
echo "Testing FA3 stable wheel still works with currently built torch"
echo "Installing ABI Stable FA3 wheel"
# The wheel was built on https://github.com/Dao-AILab/flash-attention/commit/b3846b059bf6b143d1cd56879933be30a9f78c81
# on torch nightly torch==2.9.0.dev20250830+cu129
$MAYBE_SUDO pip -q install https://s3.amazonaws.com/ossci-linux/wheels/flash_attn_3-3.0.0b1-cp39-abi3-linux_x86_64.whl
pushd flash-attention/hopper
export PYTHONPATH=$PWD
pytest -v -s \
"test_flash_attn.py::test_flash_attn_output[1-1-192-False-False-False-0.0-False-False-mha-dtype0]" \
"test_flash_attn.py::test_flash_attn_varlen_output[511-1-64-True-False-False-0.0-False-False-gqa-dtype2]" \
"test_flash_attn.py::test_flash_attn_kvcache[1-128-128-False-False-True-None-0.0-False-False-True-False-True-False-gqa-dtype0]" \
"test_flash_attn.py::test_flash_attn_race_condition[97-97-192-True-dtype0]" \
"test_flash_attn.py::test_flash_attn_combine[2-3-64-dtype1]" \
"test_flash_attn.py::test_flash3_bw_compatibility"
popd

View File

@ -70,7 +70,7 @@ sccache --zero-stats
sccache --show-stats
# Build the wheel
python -m build --wheel --no-build-isolation
python setup.py bdist_wheel
if ($LASTEXITCODE -ne 0) { exit 1 }
# Install the wheel locally

View File

@ -38,12 +38,10 @@ if errorlevel 1 goto fail
if not errorlevel 0 goto fail
:: Update CMake
:: TODO: Investigate why this helps MKL detection, even when CMake from choco is not used
call choco upgrade -y cmake --no-progress --installargs 'ADD_CMAKE_TO_PATH=System' --apply-install-arguments-to-dependencies --version=3.27.9
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
:: TODO: Move to .ci/docker/requirements-ci.txt
call pip install mkl==2024.2.0 mkl-static==2024.2.0 mkl-include==2024.2.0
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
@ -132,7 +130,7 @@ if "%USE_CUDA%"=="1" (
:: Print all existing environment variable for debugging
set
python -m build --wheel --no-isolation
python setup.py bdist_wheel
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
sccache --show-stats

View File

@ -25,7 +25,7 @@ echo Copying over test times file
robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
echo Run nn tests
python run_test.py --exclude-jit-executor --exclude-distributed-tests --exclude-quantization-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
python run_test.py --exclude-jit-executor --exclude-distributed-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
if ERRORLEVEL 1 goto fail
popd

View File

@ -37,8 +37,27 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
export PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda"
fi
# TODO: Move this to .ci/docker/requirements-ci.txt
python -m pip install "psutil==5.9.1" "pynvml==11.4.1" "pytest-shard==0.1.2"
# TODO: Move both of them to Windows AMI
python -m pip install tensorboard==2.13.0 protobuf==5.29.4 pytest-subtests==0.13.1
# Copied from https://github.com/pytorch/test-infra/blob/be01a40157c36cd5a48391fdf44a7bc3ebd4c7e3/aws/ami/windows/scripts/Installers/Install-Pip-Dependencies.ps1#L16 with some adjustments
# pytest-rerunfailures==10.3 as 10.2 fails with INTERNALERROR> pluggy._manager.PluginValidationError: unknown hook 'pytest_configure_node'
# scipy from 1.6.3 to 1.10
# expecttest from 0.1.3 to 0.3.0
# xdoctest from 1.0.2 to 1.3.0
python -m pip install "future==0.18.2" "hypothesis==5.35.1" "expecttest==0.3.0" "librosa>=0.6.2" "scipy==1.10.1" "psutil==5.9.1" "pynvml==11.4.1" "pillow==9.2.0" "unittest-xml-reporting<=3.2.0,>=2.0.0" "pytest==7.1.3" "pytest-xdist==2.5.0" "pytest-flakefinder==1.1.0" "pytest-rerunfailures==10.3" "pytest-shard==0.1.2" "sympy==1.11.1" "xdoctest==1.3.0" "pygments==2.12.0" "opt-einsum>=3.3" "networkx==2.8.8" "mpmath==1.2.1" "pytest-cpp==2.3.0" "boto3==1.35.42"
# Install Z3 optional dependency for Windows builds.
python -m pip install z3-solver==4.15.1.0
# Install tlparse for test\dynamo\test_structured_trace.py UTs.
python -m pip install tlparse==0.4.0
# Install parameterized
python -m pip install parameterized==0.8.1
# Install pulp for testing ilps under torch\distributed\_tools
python -m pip install pulp==2.9.0
run_tests() {
# Run nvidia-smi if available

View File

@ -48,7 +48,7 @@ sccache --zero-stats
sccache --show-stats
:: Call PyTorch build script
python -m build --wheel --no-isolation --outdir "%PYTORCH_FINAL_PACKAGE_DIR%"
python setup.py bdist_wheel -d "%PYTORCH_FINAL_PACKAGE_DIR%"
:: show sccache stats
sccache --show-stats

View File

@ -37,10 +37,10 @@ IF "%CUDA_PATH_V128%"=="" (
)
IF "%BUILD_VISION%" == "" (
set TORCH_CUDA_ARCH_LIST=6.1;7.0;7.5;8.0;8.6;9.0;10.0;12.0
set TORCH_CUDA_ARCH_LIST=7.0;7.5;8.0;8.6;9.0;10.0;12.0
set TORCH_NVCC_FLAGS=-Xfatbin -compress-all
) ELSE (
set NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_100,code=compute_100 -gencode=arch=compute_120,code=compute_120
set NVCC_FLAGS=-D__CUDA_NO_HALF_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=compute_80 -gencode=arch=compute_86,code=compute_86 -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_100,code=compute_100 -gencode=arch=compute_120,code=compute_120
)
set "CUDA_PATH=%CUDA_PATH_V128%"

View File

@ -28,5 +28,5 @@ start /wait "" python-amd64.exe /quiet InstallAllUsers=1 PrependPath=0 Include_t
if errorlevel 1 exit /b 1
set "PATH=%CD%\Python\Scripts;%CD%\Python;%PATH%"
%PYTHON_EXEC% -m pip install --upgrade pip setuptools packaging wheel build
%PYTHON_EXEC% -m pip install --upgrade pip setuptools packaging wheel
if errorlevel 1 exit /b 1

View File

@ -86,7 +86,7 @@ copy /Y "%LIBTORCH_PREFIX%-%PYTORCH_BUILD_VERSION%.zip" "%PYTORCH_FINAL_PACKAGE_
goto build_end
:pytorch
%PYTHON_EXEC% -m build --wheel --no-isolation --outdir "%PYTORCH_FINAL_PACKAGE_DIR%"
%PYTHON_EXEC% setup.py bdist_wheel -d "%PYTORCH_FINAL_PACKAGE_DIR%"
:build_end
IF ERRORLEVEL 1 exit /b 1

View File

@ -63,7 +63,7 @@ if errorlevel 1 exit /b 1
call %CONDA_HOME%\condabin\activate.bat testenv
if errorlevel 1 exit /b 1
call conda install -y -q -c conda-forge libuv=1.51
call conda install -y -q -c conda-forge libuv=1.39
call conda install -y -q intel-openmp
echo "install and test libtorch"

View File

@ -18,7 +18,7 @@ if "%DESIRED_PYTHON%" == "3.9" %PYTHON_EXEC% -m pip install numpy==2.0.2 cmake
%PYTHON_EXEC% -m pip install pyyaml
%PYTHON_EXEC% -m pip install mkl-include mkl-static
%PYTHON_EXEC% -m pip install boto3 requests ninja typing_extensions setuptools==72.1.0
%PYTHON_EXEC% -m pip install boto3 ninja typing_extensions setuptools==72.1.0
where cmake.exe

View File

@ -85,7 +85,7 @@ mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
# Create an isolated directory to store this builds pytorch checkout and conda
# installation
if [[ -z "$MAC_PACKAGE_WORK_DIR" ]]; then
MAC_PACKAGE_WORK_DIR="$(pwd)/tmp_wheel_${DESIRED_PYTHON}_$(date +%H%M%S)"
MAC_PACKAGE_WORK_DIR="$(pwd)/tmp_wheel_conda_${DESIRED_PYTHON}_$(date +%H%M%S)"
fi
mkdir -p "$MAC_PACKAGE_WORK_DIR" || true
if [[ -n ${GITHUB_ACTIONS} ]]; then
@ -96,11 +96,11 @@ fi
whl_tmp_dir="${MAC_PACKAGE_WORK_DIR}/dist"
mkdir -p "$whl_tmp_dir"
mac_version='macosx-11_0-arm64'
mac_version='macosx_11_0_arm64'
libtorch_arch='arm64'
# Create a consistent wheel package name to rename the wheel to
wheel_filename_new="${TORCH_PACKAGE_NAME}-${build_version}${build_number_prefix}-cp${python_nodot}-none-${mac_version//[-,]/_}.whl"
wheel_filename_new="${TORCH_PACKAGE_NAME}-${build_version}${build_number_prefix}-cp${python_nodot}-none-${mac_version}.whl"
###########################################################
@ -125,6 +125,7 @@ popd
export TH_BINARY_BUILD=1
export INSTALL_TEST=0 # dont install test binaries into site-packages
export MACOSX_DEPLOYMENT_TARGET=11.0
export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
EXTRA_CONDA_INSTALL_FLAGS=""
CONDA_ENV_CREATE_FLAGS=""
@ -132,20 +133,25 @@ RENAME_WHEEL=true
case $desired_python in
3.14t)
echo "Using 3.14 deps"
mac_version='macosx-11.0-arm64'
NUMPY_PINNED_VERSION="==2.1.0"
CONDA_ENV_CREATE_FLAGS="python-freethreading"
EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge/label/python_rc -c conda-forge"
desired_python="3.14.0rc1"
RENAME_WHEEL=false
;;
3.14)
echo "Using 3.14t deps"
mac_version='macosx-11.0-arm64'
NUMPY_PINNED_VERSION="==2.1.0"
EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge/label/python_rc -c conda-forge"
desired_python="3.14.0rc1"
RENAME_WHEEL=false
;;
3.13t)
echo "Using 3.13t deps"
mac_version='macosx-11.0-arm64'
echo "Using 3.13 deps"
NUMPY_PINNED_VERSION="==2.1.0"
CONDA_ENV_CREATE_FLAGS="python-freethreading"
EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge"
desired_python="3.13"
RENAME_WHEEL=false
;;
3.13)
@ -170,12 +176,17 @@ case $desired_python in
;;
esac
# Install into a fresh env
tmp_env_name="wheel_py$python_nodot"
conda create ${EXTRA_CONDA_INSTALL_FLAGS} -yn "$tmp_env_name" python="$desired_python" ${CONDA_ENV_CREATE_FLAGS}
source activate "$tmp_env_name"
PINNED_PACKAGES=(
"numpy${NUMPY_PINNED_VERSION}"
)
python -mvenv ~/${desired_python}-build
source ~/${desired_python}-build/bin/activate
retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements.txt"
retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements-build.txt"
pip install requests ninja typing-extensions
retry pip install -r "${pytorch_rootdir}/requirements.txt" || true
retry brew install libomp
# For USE_DISTRIBUTED=1 on macOS, need libuv, which is build as part of tensorpipe submodule
@ -186,11 +197,11 @@ export USE_QNNPACK=OFF
export BUILD_TEST=OFF
pushd "$pytorch_rootdir"
echo "Calling -m build --wheel --no-isolation at $(date)"
echo "Calling setup.py bdist_wheel at $(date)"
_PYTHON_HOST_PLATFORM=${mac_version} ARCHFLAGS="-arch arm64" python -m build --wheel --no-isolation --outdir "$whl_tmp_dir" -C--plat-name="${mac_version//[-.]/_}"
python setup.py bdist_wheel -d "$whl_tmp_dir" --plat-name ${mac_version}
echo "Finished -m build --wheel --no-isolation at $(date)"
echo "Finished setup.py bdist_wheel at $(date)"
if [[ $package_type != 'libtorch' ]]; then
echo "delocating wheel dependencies"

View File

@ -71,14 +71,7 @@ export PYTORCH_BUILD_NUMBER=1
# Set triton version as part of PYTORCH_EXTRA_INSTALL_REQUIREMENTS
TRITON_VERSION=$(cat $PYTORCH_ROOT/.ci/docker/triton_version.txt)
# Here PYTORCH_EXTRA_INSTALL_REQUIREMENTS is already set for the all the wheel builds hence append TRITON_CONSTRAINT
TRITON_CONSTRAINT="platform_system == 'Linux' and platform_machine == 'x86_64'"
# CUDA 12.9/13.0 builds have triton for Linux and Linux aarch64 binaries.
if [[ "$DESIRED_CUDA" == "cu129" ]] || [[ "$DESIRED_CUDA" == "cu130" ]]; then
TRITON_CONSTRAINT="platform_system == 'Linux'"
fi
TRITON_CONSTRAINT="platform_system == 'Linux'"
if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" && ! "$PYTORCH_BUILD_VERSION" =~ .*xpu.* ]]; then
TRITON_REQUIREMENT="triton==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"

View File

@ -0,0 +1,47 @@
#!/bin/bash
# =================== The following code **should** be executed inside Docker container ===================
# Install dependencies
sudo apt-get -y update
sudo apt-get -y install expect-dev
# This is where the local pytorch install in the docker image is located
pt_checkout="/var/lib/jenkins/workspace"
source "$pt_checkout/.ci/pytorch/common_utils.sh"
echo "functorch_doc_push_script.sh: Invoked with $*"
set -ex
version=${DOCS_VERSION:-nightly}
echo "version: $version"
# Build functorch docs
pushd $pt_checkout/functorch/docs
pip -q install -r requirements.txt
make html
popd
git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
pushd functorch_ghpages
if [ $version == "main" ]; then
version=nightly
fi
git rm -rf "$version" || true
mv "$pt_checkout/functorch/docs/build/html" "$version"
git add "$version" || true
git status
git config user.email "soumith+bot@pytorch.org"
git config user.name "pytorchbot"
# If there aren't changes, don't make a commit; push is no-op
git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
git status
if [[ "${WITH_PUSH:-}" == true ]]; then
git push -u origin gh-pages
fi
popd
# =================== The above code **should** be executed inside Docker container ===================

View File

@ -69,8 +69,6 @@ readability-string-compare,
'
HeaderFilterRegex: '^(aten/|c10/|torch/).*$'
WarningsAsErrors: '*'
LineFilter:
- name: '/usr/include/.*'
CheckOptions:
cppcoreguidelines-special-member-functions.AllowSoleDefaultDtor: true
cppcoreguidelines-special-member-functions.AllowImplicitlyDeletedCopyOrMove: true

View File

@ -73,7 +73,7 @@ exclude =
./docs/src,
./functorch/docs,
./functorch/examples,
./functorch/docs/source/tutorials,
./functorch/notebooks,
./scripts,
./test/generated_type_hints_smoketest.py,
./third_party,

View File

@ -1,10 +1,6 @@
---
name: "⚠️ CI SEV"
about: Tracking incidents for PyTorch's CI infra.
title: ''
labels: ''
assignees: ''
---
> NOTE: Remember to label this issue with "`ci: sev`"

View File

@ -1,18 +0,0 @@
---
name: DISABLE AUTOREVERT
about: Disables autorevert when open
title: "❌​\U0001F519 [DISABLE AUTOREVERT]"
labels: 'ci: disable-autorevert'
assignees: ''
---
This issue, while open, disables the autorevert functionality.
More details can be found [here](https://github.com/pytorch/test-infra/blob/main/aws/lambda/pytorch-auto-revert/README.md)
## Why are you disabling autorevert?
## Links to any issues/commits/errors that shows the source of problem

View File

@ -1,10 +1,8 @@
---
name: Disable CI jobs (PyTorch Dev Infra only)
about: Use this template to disable CI jobs
title: DISABLED [WORKFLOW_NAME] / [PLATFORM_NAME] / [JOB_NAME]
labels: 'module: ci'
assignees: ''
title: "DISABLED [WORKFLOW_NAME] / [PLATFORM_NAME] / [JOB_NAME]"
labels: "module: ci"
---
> For example, DISABLED pull / win-vs2022-cpu-py3 / test (default). Once

View File

@ -21,10 +21,6 @@ self-hosted-runner:
- linux.arm64.2xlarge.ephemeral
- linux.arm64.m7g.4xlarge
- linux.arm64.m7g.4xlarge.ephemeral
- linux.arm64.r7g.12xlarge.memory
- linux.aws.h100
- linux.aws.h100.4
- linux.aws.h100.8
- linux.4xlarge.nvidia.gpu
- linux.8xlarge.nvidia.gpu
- linux.16xlarge.nvidia.gpu

View File

@ -264,7 +264,7 @@ def unzip_artifact_and_replace_files() -> None:
change_content_to_new_version(f"artifacts/dist/{old_stem}/torch/version.py")
for file in Path(f"artifacts/dist/{old_stem}").glob(
"*.dist-info/*",
"*.dist-info/**",
):
change_content_to_new_version(file)

View File

@ -23,6 +23,9 @@ runs:
run: |
.github\scripts\kill_active_ssh_sessions.ps1
- name: Clean up leftover processes on non-ephemeral Windows runner
uses: pytorch/test-infra/.github/actions/cleanup-runner@main
# Cleaning up Windows workspace sometimes fails flakily with device or resource busy
# error, meaning one or more processes haven't stopped completely yet. So trying to
# retry this step several time similar to how checkout-pytorch GHA does

View File

@ -1 +1 @@
87ff22e49ed0e92576c4935ccb8c143daac4a3cd
27fc2493d383354a008106f22f3be232badee9a1

View File

@ -1 +1 @@
08ae0af1395c8d8471f4025deb6af9aef90b342f
7f1de94a4c2d14f59ad4ca84538c36084ea6b2c8

View File

@ -1 +1 @@
0fc62aa26a30ed7ca419d285f285cb5ba02c4394
r2.9

View File

@ -82,10 +82,16 @@ RUN if command -v apt-get >/dev/null; then \
apt-get update -y \
&& apt-get install -y ccache software-properties-common git curl wget sudo vim; \
else \
dnf install -y git curl wget sudo; \
dnf install -y git curl wget sudo vim; \
fi \
&& python3 --version && python3 -m pip --version
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# Install uv for faster pip installs if not existed
RUN --mount=type=cache,target=/root/.cache/uv \
if ! python3 -m uv --version >/dev/null 2>&1; then \
@ -202,7 +208,7 @@ ARG max_jobs=16
ENV MAX_JOBS=${max_jobs}
ARG nvcc_threads=4
ENV NVCC_THREADS=$nvcc_threads
ARG torch_cuda_arch_list='8.0 8.6 8.9 9.0'
ARG torch_cuda_arch_list='8.0;8.6;8.9;9.0'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
ARG USE_SCCACHE
@ -214,16 +220,11 @@ ARG SCCACHE_S3_NO_CREDENTIALS=0
RUN --mount=type=cache,target=/root/.cache/uv \
--mount=type=bind,source=.git,target=.git \
if [ "$USE_SCCACHE" = "1" ]; then \
echo "Installing sccache..."; \
if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
SCCACHE_ARCHIVE="sccache-v0.8.1-aarch64-unknown-linux-musl"; \
else \
SCCACHE_ARCHIVE="sccache-v0.8.1-x86_64-unknown-linux-musl"; \
fi; \
curl -L -o sccache.tar.gz "https://github.com/mozilla/sccache/releases/download/v0.8.1/${SCCACHE_ARCHIVE}.tar.gz" \
echo "Installing sccache..." \
&& curl -L -o sccache.tar.gz https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-unknown-linux-musl.tar.gz \
&& tar -xzf sccache.tar.gz \
&& sudo mv "${SCCACHE_ARCHIVE}"/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz "${SCCACHE_ARCHIVE}" \
&& sudo mv sccache-v0.8.1-x86_64-unknown-linux-musl/sccache /usr/bin/sccache \
&& rm -rf sccache.tar.gz sccache-v0.8.1-x86_64-unknown-linux-musl \
&& export SCCACHE_BUCKET=${SCCACHE_BUCKET_NAME} \
&& export SCCACHE_REGION=${SCCACHE_REGION_NAME} \
&& export SCCACHE_S3_NO_CREDENTIALS=${SCCACHE_S3_NO_CREDENTIALS} \
@ -284,7 +285,7 @@ RUN if command -v apt-get >/dev/null; then \
&& ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
&& curl -sS ${GET_PIP_URL} | python${PYTHON_VERSION}; \
else \
dnf install -y git curl wget sudo; \
dnf install -y git curl wget sudo vim; \
fi \
&& python3 --version && python3 -m pip --version
@ -297,28 +298,22 @@ RUN echo "[INFO] Listing current directory before torch install step:" && \
echo "[INFO] Showing torch_build_versions.txt content:" && \
cat torch_build_versions.txt
# Install build and runtime dependencies, this is needed for flashinfer install
COPY requirements/build.txt requirements/build.txt
COPY use_existing_torch.py use_existing_torch.py
RUN python3 use_existing_torch.py
RUN cat requirements/build.txt
# Workaround for https://github.com/openai/triton/issues/2507 and
# https://github.com/pytorch/pytorch/issues/107960 -- hopefully
# this won't be needed for future versions of this docker image
# or future versions of triton.
RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
# Install uv for faster pip installs if not existed
RUN --mount=type=cache,target=/root/.cache/uv \
if ! python3 -m uv --version > /dev/null 2>&1; then \
python3 -m pip install uv==0.8.4; \
fi
ENV UV_HTTP_TIMEOUT=500
ENV UV_INDEX_STRATEGY="unsafe-best-match"
# Use copy mode to avoid hardlink failures with Docker cache mounts
ENV UV_LINK_MODE=copy
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system -r requirements/build.txt
# Default mount file as placeholder, this just avoid the mount error
ARG TORCH_WHEELS_PATH="./requirements"
# Install torch, torchaudio and torchvision
@ -344,11 +339,13 @@ RUN --mount=type=cache,target=/root/.cache/uv \
# Install xformers wheel from previous stage
RUN --mount=type=cache,target=/root/.cache/uv \
uv pip install --system /wheels/xformers/*.whl --verbose
# Build flashinfer from source.
ARG torch_cuda_arch_list='8.0;8.9;9.0a;10.0a;12.0'
# install package for build flashinfer
# see issue: https://github.com/flashinfer-ai/flashinfer/issues/738
RUN pip install build==1.3.0
RUN pip freeze | grep -E 'setuptools|packaging|build'
ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}

View File

@ -1,22 +0,0 @@
import glob
import os
requires_files = glob.glob("requirements/*.txt")
requires_files += ["pyproject.toml"]
for file in requires_files:
if not os.path.exists(file):
print(f"!!! skipping missing {file}")
continue
print(f">>> cleaning {file}")
with open(file) as f:
lines = f.readlines()
if "torch" in "".join(lines).lower():
print("removed:")
with open(file, "w") as f:
for line in lines:
if "torch" not in line.lower():
f.write(line)
print(f"<<< done cleaning {file}")
print()

3
.github/labeler.yml vendored
View File

@ -130,6 +130,3 @@
- torch/csrc/inductor/aoti_include/**
- torchgen/aoti/**
- torchgen/gen_aoti_c_shim.py
"ciflow/vllm":
- .github/ci_commit_pins/vllm.txt

View File

@ -525,21 +525,6 @@
- Lint
- pull
- name: typechecking
patterns:
- 'pyrefly.toml'
- 'mypy.ini'
- 'mypy-strict.ini'
approved_by:
- lolpack
- maggiemoss
- ndmitchell
- kinto0
mandatory_checks_name:
- EasyCLA
- Lint
- pull
- name: superuser
patterns:
- '*'

View File

@ -1,44 +1,41 @@
tracking_issue: 24422
ciflow_tracking_issue: 64124
ciflow_push_tags:
- ciflow/b200
- ciflow/b200-symm-mem
- ciflow/binaries
- ciflow/binaries_libtorch
- ciflow/binaries_wheel
- ciflow/h100
- ciflow/h100-cutlass-backend
- ciflow/h100-distributed
- ciflow/h100-symm-mem
- ciflow/triton_binaries
- ciflow/inductor
- ciflow/inductor-cu126
- ciflow/inductor-micro-benchmark
- ciflow/inductor-micro-benchmark-cpu-x86
- ciflow/inductor-perf-compare
- ciflow/inductor-perf-test-nightly-rocm
- ciflow/inductor-perf-test-nightly-x86-zen
- ciflow/inductor-periodic
- ciflow/inductor-rocm
- ciflow/inductor-perf-test-nightly-rocm
- ciflow/inductor-perf-compare
- ciflow/inductor-micro-benchmark
- ciflow/inductor-micro-benchmark-cpu-x86
- ciflow/inductor-perf-test-nightly-x86-zen
- ciflow/inductor-cu126
- ciflow/linux-aarch64
- ciflow/mps
- ciflow/nightly
- ciflow/op-benchmark
- ciflow/periodic
- ciflow/periodic-rocm-mi300
- ciflow/pull
- ciflow/quantization-periodic
- ciflow/riscv64
- ciflow/rocm
- ciflow/rocm-mi300
- ciflow/s390
- ciflow/riscv64
- ciflow/slow
- ciflow/torchbench
- ciflow/triton_binaries
- ciflow/trunk
- ciflow/unstable
- ciflow/vllm
- ciflow/win-arm64
- ciflow/xpu
- ciflow/vllm
- ciflow/torchbench
- ciflow/op-benchmark
- ciflow/pull
- ciflow/h100
- ciflow/h100-distributed
- ciflow/win-arm64
- ciflow/h100-symm-mem
- ciflow/h100-cutlass-backend
retryable_workflows:
- pull
- trunk
@ -47,4 +44,4 @@ retryable_workflows:
- inductor-A100-perf-nightly
labeler_config: labeler.yml
label_to_label_config: label_to_label.yml
mergebot: true
mergebot: True

View File

@ -0,0 +1,36 @@
boto3==1.35.42
cmake==3.27.*
expecttest==0.3.0
fbscribelogger==0.1.7
filelock==3.18.0
hypothesis==6.56.4
librosa>=0.6.2
mpmath==1.3.0
networkx==2.8.7
ninja==1.10.2.4
numba==0.59.0
numpy==1.26.4
opt-einsum>=3.3
optree==0.13.0
packaging==23.1
parameterized==0.8.1
pillow==10.3.0
protobuf==5.29.4
psutil==5.9.8
pygments==2.15.0
pytest-cpp==2.3.0
pytest-flakefinder==1.1.0
pytest-rerunfailures==10.3
pytest-subtests==0.13.1
pytest-xdist==3.3.1
pytest==7.3.2
pyyaml==6.0.2
scipy==1.12.0
setuptools==72.1.0
sympy==1.13.3
tlparse==0.4.0
tensorboard==2.13.0
typing-extensions==4.12.2
unittest-xml-reporting<=3.2.0,>=2.0.0
xdoctest==1.1.0
z3-solver==4.15.1.0

View File

@ -39,9 +39,7 @@ def main() -> None:
pull_request_label_names = [label.name for label in pull_request_labels]
issue_label_names = [label.name for label in issue_labels]
labels_to_add = [
label
for label in issue_label_names
if label not in pull_request_label_names and label != "actionable"
label for label in issue_label_names if label not in pull_request_label_names
]
if not labels_to_add:
print("The pull request already has the same labels.")

View File

@ -41,9 +41,9 @@ SUPPORTED_PERIODICAL_MODES: dict[str, Callable[[Optional[str]], bool]] = {
}
# The link to the published list of disabled jobs
DISABLED_JOBS_URL = "https://ossci-metrics.s3.amazonaws.com/disabled-jobs.json"
DISABLED_JOBS_URL = "https://ossci-metrics.s3.amazonaws.com/disabled-jobs.json?versionId=hjktHz2WOejHpxKpkqpDknTt5rMTM9KK"
# and unstable jobs
UNSTABLE_JOBS_URL = "https://ossci-metrics.s3.amazonaws.com/unstable-jobs.json"
UNSTABLE_JOBS_URL = "https://ossci-metrics.s3.amazonaws.com/unstable-jobs.json?versionId=wrjdvvQTJxgvMO.rGw5MEuMsj6XbjuV7"
# Some constants used to handle disabled and unstable jobs
JOB_NAME_SEP = "/"

View File

@ -30,7 +30,7 @@ CUDA_ARCHES_CUDNN_VERSION = {
}
# NOTE: Please also update the ROCm sources in `PIP_SOURCES` in tools/nightly.py when changing this
ROCM_ARCHES = ["6.4", "7.0"]
ROCM_ARCHES = ["6.3", "6.4"]
XPU_ARCHES = ["xpu"]
@ -53,8 +53,8 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusolver-cu12==11.7.1.2; platform_system == 'Linux' | "
"nvidia-cusparse-cu12==12.5.4.2; platform_system == 'Linux' | "
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | "
"nvidia-nvtx-cu12==12.6.77; platform_system == 'Linux' | "
"nvidia-nvjitlink-cu12==12.6.85; platform_system == 'Linux' | "
"nvidia-cufile-cu12==1.11.1.6; platform_system == 'Linux'"
@ -70,8 +70,8 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusolver-cu12==11.7.3.90; platform_system == 'Linux' | "
"nvidia-cusparse-cu12==12.5.8.93; platform_system == 'Linux' | "
"nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.28.3; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.24; platform_system == 'Linux' | "
"nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
"nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | "
"nvidia-nvtx-cu12==12.8.90; platform_system == 'Linux' | "
"nvidia-nvjitlink-cu12==12.8.93; platform_system == 'Linux' | "
"nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'"
@ -87,7 +87,7 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
"nvidia-cusolver==12.0.3.29; platform_system == 'Linux' | "
"nvidia-cusparse==12.6.2.49; platform_system == 'Linux' | "
"nvidia-cusparselt-cu13==0.8.0; platform_system == 'Linux' | "
"nvidia-nccl-cu13==2.28.3; platform_system == 'Linux' | "
"nvidia-nccl-cu13==2.27.7; platform_system == 'Linux' | "
"nvidia-nvshmem-cu13==3.3.24; platform_system == 'Linux' | "
"nvidia-nvtx==13.0.39; platform_system == 'Linux' | "
"nvidia-nvjitlink==13.0.39; platform_system == 'Linux' | "

View File

@ -127,6 +127,53 @@ LINUX_BINARY_BUILD_WORFKLOWS = [
),
]
ROCM_SMOKE_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="manywheel",
build_variant="rocm",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX,
arches=["6.4"],
python_versions=["3.10"],
),
ciflow_config=CIFlowConfig(
labels={
LABEL_CIFLOW_BINARIES,
LABEL_CIFLOW_BINARIES_WHEEL,
LABEL_CIFLOW_ROCM,
},
isolated_workflow=True,
),
branches="main",
),
]
LINUX_BINARY_SMOKE_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="manywheel",
build_configs=generate_binary_build_matrix.generate_wheels_matrix(
OperatingSystem.LINUX,
arches=["12.8"],
python_versions=["3.12"],
),
branches="main",
),
BinaryBuildWorkflow(
os=OperatingSystem.LINUX,
package_type="libtorch",
build_variant=generate_binary_build_matrix.RELEASE,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.LINUX,
generate_binary_build_matrix.RELEASE,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
),
]
WINDOWS_BINARY_BUILD_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
@ -212,6 +259,39 @@ WINDOWS_BINARY_BUILD_WORKFLOWS = [
),
]
WINDOWS_BINARY_SMOKE_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="libtorch",
build_variant=generate_binary_build_matrix.RELEASE,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.WINDOWS,
generate_binary_build_matrix.RELEASE,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
ciflow_config=CIFlowConfig(
isolated_workflow=True,
),
),
BinaryBuildWorkflow(
os=OperatingSystem.WINDOWS,
package_type="libtorch",
build_variant=generate_binary_build_matrix.DEBUG,
build_configs=generate_binary_build_matrix.generate_libtorch_matrix(
OperatingSystem.WINDOWS,
generate_binary_build_matrix.DEBUG,
arches=["cpu"],
libtorch_variants=["shared-with-deps"],
),
branches="main",
ciflow_config=CIFlowConfig(
isolated_workflow=True,
),
),
]
MACOS_BINARY_BUILD_WORKFLOWS = [
BinaryBuildWorkflow(
os=OperatingSystem.MACOS_ARM64,
@ -292,10 +372,23 @@ def main() -> None:
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
S390X_BINARY_BUILD_WORKFLOWS,
),
(
# Give rocm it's own workflow file
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
ROCM_SMOKE_WORKFLOWS,
),
(
jinja_env.get_template("linux_binary_build_workflow.yml.j2"),
LINUX_BINARY_SMOKE_WORKFLOWS,
),
(
jinja_env.get_template("windows_binary_build_workflow.yml.j2"),
WINDOWS_BINARY_BUILD_WORKFLOWS,
),
(
jinja_env.get_template("windows_binary_build_workflow.yml.j2"),
WINDOWS_BINARY_SMOKE_WORKFLOWS,
),
(
jinja_env.get_template("macos_binary_build_workflow.yml.j2"),
MACOS_BINARY_BUILD_WORKFLOWS,

View File

@ -1,94 +0,0 @@
#!/usr/bin/env bash
set -eux
torch_version=$(unzip -p torch-* '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
nightly=$(echo ${torch_version} | cut -d'.' -f4)
# Copied from .ci/manywheel/build_common.sh
make_wheel_record() {
fpath=$1
if echo $fpath | grep RECORD >/dev/null 2>&1; then
echo "$fpath,,"
else
fhash=$(openssl dgst -sha256 -binary $fpath | openssl base64 | sed -e 's/+/-/g' | sed -e 's/\//_/g' | sed -e 's/=//g')
fsize=$(ls -nl $fpath | awk '{print $5}')
echo "$fpath,sha256=$fhash,$fsize"
fi
}
change_wheel_version() {
local package=$1
local wheel=$2
local f_version=$3
local t_version=$4
# Extract the wheel
${PYTHON_EXECUTABLE} -mwheel unpack $wheel
mv "${package}-${f_version}" "${package}-${t_version}"
# Change the version from f_version to t_version in the dist-info dir
pushd "${package}-${t_version}"
mv "${package}-${f_version}.dist-info" "${package}-${t_version}.dist-info"
pushd "${package}-${t_version}.dist-info"
sed -i "s/${package}-${f_version}.dist-info/${package}-${t_version}.dist-info/g" RECORD
# Update the version in METADATA and its SHA256 hash
sed -i "s/Version: ${f_version}/Version: ${t_version}/g" METADATA
# then add PyTorch nightly dependency of vLLM
if [[ "${package}" == vllm ]] || [[ "${package}" == xformers ]]; then
sed -i "/License-File/a\Requires-Dist: torch==${torch_version}" METADATA
fi
sed -i '/METADATA,sha256/d' RECORD
popd
make_wheel_record "${package}-${t_version}.dist-info/METADATA" >> "${package}-${t_version}.dist-info/RECORD"
popd
# Repack the wheel
${PYTHON_EXECUTABLE} -mwheel pack "${package}-${t_version}"
# Clean up
rm -rf "${package}-${t_version}"
}
repackage_wheel() {
local package=$1
pushd $package
local orig_wheel=$(find . -name *${package//-/_}*)
local orig_version=$(unzip -p $orig_wheel '**/METADATA' | grep '^Version: ' | cut -d' ' -f2)
local version=""
if [[ "${package}" == vllm ]]; then
# Copied from vllm/.buildkite/scripts/upload-wheels.sh
version=1.0.0
else
version=$(echo $orig_version | tr '.+' '.' | cut -d'.' -f1-3)
fi
local nightly_version=$version.$nightly
# Use nightly version
change_wheel_version ${package//-/_} $orig_wheel $orig_version $nightly_version
# Clean up
rm "${orig_wheel}"
auditwheel repair --plat $PLATFORM *.whl \
--exclude libc10* --exclude libtorch* --exclude libcu* --exclude libnv*
local repair_wheel=$(find wheelhouse -name *${PLATFORM}*)
local repair_wheel=$(basename ${repair_wheel})
popd
cp ${package}/wheelhouse/${repair_wheel} .
rm -rf $package
}
# Require to re-package the wheel
${PYTHON_EXECUTABLE} -mpip install wheel==0.45.1
pushd externals/vllm/wheels
for package in xformers flashinfer-python vllm; do
repackage_wheel $package
done
popd

View File

@ -32,7 +32,7 @@ concurrency:
{%- macro setup_ec2_windows() -%}
!{{ display_ec2_information() }}
- name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
continue-on-error: true
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}

View File

@ -56,7 +56,7 @@ jobs:
get-label-type:
if: github.repository_owner == 'pytorch'
name: get-label-type
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@release/2.9
with:
triggering_actor: ${{ github.triggering_actor }}
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
@ -71,7 +71,7 @@ jobs:
with:!{{ upload.binary_env_as_input(config) }}
{%- if "aarch64" in build_environment %}
runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
runs_on: linux.arm64.r7g.12xlarge.memory
runs_on: linux.arm64.m7g.4xlarge.ephemeral
ALPINE_IMAGE: "arm64v8/alpine"
{%- elif "s390x" in build_environment %}
runs_on: linux.s390x
@ -138,7 +138,7 @@ jobs:
contents: read
steps:
- name: Setup XPU
uses: pytorch/pytorch/.github/actions/setup-xpu@main
uses: pytorch/pytorch/.github/actions/setup-xpu@release/2.9
- name: configure aws credentials
id: aws_creds
uses: aws-actions/configure-aws-credentials@v4
@ -153,10 +153,10 @@ jobs:
with:
name: !{{ config["build_name"] }}
path: "${{ runner.temp }}/artifacts/"
!{{ common.checkout(deep_clone=False, directory="pytorch") }}
!{{ common.checkout(deep_clone=False, directory="pytorch", checkout_pr_head=False) }}
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
docker-image-name: !{{ config["container_image"] }}
@ -164,7 +164,7 @@ jobs:
docker-build-dir: .ci/docker
working-directory: pytorch
- name: Pull Docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Test Pytorch binary
@ -185,7 +185,7 @@ jobs:
with:
name: !{{ config["build_name"] }}
path: "${{ runner.temp }}/artifacts/"
!{{ common.checkout(deep_clone=False, directory="pytorch") }}
!{{ common.checkout(deep_clone=False, directory="pytorch", checkout_pr_head=False) }}
- name: ROCm set GPU_FLAG
run: |
echo "GPU_FLAG=--device=/dev/mem --device=/dev/kfd --device=/dev/dri --group-add video --group-add daemon" >> "${GITHUB_ENV}"
@ -199,7 +199,7 @@ jobs:
role-duration-seconds: 18000
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
docker-image-name: !{{ config["container_image"] }}
@ -207,7 +207,7 @@ jobs:
docker-build-dir: .ci/docker
working-directory: pytorch
- name: Pull Docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Test Pytorch binary

View File

@ -22,16 +22,6 @@ name: !{{ build_environment }}
echo "MAC_PACKAGE_WORK_DIR=${RUNNER_TEMP}" >> "${GITHUB_ENV}"
{%- endmacro %}
{%- macro setup_python(py_ver) -%}
- name: Setup Python
uses: actions/setup-python@v6
with:
# TODO: Removeme once 3.14 is out
# .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
python-version: "!{{ (py_ver.strip('t') + '.4') if '3.14' not in py_ver else '3.14.0-rc.2' }}"
freethreaded: !{{ "true" if py_ver.endswith('t') else "false" }}
{%- endmacro %}
on:
# TODO: Migrate to new ciflow trigger, reference https://github.com/pytorch/pytorch/pull/70321
push:
@ -71,13 +61,23 @@ jobs:
{%- endif %}
steps:
!{{ set_runner_specific_vars() }}
!{{ setup_python(config.get("python_version", "3.10")) }}
!{{ common.checkout(deep_clone=False, directory="pytorch") }}
- name: Install conda and dependencies
run: |
# Install conda, setup-miniconda messes with the path that messes with the ruby stuff we do later on
curl --retry 3 --retry-all-errors -o "${RUNNER_TEMP}/conda.sh" "https://repo.anaconda.com/miniconda/Miniconda3-py310_23.5.2-0-MacOSX-$(uname -m).sh"
chmod +x "${RUNNER_TEMP}/conda.sh"
/bin/bash "${RUNNER_TEMP}/conda.sh" -b -p "${RUNNER_TEMP}/anaconda"
echo "${RUNNER_TEMP}/anaconda/bin" >> "${GITHUB_PATH}"
!{{ common.checkout(deep_clone=False, directory="pytorch", checkout_pr_head=False) }}
- name: Populate binary env
run: |
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
"${PYTORCH_ROOT}/.circleci/scripts/binary_populate_env.sh"
- name: Build PyTorch binary
run: |
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
set -eux -o pipefail
# shellcheck disable=SC1090
source "${BINARY_ENV_FILE:-/Users/distiller/project/env}"
@ -94,6 +94,8 @@ jobs:
{%- if config["package_type"] == "wheel" %}
- name: Test PyTorch wheel
run: |
# shellcheck disable=SC1091
source "${RUNNER_TEMP}/anaconda/bin/activate"
set -eux -o pipefail
# shellcheck disable=SC1090
source "${BINARY_ENV_FILE:-/Users/distiller/project/env}"
@ -104,9 +106,33 @@ jobs:
SMOKE_TEST_PARAMS=""
EXTRA_CONDA_INSTALL_FLAGS=""
CONDA_ENV_CREATE_FLAGS=""
# shellcheck disable=SC2153
case $DESIRED_PYTHON in
3.14t)
CONDA_ENV_CREATE_FLAGS="python-freethreading"
EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge/label/python_rc -c conda-forge"
desired_python="3.14.0rc1"
;;
3.14)
EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge/label/python_rc -c conda-forge"
desired_python="3.14.0rc1"
;;
3.13t)
CONDA_ENV_CREATE_FLAGS="python-freethreading"
EXTRA_CONDA_INSTALL_FLAGS="-c conda-forge"
desired_python="3.13"
;;
*)
# shellcheck disable=SC2153
desired_python=${DESIRED_PYTHON}
;;
esac
# shellcheck disable=SC2086
python -mvenv test_venv
source test_venv/bin/activate
conda create -yn "test_conda_env" python="$desired_python" ${CONDA_ENV_CREATE_FLAGS} ${EXTRA_CONDA_INSTALL_FLAGS}
conda activate test_conda_env
pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
# shellcheck disable=SC2086

View File

@ -64,7 +64,7 @@ jobs:
get-label-type:
if: github.repository_owner == 'pytorch'
name: get-label-type
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@release/2.9
with:
triggering_actor: ${{ github.triggering_actor }}
issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
@ -135,7 +135,7 @@ jobs:
{%- else %}
!{{ set_runner_specific_vars() }}
!{{ common.setup_ec2_windows() }}
!{{ common.checkout(deep_clone=False, directory="pytorch") }}
!{{ common.checkout(deep_clone=False, directory="pytorch", checkout_pr_head=False) }}
{%- endif %}
- name: Populate binary env
shell: bash
@ -211,7 +211,7 @@ jobs:
"pytorch/.ci/pytorch/windows/arm64/bootstrap_rust.bat"
{%- else %}
!{{ common.setup_ec2_windows() }}
!{{ common.checkout(deep_clone=False, directory="pytorch") }}
!{{ common.checkout(deep_clone=False, directory="pytorch", checkout_pr_head=False) }}
!{{ set_runner_specific_vars() }}
{%- endif %}
- uses: !{{ common.download_artifact_action }}

View File

@ -47,7 +47,7 @@ jobs:
reenabled-issues: ${{ steps.filter.outputs.reenabled-issues }}
steps:
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
fetch-depth: 1
submodules: false
@ -69,25 +69,25 @@ jobs:
runs-on: ${{ matrix.runner }}
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
# [see note: pytorch repo ref]
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
- name: Setup Linux
uses: ./.github/actions/setup-linux
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
docker-image-name: ${{ inputs.docker-image-name }}
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -97,7 +97,7 @@ jobs:
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
uses: pytorch/test-infra/.github/actions/setup-nvidia@release/2.9
if: ${{ inputs.cuda-version != 'cpu' && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' }}
- name: Output disk space left
@ -209,5 +209,5 @@ jobs:
file-suffix: bazel-${{ github.job }}_${{ steps.get-job-id.outputs.job-id }}
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
uses: pytorch/test-infra/.github/actions/teardown-linux@release/2.9
if: always()

View File

@ -142,13 +142,13 @@ jobs:
- name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
if: inputs.build_environment != 'linux-s390x-binary-manywheel'
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
continue-on-error: true
with:
github-secret: ${{ secrets.github-token }}
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
no-sudo: ${{ inputs.build_environment == 'linux-aarch64-binary-manywheel' || inputs.build_environment == 'linux-s390x-binary-manywheel' }}
@ -178,7 +178,6 @@ jobs:
- name: Checkout PyTorch to pytorch dir
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
submodules: recursive
path: pytorch
show-progress: false
@ -213,9 +212,9 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' && inputs.build_environment != 'linux-s390x-binary-manywheel' }}
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
# If doing this in main or release branch, use docker.io. Otherwise
# If doing this in release/2.9 or release branch, use docker.io. Otherwise
# use ECR
docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
docker-image-name: ${{ inputs.DOCKER_IMAGE }}
@ -227,7 +226,7 @@ jobs:
- name: Pull Docker image
if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' && inputs.build_environment != 'linux-s390x-binary-manywheel' }}
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -283,7 +282,7 @@ jobs:
- name: Teardown Linux
if: always() && inputs.build_environment != 'linux-s390x-binary-manywheel'
uses: pytorch/test-infra/.github/actions/teardown-linux@main
uses: pytorch/test-infra/.github/actions/teardown-linux@release/2.9
- name: Chown workspace
if: always() && inputs.build_environment != 'linux-s390x-binary-manywheel'

View File

@ -125,14 +125,14 @@ jobs:
- name: "[FB EMPLOYEES] Enable SSH (Click me for login details)"
if: inputs.build_environment != 'linux-s390x-binary-manywheel'
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
continue-on-error: true
with:
github-secret: ${{ secrets.github-token }}
# Setup the environment
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
no-sudo: ${{ inputs.build_environment == 'linux-aarch64-binary-manywheel' || inputs.build_environment == 'linux-s390x-binary-manywheel' }}
@ -155,7 +155,6 @@ jobs:
- name: Checkout PyTorch to pytorch dir
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
with:
ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }}
submodules: recursive
show-progress: false
path: pytorch
@ -186,7 +185,7 @@ jobs:
path: "${{ runner.temp }}/artifacts/"
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
uses: pytorch/test-infra/.github/actions/setup-nvidia@release/2.9
if: ${{ inputs.GPU_ARCH_TYPE == 'cuda' && steps.filter.outputs.is-test-matrix-empty == 'False' }}
- name: configure aws credentials
@ -201,7 +200,7 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' && inputs.build_environment != 'linux-s390x-binary-manywheel' }}
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
docker-registry: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/') && '308535385114.dkr.ecr.us-east-1.amazonaws.com' || 'docker.io' }}
docker-image-name: ${{ inputs.DOCKER_IMAGE }}
@ -211,7 +210,7 @@ jobs:
- name: Pull Docker image
if: ${{ steps.filter.outputs.is-test-matrix-empty == 'False' && inputs.build_environment != 'linux-s390x-binary-manywheel' }}
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -223,7 +222,7 @@ jobs:
- name: Teardown Linux
if: always() && inputs.build_environment != 'linux-s390x-binary-manywheel'
uses: pytorch/test-infra/.github/actions/teardown-linux@main
uses: pytorch/test-infra/.github/actions/teardown-linux@release/2.9
- name: Chown workspace
if: always() && inputs.build_environment != 'linux-s390x-binary-manywheel'

View File

@ -81,7 +81,7 @@ jobs:
SHA1: ${{ github.event.pull_request.head.sha || github.sha }}
steps:
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
no-sudo: true

View File

@ -75,12 +75,16 @@ jobs:
runner: ${{ inputs.runner_prefix }}linux.2xlarge
# It takes less than 30m to finish python docs unless there are issues
timeout-minutes: 30
- docs_type: functorch
runner: ${{ inputs.runner_prefix }}linux.2xlarge
# It takes less than 15m to finish functorch docs unless there are issues
timeout-minutes: 15
# Set a fixed name for this job instead of using the current matrix-generated name, i.e. build-docs (cpp, linux.12xlarge, 180)
# The current name requires updating the database last docs push query from test-infra every time the matrix is updated
name: build-docs-${{ matrix.docs_type }}-${{ inputs.push }}
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
instructions: |
@ -91,7 +95,7 @@ jobs:
# [see note: pytorch repo ref]
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
- name: Setup Linux
uses: ./.github/actions/setup-linux
@ -106,12 +110,12 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
docker-image-name: ${{ inputs.docker-image }}
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -207,6 +211,16 @@ jobs:
path: cppdocs/
s3-prefix: pytorch/pytorch/${{ github.event.pull_request.number }}/cppdocs
- name: Upload functorch Docs Preview
uses: seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a # v5.1.0
if: ${{ github.event_name == 'pull_request' && matrix.docs_type == 'functorch' && steps.build-docs.outcome == 'success' }}
with:
retention-days: 14
s3-bucket: doc-previews
if-no-files-found: error
path: functorch_ghpages/nightly/
s3-prefix: pytorch/pytorch/${{ github.event.pull_request.number }}/functorchdocs
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
uses: pytorch/test-infra/.github/actions/teardown-linux@release/2.9
if: always()

View File

@ -2,12 +2,6 @@ name: Get Changed Files
on:
workflow_call:
inputs:
all_files:
description: "Whether to return all files instead of just changed files"
required: false
type: boolean
default: false
outputs:
changed-files:
description: "List of changed files (space-separated) or '*' if not in a PR"
@ -32,23 +26,17 @@ jobs:
# Get the PR number from the github context
PR_NUMBER="${{ github.event.number }}"
# Check if all_files is requested
if [ "${{ inputs.all_files }}" = "true" ]; then
echo "all_files input is true, returning all files"
echo "changed-files=*" >> "$GITHUB_OUTPUT"
else
# Use gh CLI to get changed files in the PR with explicit repo
CHANGED_FILES=$(gh api repos/${{ github.repository }}/pulls/$PR_NUMBER/files --paginate --jq '.[] | select(.status != "removed") | .filename' | tr '\n' ' ' | sed 's/ $//')
# Use gh CLI to get changed files in the PR with explicit repo
CHANGED_FILES=$(gh api repos/${{ github.repository }}/pulls/$PR_NUMBER/files --paginate --jq '.[] | select(.status != "removed") | .filename' | tr '\n' ' ' | sed 's/ $//')
if [ -z "$CHANGED_FILES" ]; then
echo "No changed files found, setting to '*'"
CHANGED_FILES="*"
fi
echo "Changed files: $CHANGED_FILES"
echo "changed-files=$CHANGED_FILES" >> "$GITHUB_OUTPUT"
if [ -z "$CHANGED_FILES" ]; then
echo "No changed files found, setting to '*'"
CHANGED_FILES="*"
fi
echo "Changed files: $CHANGED_FILES"
echo "changed-files=$CHANGED_FILES" >> "$GITHUB_OUTPUT"
else
echo "Not in PR context, setting changed files to '*'"
echo "changed-files=*" >> "$GITHUB_OUTPUT"

View File

@ -11,7 +11,7 @@ on:
jobs:
lint-urls:
if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'skip-url-lint') }}
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@release/2.9
with:
job-name: lint-urls
timeout: 120
@ -37,7 +37,7 @@ jobs:
lint-xrefs:
if: ${{ github.event_name != 'pull_request' || !contains(github.event.pull_request.labels.*.name, 'skip-xref-lint') }}
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main
uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@release/2.9
with:
job-name: lint-xrefs
timeout: 60

View File

@ -134,7 +134,7 @@ jobs:
test-matrix: ${{ steps.filter.outputs.test-matrix }}
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
@ -147,7 +147,7 @@ jobs:
# checkout because when we run this action we don't *have* a local
# checkout. In other cases you should prefer a local checkout.
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
no-sudo: true
@ -183,7 +183,7 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
docker-image-name: ${{ inputs.docker-image-name }}
@ -199,7 +199,7 @@ jobs:
echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
if: inputs.build-environment != 'linux-s390x-binary-manywheel' && steps.use-old-whl.outputs.reuse != 'true'
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -457,7 +457,7 @@ jobs:
artifact_prefix: usage_log_build_${{ steps.get-job-id.outputs.job-id }}
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
uses: pytorch/test-infra/.github/actions/teardown-linux@release/2.9
if: always() && inputs.build-environment != 'linux-s390x-binary-manywheel'
- name: Cleanup docker

View File

@ -1,255 +0,0 @@
# The point of this workflow is to test that a FA3 wheel that was built based off the
# stable ABI as of torch nightly 20250830 can still run on the newer torch.
#
# This workflow is very similar to the _linux-test.yml workflow, with the following
# differences:
# 1. It is simpler (there is no test matrix)
# 2. It pulls flash-attention as a secondary repository in order to access the tests.
# Note that it does not BUILD anything from flash-attention, as we have a prebuilt
# wheel. We pull flash-attention only to run a few tests.
# 3. It runs only FA3 tests. No PyTorch tests are run.
name: linux-test-stable-fa3
on:
workflow_call:
inputs:
build-environment:
required: true
type: string
description: Top-level label for what's being built/tested.
docker-image:
required: true
type: string
description: Docker image to run in.
timeout-minutes:
required: false
type: number
default: 30
description: |
Set the maximum (in minutes) how long the workflow should take to finish
s3-bucket:
description: S3 bucket to download artifact
required: false
type: string
default: "gha-artifacts"
secrets:
HUGGING_FACE_HUB_TOKEN:
required: false
description: |
HF Auth token to avoid rate limits when downloading models or datasets from hub
VLLM_TEST_HUGGING_FACE_TOKEN:
required: false
description: |
HF Auth token to test vllm
SCRIBE_GRAPHQL_ACCESS_TOKEN:
required: false
description: |
FB app token to write to scribe endpoint
env:
GIT_DEFAULT_BRANCH: ${{ github.event.repository.default_branch }}
jobs:
test:
# Don't run on forked repos
if: github.repository_owner == 'pytorch'
runs-on: linux.aws.h100
timeout-minutes: ${{ inputs.timeout-minutes || 30 }}
permissions:
id-token: write
contents: read
steps:
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
with:
no-sudo: true
- name: Checkout flash-attention as a secondary repository
uses: actions/checkout@v4
with:
repository: Dao-AILab/flash-attention
path: flash-attention
- name: Setup Linux
uses: ./.github/actions/setup-linux
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
with:
docker-image-name: ${{ inputs.docker-image }}
- name: Use following to pull public copy of the image
id: print-ghcr-mirror
env:
ECR_DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
shell: bash
run: |
tag=${ECR_DOCKER_IMAGE##*:}
echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Check if in a container runner
shell: bash
id: check_container_runner
run: echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
- name: Setup GPU_FLAG for docker run
id: setup-gpu-flag
run: echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}"
- name: Setup SCCACHE_SERVER_PORT environment for docker run when on container
id: setup-sscache-port-flag
run: echo "SCCACHE_SERVER_PORT_DOCKER_FLAG=-e SCCACHE_SERVER_PORT=$((RUNNER_UID + 4226))" >> "${GITHUB_ENV}"
if: ${{ steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'true' }}
- name: Get workflow job id
id: get-job-id
uses: ./.github/actions/get-workflow-job-id
if: always()
with:
github-token: ${{ secrets.GITHUB_TOKEN }}
- name: Download build artifacts
uses: ./.github/actions/download-build-artifacts
with:
name: ${{ inputs.build-environment }}
s3-bucket: ${{ inputs.s3-bucket }}
- name: Parse ref
id: parse-ref
run: .github/scripts/parse_ref.py
- name: Set Test step time
id: test-timeout
shell: bash
env:
JOB_TIMEOUT: ${{ inputs.timeout-minutes }}
run: |
echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
- name: Preserve github env variables for use in docker
shell: bash
run: |
env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
- name: Test
id: test
timeout-minutes: ${{ fromJson(steps.test-timeout.outputs.timeout) }}
env:
BUILD_ENVIRONMENT: ${{ inputs.build-environment }}
PR_NUMBER: ${{ github.event.pull_request.number }}
GITHUB_REPOSITORY: ${{ github.repository }}
GITHUB_WORKFLOW: ${{ github.workflow }}
GITHUB_JOB: ${{ github.job }}
GITHUB_RUN_ID: ${{ github.run_id }}
GITHUB_RUN_NUMBER: ${{ github.run_number }}
GITHUB_RUN_ATTEMPT: ${{ github.run_attempt }}
JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
JOB_NAME: ${{ steps.get-job-id.outputs.job-name }}
BRANCH: ${{ steps.parse-ref.outputs.branch }}
SHA1: ${{ github.event.pull_request.head.sha || github.sha }}
BASE_SHA: ${{ github.event.pull_request.base.sha || github.sha }}
SHM_SIZE: '2g'
DOCKER_IMAGE: ${{ inputs.docker-image }}
VLLM_TEST_HUGGING_FACE_TOKEN: ${{ secrets.VLLM_TEST_HUGGING_FACE_TOKEN }}
HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
SCRIBE_GRAPHQL_ACCESS_TOKEN: ${{ secrets.SCRIBE_GRAPHQL_ACCESS_TOKEN }}
ARTIFACTS_FILE_SUFFIX: ${{ github.job }}-${{ steps.get-job-id.outputs.job-id }}
run: |
set -x
TEST_COMMAND=.ci/pytorch/test_fa3_abi_stable.sh
# Leaving 1GB for the runner and other things
TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo)
# https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap
# comes from https://github.com/pytorch/test-infra/pull/6058
TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3))
SHM_OPTS="--shm-size=${SHM_SIZE}"
JENKINS_USER="--user jenkins"
DOCKER_SHELL_CMD=
# detached container should get cleaned up by teardown_ec2_linux
# TODO: Stop building test binaries as part of the build phase
# Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice
# shellcheck disable=SC2086,SC2090
container_name=$(docker run \
${GPU_FLAG:-} \
${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
-e BUILD_ENVIRONMENT \
-e PR_NUMBER \
-e GITHUB_ACTIONS \
-e GITHUB_REPOSITORY \
-e GITHUB_WORKFLOW \
-e GITHUB_JOB \
-e GITHUB_RUN_ID \
-e GITHUB_RUN_NUMBER \
-e GITHUB_RUN_ATTEMPT \
-e JOB_ID \
-e JOB_NAME \
-e BASE_SHA \
-e BRANCH \
-e SHA1 \
-e MAX_JOBS="$(nproc --ignore=2)" \
-e HUGGING_FACE_HUB_TOKEN \
-e VLLM_TEST_HUGGING_FACE_TOKEN \
-e SCRIBE_GRAPHQL_ACCESS_TOKEN \
-e ARTIFACTS_FILE_SUFFIX \
--memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \
--memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--security-opt seccomp=unconfined \
--cap-add=SYS_PTRACE \
--ipc=host \
${SHM_OPTS} \
--tty \
--detach \
--name="${container_name}" \
${JENKINS_USER} \
-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
-w /var/lib/jenkins/workspace \
"${DOCKER_IMAGE}" \
${DOCKER_SHELL_CMD}
)
echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
- name: Collect backtraces from coredumps (if any)
if: always()
run: |
# shellcheck disable=SC2156
find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
- name: Store Core dumps on S3
uses: seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a # v5.1.0
if: failure()
with:
name: coredumps-fa3-stable-abi-smoke-tests
retention-days: 14
if-no-files-found: ignore
path: ./**/core.[1-9]*
- name: Upload utilization stats
if: ${{ always() && steps.test.conclusion && steps.test.conclusion != 'skipped' }}
continue-on-error: true
uses: ./.github/actions/upload-utilization-stats
with:
job_id: ${{ steps.get-job-id.outputs.job-id }}
job_name: ${{ steps.get-job-id.outputs.job-name }}
workflow_name: ${{ github.workflow }}
workflow_run_id: ${{github.run_id}}
workflow_attempt: ${{github.run_attempt}}
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always() && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false'

View File

@ -99,7 +99,7 @@ jobs:
contents: read
steps:
- name: Setup SSH (Click me for login details)
uses: pytorch/test-infra/.github/actions/setup-ssh@main
uses: pytorch/test-infra/.github/actions/setup-ssh@release/2.9
if: ${{ !contains(matrix.runner, 'b200') && inputs.build-environment != 'linux-s390x-binary-manywheel' }}
with:
github-secret: ${{ secrets.GITHUB_TOKEN }}
@ -108,7 +108,7 @@ jobs:
docker exec -it $(docker container ps --format '{{.ID}}') bash
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
no-sudo: true
@ -139,7 +139,7 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
docker-image-name: ${{ inputs.docker-image }}
@ -155,7 +155,7 @@ jobs:
echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -167,7 +167,7 @@ jobs:
- name: Install nvidia driver, nvidia-docker runtime, set GPU_FLAG
id: install-nvidia-driver
uses: pytorch/test-infra/.github/actions/setup-nvidia@main
uses: pytorch/test-infra/.github/actions/setup-nvidia@release/2.9
with:
driver-version: ${{ matrix.config == 'legacy_nvidia_driver' && '525.105.17' || '580.82.07' }}
if: ${{ contains(inputs.build-environment, 'cuda') && !contains(matrix.config, 'nogpu') && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false' && !contains(matrix.runner, 'b200') }}
@ -420,7 +420,7 @@ jobs:
aws-region: us-east-1
- name: Upload the benchmark results
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@main
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@release/2.9
if: inputs.build-environment != 'linux-s390x-binary-manywheel'
with:
benchmark-results-dir: test/test-reports
@ -478,7 +478,7 @@ jobs:
workflow_attempt: ${{github.run_attempt}}
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
uses: pytorch/test-infra/.github/actions/teardown-linux@release/2.9
if: always() && steps.check_container_runner.outputs.IN_CONTAINER_RUNNER == 'false'
# NB: We are currently having an intermittent GPU-related issue on G5 runners with

View File

@ -67,11 +67,11 @@ jobs:
test-matrix: ${{ steps.filter.outputs.test-matrix }}
steps:
- name: Clean up disk space before running MacOS workflow
uses: pytorch/test-infra/.github/actions/check-disk-space@main
uses: pytorch/test-infra/.github/actions/check-disk-space@release/2.9
# [see note: pytorch repo ref]
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
- name: Set xcode version
env:
@ -82,10 +82,10 @@ jobs:
fi
- name: Setup Python
uses: pytorch/test-infra/.github/actions/setup-python@main
uses: pytorch/test-infra/.github/actions/setup-python@release/2.9
with:
python-version: ${{ inputs.python-version }}
pip-requirements-file: .ci/docker/requirements-ci.txt
pip-requirements-file: .github/requirements/pip-requirements-macOS.txt
- name: Install sccache (only for non-forked PRs, and pushes to trunk)
uses: nick-fields/retry@7152eba30c6575329ac0576536151aca5a72780e # v3.0.0
@ -188,4 +188,4 @@ jobs:
- name: Clean up disk space
if: always()
continue-on-error: true
uses: pytorch/test-infra/.github/actions/check-disk-space@main
uses: pytorch/test-infra/.github/actions/check-disk-space@release/2.9

View File

@ -105,11 +105,11 @@ jobs:
done
- name: Clean up disk space before running MacOS workflow
uses: pytorch/test-infra/.github/actions/check-disk-space@main
uses: pytorch/test-infra/.github/actions/check-disk-space@release/2.9
# [see note: pytorch repo ref]
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
- name: Get workflow job id
id: get-job-id
@ -119,10 +119,10 @@ jobs:
github-token: ${{ secrets.GITHUB_TOKEN }}
- name: Setup Python
uses: pytorch/test-infra/.github/actions/setup-python@main
uses: pytorch/test-infra/.github/actions/setup-python@release/2.9
with:
python-version: ${{ inputs.python-version }}
pip-requirements-file: .ci/docker/requirements-ci.txt
pip-requirements-file: .github/requirements/pip-requirements-macOS.txt
- name: Start monitoring script
id: monitor-script
@ -257,7 +257,7 @@ jobs:
file-suffix: ${{ github.job }}-${{ matrix.config }}-${{ matrix.shard }}-${{ matrix.num_shards }}-${{ matrix.runner }}_${{ steps.get-job-id.outputs.job-id }}
- name: Upload the benchmark results
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@main
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@release/2.9
with:
benchmark-results-dir: test/test-reports
dry-run: false
@ -287,4 +287,4 @@ jobs:
- name: Clean up disk space
if: always()
continue-on-error: true
uses: pytorch/test-infra/.github/actions/check-disk-space@main
uses: pytorch/test-infra/.github/actions/check-disk-space@release/2.9

View File

@ -62,11 +62,6 @@ on:
required: false
type: number
default: 1
secrets:
HUGGING_FACE_HUB_TOKEN:
required: false
description: |
HF Auth token to avoid rate limits when downloading models or datasets from hub
env:
GIT_DEFAULT_BRANCH: ${{ github.event.repository.default_branch }}
@ -81,11 +76,12 @@ jobs:
strategy:
matrix: ${{ fromJSON(inputs.test-matrix) }}
fail-fast: false
runs-on: ${{ matrix.runner }}
timeout-minutes: ${{ matrix.mem_leak_check == 'mem_leak_check' && 600 || inputs.timeout-minutes }}
runs-on: ${{ matrix.runner }}
steps:
# [see note: pytorch repo ref]
- name: Checkout PyTorch
uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
with:
no-sudo: true
@ -117,12 +113,12 @@ jobs:
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
uses: pytorch/test-infra/.github/actions/calculate-docker-image@release/2.9
with:
docker-image-name: ${{ inputs.docker-image }}
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
uses: pytorch/test-infra/.github/actions/pull-docker-image@release/2.9
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
@ -135,9 +131,6 @@ jobs:
- name: Start monitoring script
id: monitor-script
if: ${{ !inputs.disable-monitor }}
shell: bash
continue-on-error: true
env:
JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
JOB_NAME: ${{ steps.get-job-id.outputs.job-name }}
@ -145,6 +138,9 @@ jobs:
WORKFLOW_RUN_ID: ${{github.run_id}}
MONITOR_LOG_INTERVAL: ${{ inputs.monitor-log-interval }}
MONITOR_DATA_COLLECT_INTERVAL: ${{ inputs.monitor-data-collect-interval }}
if: ${{ !inputs.disable-monitor }}
shell: bash
continue-on-error: true
run: |
python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7
python3 -m tools.stats.monitor --log-interval "$MONITOR_LOG_INTERVAL" --data-collect-interval "$MONITOR_DATA_COLLECT_INTERVAL" > usage_log.txt 2>&1 &
@ -182,12 +178,6 @@ jobs:
run: |
echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
- name: Preserve github env variables for use in docker
shell: bash
run: |
env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
- name: Test
id: test
env:
@ -203,22 +193,20 @@ jobs:
JOB_NAME: ${{ steps.get-job-id.outputs.job-name }}
BRANCH: ${{ steps.parse-ref.outputs.branch }}
SHA1: ${{ github.event.pull_request.head.sha || github.sha }}
BASE_SHA: ${{ github.event.pull_request.base.sha || github.sha }}
TEST_CONFIG: ${{ matrix.config }}
SHARD_NUMBER: ${{ matrix.shard }}
NUM_TEST_SHARDS: ${{ matrix.num_shards }}
REENABLED_ISSUES: ${{ steps.keep-going.outputs.reenabled-issues }}
CONTINUE_THROUGH_ERROR: ${{ steps.keep-going.outputs.keep-going }}
VERBOSE_TEST_LOGS: ${{ steps.keep-going.outputs.ci-verbose-test-logs }}
TEST_SHOWLOCALS: ${{ steps.keep-going.outputs.ci-test-showlocals }}
NO_TEST_TIMEOUT: ${{ steps.keep-going.outputs.ci-no-test-timeout }}
NO_TD: ${{ steps.keep-going.outputs.ci-no-td }}
TEST_CONFIG: ${{ matrix.config }}
SHARD_NUMBER: ${{ matrix.shard }}
NUM_TEST_SHARDS: ${{ matrix.num_shards }}
REENABLED_ISSUES: ${{ steps.keep-going.outputs.reenabled-issues }}
DOCKER_IMAGE: ${{ inputs.docker-image }}
PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: ${{ matrix.mem_leak_check && '1' || '0' }}
PYTORCH_TEST_RERUN_DISABLED_TESTS: ${{ matrix.rerun_disabled_tests && '1' || '0' }}
TESTS_TO_INCLUDE: ${{ inputs.tests-to-include }}
DASHBOARD_TAG: ${{ inputs.dashboard-tag }}
HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGING_FACE_HUB_TOKEN }}
timeout-minutes: ${{ fromJson(steps.test-timeout.outputs.timeout) }}
run: |
set -x
@ -248,7 +236,6 @@ jobs:
-e GITHUB_RUN_ATTEMPT \
-e JOB_ID \
-e JOB_NAME \
-e BASE_SHA \
-e BRANCH \
-e SHA1 \
-e AWS_DEFAULT_REGION \
@ -266,12 +253,10 @@ jobs:
-e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
-e PYTORCH_TEST_RERUN_DISABLED_TESTS \
-e TESTS_TO_INCLUDE \
-e HUGGING_FACE_HUB_TOKEN \
-e DASHBOARD_TAG \
--env-file="${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}" \
--ulimit stack=10485760:83886080 \
--ulimit core=0 \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--security-opt seccomp=unconfined \
--cap-add=SYS_PTRACE \
--shm-size="8g" \
@ -345,7 +330,7 @@ jobs:
aws-region: us-east-1
- name: Upload the benchmark results
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@main
uses: pytorch/test-infra/.github/actions/upload-benchmark-results@release/2.9
with:
benchmark-results-dir: test/test-reports
dry-run: false

View File

@ -59,7 +59,7 @@ jobs:
PR_NUMBER: ${{ github.event.pull_request.number }}
steps:
# - name: Checkout PyTorch
# uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
# uses: pytorch/pytorch/.github/actions/checkout-pytorch@release/2.9
# with:
# fetch-depth: 1
# submodules: true

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