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Author SHA1 Message Date
7eef782636 Link LazyLinalg with cusolver statically when needed (#79324) (#79522)
By copy-n-pasting the static linking logic from `libtorch_cuda` if
lazylinalg is not enabled

Pull Request resolved: https://github.com/pytorch/pytorch/pull/79324
Approved by: https://github.com/atalman

Co-authored-by: Nikita Shulga <nshulga@fb.com>
2022-06-14 08:35:57 -07:00
fa01ea406a Add docs for Python Registration (#79481)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78753

Approved by: https://github.com/ezyang, https://github.com/albanD
2022-06-14 08:09:21 -04:00
21e1282098 [CUDA graphs] Allows Adam and AdamW to be capture-safe (#77862) (#79472)
Near term fix for https://github.com/pytorch/pytorch/issues/76368.

Q. Why does the user need to request `capturable=True` in the optimizer constructor? Why can't capture safety be completely automatic?
A. We need to set up capture-safe (device-side) state variables before capture. If we don't, and step() internally detects capture is underway, it's too late: the best we could do is create a device state variable and copy the current CPU value into it, which is not something we want baked into the graph.

Q. Ok, why not just do the capture-safe approach with device-side state variables all the time?
A. It incurs several more kernel launches per parameter, which could really add up and regress cpu overhead for ungraphed step()s. If the optimizer won't be captured, we should allow step() to stick with its current cpu-side state handling.

Q. But cuda RNG is a stateful thing that maintains its state on the cpu outside of capture and replay, and we capture it automatically. Why can't we do the same thing here?
A. The graph object can handle RNG generator increments because its capture_begin, capture_end, and replay() methods can see and access generator object. But the graph object has no explicit knowledge of or access to optimizer steps in its capture scope. We could let the user tell the graph object what optimizers will be stepped in its scope, ie something like
```python
graph.will_use_optimizer(opt)
graph.capture_begin()
...
```
but that seems clunkier than an optimizer constructor arg.

I'm open to other ideas, but right now I think constructor arg is necessary and the least bad approach.

Long term, https://github.com/pytorch/pytorch/issues/71274 is a better fix.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77862
Approved by: https://github.com/ezyang
2022-06-14 08:06:07 -04:00
2d3d6f9d05 cherry-pick (#79455)
Co-authored-by: Mike Ruberry <mruberry@fb.com>
2022-06-13 18:52:31 -04:00
da93b1cbeb [CI] Turn flaky test signal to green (#79220) (#79220) (#79416)
Summary:
This implements the RFC #73573

Pull Request resolved: https://github.com/pytorch/pytorch/pull/79220
Approved by: https://github.com/suo

Test Plan: contbuild & OSS CI, see 1bc8c87322

Reviewed By: osalpekar

Differential Revision: D37059423

Pulled By: osalpekar

fbshipit-source-id: c73d326e3aca834221cd003157f960e5bc02960a

Co-authored-by: Jane Xu (Meta Employee) <janeyx@fb.com>
2022-06-13 17:15:34 -04:00
d67c72cb53 Removing cublas static linking (#79280) (#79417)
Removing cublas static linking

Test:  https://github.com/pytorch/pytorch/runs/6837323424?check_suite_focus=true

```
(base) atalman@atalman-dev-workstation-d4c889c8-2k8hl:~/whl_test/torch/lib$ ldd libtorch_cuda.so
	linux-vdso.so.1 (0x00007fffe8f6a000)
	libc10_cuda.so (0x00007f6539e6a000)
	libcudart-80664282.so.10.2 (0x00007f6539be9000)
	libnvToolsExt-3965bdd0.so.1 (0x00007f65399df000)
	libpthread.so.0 => /lib/x86_64-linux-gnu/libpthread.so.0 (0x00007f65397c0000)
	libc10.so (0x00007f653952f000)
	libtorch_cpu.so (0x00007f6520921000)
	libm.so.6 => /lib/x86_64-linux-gnu/libm.so.6 (0x00007f6520583000)
	libdl.so.2 => /lib/x86_64-linux-gnu/libdl.so.2 (0x00007f652037f000)
	libcublas.so.10 (0x00007f651c0c5000)
	librt.so.1 => /lib/x86_64-linux-gnu/librt.so.1 (0x00007f651bebd000)
	libstdc++.so.6 => /usr/lib/x86_64-linux-gnu/libstdc++.so.6 (0x00007f651bb34000)
	libgcc_s.so.1 => /lib/x86_64-linux-gnu/libgcc_s.so.1 (0x00007f651b91c000)
	libc.so.6 => /lib/x86_64-linux-gnu/libc.so.6 (0x00007f651b52b000)
	/lib64/ld-linux-x86-64.so.2 (0x00007f656aa13000)
	libgomp-a34b3233.so.1 (0x00007f651b301000)
	libcublasLt.so.10 (0x00007f651946c000)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79280
Approved by: https://github.com/seemethere
2022-06-13 16:48:13 -04:00
ef26f13df9 Install NDK 21 after GitHub update (#79024) (#79024) (#79429)
Summary:
See https://github.com/actions/virtual-environments/issues/5595

Pull Request resolved: https://github.com/pytorch/pytorch/pull/79024
Approved by: https://github.com/janeyx99

Test Plan: contbuild & OSS CI, see 0be9df4e85

Reviewed By: osalpekar

Differential Revision: D36993242

Pulled By: kit1980

fbshipit-source-id: c2e76fee4eaf0b1474cb7221721cbb798c319001

Co-authored-by: Sergii Dymchenko (Meta Employee) <sdym@fb.com>
2022-06-13 15:07:45 -04:00
4a9779aa4d [DataPipe] Correcting deprecation version (#79309)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79302

Approved by: https://github.com/ejguan

Co-authored-by: PyTorch MergeBot <pytorchmergebot@users.noreply.github.com>
2022-06-10 15:23:18 -07:00
9a94ddc081 Fix _free_weak_ref error (#79315)
Fixes #74016

This is a cherry pick of  https://github.com/pytorch/pytorch/pull/78575 into release/1.12 branch
Approved by: https://github.com/ezyang
2022-06-10 15:14:11 -07:00
dee3dc6070 MPS: add layer_norm_backward (#79189) (#79276)
Layernorm backward

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/79189
Approved by: https://github.com/razarmehr, https://github.com/albanD
2022-06-10 10:20:43 -07:00
30fce6836f Fix jit schema_matching ignoring self resulting in wrong operator schema (#79249)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79101

Approved by: https://github.com/gmagogsfm, https://github.com/eellison
2022-06-10 11:51:42 -04:00
0f93212516 adding a quick link to nvfuser README.md in jit doc for 1.12 release (#78160) (#79221)
adding a link to github 1.12 release branch nvfuser README.md in jit doc

Note that this PR is intended to be cherry-picked by 1.12 release, we'll have a follow up PR to update the link once this PR is merged.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78160
Approved by: https://github.com/davidberard98

Co-authored-by: jjsjann123 <alex.jann2012@gmail.com>
2022-06-09 14:33:59 -04:00
eqy
585417e935 [DDP] Cherrypick support other memory formats #79060 (#79071)
* check in

* add test
2022-06-09 14:25:17 -04:00
bd93fe635e Foward fix sharding bug for DL (#79124) (#79129)
This PR solves a bug introduced by #79041

`torch.utils.data.graph_settings.apply_sharding` changes the datapipe in-place and returns `None`

It would resolve the Error in TorchData. See: https://github.com/pytorch/data/actions/runs/2461030312
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79124
Approved by: https://github.com/VitalyFedyunin
2022-06-09 14:21:33 -04:00
cc6e2d3035 Package config/template files with torchgen (#78942) (#79123)
Package config/template files with torchgen

This PR packages native_functions.yaml, tags.yaml and ATen/templates
with torchgen.

This PR:
- adds a step to setup.py to copy the relevant files over into torchgen
- adds a docstring for torchgen (so `import torchgen; help(torchgen)`
says something)
- adds a helper function in torchgen so you can get the torchgen root
directory (and figure out where the packaged files are)
- changes some scripts to explicitly pass the location of torchgen,
which will be helpful for the first item in the Future section.

Future
======

- torchgen, when invoked from the command line, should use sources
in torchgen/packaged instead of aten/src. I'm unable to do this because
people (aka PyTorch CI) invokes `python -m torchgen.gen` without
installing torchgen.
- the source of truth for all of these files should be in torchgen.
This is a bit annoying to execute on due to potential merge conflicts
and dealing with merge systems
- CI and testing. The way things are set up right now is really fragile,
we should have a CI job for torchgen.

Test Plan
=========
I ran the following locally:

```
python -m torchgen.gen -s torchgen/packaged
```
and verified that it outputted files.

Furthermore, I did a setup.py install and checked that the files are
actually being packaged with torchgen.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78942
Approved by: https://github.com/ezyang
2022-06-09 14:20:32 -04:00
127922d451 Fix sharding strategy for distributed DL (#79041) (#79063)
1. Change the sharding strategy from sharding by worker first then by rank to sharding in the order of rank then workers.
2. Change to fetch Rank and World size in main process for the sake of `spawn`.

For the change 1:
Before this PR, for the case when dataset can not be evenly divided by `worker_num * world_size`, more data will be retrieved by workers in first RANKs.
Using the following example:
- dataset size: 100
- world_size: 4
- num_worker: 2

The number of data retrieved by each rank before this PR
- Rank 0: 26
- Rank 1: 26
- Rank 2: 24
- Rank 3: 24

The number of data retrieved by each rank after this PR
- Rank 0: 25
- Rank 1: 25
- Rank 2: 25
- Rank 3: 25

For the change 2:
Before this PR, `dist` functions are invoked inside worker processes. It's fine when the worker processes are forked from the parent process. All environment variables are inherited and exposed to these `dist` functions. However, when the worker processes are spawned, they won't be able to access to these environment variables, then the dataset won't be sharded by rank.
After this PR, `_sharding_worker_init_fn` should be working for both `spawn` and `fork` case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79041
Approved by: https://github.com/VitalyFedyunin, https://github.com/NivekT
2022-06-07 21:24:27 -04:00
4c3742be4b Add check for no grad in transformer encoder nestedtensor conversion (#78832) (#78832) (#79029)
Summary:
Before, we allowed inputs with grad to be converted to NestedTensors. Autograd attempts to find the size of the NestedTensor, but NestedTensor throws an exception for its size function. This causes all calls to nn.TransformerEncoder with grad enabled to fail.

Fix: we add a check for no grad in transformer encoder so we do not convert tensor with grad to nestedtensor.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78832
Approved by: https://github.com/cpuhrsch, https://github.com/jbschlosser

Test Plan: contbuild & OSS CI, see 1f819ee965

Reviewed By: frank-wei, mikekgfb

Differential Revision: D36907614

Pulled By: erichan1

fbshipit-source-id: 576be36530da81c1eff59ac427ae860bfb402106
2022-06-07 21:23:27 -04:00
f12a1ff7f9 [1.12][DataPipe] Disable profiler for IterDataPipe by default and add deprecation of functional DataPipe names (#79027)
* [DataPipe] Disable profiler for IterDataPipe by default

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78674

Approved by: https://github.com/VitalyFedyunin

* [DataPipe] Add function for deprecation of functional DataPipe names

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78970

Approved by: https://github.com/ejguan
2022-06-07 17:48:48 -04:00
f913b4d9fb [quant] Skip some broken tests due to hypothesis
Summary:
Some quantization tests failed when we didn't touch any code related to the tests, all of them
are using hypothesis, it's likely that hypothesis is the problem. We will skip these tests for now and
gradually remove all hypothesis tests from quantization test code, or skip running the hypothesis tests in CI

Test Plan:
ossci

Reviewers:

Subscribers:

Tasks:

Tags:

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78302

Approved by: https://github.com/suo, https://github.com/dzdang

(cherry picked from commit 716f76716a842482947efbdb54ea6bf6de3577e1)
2022-06-07 10:05:29 -07:00
9229e451b2 Guard test_sparse_csr.test_mm on CUDA11+ (#77965)
Fixes #77944

Pull Request resolved: https://github.com/pytorch/pytorch/pull/77965
Approved by: https://github.com/albanD, https://github.com/malfet

(cherry picked from commit a8467de6fa1657a6f2b3f0b426a873c6e98ce5ce)
2022-06-07 10:02:31 -07:00
d064733915 Fix coreml ios workflow (#78356)
Which were broken by https://pypi.org/project/protobuf/4.21.0/ release
Fix by installing pinned version of coremltools with pinned version of protobuf

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78356
Approved by: https://github.com/atalman

(cherry picked from commit a4723d5a5f11f974f9d9ccd83564f3de5de818c5)
2022-06-07 09:51:50 -07:00
9d67727edf [FSDP][Docs] Fix typo in full_optim_state_dict() (#78981)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78784

Approved by: https://github.com/rohan-varma
2022-06-07 10:10:32 -04:00
ec86ed25e9 Run MPS tests (#78723)
This adds a workflow, that is executed on MacOS 12.3+ machines and runs just test_mps.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78723
Approved by: https://github.com/albanD, https://github.com/kulinseth

(cherry picked from commit f7ac389e71e55f84651141c01334dea668b3f90c)
2022-06-07 06:57:52 -07:00
2deba51e72 [MPS] Do not pass linker command to a compiler (#78630)
`-weak_framework` is a linker rather than a compiler option and as such
it should not be passed as CXX flag
Also, use `string(APPEND` rather than `set(FOO "$(FOO) ...)`

Likely fixes our ability to use `sccache` for MacOS CI builds, see https://github.com/pytorch/pytorch/issues/78375#issuecomment-1143697183
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78630
Approved by: https://github.com/albanD

(cherry picked from commit 634954c55c05b0c0905b2299308dd9152e08af92)
2022-06-07 06:56:57 -07:00
e9a12ec87f update mps note with more details (#78669)
Follow up to the comments in https://github.com/pytorch/pytorch/pull/77767#pullrequestreview-978807521
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78669
Approved by: https://github.com/kulinseth, https://github.com/anjali411

(cherry picked from commit b30b1f3decfd2b51ac2250b00a8ae7049143d855)
2022-06-07 06:53:59 -07:00
2a8e3ee91e Update codeowners for MPS (#78727)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78727
Approved by: https://github.com/malfet

(cherry picked from commit 48c3d8573918cf47f8091e9b5e7cea7aa0785ad4)
2022-06-07 06:52:53 -07:00
47d558e862 [MPS] Add arange_mps_out implementation (#78789)
Mostly by factoring out shader logic from `linspace_out_mps` implementation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78789
Approved by: https://github.com/albanD, https://github.com/kulinseth
2022-06-07 06:52:17 -07:00
bc0a9abad2 MPS: Fix issues with view tensors and linspace. (#78690)
Fixes: #https://github.com/pytorch/pytorch/issues/78642, https://github.com/pytorch/pytorch/issues/78511
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78690
Approved by: https://github.com/razarmehr, https://github.com/DenisVieriu97

(cherry picked from commit 4858c56334aa2b09b1ba10d0a3547ef01edda363)
2022-06-07 06:51:17 -07:00
fa7d872ce3 MPS: add linespace op (#78570)
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78570
Approved by: https://github.com/malfet

(cherry picked from commit a3bdafece3a07aea186e34abc28e2540aa078393)
2022-06-07 06:51:08 -07:00
d1d2be89fd Add test case for issue: https://github.com/pytorch/pytorch/issues/77851 (#78547)
The test works fine now.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78547
Approved by: https://github.com/kulinseth

(cherry picked from commit aa62b3e003b53a0b36e04005fe5fdc8e2dda0253)
2022-06-07 06:50:53 -07:00
0e58e3374e MPS: Implement aten::count_nonzero.dim_IntList (#78169)
- See: #77764

Implements the `aten::count_nonzero.dim_IntList` operator (as used by [torch.count_nonzero](https://pytorch.org/docs/stable/generated/torch.count_nonzero.html)) for [MPS](https://pytorch.org/blog/introducing-accelerated-pytorch-training-on-mac/).

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78169
Approved by: https://github.com/malfet, https://github.com/kulinseth, https://github.com/albanD

(cherry picked from commit f42b42d3eb9af4ea1d09f00a13e9b6dc9efcc0f8)
2022-06-07 06:50:41 -07:00
e3e753161c MPS: Fix crashes in view tensors due to buffer size mismatch (#78496)
Fixes #78247, #77886

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78496
Approved by: https://github.com/albanD, https://github.com/malfet

(cherry picked from commit 017b0ae9431ae3780a4eb9bf6d8865dfcd02cd92)
2022-06-07 06:50:33 -07:00
dc2b2f09d7 Speed up test_mps from 9min to 25s
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78488

Approved by: https://github.com/kulinseth

(cherry picked from commit bde246fcc60372c0ce7ee16dd5e3dc7652a36867)
2022-06-07 06:50:20 -07:00
19ebdd7eab Remove prints and add proper asserts
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78454

Approved by: https://github.com/kulinseth

(cherry picked from commit 02551a002575d1a40d6a6c7d6c7f319ef1b3ad2f)
2022-06-07 06:50:13 -07:00
f8160b113e MPS: Fixes the as_strided_mps implementation for contiguous view operations (#78440)
Fixes https://github.com/pytorch/pytorch/issues/78107; https://github.com/pytorch/pytorch/issues/77750

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78440
Approved by: https://github.com/malfet

(cherry picked from commit d63db52349ae3cffd6f762c9027e7363a6271d27)
2022-06-07 06:50:04 -07:00
3e8119bf9a MPS: Fix the memory growing issue and BERT_pytorch network crash fix. (#78006)
Fixes #77753

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78006
Approved by: https://github.com/albanD

(cherry picked from commit cbdb694f158b8471d71822873c3ac130203cc218)
2022-06-07 06:49:56 -07:00
6660df9f22 [MPS] Fix copy_kernel_mps (#78428)
By passing `storage_offset` of source and destination Tensors
This fixes following simple usecase:
```
python3` -c "import torch;x=torch.zeros(3, 3, device='mps'); x[1, 1]=1;print(x)"
```

Add test to validate it would not regress in the future

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78428
Approved by: https://github.com/kulinseth

(cherry picked from commit 437ecfc4612b73ada1f99de94f3c79de6b08f99a)
2022-06-07 06:43:55 -07:00
8b7e19a87b MPS: Eye op (#78408)
This can be used as a reference PR was to add Op in MPS backend.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78408
Approved by: https://github.com/albanD

(cherry picked from commit 8552acbd7435eadb184e0cedc21df64d3bf30329)
2022-06-07 06:43:48 -07:00
9828013233 [mps] Do not use malloc/free in Indexing.mm (#78409)
Especially allocating just 2 int64 on heap is somewhat wasteful (and they are leaked if function returns earlier)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78409
Approved by: https://github.com/seemethere, https://github.com/kulinseth

(cherry picked from commit aefb4c9fba0edf5a71a245e4fd8f5ac1d65beeac)
2022-06-07 06:43:41 -07:00
53fc6dc3db MPS: Add adaptive max pool2d op (#78410)
Adaptive max pool 2d forward and backward with test

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78410
Approved by: https://github.com/albanD

(cherry picked from commit 2e32d5fcd8de75dc2695d940925e5be181a06b54)
2022-06-07 06:43:33 -07:00
52435c6b1f MPS: add ranked tensors for addcmul ops instead of constants and update version_check (#78354)
This is a reland of https://github.com/pytorch/pytorch/pull/78312 with syntax error and formating fixed in `MPSDevice.mm`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78354
Approved by: https://github.com/kulinseth

(cherry picked from commit 45462baf7e2ef00a9aa912e2a045b20bc3ed80d3)
2022-06-07 06:43:21 -07:00
9a66061326 Fix the MPS Heap volatility (#78230)
Fixes #77829
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78230
Approved by: https://github.com/malfet

(cherry picked from commit c8ab55b2939c4cd5cd8d2e0605fdfd09e8eff294)
2022-06-07 06:42:00 -07:00
eef0ec541e Use random seed in normal_mps_out (#78010)
Fixes #78009.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78010
Approved by: https://github.com/kulinseth

(cherry picked from commit 51c4c79e3d4e600baa6a53a2afcf99ef8db5dbe0)
2022-06-07 06:41:26 -07:00
0ffefea581 Fix typo in testname (#78258)
`test_linear2D_no_bias_backwarwd` -> `test_linear2D_no_bias_backward`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78258
Approved by: https://github.com/kulinseth, https://github.com/janeyx99

(cherry picked from commit 705082656a9dd2c9f243da01f28a195b94b24d66)
2022-06-07 06:41:18 -07:00
7e12cfb29d [MPS] Lazy initialize allocators (#78227)
Do not construct MPS allocators at load time, but rather create them
lazily when needed

This significantly reduces `libtorch.dylib` load time and prevents weird
flicker, when during import torch when Intel MacBook runs switches from
integrated to discrete graphics

Before the change `python3 -c "import timeit;import importlib;print(timeit.timeit(lambda: importlib.import_module('torch'), number=1))"` takes about 1 sec, after the change it drops down to .6 sec

Minor changes:
 - Deleted unused `__block id<MTLBuffer> buf = nil;` from
   HeapAllocatorImpl
 - Add braces for single line if statements

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78227
Approved by: https://github.com/kulinseth, https://github.com/albanD

(cherry picked from commit 2679aa47897232827771ad7bb18e14bb4be3cae8)
2022-06-07 06:41:09 -07:00
24b9bd4398 [MPS] Add version check (#78192)
Use `instancesRespondToSelector:` to test the presence of
`optimizationLevel` in `MPSGraphCompilationDescriptor`, which according
to
https://developer.apple.com/documentation/metalperformanceshadersgraph/mpsgraphcompilationdescriptor/3922624-optimizationlevel
is only available on 12.3 or newer

This works around a limitations of `@available(macOS 12.3, *)` macro in
shared libraries dynamically loaded by apps targeting older runtime.
And deployment target for macos Python conda binaries is 10.14:
```
% otool -l `which python3`
...
Load command 9
      cmd LC_BUILD_VERSION
  cmdsize 32
 platform 1
    minos 10.14
      sdk 10.14
...
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78192
Approved by: https://github.com/atalman, https://github.com/seemethere

(cherry picked from commit b7bb34d7625d95e5088638721dcc07c2bc5e2ade)
2022-06-07 06:41:02 -07:00
5342e76039 Convert MPS Tensor data using MPSGraph API (#78092)
Fixes #78091
If you are already working on this, simply disregard this or take what may be helpful. This is my attempt at MPS-native Tensor datatype conversion. It works for everything tested ~~but is currently only implemented for MPS-to-MPS copy, not MPS-to-X or X-to-MPS, but the same approach could easily be used~~.

Before:
```python
In [5]: pt.full((40,), -10.3, device="mps")
Out[5]:
tensor([-10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000], device='mps:0')

In [6]: pt.full((40,), -10.3, device="mps").int()
Out[6]:
tensor([-1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883,
        -1054552883, -1054552883, -1054552883, -1054552883, -1054552883],
       device='mps:0', dtype=torch.int32)

In [7]: pt.full((40,), -10.3, device="mps").int().float()
Out[7]:
tensor([-10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000], device='mps:0')

In [8]: pt.full((40,), -10.3, device="mps").int().float().bool()
Out[8]:
tensor([ True, False, False,  True,  True, False, False,  True,  True, False,
        False,  True,  True, False, False,  True,  True, False, False,  True,
         True, False, False,  True,  True, False, False,  True,  True, False,
        False,  True,  True, False, False,  True,  True, False, False,  True],
       device='mps:0')
```

After:
```python
In [3]: pt.full((40,), -10.3, device="mps")
Out[3]:
tensor([-10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000, -10.3000,
        -10.3000, -10.3000, -10.3000, -10.3000, -10.3000], device='mps:0')

In [4]: pt.full((40,), -10.3, device="mps").int()
Out[4]:
tensor([-10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
        -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10,
        -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10, -10],
       device='mps:0', dtype=torch.int32)

In [5]: pt.full((40,), -10.3, device="mps").int().float()
Out[5]:
tensor([-10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10.,
        -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10.,
        -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10., -10.,
        -10., -10., -10., -10.], device='mps:0')

In [6]: pt.full((40,), -10.3, device="mps").int().float().bool()
Out[6]:
tensor([True, True, True, True, True, True, True, True, True, True, True, True,
        True, True, True, True, True, True, True, True, True, True, True, True,
        True, True, True, True, True, True, True, True, True, True, True, True,
        True, True, True, True], device='mps:0')
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78092
Approved by: https://github.com/kulinseth, https://github.com/malfet

(cherry picked from commit a52bfe2c5d8588b8f9e83e0beecdd18a1d672d0e)
2022-06-07 06:40:54 -07:00
08d70ab718 [MPS] Fix torch.mps.is_available() (#78121)
By introducing `at:mps::is_available()` and changing `torch._C._is_mps_available` from property to memoizable callable

Also, if `_mtl_device` is released in MPSDevice destructor, shouldn't it be retained in the constructor

Looks like GitHubActions Mac runner does not have any Metal devices available, according to https://github.com/malfet/deleteme/runs/6560871657?check_suite_focus=true#step:3:15

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78121
Approved by: https://github.com/albanD

(cherry picked from commit 6244daa6a9a27463f63235d88b9f728c91243a08)
2022-06-07 06:40:23 -07:00
207bde1ee8 Add ignore for -Wunsupported-availability-guard
This failed internal builds so just upstreaming the internal fix

Signed-off-by: Eli Uriegas <eliuriegasfb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/77995

Approved by: https://github.com/bigfootjon, https://github.com/malfet

(cherry picked from commit a9a99a901e953cf35edd010e17ee0ed4d2f347af)
2022-06-07 06:40:18 -07:00
51428a8f43 Fix a few issues on assert/double error/legacy constructor (#77966)
Fixes https://github.com/pytorch/pytorch/issues/77960, https://github.com/pytorch/pytorch/issues/77957, https://github.com/pytorch/pytorch/issues/77781
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77966
Approved by: https://github.com/soulitzer, https://github.com/kulinseth

(cherry picked from commit 04ac80c73a9f525322a8b622659a27ad065698ea)
2022-06-07 06:38:32 -07:00
c40f18454d [DataLoader] Apply sharding settings in dist when num_workers is 0 (#78967)
ghstack-source-id: 9c53e8c9adb3ac7c80ebf22a476385509b252511
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78950

Co-authored-by: Vitaly Fedyunin <vitaly.fedyunin@gmail.com>
2022-06-07 09:15:35 -04:00
8a5156a050 [DataPipe] Adding functional API for FileLister (#78419) (#78948)
Fixes #78263

Follow-up from pytorch/data#387. This adds a functional API `list_files()` to `FileListerDataPipe`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78419
Approved by: https://github.com/NivekT, https://github.com/ejguan

Co-authored-by: Robert Xiu <xiurobert@gmail.com>
2022-06-07 09:13:09 -04:00
04d75d2008 Make ShufflerDataPipe deterministic for persistent DL and distributed DL (#78765) (#78927)
Fixes https://github.com/pytorch/data/issues/426

This PR introduces two main changes:
- It ensures the `ShufflerDataPipe` would share the same seed across distributed processes.
- Users can reset `shuffle` for persistent workers per epoch.

Detail:
- `shared_seed` is shared across distributed and worker processes. It will seed a `shared_rng` to provide seeds to each `ShufflerDataPipe` in the pipeline
- `worker_loop` now accepts a new argument of `shared_seed` to accept this shared seed.
- The `shared_seed` is attached to `_ResumeIteration` for resetting seed per epoch for `persistent worker`
- I choose not to touch `base_seed` simply for BC issue

I used this [script](https://gist.github.com/ejguan/d88f75fa822cb696ab1bc5bc25844f47) to test the result with `world_size=4`. Please check the result in: https://gist.github.com/ejguan/6ee2d2de12ca57f9eb4b97ef5a0e300b

You can see there isn't any duplicated/missing element for each epoch. And, with the same seed, the order of data remains the same across epochs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78765
Approved by: https://github.com/VitalyFedyunin
2022-06-07 09:03:26 -04:00
2652da29ab Avoid CPU Sync in SyncBatchNorm When Capturing CUDA Graphs (#78810)
We recently updated `SyncBatchNorm` to support empty input batches.
The new code removes stats from ranks with empty inputs. However,
this change breaks CUDA graph capture as it forces CPU sync. This
commit uses `is_current_stream_capturing()` to guard the new code
path, and only run the new code when not capturing CUA Graphs. To
support empty inputs with CUDA graph capturing, we might need to
update CUDA kernels for `batch_norm_backward_elemt` and
`batch_norm_gather_stats_with_counts`. See #78656.

Fixes #78549

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78666

Approved by: https://github.com/albanD
2022-06-06 09:39:03 -04:00
aa8911885b [chalf] warn once on creating a chalf tensor (#78245) (#78710)
`chalf` is experimental as the op coverage is low.

Following script raises 6 warnings if `set_warn_always(True)` else raises only 1 warning.
```python
import torch
torch.set_warn_always(True)
device='cpu'
t = torch.randn(3, dtype=torch.chalf, device=device)
y = torch.rand(3, dtype=torch.chalf, device=device)
# Allocates new tensor for result
t + y

device='cuda'
t = torch.randn(3, dtype=torch.chalf, device=device)
y = torch.rand(3, dtype=torch.chalf, device=device)

# Allocates new tensor for result
t + y

```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78245
Approved by: https://github.com/anjali411
2022-06-03 10:51:18 -04:00
528710ec89 [DataLoader] DataLoader now automatically apply sharding to DataPipes (#78762)
ghstack-source-id: ac918b064cd09cd68a04c28238481c76b46b4010
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78631

Co-authored-by: Vitaly Fedyunin <vitaly.fedyunin@gmail.com>
2022-06-03 10:21:55 -04:00
de53f70e1d [GHA] attempt to re-enable mac test workflows (#78000) (#78749)
Our mac tests have not been running since #77645 because of
<img width="1386" alt="image" src="https://user-images.githubusercontent.com/31798555/169602783-988a265a-ce4a-41a7-8f13-3eb4615b0d6f.png">

https://github.com/pytorch/pytorch/actions/runs/2345334995
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78000
Approved by: https://github.com/malfet

Co-authored-by: Jane Xu <janeyx@fb.com>
2022-06-02 15:25:22 -04:00
39ebb3e06e fix set item to scalar tensor missing gradient info (#78746)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78246

Approved by: https://github.com/ngimel
2022-06-02 15:04:36 -04:00
fd3cc823ce [DataPipe] Lazily generate exception message for performance (#78726)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78673

Approved by: https://github.com/ejguan
2022-06-02 14:26:38 -04:00
5bb7c617f6 [docs][nn] conv: complex support note (#78351) (#78709)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78351
Approved by: https://github.com/anjali411, https://github.com/jbschlosser
2022-06-02 14:23:29 -04:00
8a627381c9 [DataLoader] Minor documentation improvement (#78548)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78404

Approved by: https://github.com/ejguan
2022-06-01 14:05:54 -04:00
f56e16a70f [ONNX] Fix typo when comparing DeviceObjType (#78085) (#78370)
#77423 Introduced a typo in

1db9be70a7/torch/onnx/symbolic_opset9.py (L5012-L5017)

where the string `DeviceObjType` was replaced with `_C.DeviceObjType`. This PR reverts the changes to the strings.

**Tested:**

With torchvision,

```
pytest test/test_onnx.py::TestONNXExporter::test_mask_rcnn
pytest -n auto test/test_onnx.py::TestONNXExporter
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78085
Approved by: https://github.com/datumbox, https://github.com/BowenBao, https://github.com/ezyang

Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
2022-05-26 13:05:36 -07:00
c93a7f8bea Update PyTorch/XLA git clone branch name for 1.12 (#78315) 2022-05-25 16:06:39 -07:00
919b53c5e7 [Profiler] Fix segfault in AppendOnlyList (#78084) 2022-05-24 17:21:45 -04:00
2ad18abc49 [MPS] Initialize MPSDevice::_mtl_device property to nil (#78136) (#78204)
This prevents `import torch` accidentally crash on machines with no metal devices

Should prevent crashes reported in https://github.com/pytorch/pytorch/pull/77662#issuecomment-1134637986 and https://github.com/pytorch/functorch/runs/6560056366?check_suite_focus=true

Backtrace to the crash:
```
(lldb) bt
* thread #1, stop reason = signal SIGSTOP
  * frame #0: 0x00007fff7202be57 libobjc.A.dylib`objc_msgSend + 23
    frame #1: 0x000000010fd9f524 libtorch_cpu.dylib`at::mps::HeapAllocator::MPSHeapAllocatorImpl::MPSHeapAllocatorImpl() + 436
    frame #2: 0x000000010fda011d libtorch_cpu.dylib`_GLOBAL__sub_I_MPSAllocator.mm + 125
    frame #3: 0x000000010ada81e3 dyld`ImageLoaderMachO::doModInitFunctions(ImageLoader::LinkContext const&) + 535
    frame #4: 0x000000010ada85ee dyld`ImageLoaderMachO::doInitialization(ImageLoader::LinkContext const&) + 40(lldb) up
frame #1: 0x000000010fd9f524 libtorch_cpu.dylib`at::mps::HeapAllocator::MPSHeapAllocatorImpl::MPSHeapAllocatorImpl() + 436
libtorch_cpu.dylib`at::mps::HeapAllocator::MPSHeapAllocatorImpl::MPSHeapAllocatorImpl:
->  0x10fd9f524 <+436>: movq   %rax, 0x1b0(%rbx)
    0x10fd9f52b <+443>: movw   $0x0, 0x1b8(%rbx)
    0x10fd9f534 <+452>: addq   $0x8, %rsp
    0x10fd9f538 <+456>: popq   %rbx
(lldb) disassemble
 ...
    0x10fd9f514 <+420>: movq   0xf19ad15(%rip), %rsi     ; "maxBufferLength"
    0x10fd9f51b <+427>: movq   %r14, %rdi
    0x10fd9f51e <+430>: callq  *0xeaa326c(%rip)          ; (void *)0x00007fff7202be40: objc_msgSend
```

which corresponds to `[m_device maxBufferLength]` call, where `m_device` is not initialized in
2ae3c59e4b/aten/src/ATen/mps/MPSAllocator.h (L171)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/78136
Approved by: https://github.com/seemethere

Co-authored-by: Nikita Shulga <nshulga@fb.com>
2022-05-24 17:03:38 -04:00
9596b999f8 Fix unit tests (#78056) 2022-05-24 17:00:57 -04:00
baabb4cb96 MPS: Add back the memory leak fixes. (#77964) (#78198)
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/77964
Approved by: https://github.com/albanD

Co-authored-by: Kulin Seth <kulinseth@gmail.com>
2022-05-24 16:36:16 -04:00
906a6e1df9 Fixing release rc build names (#78174) 2022-05-24 15:04:36 -04:00
974f7f8080 [1.12] Remove torch.vmap (#78021) 2022-05-23 10:30:23 -07:00
8abf37d74e ci: Pin builder to release/1.12 (#77986)
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
2022-05-20 14:00:44 -04:00
8ff2bc0c01 Release 1.12 Install torch from test channel, Pin builder and xla repo (#77983) 2022-05-20 10:51:22 -07:00
a119b7f6d4 retry - enable NVFuser by default
Enable NVFuser in OSS.

Retry of #77213, because it was breaking torchvision tests.

Fix in #77471 has been verified by jjsjann123

Pull Request resolved: https://github.com/pytorch/pytorch/pull/77579

Approved by: https://github.com/eellison, https://github.com/malfet, https://github.com/atalman, https://github.com/seemethere
2022-05-20 10:31:49 -07:00
22781 changed files with 1261369 additions and 2419616 deletions

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@ -1,4 +0,0 @@
# We do not use this library in our Bazel build. It contains an
# infinitely recursing symlink that makes Bazel very unhappy.
third_party/ittapi/
third_party/opentelemetry-cpp

109
.bazelrc
View File

@ -1,8 +1,8 @@
build --cxxopt=--std=c++17
build --cxxopt=--std=c++14
build --copt=-I.
# Bazel does not support including its cc_library targets as system
# headers. We work around this for generated code
# (e.g. torch/headeronly/macros/cmake_macros.h) by making the generated directory a
# (e.g. c10/macros/cmake_macros.h) by making the generated directory a
# system include path.
build --copt=-isystem --copt bazel-out/k8-fastbuild/bin
build --copt=-isystem --copt bazel-out/darwin-fastbuild/bin
@ -13,102 +13,15 @@ build:no-tty --curses no
build:no-tty --progress_report_interval 10
build:no-tty --show_progress_rate_limit 10
# Build with GPU support by default.
build --define=cuda=true
# rules_cuda configuration
build --@rules_cuda//cuda:enable_cuda
build --@rules_cuda//cuda:cuda_targets=sm_52
build --@rules_cuda//cuda:compiler=nvcc
build --repo_env=CUDA_PATH=/usr/local/cuda
# Configuration to build without GPU support
build:cpu-only --define=cuda=false
# Configuration to build with GPU support
build:gpu --define=cuda=true
# define a separate build folder for faster switching between configs
build:cpu-only --platform_suffix=-cpu-only
build:gpu --platform_suffix=-gpu
# See the note on the config-less build for details about why we are
# doing this. We must also do it for the "-cpu-only" platform suffix.
build --copt=-isystem --copt=bazel-out/k8-fastbuild-cpu-only/bin
# doing this. We must also do it for the "-gpu" platform suffix.
build --copt=-isystem --copt=bazel-out/k8-fastbuild-gpu/bin
# rules_cuda configuration
build:cpu-only --@rules_cuda//cuda:enable_cuda=False
# Definition of --config=shell
# interactive shell immediately before execution
build:shell --run_under="//tools/bazel_tools:shellwrap"
# Disable all warnings for external repositories. We don't care about
# their warnings.
build --per_file_copt=^external/@-w
# Set additional warnings to error level.
#
# Implementation notes:
# * we use file extensions to determine if we are using the C++
# compiler or the cuda compiler
# * we use ^// at the start of the regex to only permit matching
# PyTorch files. This excludes external repos.
#
# Note that because this is logically a command-line flag, it is
# considered the word on what warnings are enabled. This has the
# unfortunate consequence of preventing us from disabling an error at
# the target level because those flags will come before these flags in
# the action invocation. Instead we provide per-file exceptions after
# this.
#
# On the bright side, this means we don't have to more broadly apply
# the exceptions to an entire target.
#
# Looking for CUDA flags? We have a cu_library macro that we can edit
# directly. Look in //tools/rules:cu.bzl for details. Editing the
# macro over this has the following advantages:
# * making changes does not require discarding the Bazel analysis
# cache
# * it allows for selective overrides on individual targets since the
# macro-level opts will come earlier than target level overrides
build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=all
# The following warnings come from -Wall. We downgrade them from error
# to warnings here.
#
# We intentionally use #pragma unroll, which is compiler specific.
build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-error=unknown-pragmas
build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=extra
# The following warnings come from -Wextra. We downgrade them from error
# to warnings here.
#
# unused-parameter-compare has a tremendous amount of violations in the
# codebase. It will be a lot of work to fix them, just disable it for
# now.
build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-parameter
# missing-field-parameters has both a large number of violations in
# the codebase, but it also is used pervasively in the Python C
# API. There are a couple of catches though:
# * we use multiple versions of the Python API and hence have
# potentially multiple different versions of each relevant
# struct. They may have different numbers of fields. It will be
# unwieldy to support multiple versions in the same source file.
# * Python itself for many of these structs recommends only
# initializing a subset of the fields. We should respect the API
# usage conventions of our dependencies.
#
# Hence, we just disable this warning altogether. We may want to clean
# up some of the clear-cut cases that could be risky, but we still
# likely want to have this disabled for the most part.
build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-missing-field-initializers
build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-function
build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-variable
build --per_file_copt='//:aten/src/ATen/RegisterCompositeExplicitAutograd\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterCompositeImplicitAutograd\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterMkldnnCPU\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterNestedTensorCPU\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterQuantizedCPU\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterSparseCPU\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterSparseCsrCPU\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterNestedTensorMeta\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterSparseMeta\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterQuantizedMeta\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:aten/src/ATen/RegisterZeroTensor\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:torch/csrc/lazy/generated/RegisterAutogradLazy\.cpp$'@-Wno-error=unused-function
build --per_file_copt='//:torch/csrc/lazy/generated/RegisterLazy\.cpp$'@-Wno-error=unused-function
build:gpu --@rules_cuda//cuda:enable_cuda
build:gpu --@rules_cuda//cuda:cuda_targets=sm_52
build:gpu --@rules_cuda//cuda:compiler=nvcc
build:gpu --repo_env=CUDA_PATH=/usr/local/cuda

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@ -1 +1 @@
6.5.0
4.2.1

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@ -1,15 +0,0 @@
version: 1
paths:
include:
- "**/*.py"
exclude:
- ".*"
- ".*/**"
- "**/.*/**"
- "**/.*"
- "**/_*/**"
- "**/_*.py"
- "**/test/**"
- "**/benchmarks/**"
- "**/test_*.py"
- "**/*_test.py"

15
.buckconfig.oss Normal file
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@ -0,0 +1,15 @@
[buildfile]
name = BUILD.buck
[repositories]
bazel_skylib = third_party/bazel-skylib/
[download]
in_build = true
[cxx]
cxxflags = -std=c++17
should_remap_host_platform = true
[project]
default_flavors_mode=all

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@ -1,19 +0,0 @@
# Aarch64 (ARM/Graviton) Support Scripts
Scripts for building aarch64 PyTorch PIP Wheels. These scripts build the following wheels:
* torch
* torchvision
* torchaudio
* torchtext
* torchdata
## Aarch64_ci_build.sh
This script is design to support CD operations within PyPi manylinux aarch64 container, and be executed in the container. It prepares the container and then executes __aarch64_wheel_ci_build.py__ to build the wheels. The script "assumes" the PyTorch repo is located at: ```/pytorch``` and will put the wheels into ```/artifacts```.
### Usage
```DESIRED_PYTHON=<PythonVersion> aarch64_ci_build.sh```
__NOTE:__ CI build is currently __EXPERMINTAL__
## Build_aarch64_wheel.py
This app allows a person to build using AWS EC3 resources and requires AWS-CLI and Boto3 with AWS credentials to support building EC2 instances for the wheel builds. Can be used in a codebuild CD or from a local system.
### Usage
```build_aarch64_wheel.py --key-name <YourPemKey> --use-docker --python 3.8 --branch <RCtag>```

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@ -1,49 +0,0 @@
#!/bin/bash
set -eux -o pipefail
GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
# Set CUDA architecture lists to match x86 build_cuda.sh
if [[ "$GPU_ARCH_VERSION" == *"12.6"* ]]; then
export TORCH_CUDA_ARCH_LIST="8.0;9.0"
elif [[ "$GPU_ARCH_VERSION" == *"12.8"* ]]; then
export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
elif [[ "$GPU_ARCH_VERSION" == *"13.0"* ]]; then
export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;11.0;12.0+PTX"
fi
# Compress the fatbin with -compress-mode=size for CUDA 13
if [[ "$DESIRED_CUDA" == *"13"* ]]; then
export TORCH_NVCC_FLAGS="-compress-mode=size"
fi
SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
source $SCRIPTPATH/aarch64_ci_setup.sh
###############################################################################
# Run aarch64 builder python
###############################################################################
cd /
# adding safe directory for git as the permissions will be
# on the mounted pytorch repo
git config --global --add safe.directory /pytorch
pip install -r /pytorch/requirements.txt
pip install auditwheel==6.2.0 wheel
if [ "$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
else
echo "BASE_CUDA_VERSION is set to: $DESIRED_CUDA"
export USE_SYSTEM_NCCL=1
# Check if we should use NVIDIA libs from PyPI (similar to x86 build_cuda.sh logic)
if [[ -z "$PYTORCH_EXTRA_INSTALL_REQUIREMENTS" ]]; then
echo "Bundling CUDA libraries with wheel for aarch64."
else
echo "Using nvidia libs from pypi for aarch64."
echo "Updated PYTORCH_EXTRA_INSTALL_REQUIREMENTS for aarch64: $PYTORCH_EXTRA_INSTALL_REQUIREMENTS"
export USE_NVIDIA_PYPI_LIBS=1
fi
python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
fi

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@ -1,21 +0,0 @@
#!/bin/bash
set -eux -o pipefail
# This script is used to prepare the Docker container for aarch64_ci_wheel_build.py python script
# By creating symlinks from desired /opt/python to /usr/local/bin/
NUMPY_VERSION=2.0.2
if [[ "$DESIRED_PYTHON" == "3.13" || "$DESIRED_PYTHON" == "3.13t" ]]; then
NUMPY_VERSION=2.1.2
fi
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"
source $SCRIPTPATH/../manywheel/set_desired_python.sh
pip install -q numpy==${NUMPY_VERSION} pyyaml==6.0.2 scons==4.7.0 ninja==1.11.1 patchelf==0.17.2
for tool in python python3 pip pip3 ninja scons patchelf; do
ln -sf ${DESIRED_PYTHON_BIN_DIR}/${tool} /usr/local/bin;
done
python --version

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@ -1,382 +0,0 @@
#!/usr/bin/env python3
# encoding: UTF-8
import os
import shutil
from subprocess import check_call, check_output
def list_dir(path: str) -> list[str]:
"""'
Helper for getting paths for Python
"""
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()
for i, line in enumerate(lines):
if line.startswith("Tag:"):
lines[i] = line.replace("-linux_", "-manylinux_2_28_")
print(f"Updated tag from {line} to {lines[i]}")
break
with open(filename, "w") as f:
f.writelines(lines)
def patch_library_rpath(
folder: str,
lib_name: str,
use_nvidia_pypi_libs: bool = False,
desired_cuda: str = "",
) -> None:
"""Apply patchelf to set RPATH for a library in torch/lib"""
lib_path = f"{folder}/tmp/torch/lib/{lib_name}"
if use_nvidia_pypi_libs:
# For PyPI NVIDIA libraries, construct CUDA RPATH
cuda_rpaths = [
"$ORIGIN/../../nvidia/cudnn/lib",
"$ORIGIN/../../nvidia/nvshmem/lib",
"$ORIGIN/../../nvidia/nccl/lib",
"$ORIGIN/../../nvidia/cusparselt/lib",
]
if "130" in desired_cuda:
cuda_rpaths.append("$ORIGIN/../../nvidia/cu13/lib")
else:
cuda_rpaths.extend(
[
"$ORIGIN/../../nvidia/cublas/lib",
"$ORIGIN/../../nvidia/cuda_cupti/lib",
"$ORIGIN/../../nvidia/cuda_nvrtc/lib",
"$ORIGIN/../../nvidia/cuda_runtime/lib",
"$ORIGIN/../../nvidia/cufft/lib",
"$ORIGIN/../../nvidia/curand/lib",
"$ORIGIN/../../nvidia/cusolver/lib",
"$ORIGIN/../../nvidia/cusparse/lib",
"$ORIGIN/../../nvidia/nvtx/lib",
"$ORIGIN/../../nvidia/cufile/lib",
]
)
# Add $ORIGIN for local torch libs
rpath = ":".join(cuda_rpaths) + ":$ORIGIN"
else:
# For bundled libraries, just use $ORIGIN
rpath = "$ORIGIN"
if os.path.exists(lib_path):
os.system(
f"cd {folder}/tmp/torch/lib/; "
f"patchelf --set-rpath '{rpath}' --force-rpath {lib_name}"
)
def copy_and_patch_library(
src_path: str,
folder: str,
use_nvidia_pypi_libs: bool = False,
desired_cuda: str = "",
) -> None:
"""Copy a library to torch/lib and patch its RPATH"""
if os.path.exists(src_path):
lib_name = os.path.basename(src_path)
shutil.copy2(src_path, f"{folder}/tmp/torch/lib/{lib_name}")
patch_library_rpath(folder, lib_name, use_nvidia_pypi_libs, desired_cuda)
def package_cuda_wheel(wheel_path, desired_cuda) -> None:
"""
Package the cuda wheel libraries
"""
folder = os.path.dirname(wheel_path)
os.mkdir(f"{folder}/tmp")
os.system(f"unzip {wheel_path} -d {folder}/tmp")
# Delete original wheel since it will be repackaged
os.system(f"rm {wheel_path}")
# Check if we should use PyPI NVIDIA libraries or bundle system libraries
use_nvidia_pypi_libs = os.getenv("USE_NVIDIA_PYPI_LIBS", "0") == "1"
if use_nvidia_pypi_libs:
print("Using nvidia libs from pypi - skipping CUDA library bundling")
# For PyPI approach, we don't bundle CUDA libraries - they come from PyPI packages
# We only need to bundle non-NVIDIA libraries
minimal_libs_to_copy = [
"/lib64/libgomp.so.1",
"/usr/lib64/libgfortran.so.5",
"/acl/build/libarm_compute.so",
"/acl/build/libarm_compute_graph.so",
"/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_lapack_core.so.0",
"/usr/local/lib/libnvpl_blas_core.so.0",
]
# Copy minimal libraries to unzipped_folder/torch/lib
for lib_path in minimal_libs_to_copy:
copy_and_patch_library(lib_path, folder, use_nvidia_pypi_libs, desired_cuda)
# Patch torch libraries used for searching libraries
torch_libs_to_patch = [
"libtorch.so",
"libtorch_cpu.so",
"libtorch_cuda.so",
"libtorch_cuda_linalg.so",
"libtorch_global_deps.so",
"libtorch_python.so",
"libtorch_nvshmem.so",
"libc10.so",
"libc10_cuda.so",
"libcaffe2_nvrtc.so",
"libshm.so",
]
for lib_name in torch_libs_to_patch:
patch_library_rpath(folder, lib_name, use_nvidia_pypi_libs, desired_cuda)
else:
print("Bundling CUDA libraries with wheel")
# Original logic for bundling system CUDA libraries
# Common libraries for all CUDA versions
common_libs = [
# Non-NVIDIA system libraries
"/lib64/libgomp.so.1",
"/usr/lib64/libgfortran.so.5",
"/acl/build/libarm_compute.so",
"/acl/build/libarm_compute_graph.so",
# Common CUDA libraries (same for all versions)
"/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_lapack_core.so.0",
"/usr/local/lib/libnvpl_blas_core.so.0",
"/usr/local/cuda/extras/CUPTI/lib64/libnvperf_host.so",
"/usr/local/cuda/lib64/libcudnn.so.9",
"/usr/local/cuda/lib64/libcusparseLt.so.0",
"/usr/local/cuda/lib64/libcurand.so.10",
"/usr/local/cuda/lib64/libnccl.so.2",
"/usr/local/cuda/lib64/libnvshmem_host.so.3",
"/usr/local/cuda/lib64/libcudnn_adv.so.9",
"/usr/local/cuda/lib64/libcudnn_cnn.so.9",
"/usr/local/cuda/lib64/libcudnn_graph.so.9",
"/usr/local/cuda/lib64/libcudnn_ops.so.9",
"/usr/local/cuda/lib64/libcudnn_engines_runtime_compiled.so.9",
"/usr/local/cuda/lib64/libcudnn_engines_precompiled.so.9",
"/usr/local/cuda/lib64/libcudnn_heuristic.so.9",
"/usr/local/cuda/lib64/libcufile.so.0",
"/usr/local/cuda/lib64/libcufile_rdma.so.1",
"/usr/local/cuda/lib64/libcusparse.so.12",
]
# CUDA version-specific libraries
if "13" in desired_cuda:
minor_version = desired_cuda[-1]
version_specific_libs = [
"/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.13",
"/usr/local/cuda/lib64/libcublas.so.13",
"/usr/local/cuda/lib64/libcublasLt.so.13",
"/usr/local/cuda/lib64/libcudart.so.13",
"/usr/local/cuda/lib64/libcufft.so.12",
"/usr/local/cuda/lib64/libcusolver.so.12",
"/usr/local/cuda/lib64/libnvJitLink.so.13",
"/usr/local/cuda/lib64/libnvrtc.so.13",
f"/usr/local/cuda/lib64/libnvrtc-builtins.so.13.{minor_version}",
]
elif "12" in desired_cuda:
# Get the last character for libnvrtc-builtins version (e.g., "129" -> "9")
minor_version = desired_cuda[-1]
version_specific_libs = [
"/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12",
"/usr/local/cuda/lib64/libcublas.so.12",
"/usr/local/cuda/lib64/libcublasLt.so.12",
"/usr/local/cuda/lib64/libcudart.so.12",
"/usr/local/cuda/lib64/libcufft.so.11",
"/usr/local/cuda/lib64/libcusolver.so.11",
"/usr/local/cuda/lib64/libnvJitLink.so.12",
"/usr/local/cuda/lib64/libnvrtc.so.12",
f"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.{minor_version}",
]
else:
raise ValueError(f"Unsupported CUDA version: {desired_cuda}.")
# Combine all libraries
libs_to_copy = common_libs + version_specific_libs
# Copy libraries to unzipped_folder/torch/lib
for lib_path in libs_to_copy:
copy_and_patch_library(lib_path, folder, use_nvidia_pypi_libs, desired_cuda)
# Make sure the wheel is tagged with manylinux_2_28
for f in os.scandir(f"{folder}/tmp/"):
if f.is_dir() and f.name.endswith(".dist-info"):
replace_tag(f"{f.path}/WHEEL")
break
os.system(f"wheel pack {folder}/tmp/ -d {folder}")
os.system(f"rm -rf {folder}/tmp/")
def complete_wheel(folder: str) -> str:
"""
Complete wheel build and put in artifact location
"""
wheel_name = list_dir(f"/{folder}/dist")[0]
# Please note for cuda we don't run auditwheel since we use custom script to package
# the cuda dependencies to the wheel file using update_wheel() method.
# However we need to make sure filename reflects the correct Manylinux platform.
if "pytorch" in folder and not enable_cuda:
print("Repairing Wheel with AuditWheel")
check_call(["auditwheel", "repair", f"dist/{wheel_name}"], cwd=folder)
repaired_wheel_name = list_dir(f"/{folder}/wheelhouse")[0]
print(f"Moving {repaired_wheel_name} wheel to /{folder}/dist")
os.rename(
f"/{folder}/wheelhouse/{repaired_wheel_name}",
f"/{folder}/dist/{repaired_wheel_name}",
)
else:
repaired_wheel_name = list_dir(f"/{folder}/dist")[0]
print(f"Copying {repaired_wheel_name} to artifacts")
shutil.copy2(
f"/{folder}/dist/{repaired_wheel_name}", f"/artifacts/{repaired_wheel_name}"
)
return repaired_wheel_name
def parse_arguments():
"""
Parse inline arguments
"""
from argparse import ArgumentParser
parser = ArgumentParser("AARCH64 wheels python CD")
parser.add_argument("--debug", action="store_true")
parser.add_argument("--build-only", action="store_true")
parser.add_argument("--test-only", type=str)
parser.add_argument("--enable-mkldnn", action="store_true")
parser.add_argument("--enable-cuda", action="store_true")
return parser.parse_args()
if __name__ == "__main__":
"""
Entry Point
"""
args = parse_arguments()
enable_mkldnn = args.enable_mkldnn
enable_cuda = args.enable_cuda
branch = check_output(
["git", "rev-parse", "--abbrev-ref", "HEAD"], cwd="/pytorch"
).decode()
print("Building PyTorch wheel")
build_vars = ""
# MAX_JOB=5 is not required for CPU backend (see commit 465d98b)
if enable_cuda:
build_vars += "MAX_JOBS=5 "
# Handle PyPI NVIDIA libraries vs bundled libraries
use_nvidia_pypi_libs = os.getenv("USE_NVIDIA_PYPI_LIBS", "0") == "1"
if use_nvidia_pypi_libs:
print("Configuring build for PyPI NVIDIA libraries")
# Configure for dynamic linking (matching x86 logic)
build_vars += "ATEN_STATIC_CUDA=0 USE_CUDA_STATIC_LINK=0 USE_CUPTI_SO=1 "
else:
print("Configuring build for bundled NVIDIA libraries")
# Keep existing static linking approach - already configured above
override_package_version = os.getenv("OVERRIDE_PACKAGE_VERSION")
desired_cuda = os.getenv("DESIRED_CUDA")
if override_package_version is not None:
version = override_package_version
build_vars += (
f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version} PYTORCH_BUILD_NUMBER=1 "
)
elif branch in ["nightly", "main"]:
build_date = (
check_output(["git", "log", "--pretty=format:%cs", "-1"], cwd="/pytorch")
.decode()
.replace("-", "")
)
version = (
check_output(["cat", "version.txt"], cwd="/pytorch").decode().strip()[:-2]
)
if enable_cuda:
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date}+{desired_cuda} PYTORCH_BUILD_NUMBER=1 "
else:
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date} PYTORCH_BUILD_NUMBER=1 "
elif branch.startswith(("v1.", "v2.")):
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 "
"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=/OpenBLAS "
else:
print("build pytorch without mkldnn backend")
os.system(f"cd /pytorch; {build_vars} python3 setup.py bdist_wheel")
if enable_cuda:
print("Updating Cuda Dependency")
filename = os.listdir("/pytorch/dist/")
wheel_path = f"/pytorch/dist/{filename[0]}"
package_cuda_wheel(wheel_path, desired_cuda)
pytorch_wheel_name = complete_wheel("/pytorch/")
print(f"Build Complete. Created {pytorch_wheel_name}..")

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#!/usr/bin/env python3
import os
import shutil
import sys
from subprocess import check_call
from tempfile import TemporaryDirectory
from auditwheel.elfutils import elf_file_filter
from auditwheel.lddtree import lddtree
from auditwheel.patcher import Patchelf
from auditwheel.repair import copylib
from auditwheel.wheeltools import InWheelCtx
def replace_tag(filename):
with open(filename) as f:
lines = f.read().split("\\n")
for i, line in enumerate(lines):
if not line.startswith("Tag: "):
continue
lines[i] = line.replace("-linux_", "-manylinux2014_")
print(f"Updated tag from {line} to {lines[i]}")
with open(filename, "w") as f:
f.write("\\n".join(lines))
class AlignedPatchelf(Patchelf):
def set_soname(self, file_name: str, new_soname: str) -> None:
check_call(
["patchelf", "--page-size", "65536", "--set-soname", new_soname, file_name]
)
def replace_needed(self, file_name: str, soname: str, new_soname: str) -> None:
check_call(
[
"patchelf",
"--page-size",
"65536",
"--replace-needed",
soname,
new_soname,
file_name,
]
)
def embed_library(whl_path, lib_soname, update_tag=False):
patcher = AlignedPatchelf()
out_dir = TemporaryDirectory()
whl_name = os.path.basename(whl_path)
tmp_whl_name = os.path.join(out_dir.name, whl_name)
with InWheelCtx(whl_path) as ctx:
torchlib_path = os.path.join(ctx._tmpdir.name, "torch", "lib")
ctx.out_wheel = tmp_whl_name
new_lib_path, new_lib_soname = None, None
for filename, _ in elf_file_filter(ctx.iter_files()):
if not filename.startswith("torch/lib"):
continue
libtree = lddtree(filename)
if lib_soname not in libtree["needed"]:
continue
lib_path = libtree["libs"][lib_soname]["path"]
if lib_path is None:
print(f"Can't embed {lib_soname} as it could not be found")
break
if lib_path.startswith(torchlib_path):
continue
if new_lib_path is None:
new_lib_soname, new_lib_path = copylib(lib_path, torchlib_path, patcher)
patcher.replace_needed(filename, lib_soname, new_lib_soname)
print(f"Replacing {lib_soname} with {new_lib_soname} for {filename}")
if update_tag:
# Add manylinux2014 tag
for filename in ctx.iter_files():
if os.path.basename(filename) != "WHEEL":
continue
replace_tag(filename)
shutil.move(tmp_whl_name, whl_path)
if __name__ == "__main__":
embed_library(
sys.argv[1], "libgomp.so.1", len(sys.argv) > 2 and sys.argv[2] == "--update-tag"
)

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@ -1,12 +0,0 @@
# Jenkins
The scripts in this directory are the entrypoint for testing Caffe2.
The environment variable `BUILD_ENVIRONMENT` is expected to be set to
the build environment you intend to test. It is a hint for the build
and test scripts to configure Caffe2 a certain way and include/exclude
tests. Docker images, they equal the name of the image itself. For
example: `py2-cuda9.0-cudnn7-ubuntu16.04`. The Docker images that are
built on Jenkins and are used in triggered builds already have this
environment variable set in their manifest. Also see
`./docker/jenkins/*/Dockerfile` and search for `BUILD_ENVIRONMENT`.

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@ -1,36 +0,0 @@
set -ex
LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
TEST_DIR="$ROOT_DIR/test"
gtest_reports_dir="${TEST_DIR}/test-reports/cpp"
pytest_reports_dir="${TEST_DIR}/test-reports/python"
# Figure out which Python to use
PYTHON="$(which python)"
if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
PYTHON=$(which "python${BASH_REMATCH[1]}")
fi
if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
# HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
unset HIP_PLATFORM
if which sccache > /dev/null; then
# Save sccache logs to file
sccache --stop-server || true
rm -f ~/sccache_error.log || true
SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
# Report sccache stats for easier debugging
sccache --zero-stats
fi
fi
# /usr/local/caffe2 is where the cpp bits are installed to in cmake-only
# builds. In +python builds the cpp tests are copied to /usr/local/caffe2 so
# that the test code in .ci/test.sh is the same
INSTALL_PREFIX="/usr/local/caffe2"
mkdir -p "$gtest_reports_dir" || true
mkdir -p "$pytest_reports_dir" || true
mkdir -p "$INSTALL_PREFIX" || true

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@ -1,168 +0,0 @@
#!/bin/bash
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
pip install click mock tabulate networkx==2.0
pip -q install "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
fi
# Skip tests in environments where they are not built/applicable
if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
echo 'Skipping tests'
exit 0
fi
# These additional packages are needed for circleci ROCm builds.
if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
# Need networkx 2.0 because bellmand_ford was moved in 2.1 . Scikit-image by
# defaults installs the most recent networkx version, so we install this lower
# version explicitly before scikit-image pulls it in as a dependency
pip install networkx==2.0
# click - onnx
pip install --progress-bar off click protobuf tabulate virtualenv mock typing-extensions
fi
# Find where cpp tests and Caffe2 itself are installed
if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
# For cmake only build we install everything into /usr/local
cpp_test_dir="$INSTALL_PREFIX/cpp_test"
ld_library_path="$INSTALL_PREFIX/lib"
else
# For Python builds we install into python
# cd to /usr first so the python import doesn't get confused by any 'caffe2'
# directory in cwd
python_installation="$(dirname $(dirname $(cd /usr && $PYTHON -c 'import os; import caffe2; print(os.path.realpath(caffe2.__file__))')))"
caffe2_pypath="$python_installation/caffe2"
cpp_test_dir="$python_installation/torch/test"
ld_library_path="$python_installation/torch/lib"
fi
################################################################################
# C++ tests #
################################################################################
# Only run cpp tests in the first shard, don't run cpp tests a second time in the second shard
if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
echo "Running C++ tests.."
for test in $(find "$cpp_test_dir" -executable -type f); do
case "$test" in
# skip tests we know are hanging or bad
*/mkl_utils_test|*/aten/integer_divider_test)
continue
;;
*/scalar_tensor_test|*/basic|*/native_test)
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
continue
else
LD_LIBRARY_PATH="$ld_library_path" "$test"
fi
;;
*/*_benchmark)
LD_LIBRARY_PATH="$ld_library_path" "$test" --benchmark_color=false
;;
*)
# Currently, we use a mixture of gtest (caffe2) and Catch2 (ATen). While
# planning to migrate to gtest as the common PyTorch c++ test suite, we
# currently do NOT use the xml test reporter, because Catch doesn't
# support multiple reporters
# c.f. https://github.com/catchorg/Catch2/blob/master/docs/release-notes.md#223
# which means that enabling XML output means you lose useful stdout
# output for Jenkins. It's more important to have useful console
# output than it is to have XML output for Jenkins.
# Note: in the future, if we want to use xml test reporter once we switch
# to all gtest, one can simply do:
LD_LIBRARY_PATH="$ld_library_path" \
"$test" --gtest_output=xml:"$gtest_reports_dir/$(basename $test).xml"
;;
esac
done
fi
################################################################################
# Python tests #
################################################################################
if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
exit 0
fi
# If pip is installed as root, we must use sudo.
# CircleCI docker images could install conda as jenkins user, or use the OS's python package.
PIP=$(which pip)
PIP_USER=$(stat --format '%U' $PIP)
CURRENT_USER=$(id -u -n)
if [[ "$PIP_USER" = root && "$CURRENT_USER" != root ]]; then
MAYBE_SUDO=sudo
fi
# Uninstall pre-installed hypothesis and coverage to use an older version as newer
# versions remove the timeout parameter from settings which ideep/conv_transpose_test.py uses
$MAYBE_SUDO pip -q uninstall -y hypothesis
$MAYBE_SUDO pip -q uninstall -y coverage
# "pip install hypothesis==3.44.6" from official server is unreliable on
# CircleCI, so we host a copy on S3 instead
$MAYBE_SUDO pip -q install attrs==18.1.0 -f https://s3.amazonaws.com/ossci-linux/wheels/attrs-18.1.0-py2.py3-none-any.whl
$MAYBE_SUDO pip -q install coverage==4.5.1 -f https://s3.amazonaws.com/ossci-linux/wheels/coverage-4.5.1-cp36-cp36m-macosx_10_12_x86_64.whl
$MAYBE_SUDO pip -q install hypothesis==3.44.6 -f https://s3.amazonaws.com/ossci-linux/wheels/hypothesis-3.44.6-py3-none-any.whl
# Collect additional tests to run (outside caffe2/python)
EXTRA_TESTS=()
# CUDA builds always include NCCL support
if [[ "$BUILD_ENVIRONMENT" == *-cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *-rocm* ]]; then
EXTRA_TESTS+=("$caffe2_pypath/contrib/nccl")
fi
rocm_ignore_test=()
if [[ $BUILD_ENVIRONMENT == *-rocm* ]]; then
# Currently these tests are failing on ROCM platform:
# On ROCm, RCCL (distributed) development isn't complete.
# https://github.com/ROCmSoftwarePlatform/rccl
rocm_ignore_test+=("--ignore $caffe2_pypath/python/data_parallel_model_test.py")
# This test has been flaky in ROCm CI (but note the tests are
# cpu-only so should be unrelated to ROCm)
rocm_ignore_test+=("--ignore $caffe2_pypath/python/operator_test/blobs_queue_db_test.py")
# This test is skipped on Jenkins(compiled without MKL) and otherwise known flaky
rocm_ignore_test+=("--ignore $caffe2_pypath/python/ideep/convfusion_op_test.py")
# This test is skipped on Jenkins(compiled without MKL) and causing segfault on Circle
rocm_ignore_test+=("--ignore $caffe2_pypath/python/ideep/pool_op_test.py")
fi
echo "Running Python tests.."
# locale setting is required by click package
for loc in "en_US.utf8" "C.UTF-8"; do
if locale -a | grep "$loc" >/dev/null 2>&1; then
export LC_ALL="$loc"
export LANG="$loc"
break;
fi
done
# Some Caffe2 tests fail when run using AVX512 ISA, see https://github.com/pytorch/pytorch/issues/66111
export DNNL_MAX_CPU_ISA=AVX2
# Should still run even in the absence of SHARD_NUMBER
if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
# TODO(sdym@meta.com) remove this when the linked issue resolved.
# py is temporary until https://github.com/Teemu/pytest-sugar/issues/241 is fixed
pip install py==1.11.0
pip install pytest-sugar
# NB: Warnings are disabled because they make it harder to see what
# the actual erroring test is
"$PYTHON" \
-m pytest \
-x \
-v \
--disable-warnings \
--junit-xml="$pytest_reports_dir/result.xml" \
--ignore "$caffe2_pypath/python/test/executor_test.py" \
--ignore "$caffe2_pypath/python/operator_test/matmul_op_test.py" \
--ignore "$caffe2_pypath/python/operator_test/pack_ops_test.py" \
--ignore "$caffe2_pypath/python/mkl/mkl_sbn_speed_test.py" \
--ignore "$caffe2_pypath/python/trt/test_pt_onnx_trt.py" \
${rocm_ignore_test[@]} \
"$caffe2_pypath/python" \
"${EXTRA_TESTS[@]}"
fi

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@ -1,139 +0,0 @@
# Docker images for GitHub CI and CD
This directory contains everything needed to build the Docker images
that are used in our CI.
The Dockerfiles located in subdirectories are parameterized to
conditionally run build stages depending on build arguments passed to
`docker build`. This lets us use only a few Dockerfiles for many
images. The different configurations are identified by a freeform
string that we call a _build environment_. This string is persisted in
each image as the `BUILD_ENVIRONMENT` environment variable.
See `build.sh` for valid build environments (it's the giant switch).
## Docker CI builds
* `build.sh` -- dispatch script to launch all builds
* `common` -- scripts used to execute individual Docker build stages
* `ubuntu` -- Dockerfile for Ubuntu image for CPU build and test jobs
* `ubuntu-cuda` -- Dockerfile for Ubuntu image with CUDA support for nvidia-docker
* `ubuntu-rocm` -- Dockerfile for Ubuntu image with ROCm support
* `ubuntu-xpu` -- Dockerfile for Ubuntu image with XPU support
### Docker CD builds
* `conda` - Dockerfile and build.sh to build Docker images used in nightly conda builds
* `manywheel` - Dockerfile and build.sh to build Docker images used in nightly manywheel builds
* `libtorch` - Dockerfile and build.sh to build Docker images used in nightly libtorch builds
## Usage
```bash
# Build a specific image
./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
# Set flags (see build.sh) and build image
sudo bash -c 'TRITON=1 ./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
```
## [Guidance] Adding a New Base Docker Image
### Background
The base Docker images in directory `.ci/docker/` are built by the `docker-builds.yml` workflow. Those images are used throughout the PyTorch CI/CD pipeline. You should only create or modify a base Docker image if you need specific environment changes or dependencies before building PyTorch on CI.
1. **Automatic Rebuilding**:
- The Docker image building process is triggered automatically when changes are made to files in the `.ci/docker/*` directory
- This ensures all images stay up-to-date with the latest dependencies and configurations
2. **Image Reuse in PyTorch Build Workflows** (example: linux-build):
- The images generated by `docker-builds.yml` are reused in `_linux-build.yml` through the `calculate-docker-image` step
- The `_linux-build.yml` workflow:
- Pulls the Docker image determined by the `calculate-docker-image` step
- Runs a Docker container with that image
- Executes `.ci/pytorch/build.sh` inside the container to build PyTorch
3. **Usage in Test Workflows** (example: linux-test):
- The same Docker images are also used in `_linux-test.yml` for running tests
- The `_linux-test.yml` workflow follows a similar pattern:
- It uses the `calculate-docker-image` step to determine which Docker image to use
- It pulls the Docker image and runs a container with that image
- It installs the wheels from the artifacts generated by PyTorch build jobs
- It executes test scripts (like `.ci/pytorch/test.sh` or `.ci/pytorch/multigpu-test.sh`) inside the container
### Understanding File Purposes
#### `.ci/docker/build.sh` vs `.ci/pytorch/build.sh`
- **`.ci/docker/build.sh`**:
- Used for building base Docker images
- Executed by the `docker-builds.yml` workflow to pre-build Docker images for CI
- Contains configurations for different Docker build environments
- **`.ci/pytorch/build.sh`**:
- Used for building PyTorch inside a Docker container
- Called by workflows like `_linux-build.yml` after the Docker container is started
- Builds PyTorch wheels and other artifacts
#### `.ci/docker/ci_commit_pins/` vs `.github/ci_commit_pins`
- **`.ci/docker/ci_commit_pins/`**:
- Used for pinning dependency versions during base Docker image building
- Ensures consistent environments for building PyTorch
- Changes here trigger base Docker image rebuilds
- **`.github/ci_commit_pins`**:
- Used for pinning dependency versions during PyTorch building and tests
- Ensures consistent dependencies for PyTorch across different builds
- Used by build scripts running inside Docker containers
### Step-by-Step Guide for Adding a New Base Docker Image
#### 1. Add Pinned Commits (If Applicable)
We use pinned commits for build stability. The `nightly.yml` workflow checks and updates pinned commits for certain repository dependencies daily.
If your new Docker image needs a library installed from a specific pinned commit or built from source:
1. Add the repository you want to track in `nightly.yml` and `merge-rules.yml`
2. Add the initial pinned commit in `.ci/docker/ci_commit_pins/`. The text filename should match the one defined in step 1
#### 2. Configure the Base Docker Image
1. **Add new Base Docker image configuration** (if applicable):
Add the configuration in `.ci/docker/build.sh`. For example:
```bash
pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-new1)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
NEW_ARG_1=yes
;;
```
2. **Add build arguments to Docker build command**:
If you're introducing a new argument to the Docker build, make sure to add it in the Docker build step in `.ci/docker/build.sh`:
```bash
docker build \
....
--build-arg "NEW_ARG_1=${NEW_ARG_1}"
```
3. **Update Dockerfile logic**:
Update the Dockerfile to use the new argument. For example, in `ubuntu/Dockerfile`:
```dockerfile
ARG NEW_ARG_1
# Set up environment for NEW_ARG_1
RUN if [ -n "${NEW_ARG_1}" ]; then bash ./do_something.sh; fi
```
4. **Add the Docker configuration** in `.github/workflows/docker-builds.yml`:
The `docker-builds.yml` workflow pre-builds the Docker images whenever changes occur in the `.ci/docker/` directory. This includes the
pinned commit updates.

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@ -1,106 +0,0 @@
ARG CUDA_VERSION=12.6
ARG BASE_TARGET=cuda${CUDA_VERSION}
ARG ROCM_IMAGE=rocm/dev-almalinux-8:6.3-complete
FROM amd64/almalinux:8.10-20250519 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=11
RUN yum -y update
RUN yum -y install epel-release
# install glibc-langpack-en make sure en_US.UTF-8 locale is available
RUN yum -y install glibc-langpack-en
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
# cmake-3.18.4 from pip
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -s /usr/local/bin/cmake /usr/bin/cmake3
RUN rm -rf /usr/local/cuda-*
FROM base as openssl
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
FROM base as patchelf
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh && cp $(which patchelf) /patchelf
FROM base as conda
# Install Anaconda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
# Install CUDA
FROM base as cuda
ARG CUDA_VERSION=12.6
RUN rm -rf /usr/local/cuda-*
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
# Preserve CUDA_VERSION for the builds
ENV CUDA_VERSION=${CUDA_VERSION}
# Make things in our path by default
ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:$PATH
FROM cuda as cuda12.6
RUN bash ./install_cuda.sh 12.6
ENV DESIRED_CUDA=12.6
FROM cuda as cuda12.8
RUN bash ./install_cuda.sh 12.8
ENV DESIRED_CUDA=12.8
FROM cuda as cuda12.9
RUN bash ./install_cuda.sh 12.9
ENV DESIRED_CUDA=12.9
FROM cuda as cuda13.0
RUN bash ./install_cuda.sh 13.0
ENV DESIRED_CUDA=13.0
FROM ${ROCM_IMAGE} as rocm
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
# Install MNIST test data
FROM base as mnist
ADD ./common/install_mnist.sh install_mnist.sh
RUN bash ./install_mnist.sh
FROM base as all_cuda
COPY --from=cuda12.6 /usr/local/cuda-12.6 /usr/local/cuda-12.6
COPY --from=cuda12.8 /usr/local/cuda-12.8 /usr/local/cuda-12.8
COPY --from=cuda12.9 /usr/local/cuda-12.9 /usr/local/cuda-12.9
COPY --from=cuda13.0 /usr/local/cuda-13.0 /usr/local/cuda-13.0
# Final step
FROM ${BASE_TARGET} as final
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=patchelf /patchelf /usr/local/bin/patchelf
COPY --from=conda /opt/conda /opt/conda
# Add jni.h for java host build.
COPY ./common/install_jni.sh install_jni.sh
COPY ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
ENV PATH /opt/conda/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
COPY --from=mnist /usr/local/mnist /usr/local/mnist
RUN rm -rf /usr/local/cuda
RUN chmod o+rw /usr/local
RUN touch /.condarc && \
chmod o+rw /.condarc && \
chmod -R o+rw /opt/conda

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@ -1,70 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -exou pipefail
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGENAME:ARCHTAG"
exit 1
fi
# Go from imagename:tag to tag
DOCKER_TAG_PREFIX=$(echo "${image}" | awk -F':' '{print $2}')
CUDA_VERSION=""
ROCM_VERSION=""
EXTRA_BUILD_ARGS=""
if [[ "${DOCKER_TAG_PREFIX}" == cuda* ]]; then
# extract cuda version from image name and tag. e.g. manylinux2_28-builder:cuda12.8 returns 12.8
CUDA_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'cuda' '{print $2}')
EXTRA_BUILD_ARGS="--build-arg CUDA_VERSION=${CUDA_VERSION}"
elif [[ "${DOCKER_TAG_PREFIX}" == rocm* ]]; then
# extract rocm version from image name and tag. e.g. manylinux2_28-builder:rocm6.2.4 returns 6.2.4
ROCM_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'rocm' '{print $2}')
EXTRA_BUILD_ARGS="--build-arg ROCM_IMAGE=rocm/dev-almalinux-8:${ROCM_VERSION}-complete"
fi
case ${DOCKER_TAG_PREFIX} in
cpu)
BASE_TARGET=base
;;
cuda*)
BASE_TARGET=cuda${CUDA_VERSION}
;;
rocm*)
BASE_TARGET=rocm
;;
*)
echo "ERROR: Unknown docker tag ${DOCKER_TAG_PREFIX}"
exit 1
;;
esac
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
export DOCKER_BUILDKIT=1
TOPDIR=$(git rev-parse --show-toplevel)
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
docker build \
--target final \
--progress plain \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
--build-arg "DEVTOOLSET_VERSION=11" \
${EXTRA_BUILD_ARGS} \
-t ${tmp_tag} \
$@ \
-f "${TOPDIR}/.ci/docker/almalinux/Dockerfile" \
${TOPDIR}/.ci/docker/
if [ -n "${CUDA_VERSION}" ]; then
# Test that we're using the right CUDA compiler
docker run --rm "${tmp_tag}" nvcc --version | grep "cuda_${CUDA_VERSION}"
fi

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@ -1,463 +0,0 @@
#!/bin/bash
# The purpose of this script is to:
# 1. Extract the set of parameters to be used for a docker build based on the provided image name.
# 2. Run docker build with the parameters found in step 1.
# 3. Run the built image and print out the expected and actual versions of packages installed.
set -ex
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGE"
exit 1
fi
function extract_version_from_image_name() {
eval export $2=$(echo "${image}" | perl -n -e"/$1(\d+(\.\d+)?(\.\d+)?)/ && print \$1")
if [ "x${!2}" = x ]; then
echo "variable '$2' not correctly parsed from image='$image'"
exit 1
fi
}
function extract_all_from_image_name() {
# parts $image into array, splitting on '-'
keep_IFS="$IFS"
IFS="-"
declare -a parts=($image)
IFS="$keep_IFS"
unset keep_IFS
for part in "${parts[@]}"; do
name=$(echo "${part}" | perl -n -e"/([a-zA-Z]+)\d+(\.\d+)?(\.\d+)?/ && print \$1")
vername="${name^^}_VERSION"
# "py" is the odd one out, needs this special case
if [ "x${name}" = xpy ]; then
vername=ANACONDA_PYTHON_VERSION
fi
# skip non-conforming fields such as "pytorch", "linux" or "bionic" without version string
if [ -n "${name}" ]; then
extract_version_from_image_name "${name}" "${vername}"
fi
done
}
# Use the same pre-built XLA test image from PyTorch/XLA
if [[ "$image" == *xla* ]]; then
echo "Using pre-built XLA test image..."
exit 0
fi
if [[ "$image" == *-jammy* ]]; then
UBUNTU_VERSION=22.04
elif [[ "$image" == *-noble* ]]; then
UBUNTU_VERSION=24.04
elif [[ "$image" == *ubuntu* ]]; then
extract_version_from_image_name ubuntu UBUNTU_VERSION
fi
if [ -n "${UBUNTU_VERSION}" ]; then
OS="ubuntu"
else
echo "Unable to derive operating system base..."
exit 1
fi
DOCKERFILE="${OS}/Dockerfile"
if [[ "$image" == *rocm* ]]; then
DOCKERFILE="${OS}-rocm/Dockerfile"
elif [[ "$image" == *xpu* ]]; then
DOCKERFILE="${OS}-xpu/Dockerfile"
elif [[ "$image" == *cuda*linter* ]]; then
# Use a separate Dockerfile for linter to keep a small image size
DOCKERFILE="linter-cuda/Dockerfile"
elif [[ "$image" == *linter* ]]; then
# Use a separate Dockerfile for linter to keep a small image size
DOCKERFILE="linter/Dockerfile"
elif [[ "$image" == *riscv* ]]; then
# Use RISC-V specific Dockerfile
DOCKERFILE="ubuntu-cross-riscv/Dockerfile"
fi
_UCX_COMMIT=7836b165abdbe468a2f607e7254011c07d788152
_UCC_COMMIT=430e241bf5d38cbc73fc7a6b89155397232e3f96
if [[ "$image" == *rocm* ]]; then
_UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
_UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
fi
tag=$(echo $image | awk -F':' '{print $2}')
# It's annoying to rename jobs every time you want to rewrite a
# configuration, so we hardcode everything here rather than do it
# from scratch
case "$tag" in
pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11)
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda13.0-cudnn9-py3-gcc11)
CUDA_VERSION=13.0.0
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-vllm)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-py3-clang12-onnx)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=12
VISION=yes
ONNX=yes
;;
pytorch-linux-jammy-py3.10-clang12)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=12
VISION=yes
TRITON=yes
;;
pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
if [[ $tag =~ "jammy" ]]; then
ANACONDA_PYTHON_VERSION=3.10
else
ANACONDA_PYTHON_VERSION=3.12
fi
GCC_VERSION=11
VISION=yes
ROCM_VERSION=6.4
NINJA_VERSION=1.9.0
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
if [[ $tag =~ "benchmarks" ]]; then
INDUCTOR_BENCHMARKS=yes
fi
;;
pytorch-linux-noble-rocm-alpha-py3)
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
VISION=yes
ROCM_VERSION=7.0
NINJA_VERSION=1.9.0
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
PYTORCH_ROCM_ARCH="gfx90a;gfx942;gfx950"
;;
pytorch-linux-jammy-xpu-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
XPU_VERSION=2025.1
NINJA_VERSION=1.9.0
TRITON=yes
;;
pytorch-linux-jammy-xpu-n-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
XPU_VERSION=2025.2
NINJA_VERSION=1.9.0
TRITON=yes
;;
pytorch-linux-jammy-py3-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
TRITON=yes
DOCS=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-clang12)
ANACONDA_PYTHON_VERSION=3.10
CUDA_VERSION=12.8.1
CLANG_VERSION=12
VISION=yes
TRITON=yes
;;
pytorch-linux-jammy-py3-clang18-asan)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=18
VISION=yes
;;
pytorch-linux-jammy-py3.10-gcc11)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
TRITON=yes
DOCS=yes
UNINSTALL_DILL=yes
;;
pytorch-linux-jammy-py3-clang12-executorch)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=12
EXECUTORCH=yes
;;
pytorch-linux-jammy-py3.12-halide)
CUDA_VERSION=12.6
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
HALIDE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3.12-triton-cpu)
CUDA_VERSION=12.6
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
TRITON_CPU=yes
;;
pytorch-linux-jammy-linter)
PYTHON_VERSION=3.10
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.10-linter)
PYTHON_VERSION=3.10
CUDA_VERSION=12.8.1
;;
pytorch-linux-jammy-aarch64-py3.10-gcc11)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
ACL=yes
VISION=yes
OPENBLAS=yes
# snadampal: skipping llvm src build install because the current version
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
;;
pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
ACL=yes
VISION=yes
OPENBLAS=yes
# snadampal: skipping llvm src build install because the current version
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-noble-riscv64-py3.12-gcc14)
GCC_VERSION=14
;;
*)
# Catch-all for builds that are not hardcoded.
VISION=yes
echo "image '$image' did not match an existing build configuration"
if [[ "$image" == *py* ]]; then
extract_version_from_image_name py ANACONDA_PYTHON_VERSION
fi
if [[ "$image" == *cuda* ]]; then
extract_version_from_image_name cuda CUDA_VERSION
fi
if [[ "$image" == *rocm* ]]; then
extract_version_from_image_name rocm ROCM_VERSION
NINJA_VERSION=1.9.0
TRITON=yes
# To ensure that any ROCm config will build using conda cmake
# and thus have LAPACK/MKL enabled
fi
if [[ "$image" == *centos7* ]]; then
NINJA_VERSION=1.10.2
fi
if [[ "$image" == *gcc* ]]; then
extract_version_from_image_name gcc GCC_VERSION
fi
if [[ "$image" == *clang* ]]; then
extract_version_from_image_name clang CLANG_VERSION
fi
if [[ "$image" == *devtoolset* ]]; then
extract_version_from_image_name devtoolset DEVTOOLSET_VERSION
fi
if [[ "$image" == *glibc* ]]; then
extract_version_from_image_name glibc GLIBC_VERSION
fi
;;
esac
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
no_cache_flag=""
progress_flag=""
# Do not use cache and progress=plain when in CI
if [[ -n "${CI:-}" ]]; then
no_cache_flag="--no-cache"
progress_flag="--progress=plain"
fi
# Build image
docker build \
${no_cache_flag} \
${progress_flag} \
--build-arg "BUILD_ENVIRONMENT=${image}" \
--build-arg "LLVMDEV=${LLVMDEV:-}" \
--build-arg "VISION=${VISION:-}" \
--build-arg "UBUNTU_VERSION=${UBUNTU_VERSION}" \
--build-arg "DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}" \
--build-arg "GLIBC_VERSION=${GLIBC_VERSION}" \
--build-arg "CLANG_VERSION=${CLANG_VERSION}" \
--build-arg "ANACONDA_PYTHON_VERSION=${ANACONDA_PYTHON_VERSION}" \
--build-arg "PYTHON_VERSION=${PYTHON_VERSION}" \
--build-arg "GCC_VERSION=${GCC_VERSION}" \
--build-arg "CUDA_VERSION=${CUDA_VERSION}" \
--build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
--build-arg "KATEX=${KATEX:-}" \
--build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
--build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx90a;gfx942}" \
--build-arg "IMAGE_NAME=${IMAGE_NAME}" \
--build-arg "UCX_COMMIT=${UCX_COMMIT}" \
--build-arg "UCC_COMMIT=${UCC_COMMIT}" \
--build-arg "TRITON=${TRITON}" \
--build-arg "TRITON_CPU=${TRITON_CPU}" \
--build-arg "ONNX=${ONNX}" \
--build-arg "DOCS=${DOCS}" \
--build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
--build-arg "EXECUTORCH=${EXECUTORCH}" \
--build-arg "HALIDE=${HALIDE}" \
--build-arg "XPU_VERSION=${XPU_VERSION}" \
--build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
--build-arg "ACL=${ACL:-}" \
--build-arg "OPENBLAS=${OPENBLAS:-}" \
--build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
--build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
-f $(dirname ${DOCKERFILE})/Dockerfile \
-t "$tmp_tag" \
"$@" \
.
# NVIDIA dockers for RC releases use tag names like `11.0-cudnn9-devel-ubuntu18.04-rc`,
# for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
# find the correct image. As a result, here we have to replace the
# "$UBUNTU_VERSION" == "18.04-rc"
# with
# "$UBUNTU_VERSION" == "18.04"
UBUNTU_VERSION=$(echo ${UBUNTU_VERSION} | sed 's/-rc$//')
function drun() {
docker run --rm "$tmp_tag" "$@"
}
if [[ "$OS" == "ubuntu" ]]; then
if !(drun lsb_release -a 2>&1 | grep -qF Ubuntu); then
echo "OS=ubuntu, but:"
drun lsb_release -a
exit 1
fi
if !(drun lsb_release -a 2>&1 | grep -qF "$UBUNTU_VERSION"); then
echo "UBUNTU_VERSION=$UBUNTU_VERSION, but:"
drun lsb_release -a
exit 1
fi
fi
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
if !(drun python --version 2>&1 | grep -qF "Python $ANACONDA_PYTHON_VERSION"); then
echo "ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION, but:"
drun python --version
exit 1
fi
fi
if [ -n "$GCC_VERSION" ]; then
if [[ "$image" == *riscv* ]]; then
# Check RISC-V cross-compilation toolchain version
if !(drun riscv64-linux-gnu-gcc-${GCC_VERSION} --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
echo "RISC-V GCC_VERSION=$GCC_VERSION, but:"
drun riscv64-linux-gnu-gcc-${GCC_VERSION} --version
exit 1
fi
elif !(drun gcc --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
echo "GCC_VERSION=$GCC_VERSION, but:"
drun gcc --version
exit 1
fi
fi
if [ -n "$CLANG_VERSION" ]; then
if !(drun clang --version 2>&1 | grep -qF "clang version $CLANG_VERSION"); then
echo "CLANG_VERSION=$CLANG_VERSION, but:"
drun clang --version
exit 1
fi
fi
if [ -n "$KATEX" ]; then
if !(drun katex --version); then
echo "KATEX=$KATEX, but:"
drun katex --version
exit 1
fi
fi
HAS_TRITON=$(drun python -c "import triton" > /dev/null 2>&1 && echo "yes" || echo "no")
if [[ -n "$TRITON" || -n "$TRITON_CPU" ]]; then
if [ "$HAS_TRITON" = "no" ]; then
echo "expecting triton to be installed, but it is not"
exit 1
fi
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

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@ -1,109 +0,0 @@
ARG CENTOS_VERSION
FROM centos:${CENTOS_VERSION}
ARG CENTOS_VERSION
# Set AMD gpu targets to build for
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
# Install required packages to build Caffe2
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Update CentOS git version
RUN yum -y remove git
RUN yum -y remove git-*
RUN yum -y install https://packages.endpointdev.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm && \
sed -i 's/packages.endpoint/packages.endpointdev/' /etc/yum.repos.d/endpoint.repo
RUN yum install -y git
# Install devtoolset
ARG DEVTOOLSET_VERSION
COPY ./common/install_devtoolset.sh install_devtoolset.sh
RUN bash ./install_devtoolset.sh && rm install_devtoolset.sh
ENV BASH_ENV "/etc/profile"
# (optional) Install non-default glibc version
ARG GLIBC_VERSION
COPY ./common/install_glibc.sh install_glibc.sh
RUN if [ -n "${GLIBC_VERSION}" ]; then bash ./install_glibc.sh; fi
RUN rm install_glibc.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG BUILD_ENVIRONMENT
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
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
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
RUN rm install_rocm_magma.sh
COPY ./common/install_amdsmi.sh install_amdsmi.sh
RUN bash ./install_amdsmi.sh
RUN rm install_amdsmi.sh
ENV PATH /opt/rocm/bin:$PATH
ENV PATH /opt/rocm/hcc/bin:$PATH
ENV PATH /opt/rocm/hip/bin:$PATH
ENV PATH /opt/rocm/opencl/bin:$PATH
ENV PATH /opt/rocm/llvm/bin:$PATH
ENV MAGMA_HOME /opt/rocm/magma
ENV LANG en_US.utf8
ENV LC_ALL en_US.utf8
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
ARG TRITON
# Install triton, this needs to be done before sccache because the latter will
# try to reach out to S3, which docker build runners don't have access
ENV CMAKE_C_COMPILER cc
ENV CMAKE_CXX_COMPILER c++
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
# Install ccache/sccache (do this last, so we get priority in PATH)
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH
RUN bash ./install_cache.sh && rm install_cache.sh
# Include BUILD_ENVIRONMENT environment variable in image
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
USER jenkins
CMD ["bash"]

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@ -1 +0,0 @@
e0dda9059d082537cee36be6c5e4fe3b18c880c0

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@ -1 +0,0 @@
461c12871f336fe6f57b55d6a297f13ef209161b

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@ -1,2 +0,0 @@
transformers==4.56.0
soxr==0.5.0

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@ -1 +0,0 @@
v2.21.5-1

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@ -1 +0,0 @@
v2.27.5-1

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@ -1 +0,0 @@
v2.27.7-1

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@ -1 +0,0 @@
7fe50dc3da2069d6645d9deb8c017a876472a977

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@ -1 +0,0 @@
5d535d7a2d4b435b1b5c1177fd8f04a12b942b9a

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@ -1 +0,0 @@
74a23feff57432129df84d8099e622773cf77925

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@ -1 +0,0 @@
c7711371cace304afe265c1ffa906415ab82fc66

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@ -1 +0,0 @@
1b0418a9a454b2b93ab8d71f40e59d2297157fae

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@ -1 +0,0 @@
bbb06c0334a6772b92d24bde54956e675c8c6604

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@ -1,18 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
# Cache the test models at ~/.cache/torch/hub/
IMPORT_SCRIPT_FILENAME="/tmp/torchvision_import_script.py"
as_jenkins echo 'import torchvision; torchvision.models.mobilenet_v2(pretrained=True); torchvision.models.mobilenet_v3_large(pretrained=True);' > "${IMPORT_SCRIPT_FILENAME}"
pip_install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
# so echo the command to a file and run the file instead
conda_run python "${IMPORT_SCRIPT_FILENAME}"
# Cleaning up
conda_run pip uninstall -y torch torchvision
rm "${IMPORT_SCRIPT_FILENAME}" || true

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@ -1,40 +0,0 @@
#!/bin/bash
# Work around bug where devtoolset replaces sudo and breaks it.
if [ -n "$DEVTOOLSET_VERSION" ]; then
export SUDO=/bin/sudo
else
export SUDO=sudo
fi
as_jenkins() {
# NB: unsetting the environment variables works around a conda bug
# https://github.com/conda/conda/issues/6576
# NB: Pass on PATH and LD_LIBRARY_PATH to sudo invocation
# NB: This must be run from a directory that jenkins has access to,
# works around https://github.com/conda/conda-package-handling/pull/34
$SUDO -E -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
}
conda_install() {
# Ensure that the install command don't upgrade/downgrade Python
# This should be called as
# conda_install pkg1 pkg2 ... [-c channel]
as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION" $*
}
conda_install_through_forge() {
as_jenkins conda install -c conda-forge -q -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION" $*
}
conda_run() {
as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION --no-capture-output $*
}
pip_install() {
as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION pip install --progress-bar off $*
}
get_pinned_commit() {
cat "${1}".txt
}

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@ -1,16 +0,0 @@
set -euo pipefail
readonly version=v25.02
readonly src_host=https://github.com/ARM-software
readonly src_repo=ComputeLibrary
# Clone ACL
[[ ! -d ${src_repo} ]] && git clone ${src_host}/${src_repo}.git
cd ${src_repo}
git checkout $version
# Build with scons
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

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@ -1,5 +0,0 @@
#!/bin/bash
set -ex
cd /opt/rocm/share/amd_smi && pip install .

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@ -1,142 +0,0 @@
#!/bin/bash
set -ex
install_ubuntu() {
echo "Preparing to build sccache from source"
apt-get update
# libssl-dev will not work as it is upgraded to libssl3 in Ubuntu-22.04.
# Instead use lib and headers from OpenSSL1.1 installed in `install_openssl.sh``
apt-get install -y cargo
echo "Checking out sccache repo"
git clone https://github.com/mozilla/sccache -b v0.10.0
cd sccache
echo "Building sccache"
cargo build --release
cp target/release/sccache /opt/cache/bin
echo "Cleaning up"
cd ..
rm -rf sccache
apt-get remove -y cargo rustc
apt-get autoclean && apt-get clean
echo "Downloading old sccache binary from S3 repo for PCH builds"
curl --retry 3 https://s3.amazonaws.com/ossci-linux/sccache -o /opt/cache/bin/sccache-0.2.14a
chmod 755 /opt/cache/bin/sccache-0.2.14a
}
install_binary() {
echo "Downloading sccache binary from S3 repo"
curl --retry 3 https://s3.amazonaws.com/ossci-linux/sccache -o /opt/cache/bin/sccache
}
mkdir -p /opt/cache/bin
mkdir -p /opt/cache/lib
sed -e 's|PATH="\(.*\)"|PATH="/opt/cache/bin:\1"|g' -i /etc/environment
export PATH="/opt/cache/bin:$PATH"
# Setup compiler cache
install_ubuntu
chmod a+x /opt/cache/bin/sccache
function write_sccache_stub() {
# Unset LD_PRELOAD for ps because of asan + ps issues
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=90589
if [ $1 == "gcc" ]; then
# Do not call sccache recursively when dumping preprocessor argument
# For some reason it's very important for the first cached nvcc invocation
cat >"/opt/cache/bin/$1" <<EOF
#!/bin/sh
# sccache does not support -E flag, so we need to call the original compiler directly in order to avoid calling this wrapper recursively
for arg in "\$@"; do
if [ "\$arg" = "-E" ]; then
exec $(which $1) "\$@"
fi
done
if [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then
exec sccache $(which $1) "\$@"
else
exec $(which $1) "\$@"
fi
EOF
else
cat >"/opt/cache/bin/$1" <<EOF
#!/bin/sh
if [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then
exec sccache $(which $1) "\$@"
else
exec $(which $1) "\$@"
fi
EOF
fi
chmod a+x "/opt/cache/bin/$1"
}
write_sccache_stub cc
write_sccache_stub c++
write_sccache_stub gcc
write_sccache_stub g++
# NOTE: See specific ROCM_VERSION case below.
if [ "x$ROCM_VERSION" = x ]; then
write_sccache_stub clang
write_sccache_stub clang++
fi
if [ -n "$CUDA_VERSION" ]; then
# TODO: This is a workaround for the fact that PyTorch's FindCUDA
# implementation cannot find nvcc if it is setup this way, because it
# appears to search for the nvcc in PATH, and use its path to infer
# where CUDA is installed. Instead, we install an nvcc symlink outside
# of the PATH, and set CUDA_NVCC_EXECUTABLE so that we make use of it.
write_sccache_stub nvcc
mv /opt/cache/bin/nvcc /opt/cache/lib/
fi
if [ -n "$ROCM_VERSION" ]; then
# ROCm compiler is hcc or clang. However, it is commonly invoked via hipcc wrapper.
# hipcc will call either hcc or clang using an absolute path starting with /opt/rocm,
# causing the /opt/cache/bin to be skipped. We must create the sccache wrappers
# directly under /opt/rocm while also preserving the original compiler names.
# Note symlinks will chain as follows: [hcc or clang++] -> clang -> clang-??
# Final link in symlink chain must point back to original directory.
# Original compiler is moved one directory deeper. Wrapper replaces it.
function write_sccache_stub_rocm() {
OLDCOMP=$1
COMPNAME=$(basename $OLDCOMP)
TOPDIR=$(dirname $OLDCOMP)
WRAPPED="$TOPDIR/original/$COMPNAME"
mv "$OLDCOMP" "$WRAPPED"
printf "#!/bin/sh\nexec sccache $WRAPPED \"\$@\"" >"$OLDCOMP"
chmod a+x "$OLDCOMP"
}
if [[ -e "/opt/rocm/hcc/bin/hcc" ]]; then
# ROCm 3.3 or earlier.
mkdir /opt/rocm/hcc/bin/original
write_sccache_stub_rocm /opt/rocm/hcc/bin/hcc
write_sccache_stub_rocm /opt/rocm/hcc/bin/clang
write_sccache_stub_rocm /opt/rocm/hcc/bin/clang++
# Fix last link in symlink chain, clang points to versioned clang in prior dir
pushd /opt/rocm/hcc/bin/original
ln -s ../$(readlink clang)
popd
elif [[ -e "/opt/rocm/llvm/bin/clang" ]]; then
# ROCm 3.5 and beyond.
mkdir /opt/rocm/llvm/bin/original
write_sccache_stub_rocm /opt/rocm/llvm/bin/clang
write_sccache_stub_rocm /opt/rocm/llvm/bin/clang++
# Fix last link in symlink chain, clang points to versioned clang in prior dir
pushd /opt/rocm/llvm/bin/original
ln -s ../$(readlink clang)
popd
else
echo "Cannot find ROCm compiler."
exit 1
fi
fi

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@ -1,45 +0,0 @@
#!/bin/bash
set -ex
if [ -n "$CLANG_VERSION" ]; then
if [[ $UBUNTU_VERSION == 22.04 ]]; then
# work around ubuntu apt-get conflicts
sudo apt-get -y -f install
wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
if [[ $CLANG_VERSION == 18 ]]; then
apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-18 main"
fi
fi
sudo apt-get update
if [[ $CLANG_VERSION -ge 18 ]]; then
apt-get install -y libomp-${CLANG_VERSION}-dev libclang-rt-${CLANG_VERSION}-dev clang-"$CLANG_VERSION" llvm-"$CLANG_VERSION"
else
apt-get install -y --no-install-recommends clang-"$CLANG_VERSION" llvm-"$CLANG_VERSION"
fi
# Install dev version of LLVM.
if [ -n "$LLVMDEV" ]; then
sudo apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"-dev
fi
# Use update-alternatives to make this version the default
update-alternatives --install /usr/bin/clang clang /usr/bin/clang-"$CLANG_VERSION" 50
update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-"$CLANG_VERSION" 50
# Override cc/c++ to clang as well
update-alternatives --install /usr/bin/cc cc /usr/bin/clang 50
update-alternatives --install /usr/bin/c++ c++ /usr/bin/clang++ 50
# clang's packaging is a little messed up (the runtime libs aren't
# added into the linker path), so give it a little help
clang_lib=("/usr/lib/llvm-$CLANG_VERSION/lib/clang/"*"/lib/linux")
echo "$clang_lib" >/etc/ld.so.conf.d/clang.conf
ldconfig
# Cleanup package manager
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
fi

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@ -1,101 +0,0 @@
#!/bin/bash
set -ex
# Optionally install conda
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download" # @lint-ignore
CONDA_FILE="Miniforge3-Linux-$(uname -m).sh"
MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
MINOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
case "$MAJOR_PYTHON_VERSION" in
3);;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
mkdir -p /opt/conda
chown jenkins:jenkins /opt/conda
SCRIPT_FOLDER="$( cd "$(dirname "$0")" ; pwd -P )"
source "${SCRIPT_FOLDER}/common_utils.sh"
pushd /tmp
wget -q "${BASE_URL}/${CONDA_FILE}"
# NB: Manually invoke bash per https://github.com/conda/conda/issues/10431
as_jenkins bash "${CONDA_FILE}" -b -f -p "/opt/conda"
popd
# NB: Don't do this, rely on the rpath to get it right
#echo "/opt/conda/lib" > /etc/ld.so.conf.d/conda-python.conf
#ldconfig
sed -e 's|PATH="\(.*\)"|PATH="/opt/conda/bin:\1"|g' -i /etc/environment
export PATH="/opt/conda/bin:$PATH"
# Ensure we run conda in a directory that jenkins has write access to
pushd /opt/conda
# Prevent conda from updating to 4.14.0, which causes docker build failures
# See https://hud.pytorch.org/pytorch/pytorch/commit/754d7f05b6841e555cea5a4b2c505dd9e0baec1d
# Uncomment the below when resolved to track the latest conda update
# as_jenkins conda update -y -n base conda
if [[ $(uname -m) == "aarch64" ]]; then
export SYSROOT_DEP="sysroot_linux-aarch64=2.17"
else
export SYSROOT_DEP="sysroot_linux-64=2.17"
fi
# Install correct Python version
# Also ensure sysroot is using a modern GLIBC to match system compilers
as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
python="$ANACONDA_PYTHON_VERSION" \
${SYSROOT_DEP}
# libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
# which is provided in libstdcxx 12 and up.
conda_install libstdcxx-ng=12.3.0 --update-deps -c conda-forge
# Miniforge installer doesn't install sqlite by default
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
conda_install sqlite
fi
# Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
if [[ $(uname -m) != "aarch64" ]]; then
pip_install mkl==2024.2.0
pip_install mkl-static==2024.2.0
pip_install mkl-include==2024.2.0
fi
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
# and libpython-static for torch deploy
conda_install llvmdev=8.0.0 "libpython-static=${ANACONDA_PYTHON_VERSION}"
# Magma package names are concatenation of CUDA major and minor ignoring revision
# I.e. magma-cuda102 package corresponds to CUDA_VERSION=10.2 and CUDA_VERSION=10.2.89
# Magma is installed from a tarball in the ossci-linux bucket into the conda env
if [ -n "$CUDA_VERSION" ]; then
conda_run ${SCRIPT_FOLDER}/install_magma_conda.sh $(cut -f1-2 -d'.' <<< ${CUDA_VERSION})
fi
if [[ "$UBUNTU_VERSION" == "24.04"* ]] ; then
conda_install_through_forge libstdcxx-ng=14
fi
# Install some other packages, including those needed for Python test reporting
pip_install -r /opt/conda/requirements-ci.txt
if [ -n "$DOCS" ]; then
apt-get update
apt-get -y install expect-dev
# We are currently building docs with python 3.8 (min support version)
pip_install -r /opt/conda/requirements-docs.txt
fi
popd
fi

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@ -1,20 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
# Anaconda
# Latest anaconda is using openssl-3 which is incompatible with all currently published versions of git
# Which are using openssl-1.1.1, see https://anaconda.org/anaconda/git/files?version=2.40.1 for example
MINICONDA_URL=https://repo.anaconda.com/miniconda/Miniconda3-py311_23.5.2-0-Linux-x86_64.sh
wget -q $MINICONDA_URL
# NB: Manually invoke bash per https://github.com/conda/conda/issues/10431
bash $(basename "$MINICONDA_URL") -b -p /opt/conda
rm $(basename "$MINICONDA_URL")
export PATH=/opt/conda/bin:$PATH
# See https://github.com/pytorch/builder/issues/1473
# Pin conda to 23.5.2 as it's the last one compatible with openssl-1.1.1
conda install -y conda=23.5.2 conda-build anaconda-client git ninja
# The cmake version here needs to match with the minimum version of cmake
# supported by PyTorch (3.18). There is only 3.18.2 on anaconda
/opt/conda/bin/pip3 install cmake==3.18.2
conda remove -y --force patchelf

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@ -1,107 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -uex -o pipefail
PYTHON_DOWNLOAD_URL=https://www.python.org/ftp/python
GET_PIP_URL=https://bootstrap.pypa.io/get-pip.py
# Python versions to be installed in /opt/$VERSION_NO
CPYTHON_VERSIONS=${CPYTHON_VERSIONS:-"3.9.0 3.10.1 3.11.0 3.12.0 3.13.0 3.13.0t 3.14.0 3.14.0t"}
function check_var {
if [ -z "$1" ]; then
echo "required variable not defined"
exit 1
fi
}
function do_cpython_build {
local py_ver=$1
local py_folder=$2
check_var $py_ver
check_var $py_folder
tar -xzf Python-$py_ver.tgz
local additional_flags=""
if [[ "$py_ver" == *"t" ]]; then
additional_flags=" --disable-gil"
fi
pushd $py_folder
local prefix="/opt/_internal/cpython-${py_ver}"
mkdir -p ${prefix}/lib
if [[ -n $(which patchelf) ]]; then
local shared_flags="--enable-shared"
else
local shared_flags="--disable-shared"
fi
if [[ -z "${WITH_OPENSSL+x}" ]]; then
local openssl_flags=""
else
local openssl_flags="--with-openssl=${WITH_OPENSSL} --with-openssl-rpath=auto"
fi
# -Wformat added for https://bugs.python.org/issue17547 on Python 2.6
CFLAGS="-Wformat" ./configure --prefix=${prefix} ${openssl_flags} ${shared_flags} ${additional_flags} > /dev/null
make -j40 > /dev/null
make install > /dev/null
if [[ "${shared_flags}" == "--enable-shared" ]]; then
patchelf --set-rpath '$ORIGIN/../lib' ${prefix}/bin/python3
fi
popd
rm -rf $py_folder
# Some python's install as bin/python3. Make them available as
# bin/python.
if [ -e ${prefix}/bin/python3 ]; then
ln -s python3 ${prefix}/bin/python
fi
${prefix}/bin/python get-pip.py
if [ -e ${prefix}/bin/pip3 ] && [ ! -e ${prefix}/bin/pip ]; then
ln -s pip3 ${prefix}/bin/pip
fi
# install setuptools since python 3.12 is required to use distutils
# packaging is needed to create symlink since wheel no longer provides needed information
${prefix}/bin/pip install packaging==25.0 wheel==0.45.1 setuptools==80.9.0
local abi_tag=$(${prefix}/bin/python -c "from packaging.tags import interpreter_name, interpreter_version; import sysconfig ; from sysconfig import get_config_var; print('{0}{1}-{0}{1}{2}'.format(interpreter_name(), interpreter_version(), 't' if sysconfig.get_config_var('Py_GIL_DISABLED') else ''))")
ln -sf ${prefix} /opt/python/${abi_tag}
}
function build_cpython {
local py_ver=$1
check_var $py_ver
local py_suffix=$py_ver
local py_folder=$py_ver
# Special handling for nogil
if [[ "${py_ver}" == *"t" ]]; then
py_suffix=${py_ver::-1}
py_folder=$py_suffix
fi
# Update to rc2 due to https://github.com/python/cpython/commit/c72699086fe4
if [ "$py_suffix" == "3.14.0" ]; then
py_suffix="3.14.0rc2"
fi
wget -q $PYTHON_DOWNLOAD_URL/$py_folder/Python-$py_suffix.tgz -O Python-$py_ver.tgz
do_cpython_build $py_ver Python-$py_suffix
rm -f Python-$py_ver.tgz
}
function build_cpythons {
check_var $GET_PIP_URL
curl -sLO $GET_PIP_URL
for py_ver in $@; do
build_cpython $py_ver
done
rm -f get-pip.py
}
mkdir -p /opt/python
mkdir -p /opt/_internal
build_cpythons $CPYTHON_VERSIONS

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@ -1,185 +0,0 @@
#!/bin/bash
set -ex
arch_path=''
targetarch=${TARGETARCH:-$(uname -m)}
if [ ${targetarch} = 'amd64' ] || [ "${targetarch}" = 'x86_64' ]; then
arch_path='x86_64'
else
arch_path='sbsa'
fi
NVSHMEM_VERSION=3.3.24
function install_cuda {
version=$1
runfile=$2
major_minor=${version%.*}
rm -rf /usr/local/cuda-${major_minor} /usr/local/cuda
if [[ ${arch_path} == 'sbsa' ]]; then
runfile="${runfile}_sbsa"
fi
runfile="${runfile}.run"
wget -q https://developer.download.nvidia.com/compute/cuda/${version}/local_installers/${runfile} -O ${runfile}
chmod +x ${runfile}
./${runfile} --toolkit --silent
rm -f ${runfile}
rm -f /usr/local/cuda && ln -s /usr/local/cuda-${major_minor} /usr/local/cuda
}
function install_cudnn {
cuda_major_version=$1
cudnn_version=$2
mkdir tmp_cudnn && cd tmp_cudnn
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
filepath="cudnn-linux-${arch_path}-${cudnn_version}_cuda${cuda_major_version}-archive"
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-${arch_path}/${filepath}.tar.xz
tar xf ${filepath}.tar.xz
cp -a ${filepath}/include/* /usr/local/cuda/include/
cp -a ${filepath}/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
}
function install_nvshmem {
cuda_major_version=$1 # e.g. "12"
nvshmem_version=$2 # e.g. "3.3.9"
case "${arch_path}" in
sbsa)
dl_arch="aarch64"
;;
x86_64)
dl_arch="x64"
;;
*)
dl_arch="${arch}"
;;
esac
tmpdir="tmp_nvshmem"
mkdir -p "${tmpdir}" && cd "${tmpdir}"
# nvSHMEM license: https://docs.nvidia.com/nvshmem/api/sla.html
# This pattern is a lie as it is not consistent across versions, for 3.3.9 it was cuda_ver-arch-nvshhem-ver
filename="libnvshmem-linux-${arch_path}-${nvshmem_version}_cuda${cuda_major_version}-archive"
suffix=".tar.xz"
url="https://developer.download.nvidia.com/compute/nvshmem/redist/libnvshmem/linux-${arch_path}/${filename}${suffix}"
# download, unpack, install
wget -q "${url}"
tar xf "${filename}${suffix}"
cp -a "${filename}/include/"* /usr/local/cuda/include/
cp -a "${filename}/lib/"* /usr/local/cuda/lib64/
# cleanup
cd ..
rm -rf "${tmpdir}"
echo "nvSHMEM ${nvshmem_version} for CUDA ${cuda_major_version} (${arch_path}) installed."
}
function install_124 {
CUDNN_VERSION=9.1.0.70
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.6.2"
install_cuda 12.4.1 cuda_12.4.1_550.54.15_linux
install_cudnn 12 $CUDNN_VERSION
CUDA_VERSION=12.4 bash install_nccl.sh
CUDA_VERSION=12.4 bash install_cusparselt.sh
ldconfig
}
function install_126 {
CUDNN_VERSION=9.10.2.21
echo "Installing CUDA 12.6.3 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
install_cuda 12.6.3 cuda_12.6.3_560.35.05_linux
install_cudnn 12 $CUDNN_VERSION
install_nvshmem 12 $NVSHMEM_VERSION
CUDA_VERSION=12.6 bash install_nccl.sh
CUDA_VERSION=12.6 bash install_cusparselt.sh
ldconfig
}
function install_129 {
CUDNN_VERSION=9.10.2.21
echo "Installing CUDA 12.9.1 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
# install CUDA 12.9.1 in the same container
install_cuda 12.9.1 cuda_12.9.1_575.57.08_linux
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
install_cudnn 12 $CUDNN_VERSION
install_nvshmem 12 $NVSHMEM_VERSION
CUDA_VERSION=12.9 bash install_nccl.sh
CUDA_VERSION=12.9 bash install_cusparselt.sh
ldconfig
}
function install_128 {
CUDNN_VERSION=9.8.0.87
echo "Installing CUDA 12.8.1 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
# install CUDA 12.8.1 in the same container
install_cuda 12.8.1 cuda_12.8.1_570.124.06_linux
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
install_cudnn 12 $CUDNN_VERSION
install_nvshmem 12 $NVSHMEM_VERSION
CUDA_VERSION=12.8 bash install_nccl.sh
CUDA_VERSION=12.8 bash install_cusparselt.sh
ldconfig
}
function install_130 {
CUDNN_VERSION=9.13.0.50
echo "Installing CUDA 13.0 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
# install CUDA 13.0 in the same container
install_cuda 13.0.0 cuda_13.0.0_580.65.06_linux
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
install_cudnn 13 $CUDNN_VERSION
install_nvshmem 13 $NVSHMEM_VERSION
CUDA_VERSION=13.0 bash install_nccl.sh
CUDA_VERSION=13.0 bash install_cusparselt.sh
ldconfig
}
# idiomatic parameter and option handling in sh
while test $# -gt 0
do
case "$1" in
12.4) install_124;
;;
12.6|12.6.*) install_126;
;;
12.8|12.8.*) install_128;
;;
12.9|12.9.*) install_129;
;;
13.0|13.0.*) install_130;
;;
*) echo "bad argument $1"; exit 1
;;
esac
shift
done

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@ -1,25 +0,0 @@
#!/bin/bash
set -ex
# cudss license: https://docs.nvidia.com/cuda/cudss/license.html
mkdir tmp_cudss && cd tmp_cudss
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[1-4]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUDSS_NAME="libcudss-linux-${arch_path}-0.3.0.9_cuda12-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudss/redist/libcudss/linux-${arch_path}/${CUDSS_NAME}.tar.xz
# only for cuda 12
tar xf ${CUDSS_NAME}.tar.xz
cp -a ${CUDSS_NAME}/include/* /usr/local/cuda/include/
cp -a ${CUDSS_NAME}/lib/* /usr/local/cuda/lib64/
fi
cd ..
rm -rf tmp_cudss
ldconfig

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@ -1,41 +0,0 @@
#!/bin/bash
set -ex
# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && cd tmp_cusparselt
if [[ ${CUDA_VERSION:0:4} =~ "13" ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.8.0.4_cuda13-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} =~ ^12\.[5-9]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.7.1.0-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} == "12.4" ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.6.2.3-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
else
echo "Not sure which libcusparselt version to install for this ${CUDA_VERSION}"
fi
tar xf ${CUSPARSELT_NAME}.tar.xz
cp -a ${CUSPARSELT_NAME}/include/* /usr/local/cuda/include/
cp -a ${CUSPARSELT_NAME}/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cusparselt
ldconfig

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@ -1,25 +0,0 @@
#!/bin/bash
set -ex
if [ -n "$KATEX" ]; then
apt-get update
# Ignore error if gpg-agent doesn't exist (for Ubuntu 16.04)
apt-get install -y gpg-agent || :
curl --retry 3 -sL https://deb.nodesource.com/setup_16.x | sudo -E bash -
sudo apt-get install -y nodejs
curl --retry 3 -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
apt-get update
apt-get install -y --no-install-recommends yarn
yarn global add katex --prefix /usr/local
sudo apt-get -y install doxygen
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
fi

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@ -1,68 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
clone_executorch() {
EXECUTORCH_PINNED_COMMIT=$(get_pinned_commit executorch)
# Clone the Executorch
git clone https://github.com/pytorch/executorch.git
# and fetch the target commit
pushd executorch
git checkout "${EXECUTORCH_PINNED_COMMIT}"
git submodule update --init --recursive
popd
chown -R jenkins executorch
}
install_buck2() {
pushd executorch/.ci/docker
BUCK2_VERSION=$(cat ci_commit_pins/buck2.txt)
source common/install_buck.sh
popd
}
install_conda_dependencies() {
pushd executorch/.ci/docker
# Install conda dependencies like flatbuffer
conda_install --file conda-env-ci.txt
popd
}
install_pip_dependencies() {
pushd executorch
as_jenkins bash install_executorch.sh
# 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() {
export PYTHON_EXECUTABLE=python
export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON -DEXECUTORCH_BUILD_TESTS=ON"
as_jenkins .ci/scripts/setup-linux.sh --build-tool cmake || true
}
if [ $# -eq 0 ]; then
clone_executorch
install_buck2
install_conda_dependencies
install_pip_dependencies
pushd executorch
setup_executorch
popd
else
"$@"
fi

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@ -1,20 +0,0 @@
#!/bin/bash
set -ex
if [ -n "$GCC_VERSION" ]; then
# Need the official toolchain repo to get alternate packages
add-apt-repository ppa:ubuntu-toolchain-r/test
apt-get update
apt-get install -y g++-$GCC_VERSION
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
# Cleanup package manager
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
fi

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@ -1,48 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
COMMIT=$(get_pinned_commit halide)
test -n "$COMMIT"
# activate conda to populate CONDA_PREFIX
test -n "$ANACONDA_PYTHON_VERSION"
eval "$(conda shell.bash hook)"
conda activate py_$ANACONDA_PYTHON_VERSION
if [ -n "${UBUNTU_VERSION}" ];then
apt update
apt-get install -y lld liblld-15-dev libpng-dev libjpeg-dev libgl-dev \
libopenblas-dev libeigen3-dev libatlas-base-dev libzstd-dev
fi
pip_install numpy scipy imageio cmake ninja
git clone --depth 1 --branch release/16.x --recursive https://github.com/llvm/llvm-project.git
cmake -DCMAKE_BUILD_TYPE=Release \
-DLLVM_ENABLE_PROJECTS="clang" \
-DLLVM_TARGETS_TO_BUILD="X86;NVPTX" \
-DLLVM_ENABLE_TERMINFO=OFF -DLLVM_ENABLE_ASSERTIONS=ON \
-DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_RTTI=ON -DLLVM_BUILD_32_BITS=OFF \
-S llvm-project/llvm -B llvm-build -G Ninja
cmake --build llvm-build
cmake --install llvm-build --prefix llvm-install
export LLVM_ROOT=`pwd`/llvm-install
export LLVM_CONFIG=$LLVM_ROOT/bin/llvm-config
git clone https://github.com/halide/Halide.git
pushd Halide
git checkout ${COMMIT} && git submodule update --init --recursive
pip_install -r requirements.txt
# NOTE: pybind has a requirement for cmake > 3.5 so set the minimum cmake version here with a flag
# Context: https://github.com/pytorch/pytorch/issues/150420
cmake -G Ninja -DCMAKE_POLICY_VERSION_MINIMUM=3.5 -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake --build build
test -e ${CONDA_PREFIX}/lib/python3 || ln -s python${ANACONDA_PYTHON_VERSION} ${CONDA_PREFIX}/lib/python3
cmake --install build --prefix ${CONDA_PREFIX}
chown -R jenkins ${CONDA_PREFIX}
popd
rm -rf Halide llvm-build llvm-project llvm-install
python -c "import halide" # check for errors

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@ -1,46 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
function install_huggingface() {
pip_install -r huggingface-requirements.txt
}
function install_timm() {
local commit
commit=$(get_pinned_commit timm)
pip_install "git+https://github.com/huggingface/pytorch-image-models@${commit}"
}
function install_torchbench() {
local commit
commit=$(get_pinned_commit torchbench)
git clone https://github.com/pytorch/benchmark torchbench
pushd torchbench
git checkout "$commit"
python install.py --continue_on_fail
echo "Print all dependencies after TorchBench is installed"
python -mpip freeze
popd
chown -R jenkins torchbench
chown -R jenkins /opt/conda
}
# Pango is needed for weasyprint which is needed for doctr
conda_install pango
# Stable packages are ok here, just to satisfy TorchBench check
pip_install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
install_torchbench
install_huggingface
install_timm
# Clean up
conda_run pip uninstall -y torch torchvision torchaudio triton torchao

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@ -1,23 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
LIBPNG_VERSION=1.6.37
mkdir -p libpng
pushd libpng
wget http://download.sourceforge.net/libpng/libpng-$LIBPNG_VERSION.tar.gz
tar -xvzf libpng-$LIBPNG_VERSION.tar.gz
pushd libpng-$LIBPNG_VERSION
./configure
make
make install
popd
popd
rm -rf libpng

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@ -1,27 +0,0 @@
#!/bin/bash
set -ex
if [ -n "${UBUNTU_VERSION}" ]; then
apt update
apt-get install -y clang doxygen git graphviz nodejs npm libtinfo5
fi
# Do shallow clone of PyTorch so that we can init lintrunner in Docker build context
git clone https://github.com/pytorch/pytorch.git --depth 1
chown -R jenkins pytorch
pushd pytorch
# Install all linter dependencies
pip install -r requirements.txt
lintrunner init
# Cache .lintbin directory as part of the Docker image
cp -r .lintbin /tmp
popd
# Node dependencies required by toc linter job
npm install -g markdown-toc
# Cleaning up
rm -rf pytorch

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@ -1,27 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eou pipefail
function do_install() {
cuda_version=$1
cuda_version_nodot=${1/./}
MAGMA_VERSION="2.6.1"
magma_archive="magma-cuda${cuda_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
cuda_dir="/usr/local/cuda-${cuda_version}"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://ossci-linux.s3.us-east-1.amazonaws.com/${magma_archive}
tar -xvf "${magma_archive}"
mkdir -p "${cuda_dir}/magma"
mv include "${cuda_dir}/magma/include"
mv lib "${cuda_dir}/magma/lib"
popd
)
}
do_install $1

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@ -1,23 +0,0 @@
#!/usr/bin/env bash
# Script that installs magma from tarball inside conda environment.
# It replaces anaconda magma-cuda package which is no longer published.
# Execute it inside active conda environment.
# See issue: https://github.com/pytorch/pytorch/issues/138506
set -eou pipefail
cuda_version_nodot=${1/./}
anaconda_dir=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
MAGMA_VERSION="2.6.1"
magma_archive="magma-cuda${cuda_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://ossci-linux.s3.us-east-1.amazonaws.com/${magma_archive}
tar -xvf "${magma_archive}"
mv include/* "${anaconda_dir}/include/"
mv lib/* "${anaconda_dir}/lib"
popd
)

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@ -1,129 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
ROCM_VERSION=$1
if [[ -z $ROCM_VERSION ]]; then
echo "missing ROCM_VERSION"
exit 1;
fi
IS_UBUNTU=0
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
IS_UBUNTU=1
;;
centos|almalinux)
IS_UBUNTU=0
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
# To make version comparison easier, create an integer representation.
save_IFS="$IFS"
IFS=. ROCM_VERSION_ARRAY=(${ROCM_VERSION})
IFS="$save_IFS"
if [[ ${#ROCM_VERSION_ARRAY[@]} == 2 ]]; then
ROCM_VERSION_MAJOR=${ROCM_VERSION_ARRAY[0]}
ROCM_VERSION_MINOR=${ROCM_VERSION_ARRAY[1]}
ROCM_VERSION_PATCH=0
elif [[ ${#ROCM_VERSION_ARRAY[@]} == 3 ]]; then
ROCM_VERSION_MAJOR=${ROCM_VERSION_ARRAY[0]}
ROCM_VERSION_MINOR=${ROCM_VERSION_ARRAY[1]}
ROCM_VERSION_PATCH=${ROCM_VERSION_ARRAY[2]}
else
echo "Unhandled ROCM_VERSION ${ROCM_VERSION}"
exit 1
fi
ROCM_INT=$(($ROCM_VERSION_MAJOR * 10000 + $ROCM_VERSION_MINOR * 100 + $ROCM_VERSION_PATCH))
# Function to retry functions that sometimes timeout or have flaky failures
retry () {
$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
}
# Build custom MIOpen to use comgr for offline compilation.
## Need a sanitized ROCM_VERSION without patchlevel; patchlevel version 0 must be added to paths.
ROCM_DOTS=$(echo ${ROCM_VERSION} | tr -d -c '.' | wc -c)
if [[ ${ROCM_DOTS} == 1 ]]; then
ROCM_VERSION_NOPATCH="${ROCM_VERSION}"
ROCM_INSTALL_PATH="/opt/rocm-${ROCM_VERSION}.0"
else
ROCM_VERSION_NOPATCH="${ROCM_VERSION%.*}"
ROCM_INSTALL_PATH="/opt/rocm-${ROCM_VERSION}"
fi
MIOPEN_CMAKE_COMMON_FLAGS="
-DMIOPEN_USE_COMGR=ON
-DMIOPEN_BUILD_DRIVER=OFF
"
if [[ $ROCM_INT -ge 60200 ]] && [[ $ROCM_INT -lt 60204 ]]; then
MIOPEN_BRANCH="release/rocm-rel-6.2-staging"
else
echo "ROCm ${ROCM_VERSION} does not need any patches, do not build from source"
exit 0
fi
if [[ ${IS_UBUNTU} == 1 ]]; then
apt-get remove -y miopen-hip
else
# Workaround since almalinux manylinux image already has this and cget doesn't like that
rm -rf /usr/local/lib/pkgconfig/sqlite3.pc
# Versioned package name needs regex match
# Use --noautoremove to prevent other rocm packages from being uninstalled
yum remove -y miopen-hip* --noautoremove
fi
git clone https://github.com/ROCm/MIOpen -b ${MIOPEN_BRANCH}
pushd MIOpen
# remove .git to save disk space since CI runner was running out
rm -rf .git
# Don't build CK to save docker build time
sed -i '/composable_kernel/d' requirements.txt
## MIOpen minimum requirements
cmake -P install_deps.cmake --minimum
# clean up since CI runner was running out of disk space
rm -rf /tmp/*
if [[ ${IS_UBUNTU} == 1 ]]; then
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
else
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
fi
## Build MIOpen
mkdir -p build
cd build
PKG_CONFIG_PATH=/usr/local/lib/pkgconfig CXX=${ROCM_INSTALL_PATH}/llvm/bin/clang++ cmake .. \
${MIOPEN_CMAKE_COMMON_FLAGS} \
${MIOPEN_CMAKE_DB_FLAGS} \
-DCMAKE_PREFIX_PATH="${ROCM_INSTALL_PATH}"
make MIOpen -j $(nproc)
# Build MIOpen package
make -j $(nproc) package
# clean up since CI runner was running out of disk space
rm -rf /usr/local/cget
if [[ ${IS_UBUNTU} == 1 ]]; then
sudo dpkg -i miopen-hip*.deb
else
yum install -y miopen-*.rpm
fi
popd
rm -rf MIOpen

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@ -1,16 +0,0 @@
#!/bin/bash
set -ex
# MKL
MKL_VERSION=2024.2.0
MKLROOT=/opt/intel
mkdir -p ${MKLROOT}
pushd /tmp
python3 -mpip install wheel
python3 -mpip download -d . mkl-static==${MKL_VERSION}
python3 -m wheel unpack mkl_static-${MKL_VERSION}-py2.py3-none-manylinux1_x86_64.whl
python3 -m wheel unpack mkl_include-${MKL_VERSION}-py2.py3-none-manylinux1_x86_64.whl
mv mkl_static-${MKL_VERSION}/mkl_static-${MKL_VERSION}.data/data/lib ${MKLROOT}
mv mkl_include-${MKL_VERSION}/mkl_include-${MKL_VERSION}.data/data/include ${MKLROOT}

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@ -1,13 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
mkdir -p /usr/local/mnist/
cd /usr/local/mnist
for img in train-images-idx3-ubyte.gz train-labels-idx1-ubyte.gz t10k-images-idx3-ubyte.gz t10k-labels-idx1-ubyte.gz; do
wget -q https://ossci-datasets.s3.amazonaws.com/mnist/$img
gzip -d $img
done

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@ -1,28 +0,0 @@
#!/bin/bash
set -ex
NCCL_VERSION=""
if [[ ${CUDA_VERSION:0:2} == "11" ]]; then
NCCL_VERSION=$(cat ci_commit_pins/nccl-cu11.txt)
elif [[ ${CUDA_VERSION:0:2} == "12" ]]; then
NCCL_VERSION=$(cat ci_commit_pins/nccl-cu12.txt)
elif [[ ${CUDA_VERSION:0:2} == "13" ]]; then
NCCL_VERSION=$(cat ci_commit_pins/nccl-cu13.txt)
else
echo "Unexpected CUDA_VERSION ${CUDA_VERSION}"
exit 1
fi
if [[ -n "${NCCL_VERSION}" ]]; then
# NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
# Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
pushd nccl
make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
popd
rm -rf nccl
ldconfig
fi

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@ -1,18 +0,0 @@
#!/bin/bash
set -ex
[ -n "$NINJA_VERSION" ]
arch=$(uname -m)
if [ "$arch" == "aarch64" ]; then
url="https://github.com/ninja-build/ninja/releases/download/v${NINJA_VERSION}/ninja-linux-aarch64.zip"
else
url="https://github.com/ninja-build/ninja/releases/download/v${NINJA_VERSION}/ninja-linux.zip"
fi
pushd /tmp
wget --no-verbose --output-document=ninja-linux.zip "$url"
unzip ninja-linux.zip -d /usr/local/bin
rm -f ninja-linux.zip
popd

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@ -1,20 +0,0 @@
#!/bin/bash
set -ex
function install_nvpl {
mkdir -p /opt/nvpl/lib /opt/nvpl/include
wget https://developer.download.nvidia.com/compute/nvpl/redist/nvpl_blas/linux-sbsa/nvpl_blas-linux-sbsa-0.3.0-archive.tar.xz
tar xf nvpl_blas-linux-sbsa-0.3.0-archive.tar.xz
cp -r nvpl_blas-linux-sbsa-0.3.0-archive/lib/* /opt/nvpl/lib/
cp -r nvpl_blas-linux-sbsa-0.3.0-archive/include/* /opt/nvpl/include/
wget https://developer.download.nvidia.com/compute/nvpl/redist/nvpl_lapack/linux-sbsa/nvpl_lapack-linux-sbsa-0.2.3.1-archive.tar.xz
tar xf nvpl_lapack-linux-sbsa-0.2.3.1-archive.tar.xz
cp -r nvpl_lapack-linux-sbsa-0.2.3.1-archive/lib/* /opt/nvpl/lib/
cp -r nvpl_lapack-linux-sbsa-0.2.3.1-archive/include/* /opt/nvpl/include/
}
install_nvpl

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@ -1,38 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
retry () {
"$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
}
# ONNXRuntime should be installed before installing
# onnx-weekly. Otherwise, onnx-weekly could be
# overwritten by onnx.
pip_install \
parameterized==0.8.1 \
pytest-cov==4.0.0 \
pytest-subtests==0.10.0 \
tabulate==0.9.0 \
transformers==4.36.2
pip_install coloredlogs packaging
pip_install onnxruntime==1.22.1
pip_install onnxscript==0.4.0
# Cache the transformers model to be used later by ONNX tests. We need to run the transformers
# package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/
IMPORT_SCRIPT_FILENAME="/tmp/onnx_import_script.py"
as_jenkins echo 'import transformers; transformers.GPTJForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gptj");' > "${IMPORT_SCRIPT_FILENAME}"
# Need a PyTorch version for transformers to work
pip_install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
# so echo the command to a file and run the file instead
conda_run python "${IMPORT_SCRIPT_FILENAME}"
# Cleaning up
conda_run pip uninstall -y torch
rm "${IMPORT_SCRIPT_FILENAME}" || true

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@ -1,21 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
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="
NUM_THREADS=128
USE_OPENMP=1
NO_SHARED=0
DYNAMIC_ARCH=1
TARGET=ARMV8
CFLAGS=-O3
BUILD_BFLOAT16=1
"
make -j8 ${OPENBLAS_BUILD_FLAGS} -C ${OPENBLAS_CHECKOUT_DIR}
make -j8 ${OPENBLAS_BUILD_FLAGS} install -C ${OPENBLAS_CHECKOUT_DIR}

View File

@ -1,17 +0,0 @@
#!/bin/bash
set -ex
OPENSSL=openssl-1.1.1k
wget -q -O "${OPENSSL}.tar.gz" "https://ossci-linux.s3.amazonaws.com/${OPENSSL}.tar.gz"
tar xf "${OPENSSL}.tar.gz"
cd "${OPENSSL}"
./config --prefix=/opt/openssl -d '-Wl,--enable-new-dtags,-rpath,$(LIBRPATH)'
# NOTE: openssl install errors out when built with the -j option
NPROC=$[$(nproc) - 2]
make -j${NPROC}; make install_sw
# Link the ssl libraries to the /usr/lib folder.
sudo ln -s /opt/openssl/lib/lib* /usr/lib
cd ..
rm -rf "${OPENSSL}"

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@ -1,16 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
# Pin the version to latest release 0.17.2, building newer commit starts
# to fail on the current image
git clone -b 0.17.2 --single-branch https://github.com/NixOS/patchelf
cd patchelf
sed -i 's/serial/parallel/g' configure.ac
./bootstrap.sh
./configure
make
make install
cd ..
rm -rf patchelf

View File

@ -1,15 +0,0 @@
#!/bin/bash
set -ex
apt-get update
# Use deadsnakes in case we need an older python version
sudo add-apt-repository ppa:deadsnakes/ppa
apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python3-pip python${PYTHON_VERSION}-venv
# Use a venv because uv and some other package managers don't support --user install
ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
python -m venv /var/lib/jenkins/ci_env
source /var/lib/jenkins/ci_env/bin/activate
python -mpip install --upgrade pip
python -mpip install -r /opt/requirements-ci.txt

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@ -1,208 +0,0 @@
#!/bin/bash
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 '.' ' ');
}
install_ubuntu() {
apt-get update
# gpg-agent is not available by default
apt-get install -y --no-install-recommends gpg-agent
if [[ $(ver $UBUNTU_VERSION) -ge $(ver 22.04) ]]; then
echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' \
| sudo tee /etc/apt/preferences.d/rocm-pin-600
fi
apt-get install -y kmod
apt-get install -y wget
# Need the libc++1 and libc++abi1 libraries to allow torch._C to load at runtime
apt-get install -y libc++1
apt-get install -y libc++abi1
# Make sure rocm packages from repo.radeon.com have highest priority
cat << EOF > /etc/apt/preferences.d/rocm-pin-600
Package: *
Pin: release o=repo.radeon.com
Pin-Priority: 600
EOF
# we want the patch version of 6.4 instead
if [[ $(ver $ROCM_VERSION) -eq $(ver 6.4) ]]; then
ROCM_VERSION="${ROCM_VERSION}.2"
fi
# Default url values
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
# Add rocm repository
wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
echo "deb [arch=amd64] ${rocm_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/rocm.list
apt-get update --allow-insecure-repositories
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
rocm-dev \
rocm-utils \
rocm-libs \
rccl \
rocprofiler-dev \
roctracer-dev \
amd-smi-lib
if [[ $(ver $ROCM_VERSION) -ge $(ver 6.1) ]]; then
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated rocm-llvm-dev
fi
# precompiled miopen kernels added in ROCm 3.5, renamed in ROCm 5.5
# search for all unversioned packages
# if search fails it will abort this script; use true to avoid case where search fails
MIOPENHIPGFX=$(apt-cache search --names-only miopen-hip-gfx | awk '{print $1}' | grep -F -v . || true)
if [[ "x${MIOPENHIPGFX}" = x ]]; then
echo "miopen-hip-gfx package not available" && exit 1
else
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENHIPGFX}
fi
# ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
for kdb in /opt/rocm/share/miopen/db/*.kdb
do
sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
done
# ROCm 6.3 had a regression where initializing static code objects had significant overhead
# CI no longer builds for ROCm 6.3, but
# ROCm 6.4 did not yet fix the regression, also HIP branch names are different
if [[ $(ver $ROCM_VERSION) -ge $(ver 6.4) ]] && [[ $(ver $ROCM_VERSION) -lt $(ver 7.0) ]]; then
if [[ $(ver $ROCM_VERSION) -eq $(ver 6.4.2) ]]; then
HIP_TAG=rocm-6.4.2
CLR_HASH=74d78ba3ac4bac235d02bcb48511c30b5cfdd457 # branch release/rocm-rel-6.4.2-statco-hotfix
elif [[ $(ver $ROCM_VERSION) -eq $(ver 6.4.1) ]]; then
HIP_TAG=rocm-6.4.1
CLR_HASH=efe6c35790b9206923bfeed1209902feff37f386 # branch release/rocm-rel-6.4.1-statco-hotfix
elif [[ $(ver $ROCM_VERSION) -eq $(ver 6.4) ]]; then
HIP_TAG=rocm-6.4.0
CLR_HASH=600f5b0d2baed94d5121e2174a9de0851b040b0c # branch release/rocm-rel-6.4-statco-hotfix
fi
# clr build needs CppHeaderParser but can only find it using conda's python
python -m pip install CppHeaderParser
git clone https://github.com/ROCm/HIP -b $HIP_TAG
HIP_COMMON_DIR=$(readlink -f HIP)
git clone https://github.com/jeffdaily/clr
pushd clr
git checkout $CLR_HASH
popd
mkdir -p clr/build
pushd clr/build
# Need to point CMake to the correct python installation to find CppHeaderParser
cmake .. -DPython3_EXECUTABLE=/opt/conda/envs/py_${ANACONDA_PYTHON_VERSION}/bin/python3 -DCLR_BUILD_HIP=ON -DHIP_COMMON_DIR=$HIP_COMMON_DIR
make -j
cp hipamd/lib/libamdhip64.so.6.4.* /opt/rocm/lib/libamdhip64.so.6.4.*
popd
rm -rf HIP clr
fi
pip_install "git+https://github.com/rocm/composable_kernel@$ROCM_COMPOSABLE_KERNEL_VERSION"
# Cleanup
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
}
install_centos() {
yum update -y
yum install -y kmod
yum install -y wget
yum install -y openblas-devel
yum install -y epel-release
yum install -y dkms kernel-headers-`uname -r` kernel-devel-`uname -r`
# Add amdgpu repository
local amdgpu_baseurl
if [[ $OS_VERSION == 9 ]]; then
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/9.0/main/x86_64"
else
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/7.9/main/x86_64"
fi
echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
echo "baseurl=${amdgpu_baseurl}" >> /etc/yum.repos.d/amdgpu.repo
echo "enabled=1" >> /etc/yum.repos.d/amdgpu.repo
echo "gpgcheck=1" >> /etc/yum.repos.d/amdgpu.repo
echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/amdgpu.repo
local rocm_baseurl="http://repo.radeon.com/rocm/yum/${ROCM_VERSION}"
echo "[ROCm]" > /etc/yum.repos.d/rocm.repo
echo "name=ROCm" >> /etc/yum.repos.d/rocm.repo
echo "baseurl=${rocm_baseurl}" >> /etc/yum.repos.d/rocm.repo
echo "enabled=1" >> /etc/yum.repos.d/rocm.repo
echo "gpgcheck=1" >> /etc/yum.repos.d/rocm.repo
echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/rocm.repo
yum update -y
yum install -y \
rocm-dev \
rocm-utils \
rocm-libs \
rccl \
rocprofiler-dev \
roctracer-dev \
amd-smi-lib
# precompiled miopen kernels; search for all unversioned packages
# if search fails it will abort this script; use true to avoid case where search fails
MIOPENHIPGFX=$(yum -q search miopen-hip-gfx | grep miopen-hip-gfx | awk '{print $1}'| grep -F kdb. || true)
if [[ "x${MIOPENHIPGFX}" = x ]]; then
echo "miopen-hip-gfx package not available" && exit 1
else
yum install -y ${MIOPENHIPGFX}
fi
# ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
for kdb in /opt/rocm/share/miopen/db/*.kdb
do
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
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
}
# Install Python packages depending on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
install_ubuntu
;;
centos)
install_centos
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac

View File

@ -1,150 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
###########################
### prereqs
###########################
# Install Python packages depending on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
apt-get update -y
apt-get install -y libpciaccess-dev pkg-config
apt-get clean
;;
centos|almalinux)
yum install -y libpciaccess-devel pkgconfig
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
python3 -m pip install meson ninja
###########################
### clone repo
###########################
GIT_SSL_NO_VERIFY=true git clone https://gitlab.freedesktop.org/mesa/drm.git
pushd drm
###########################
### patch
###########################
patch -p1 <<'EOF'
diff --git a/amdgpu/amdgpu_asic_id.c b/amdgpu/amdgpu_asic_id.c
index a5007ffc..13fa07fc 100644
--- a/amdgpu/amdgpu_asic_id.c
+++ b/amdgpu/amdgpu_asic_id.c
@@ -22,6 +22,13 @@
*
*/
+#define _XOPEN_SOURCE 700
+#define _LARGEFILE64_SOURCE
+#define _FILE_OFFSET_BITS 64
+#include <ftw.h>
+#include <link.h>
+#include <limits.h>
+
#include <ctype.h>
#include <stdio.h>
#include <stdlib.h>
@@ -34,6 +41,19 @@
#include "amdgpu_drm.h"
#include "amdgpu_internal.h"
+static char *amdgpuids_path = NULL;
+static const char* amdgpuids_path_msg = NULL;
+
+static int check_for_location_of_amdgpuids(const char *filepath, const struct stat *info, const int typeflag, struct FTW *pathinfo)
+{
+ if (typeflag == FTW_F && strstr(filepath, "amdgpu.ids")) {
+ amdgpuids_path = strdup(filepath);
+ return 1;
+ }
+
+ return 0;
+}
+
static int parse_one_line(struct amdgpu_device *dev, const char *line)
{
char *buf, *saveptr;
@@ -113,10 +133,46 @@ void amdgpu_parse_asic_ids(struct amdgpu_device *dev)
int line_num = 1;
int r = 0;
+ // attempt to find typical location for amdgpu.ids file
fp = fopen(AMDGPU_ASIC_ID_TABLE, "r");
+
+ // if it doesn't exist, search
+ if (!fp) {
+
+ char self_path[ PATH_MAX ];
+ ssize_t count;
+ ssize_t i;
+
+ count = readlink( "/proc/self/exe", self_path, PATH_MAX );
+ if (count > 0) {
+ self_path[count] = '\0';
+
+ // remove '/bin/python' from self_path
+ for (i=count; i>0; --i) {
+ if (self_path[i] == '/') break;
+ self_path[i] = '\0';
+ }
+ self_path[i] = '\0';
+ for (; i>0; --i) {
+ if (self_path[i] == '/') break;
+ self_path[i] = '\0';
+ }
+ self_path[i] = '\0';
+
+ if (1 == nftw(self_path, check_for_location_of_amdgpuids, 5, FTW_PHYS)) {
+ fp = fopen(amdgpuids_path, "r");
+ amdgpuids_path_msg = amdgpuids_path;
+ }
+ }
+
+ }
+ else {
+ amdgpuids_path_msg = AMDGPU_ASIC_ID_TABLE;
+ }
+
+ // both hard-coded location and search have failed
if (!fp) {
- fprintf(stderr, "%s: %s\n", AMDGPU_ASIC_ID_TABLE,
- strerror(errno));
+ //fprintf(stderr, "amdgpu.ids: No such file or directory\n");
return;
}
@@ -132,7 +188,7 @@ void amdgpu_parse_asic_ids(struct amdgpu_device *dev)
continue;
}
- drmMsg("%s version: %s\n", AMDGPU_ASIC_ID_TABLE, line);
+ drmMsg("%s version: %s\n", amdgpuids_path_msg, line);
break;
}
@@ -150,7 +206,7 @@ void amdgpu_parse_asic_ids(struct amdgpu_device *dev)
if (r == -EINVAL) {
fprintf(stderr, "Invalid format: %s: line %d: %s\n",
- AMDGPU_ASIC_ID_TABLE, line_num, line);
+ amdgpuids_path_msg, line_num, line);
} else if (r && r != -EAGAIN) {
fprintf(stderr, "%s: Cannot parse ASIC IDs: %s\n",
__func__, strerror(-r));
EOF
###########################
### build
###########################
meson builddir --prefix=/opt/amdgpu
pushd builddir
ninja install
popd
popd

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@ -1,37 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eou pipefail
function do_install() {
rocm_version=$1
if [[ ${rocm_version} =~ ^[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
# chop off any patch version
rocm_version="${rocm_version%.*}"
fi
rocm_version_nodot=${rocm_version//./}
# 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"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://ossci-linux.s3.us-east-1.amazonaws.com/${magma_archive}
if tar -xvf "${magma_archive}"
then
mkdir -p "${rocm_dir}/magma"
mv include "${rocm_dir}/magma/include"
mv lib "${rocm_dir}/magma/lib"
else
echo "${magma_archive} not found, skipping magma install"
fi
popd
)
}
do_install $1

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@ -1,107 +0,0 @@
#!/bin/bash
set -ex
mkdir -p /opt/triton
if [ -z "${TRITON}" ] && [ -z "${TRITON_CPU}" ]; then
echo "TRITON and TRITON_CPU are not set. Exiting..."
exit 0
fi
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
get_pip_version() {
conda_run pip list | grep -w $* | head -n 1 | awk '{print $2}'
}
if [ -n "${XPU_VERSION}" ]; then
TRITON_REPO="https://github.com/intel/intel-xpu-backend-for-triton"
TRITON_TEXT_FILE="triton-xpu"
elif [ -n "${TRITON_CPU}" ]; then
TRITON_REPO="https://github.com/triton-lang/triton-cpu"
TRITON_TEXT_FILE="triton-cpu"
else
TRITON_REPO="https://github.com/triton-lang/triton"
TRITON_TEXT_FILE="triton"
fi
# The logic here is copied from .ci/pytorch/common_utils.sh
TRITON_PINNED_COMMIT=$(get_pinned_commit ${TRITON_TEXT_FILE})
if [ -n "${UBUNTU_VERSION}" ];then
apt update
apt-get install -y gpg-agent
fi
# Keep the current cmake and numpy version here, so we can reinstall them later
CMAKE_VERSION=$(get_pip_version cmake)
NUMPY_VERSION=$(get_pip_version numpy)
if [ -z "${MAX_JOBS}" ]; then
export MAX_JOBS=$(nproc)
fi
# Git checkout triton
mkdir /var/lib/jenkins/triton
chown -R jenkins /var/lib/jenkins/triton
chgrp -R jenkins /var/lib/jenkins/triton
pushd /var/lib/jenkins/
as_jenkins git clone --recursive ${TRITON_REPO} triton
cd triton
as_jenkins git checkout ${TRITON_PINNED_COMMIT}
as_jenkins git submodule update --init --recursive
# Old versions of python have setup.py in ./python; newer versions have it in ./
if [ ! -f setup.py ]; then
cd python
fi
pip_install pybind11==3.0.1
# TODO: remove patch setup.py once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
as_jenkins sed -i -e 's/https:\/\/tritonlang.blob.core.windows.net\/llvm-builds/https:\/\/oaitriton.blob.core.windows.net\/public\/llvm-builds/g' setup.py
if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}" == "7" ]]; then
# Triton needs at least gcc-9 to build
apt-get install -y g++-9
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 setup.py bdist_wheel
else
conda_run python setup.py bdist_wheel
fi
# Copy the wheel to /opt for multi stage docker builds
cp dist/*.whl /opt/triton
# Install the wheel for docker builds that don't use multi stage
pip_install dist/*.whl
# TODO: This is to make sure that the same cmake and numpy version from install conda
# script is used. Without this step, the newer cmake version (3.25.2) downloaded by
# triton build step via pip will fail to detect conda MKL. Once that issue is fixed,
# this can be removed.
#
# The correct numpy version also needs to be set here because conda claims that it
# causes inconsistent environment. Without this, conda will attempt to install the
# latest numpy version, which fails ASAN tests with the following import error: Numba
# needs NumPy 1.20 or less.
# Note that we install numpy with pip as conda might not have the version we want
if [ -n "${CMAKE_VERSION}" ]; then
pip_install "cmake==${CMAKE_VERSION}"
fi
if [ -n "${NUMPY_VERSION}" ]; then
pip_install "numpy==${NUMPY_VERSION}"
fi
# IMPORTANT: helion needs to be installed without dependencies.
# It depends on torch and triton. We don't want to install
# triton and torch from production on Docker CI images
if [[ "$ANACONDA_PYTHON_VERSION" != 3.9* ]]; then
pip_install helion --no-deps
fi

View File

@ -1,81 +0,0 @@
#!/bin/bash
set -ex
if [[ -d "/usr/local/cuda/" ]]; then
with_cuda=/usr/local/cuda/
else
with_cuda=no
fi
if [[ -d "/opt/rocm" ]]; then
with_rocm=/opt/rocm
else
with_rocm=no
fi
function install_ucx() {
set -ex
git clone --recursive https://github.com/openucx/ucx.git
pushd ucx
git checkout ${UCX_COMMIT}
git submodule update --init --recursive
./autogen.sh
./configure --prefix=$UCX_HOME \
--enable-mt \
--with-cuda=$with_cuda \
--with-rocm=$with_rocm \
--enable-profiling \
--enable-stats
time make -j
sudo make install
popd
rm -rf ucx
}
function install_ucc() {
set -ex
git clone --recursive https://github.com/openucx/ucc.git
pushd ucc
git checkout ${UCC_COMMIT}
git submodule update --init --recursive
./autogen.sh
if [[ -n "$CUDA_VERSION" && $CUDA_VERSION == 13* ]]; then
NVCC_GENCODE="-gencode=arch=compute_86,code=compute_86"
else
# We only run distributed tests on Tesla M60 and A10G
NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
fi
if [[ -n "$ROCM_VERSION" ]]; then
if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then
amdgpu_targets=`echo $PYTORCH_ROCM_ARCH | sed 's/;/ /g'`
else
amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
fi
for arch in $amdgpu_targets; do
HIP_OFFLOAD="$HIP_OFFLOAD --offload-arch=$arch"
done
else
HIP_OFFLOAD="all-arch-no-native"
fi
./configure --prefix=$UCC_HOME \
--with-ucx=$UCX_HOME \
--with-cuda=$with_cuda \
--with-nvcc-gencode="${NVCC_GENCODE}" \
--with-rocm=$with_rocm \
--with-rocm-arch="${HIP_OFFLOAD}"
time make -j
sudo make install
popd
rm -rf ucc
}
install_ucx
install_ucc

View File

@ -1,40 +0,0 @@
#!/bin/bash
set -ex
# Since version 24 the system ships with user 'ubuntu' that has id 1000
# We need a work-around to enable id 1000 usage for this script
if [[ $UBUNTU_VERSION == 24.04 ]]; then
# touch is used to disable harmless error message
touch /var/mail/ubuntu && chown ubuntu /var/mail/ubuntu && userdel -r ubuntu
fi
# Mirror jenkins user in container
# jenkins user as ec2-user should have the same user-id
echo "jenkins:x:1000:1000::/var/lib/jenkins:" >> /etc/passwd
echo "jenkins:x:1000:" >> /etc/group
# Needed on focal or newer
echo "jenkins:*:19110:0:99999:7:::" >>/etc/shadow
# Create $HOME
mkdir -p /var/lib/jenkins
chown jenkins:jenkins /var/lib/jenkins
mkdir -p /var/lib/jenkins/.ccache
chown jenkins:jenkins /var/lib/jenkins/.ccache
# Allow writing to /usr/local (for make install)
chown jenkins:jenkins /usr/local
# Allow sudo
# TODO: Maybe we shouldn't
echo 'jenkins ALL=(ALL) NOPASSWD:ALL' > /etc/sudoers.d/jenkins
# Work around bug where devtoolset replaces sudo and breaks it.
if [ -n "$DEVTOOLSET_VERSION" ]; then
SUDO=/bin/sudo
else
SUDO=sudo
fi
# Test that sudo works
$SUDO -u jenkins $SUDO -v

View File

@ -1,176 +0,0 @@
#!/bin/bash
set -xe
# Script used in CI and CD pipeline
# Intel® software for general purpose GPU capabilities.
# Refer to https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
# Users should update to the latest version as it becomes available
function install_ubuntu() {
. /etc/os-release
if [[ ! " jammy " =~ " ${VERSION_CODENAME} " ]]; then
echo "Ubuntu version ${VERSION_CODENAME} not supported"
exit
fi
apt-get update -y
apt-get install -y gpg-agent wget
# To add the online network package repository for the GPU Driver
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key \
| gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] \
https://repositories.intel.com/gpu/ubuntu ${VERSION_CODENAME}${XPU_DRIVER_VERSION} unified" \
| tee /etc/apt/sources.list.d/intel-gpu-${VERSION_CODENAME}.list
# To add the online network network package repository for the Intel Support Packages
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
| gpg --dearmor > /usr/share/keyrings/oneapi-archive-keyring.gpg.gpg
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg.gpg] \
https://apt.repos.intel.com/oneapi all main" \
| tee /etc/apt/sources.list.d/oneAPI.list
# Update the packages list and repository index
apt-get update
# The xpu-smi packages
apt-get install -y flex bison xpu-smi
if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
# Compute and Media Runtimes
apt-get install -y \
intel-opencl-icd intel-level-zero-gpu level-zero \
intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
# Development Packages
apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
else # rolling driver
apt-get install -y \
intel-opencl-icd libze-intel-gpu1 libze1 \
intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev
fi
# Install Intel Support Packages
apt-get install -y ${XPU_PACKAGES}
# Cleanup
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
}
function install_rhel() {
. /etc/os-release
if [[ "${ID}" == "rhel" ]]; then
if [[ ! " 8.8 8.9 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
echo "RHEL version ${VERSION_ID} not supported"
exit
fi
elif [[ "${ID}" == "almalinux" ]]; then
# Workaround for almalinux8 which used by quay.io/pypa/manylinux_2_28_x86_64
VERSION_ID="8.8"
fi
dnf install -y 'dnf-command(config-manager)'
# To add the online network package repository for the GPU Driver
dnf config-manager --add-repo \
https://repositories.intel.com/gpu/rhel/${VERSION_ID}${XPU_DRIVER_VERSION}/unified/intel-gpu-${VERSION_ID}.repo
# To add the online network network package repository for the Intel Support Packages
tee > /etc/yum.repos.d/oneAPI.repo << EOF
[oneAPI]
name=Intel for Pytorch GPU dev repository
baseurl=https://yum.repos.intel.com/oneapi
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
EOF
# Install Intel Support Packages
yum install -y ${XPU_PACKAGES}
# The xpu-smi packages
dnf install -y xpu-smi
# Compute and Media Runtimes
dnf install --skip-broken -y \
intel-opencl intel-media intel-mediasdk libmfxgen1 libvpl2\
level-zero intel-level-zero-gpu mesa-dri-drivers mesa-vulkan-drivers \
mesa-vdpau-drivers libdrm mesa-libEGL mesa-libgbm mesa-libGL \
mesa-libxatracker libvpl-tools intel-metrics-discovery \
intel-metrics-library intel-igc-core intel-igc-cm \
libva libva-utils intel-gmmlib libmetee intel-gsc intel-ocloc
# Development packages
dnf install -y --refresh \
intel-igc-opencl-devel level-zero-devel intel-gsc-devel libmetee-devel \
level-zero-devel
# Cleanup
dnf clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
}
function install_sles() {
. /etc/os-release
VERSION_SP=${VERSION_ID//./sp}
if [[ ! " 15sp4 15sp5 " =~ " ${VERSION_SP} " ]]; then
echo "SLES version ${VERSION_ID} not supported"
exit
fi
# To add the online network package repository for the GPU Driver
zypper addrepo -f -r \
https://repositories.intel.com/gpu/sles/${VERSION_SP}${XPU_DRIVER_VERSION}/unified/intel-gpu-${VERSION_SP}.repo
rpm --import https://repositories.intel.com/gpu/intel-graphics.key
# To add the online network network package repository for the Intel Support Packages
zypper addrepo https://yum.repos.intel.com/oneapi oneAPI
rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
# The xpu-smi packages
zypper install -y lsb-release flex bison xpu-smi
# Compute and Media Runtimes
zypper install -y intel-level-zero-gpu level-zero intel-gsc intel-opencl intel-ocloc \
intel-media-driver libigfxcmrt7 libvpl2 libvpl-tools libmfxgen1 libmfx1
# Development packages
zypper install -y libigdfcl-devel intel-igc-cm libigfxcmrt-devel level-zero-devel
# Install Intel Support Packages
zypper install -y ${XPU_PACKAGES}
}
# Default use GPU driver rolling releases
XPU_DRIVER_VERSION=""
if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
# Use GPU driver LTS releases
XPU_DRIVER_VERSION="/lts/2350"
fi
# Default use Intel® oneAPI Deep Learning Essentials 2025.1
if [[ "$XPU_VERSION" == "2025.2" ]]; then
XPU_PACKAGES="intel-deep-learning-essentials-2025.2"
else
XPU_PACKAGES="intel-deep-learning-essentials-2025.1"
fi
# The installation depends on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
install_ubuntu
;;
rhel|almalinux)
install_rhel
;;
sles)
install_sles
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac

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@ -1,117 +0,0 @@
ARG BASE_TARGET=base
ARG GPU_IMAGE=ubuntu:20.04
FROM ${GPU_IMAGE} as base
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get clean && apt-get update
RUN apt-get install -y curl locales g++ git-all autoconf automake make cmake wget unzip sudo
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
RUN locale-gen en_US.UTF-8
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
# Install openssl
FROM base as openssl
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# Install python
FROM base as python
ADD common/install_cpython.sh install_cpython.sh
RUN apt-get update -y && \
apt-get install build-essential gdb lcov libbz2-dev libffi-dev \
libgdbm-dev liblzma-dev libncurses5-dev libreadline6-dev \
libsqlite3-dev libssl-dev lzma lzma-dev tk-dev uuid-dev zlib1g-dev -y && \
bash ./install_cpython.sh && \
rm install_cpython.sh && \
apt-get clean
FROM base as conda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
FROM base as cpu
# Install Anaconda
COPY --from=conda /opt/conda /opt/conda
# Install python
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
ENV PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH
# Install MKL
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM cpu as cuda
ADD ./common/install_cuda.sh install_cuda.sh
ADD ./common/install_magma.sh install_magma.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
ENV CUDA_HOME /usr/local/cuda
FROM cuda as cuda12.6
RUN bash ./install_cuda.sh 12.6
RUN bash ./install_magma.sh 12.6
RUN ln -sf /usr/local/cuda-12.6 /usr/local/cuda
FROM cuda as cuda12.8
RUN bash ./install_cuda.sh 12.8
RUN bash ./install_magma.sh 12.8
RUN ln -sf /usr/local/cuda-12.8 /usr/local/cuda
FROM cuda as cuda12.9
RUN bash ./install_cuda.sh 12.9
RUN bash ./install_magma.sh 12.9
RUN ln -sf /usr/local/cuda-12.9 /usr/local/cuda
FROM cuda as cuda13.0
RUN bash ./install_cuda.sh 13.0
RUN bash ./install_magma.sh 13.0
RUN ln -sf /usr/local/cuda-13.0 /usr/local/cuda
# Install libibverbs for libtorch and copy to CUDA directory
RUN apt-get update -y && \
apt-get install -y libibverbs-dev librdmacm-dev && \
cp /usr/lib/x86_64-linux-gnu/libmlx5.so* /usr/local/cuda/lib64/ && \
cp /usr/lib/x86_64-linux-gnu/librdmacm.so* /usr/local/cuda/lib64/ && \
cp /usr/lib/x86_64-linux-gnu/libibverbs.so* /usr/local/cuda/lib64/ && \
cp /usr/lib/x86_64-linux-gnu/libnl* /usr/local/cuda/lib64/
FROM cpu as rocm
ARG ROCM_VERSION
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ENV MKLROOT /opt/intel
# Adding ROCM_PATH env var so that LoadHip.cmake (even with logic updated for ROCm6.0)
# find HIP works for ROCm5.7. Not needed for ROCm6.0 and above.
# Remove below when ROCm5.7 is not in support matrix anymore.
ENV ROCM_PATH /opt/rocm
# No need to install ROCm as base docker image should have full ROCm install
#ADD ./common/install_rocm.sh install_rocm.sh
ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
ADD ./common/install_rocm_magma.sh install_rocm_magma.sh
# gfortran and python needed for building magma from source for ROCm
RUN apt-get update -y && \
apt-get install gfortran -y && \
apt-get install python3 python-is-python3 -y && \
apt-get clean
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION} && rm install_rocm_magma.sh
FROM ${BASE_TARGET} as final
COPY --from=openssl /opt/openssl /opt/openssl
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh
# Install Anaconda
COPY --from=conda /opt/conda /opt/conda
# Install python
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
ENV PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH

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@ -1,67 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eoux pipefail
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGENAME:ARCHTAG"
exit 1
fi
TOPDIR=$(git rev-parse --show-toplevel)
DOCKER=${DOCKER:-docker}
# Go from imagename:tag to tag
DOCKER_TAG_PREFIX=$(echo "${image}" | awk -F':' '{print $2}')
GPU_ARCH_VERSION=""
if [[ "${DOCKER_TAG_PREFIX}" == cuda* ]]; then
# extract cuda version from image name. e.g. manylinux2_28-builder:cuda12.8 returns 12.8
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'cuda' '{print $2}')
elif [[ "${DOCKER_TAG_PREFIX}" == rocm* ]]; then
# extract rocm version from image name. e.g. manylinux2_28-builder:rocm6.2.4 returns 6.2.4
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'rocm' '{print $2}')
fi
case ${DOCKER_TAG_PREFIX} in
cpu)
BASE_TARGET=cpu
GPU_IMAGE=ubuntu:20.04
DOCKER_GPU_BUILD_ARG=""
;;
cuda*)
BASE_TARGET=cuda${GPU_ARCH_VERSION}
GPU_IMAGE=ubuntu:20.04
DOCKER_GPU_BUILD_ARG=""
;;
rocm*)
# we want the patch version of 6.4 instead
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:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
;;
*)
echo "ERROR: Unrecognized DOCKER_TAG_PREFIX: ${DOCKER_TAG_PREFIX}"
exit 1
;;
esac
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
DOCKER_BUILDKIT=1 ${DOCKER} build \
--target final \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
-t "${tmp_tag}" \
$@ \
-f "${TOPDIR}/.ci/docker/libtorch/Dockerfile" \
"${TOPDIR}/.ci/docker/"

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@ -1,48 +0,0 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install missing libomp-dev
RUN apt-get update && apt-get install -y --no-install-recommends libomp-dev && apt-get autoclean && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG PYTHON_VERSION
ARG PIP_CMAKE
# Put venv into the env vars so users don't need to activate it
ENV PATH /var/lib/jenkins/ci_env/bin:$PATH
ENV VIRTUAL_ENV /var/lib/jenkins/ci_env
COPY requirements-ci.txt /opt/requirements-ci.txt
COPY ./common/install_python.sh install_python.sh
RUN bash ./install_python.sh && rm install_python.sh /opt/requirements-ci.txt
# Install cuda and cudnn
ARG CUDA_VERSION
COPY ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu* install_cusparselt.sh
ENV DESIRED_CUDA ${CUDA_VERSION}
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
# Note that Docker build forbids copying file outside the build context
COPY ./common/install_linter.sh install_linter.sh
RUN bash ./install_linter.sh
RUN rm install_linter.sh
RUN chown -R jenkins:jenkins /var/lib/jenkins/ci_env
USER jenkins
CMD ["bash"]

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@ -1,33 +0,0 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG PYTHON_VERSION
ENV PATH /var/lib/jenkins/ci_env/bin:$PATH
ENV VIRTUAL_ENV /var/lib/jenkins/ci_env
COPY requirements-ci.txt /opt/requirements-ci.txt
COPY ./common/install_python.sh install_python.sh
RUN bash ./install_python.sh && rm install_python.sh /opt/requirements-ci.txt
# Note that Docker build forbids copying file outside the build context
COPY ./common/install_linter.sh install_linter.sh
RUN bash ./install_linter.sh
RUN rm install_linter.sh
RUN chown -R jenkins:jenkins /var/lib/jenkins/ci_env
USER jenkins
CMD ["bash"]

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@ -1,180 +0,0 @@
# syntax = docker/dockerfile:experimental
ARG BASE_CUDA_VERSION=11.8
ARG GPU_IMAGE=amd64/almalinux:8
FROM quay.io/pypa/manylinux_2_28_x86_64 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=13
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel yum-utils gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# cmake-3.18.4 from pip
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -s /usr/local/bin/cmake /usr/bin/cmake3
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# remove unnecessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
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
FROM base as cuda
ARG BASE_CUDA_VERSION=12.6
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh install_nccl.sh ci_commit_pins/nccl-cu* install_cusparselt.sh
FROM base as intel
# MKL
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM base as magma
ARG BASE_CUDA_VERSION=12.6
# Install magma
ADD ./common/install_magma.sh install_magma.sh
RUN bash ./install_magma.sh ${BASE_CUDA_VERSION} && rm install_magma.sh
FROM base as jni
# Install java jni header
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
# Install libpng
ADD ./common/install_libpng.sh install_libpng.sh
RUN bash ./install_libpng.sh && rm install_libpng.sh
FROM ${GPU_IMAGE} as common
ARG DEVTOOLSET_VERSION=13
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
glibc-langpack-en \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran \
gcc-toolset-${DEVTOOLSET_VERSION}-gdb
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
# Install LLVM version
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=base /opt/python /opt/python
COPY --from=base /usr/local/lib/ /usr/local/lib/
COPY --from=base /opt/_internal /opt/_internal
COPY --from=base /usr/local/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=intel /opt/intel /opt/intel
COPY --from=base /usr/local/bin/patchelf /usr/local/bin/patchelf
COPY --from=libpng /usr/local/bin/png* /usr/local/bin/
COPY --from=libpng /usr/local/bin/libpng* /usr/local/bin/
COPY --from=libpng /usr/local/include/png* /usr/local/include/
COPY --from=libpng /usr/local/include/libpng* /usr/local/include/
COPY --from=libpng /usr/local/lib/libpng* /usr/local/lib/
COPY --from=libpng /usr/local/lib/pkgconfig /usr/local/lib/pkgconfig
COPY --from=jni /usr/local/include/jni.h /usr/local/include/jni.h
FROM common as cpu_final
ARG BASE_CUDA_VERSION=12.6
ARG DEVTOOLSET_VERSION=13
# Install Anaconda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
ENV PATH /opt/conda/bin:$PATH
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# Install setuptools and wheel for python 3.12/3.13
RUN for cpython_version in "cp312-cp312" "cp313-cp313" "cp313-cp313t"; do \
/opt/python/${cpython_version}/bin/python -m pip install setuptools wheel; \
done;
# cmake-3.18.4 from pip; force in case cmake3 already exists
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -sf /usr/local/bin/cmake /usr/bin/cmake3
FROM cpu_final as cuda_final
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=magma /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
ENV PATH=/usr/local/cuda/bin:$PATH
FROM cpu_final as rocm_final
ARG ROCM_VERSION=6.0
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ARG DEVTOOLSET_VERSION=11
ENV LDFLAGS="-Wl,-rpath=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64 -Wl,-rpath=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib"
# Somewhere in ROCm stack, we still use non-existing /opt/rocm/hip path,
# below workaround helps avoid error
ENV ROCM_PATH /opt/rocm
# cmake-3.28.4 from pip to get enable_language(HIP)
# and avoid 3.21.0 cmake+ninja issues with ninja inserting "-Wl,--no-as-needed" in LINK_FLAGS for static linker
RUN python3 -m pip install --upgrade pip && \
python3 -mpip install cmake==3.28.4
# replace the libdrm in /opt/amdgpu with custom amdgpu.ids lookup path
ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
# ROCm 6.4 rocm-smi depends on system drm.h header
RUN yum install -y libdrm-devel
ENV MKLROOT /opt/intel
ADD ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION} && rm install_rocm_magma.sh
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh
FROM cpu_final as xpu_final
# XPU CD use rolling driver
ENV XPU_DRIVER_TYPE ROLLING
# cmake-3.28.4 from pip
RUN python3 -m pip install --upgrade pip && \
python3 -mpip install cmake==3.28.4
ADD ./common/install_xpu.sh install_xpu.sh
ENV XPU_VERSION 2025.2
RUN bash ./install_xpu.sh && rm install_xpu.sh
RUN pushd /opt/_internal && tar -xJf static-libs-for-embedding-only.tar.xz && popd

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@ -1,73 +0,0 @@
FROM quay.io/pypa/manylinux_2_28_aarch64 as base
ARG GCCTOOLSET_VERSION=13
# Language variables
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
less \
libffi-devel \
libgomp \
make \
openssl-devel \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm \
zstd \
sudo \
gcc-toolset-${GCCTOOLSET_VERSION}-gcc \
gcc-toolset-${GCCTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${GCCTOOLSET_VERSION}-gcc-gfortran \
gcc-toolset-${GCCTOOLSET_VERSION}-gdb
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
FROM base as openblas
# Install openblas
ARG OPENBLAS_VERSION
ADD ./common/install_openblas.sh install_openblas.sh
RUN bash ./install_openblas.sh && rm install_openblas.sh
FROM base as final
# remove unnecessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
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/
ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:$LD_LIBRARY_PATH

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FROM quay.io/pypa/manylinux_2_28_aarch64 as base
# Cuda ARM build needs gcc 11
ARG DEVTOOLSET_VERSION=13
# Language variables
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm \
less \
zstd \
libgomp \
sudo \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran \
gcc-toolset-${DEVTOOLSET_VERSION}-gdb
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
FROM openssl as final
# remove unnecessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
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
FROM base as cuda
ARG BASE_CUDA_VERSION
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./common/install_cusparselt.sh install_cusparselt.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh install_nccl.sh ci_commit_pins/nccl-cu* install_cusparselt.sh
FROM base as magma
ARG BASE_CUDA_VERSION
# Install magma
ADD ./common/install_magma.sh install_magma.sh
RUN bash ./install_magma.sh ${BASE_CUDA_VERSION} && rm install_magma.sh
FROM base as nvpl
# Install nvpl
ADD ./common/install_nvpl.sh install_nvpl.sh
RUN bash ./install_nvpl.sh && rm install_nvpl.sh
FROM final as cuda_final
ARG BASE_CUDA_VERSION
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
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/
RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
ENV PATH=/usr/local/cuda/bin:$PATH

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@ -1,71 +0,0 @@
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

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@ -1,141 +0,0 @@
FROM quay.io/pypa/manylinux_2_28_s390x as base
# Language variables
ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8
ENV LANGUAGE=C.UTF-8
# there is a bugfix in gcc >= 14 for precompiled headers and s390x vectorization interaction.
# with earlier gcc versions test/inductor/test_cpu_cpp_wrapper.py will fail.
ARG DEVTOOLSET_VERSION=14
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
sudo \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm \
less \
zstd \
libgomp \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${DEVTOOLSET_VERSION}-binutils \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran \
cmake \
rust \
cargo \
llvm-devel \
libzstd-devel \
python3.12-devel \
python3.12-test \
python3.12-setuptools \
python3.12-pip \
python3-virtualenv \
python3.12-pyyaml \
python3.12-numpy \
python3.12-wheel \
python3.12-cryptography \
blas-devel \
openblas-devel \
lapack-devel \
atlas-devel \
libjpeg-devel \
libxslt-devel \
libxml2-devel \
openssl-devel \
valgrind \
ninja-build
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
# installed python doesn't have development parts. Rebuild it from scratch
RUN /bin/rm -rf /opt/_internal /opt/python /usr/local/*/*
# EPEL for cmake
FROM base as patchelf
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh
RUN cp $(which patchelf) /patchelf
FROM patchelf as python
# build python
COPY manywheel/build_scripts /build_scripts
ADD ./common/install_cpython.sh /build_scripts/install_cpython.sh
ENV SSL_CERT_FILE=
RUN bash build_scripts/build.sh && rm -r build_scripts
FROM base as final
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
COPY --from=python /opt/python/cp39-cp39/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=patchelf /usr/local/bin/patchelf /usr/local/bin/patchelf
RUN alternatives --set python /usr/bin/python3.12
RUN alternatives --set python3 /usr/bin/python3.12
RUN pip-3.12 install typing_extensions
ENTRYPOINT []
CMD ["/bin/bash"]
# install test dependencies:
# - grpcio requires system openssl, bundled crypto fails to build
RUN dnf install -y \
hdf5-devel \
python3-h5py \
git
RUN env GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=True pip3 install grpcio
# cmake-3.28.0 from pip for onnxruntime
RUN python3 -mpip install cmake==3.28.0
# build onnxruntime 1.21.0 from sources.
# it is not possible to build it from sources using pip,
# so just build it from upstream repository.
# h5py is dependency of onnxruntime_training.
# h5py==3.11.0 builds with hdf5-devel 1.10.5 from repository.
# h5py 3.11.0 doesn't build with numpy >= 2.3.0.
# install newest flatbuffers version first:
# for some reason old version is getting pulled in otherwise.
# packaging package is required for onnxruntime wheel build.
RUN pip3 install flatbuffers && \
pip3 install cython 'pkgconfig>=1.5.5' 'setuptools>=77' 'numpy<2.3.0' && \
pip3 install --no-build-isolation h5py==3.11.0 && \
pip3 install packaging && \
git clone https://github.com/microsoft/onnxruntime && \
cd onnxruntime && git checkout v1.21.0 && \
git submodule update --init --recursive && \
wget https://github.com/microsoft/onnxruntime/commit/f57db79743c4d1a3553aa05cf95bcd10966030e6.patch && \
patch -p1 < f57db79743c4d1a3553aa05cf95bcd10966030e6.patch && \
./build.sh --config Release --parallel 0 --enable_pybind \
--build_wheel --enable_training --enable_training_apis \
--enable_training_ops --skip_tests --allow_running_as_root \
--compile_no_warning_as_error && \
pip3 install ./build/Linux/Release/dist/onnxruntime_training-*.whl && \
cd .. && /bin/rm -rf ./onnxruntime

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@ -1,129 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -exou pipefail
TOPDIR=$(git rev-parse --show-toplevel)
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGE:ARCHTAG"
exit 1
fi
# Go from imagename:tag to tag
DOCKER_TAG_PREFIX=$(echo "${image}" | awk -F':' '{print $2}')
GPU_ARCH_VERSION=""
if [[ "${DOCKER_TAG_PREFIX}" == cuda* ]]; then
# extract cuda version from image name. e.g. manylinux2_28-builder:cuda12.8 returns 12.8
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'cuda' '{print $2}')
elif [[ "${DOCKER_TAG_PREFIX}" == rocm* ]]; then
# extract rocm version from image name. e.g. manylinux2_28-builder:rocm6.2.4 returns 6.2.4
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'rocm' '{print $2}')
fi
MANY_LINUX_VERSION=${MANY_LINUX_VERSION:-}
DOCKERFILE_SUFFIX=${DOCKERFILE_SUFFIX:-}
OPENBLAS_VERSION=${OPENBLAS_VERSION:-}
case ${image} in
manylinux2_28-builder:cpu)
TARGET=cpu_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13"
MANY_LINUX_VERSION="2_28"
;;
manylinux2_28_aarch64-builder:cpu-aarch64)
TARGET=final
GPU_IMAGE=arm64v8/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13 --build-arg NINJA_VERSION=1.12.1"
MANY_LINUX_VERSION="2_28_aarch64"
OPENBLAS_VERSION="v0.3.30"
;;
manylinuxcxx11-abi-builder:cpu-cxx11-abi)
TARGET=final
GPU_IMAGE=""
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
MANY_LINUX_VERSION="cxx11-abi"
;;
manylinuxs390x-builder:cpu-s390x)
TARGET=final
GPU_IMAGE=s390x/almalinux:8
DOCKER_GPU_BUILD_ARG=""
MANY_LINUX_VERSION="s390x"
;;
manylinux2_28-builder:cuda11*)
TARGET=cuda_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
manylinux2_28-builder:cuda12*)
TARGET=cuda_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
MANY_LINUX_VERSION="2_28"
;;
manylinux2_28-builder:cuda13*)
TARGET=cuda_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
MANY_LINUX_VERSION="2_28"
;;
manylinuxaarch64-builder:cuda*)
TARGET=cuda_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
MANY_LINUX_VERSION="aarch64"
DOCKERFILE_SUFFIX="_cuda_aarch64"
;;
manylinux2_28-builder:rocm*)
# we want the patch version of 6.4 instead
if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
fi
TARGET=rocm_final
MANY_LINUX_VERSION="2_28"
DEVTOOLSET_VERSION="11"
GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
;;
manylinux2_28-builder:xpu)
TARGET=xpu_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
*)
echo "ERROR: Unrecognized image name: ${image}"
exit 1
;;
esac
if [[ -n ${MANY_LINUX_VERSION} && -z ${DOCKERFILE_SUFFIX} ]]; then
DOCKERFILE_SUFFIX=_${MANY_LINUX_VERSION}
fi
# Only activate this if in CI
if [ "$(uname -m)" != "s390x" ] && [ -v CI ]; then
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
fi
tmp_tag=$(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=${OPENBLAS_VERSION}" \
--target "${TARGET}" \
-t "${tmp_tag}" \
$@ \
-f "${TOPDIR}/.ci/docker/manywheel/Dockerfile${DOCKERFILE_SUFFIX}" \
"${TOPDIR}/.ci/docker/"

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@ -1,118 +0,0 @@
#!/bin/bash
# Top-level build script called from Dockerfile
# Script used only in CD pipeline
# Stop at any error, show all commands
set -ex
# openssl version to build, with expected sha256 hash of .tar.gz
# archive
OPENSSL_ROOT=openssl-1.1.1l
OPENSSL_HASH=0b7a3e5e59c34827fe0c3a74b7ec8baef302b98fa80088d7f9153aa16fa76bd1
DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
CURL_ROOT=curl-7.73.0
CURL_HASH=cf34fe0b07b800f1c01a499a6e8b2af548f6d0e044dca4a29d88a4bee146d131
AUTOCONF_ROOT=autoconf-2.69
AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969
# Dependencies for compiling Python that we want to remove from
# the final image after compiling Python
PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel libpcap-devel xz-devel libffi-devel"
if [ "$(uname -m)" != "s390x" ] ; then
PYTHON_COMPILE_DEPS="${PYTHON_COMPILE_DEPS} db4-devel"
else
PYTHON_COMPILE_DEPS="${PYTHON_COMPILE_DEPS} libdb-devel"
fi
# Libraries that are allowed as part of the manylinux1 profile
MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel mesa-libGL-devel libICE-devel libSM-devel ncurses-devel"
# Get build utilities
MY_DIR=$(dirname "${BASH_SOURCE[0]}")
source $MY_DIR/build_utils.sh
# Development tools and libraries
yum -y install bzip2 make git patch unzip bison yasm diffutils \
automake which file \
${PYTHON_COMPILE_DEPS}
# Install newest autoconf
build_autoconf $AUTOCONF_ROOT $AUTOCONF_HASH
autoconf --version
# Compile the latest Python releases.
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
build_openssl $OPENSSL_ROOT $OPENSSL_HASH
/build_scripts/install_cpython.sh
PY39_BIN=/opt/python/cp39-cp39/bin
# Our openssl doesn't know how to find the system CA trust store
# (https://github.com/pypa/manylinux/issues/53)
# And it's not clear how up-to-date that is anyway
# So let's just use the same one pip and everyone uses
$PY39_BIN/pip install certifi
ln -s $($PY39_BIN/python -c 'import certifi; print(certifi.where())') \
/opt/_internal/certs.pem
# If you modify this line you also have to modify the versions in the
# Dockerfiles:
export SSL_CERT_FILE=/opt/_internal/certs.pem
# Install newest curl
build_curl $CURL_ROOT $CURL_HASH
rm -rf /usr/local/include/curl /usr/local/lib/libcurl* /usr/local/lib/pkgconfig/libcurl.pc
hash -r
curl --version
curl-config --features
# Install patchelf (latest with unreleased bug fixes)
curl -sLOk https://nixos.org/releases/patchelf/patchelf-0.10/patchelf-0.10.tar.gz
# check_sha256sum patchelf-0.9njs2.tar.gz $PATCHELF_HASH
tar -xzf patchelf-0.10.tar.gz
(cd patchelf-0.10 && ./configure && make && make install)
rm -rf patchelf-0.10.tar.gz patchelf-0.10
# Install latest pypi release of auditwheel
$PY39_BIN/pip install auditwheel
ln -s $PY39_BIN/auditwheel /usr/local/bin/auditwheel
# Clean up development headers and other unnecessary stuff for
# final image
yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \
avahi freetype bitstream-vera-fonts \
${PYTHON_COMPILE_DEPS} || true > /dev/null 2>&1
yum -y install ${MANYLINUX1_DEPS}
yum -y clean all > /dev/null 2>&1
yum list installed
# we don't need libpython*.a, and they're many megabytes
find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f
# Strip what we can -- and ignore errors, because this just attempts to strip
# *everything*, including non-ELF files:
find /opt/_internal -type f -print0 \
| xargs -0 -n1 strip --strip-unneeded 2>/dev/null || true
# We do not need the Python test suites, or indeed the precompiled .pyc and
# .pyo files. Partially cribbed from:
# https://github.com/docker-library/python/blob/master/3.4/slim/Dockerfile # @lint-ignore
find /opt/_internal \
\( -type d -a -name test -o -name tests \) \
-o \( -type f -a -name '*.pyc' -o -name '*.pyo' \) \
-print0 | xargs -0 rm -f
for PYTHON in /opt/python/*/bin/python; do
# Smoke test to make sure that our Pythons work, and do indeed detect as
# being manylinux compatible:
$PYTHON $MY_DIR/manylinux1-check.py
# Make sure that SSL cert checking works
$PYTHON $MY_DIR/ssl-check.py
done
# Fix libc headers to remain compatible with C99 compilers.
find /usr/include/ -type f -exec sed -i 's/\bextern _*inline_*\b/extern __inline __attribute__ ((__gnu_inline__))/g' {} +
# Now we can delete our built SSL
rm -rf /usr/local/ssl

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@ -1,91 +0,0 @@
#!/bin/bash
# Helper utilities for build
# Script used only in CD pipeline
OPENSSL_DOWNLOAD_URL=https://www.openssl.org/source/old/1.1.1/ # @lint-ignore
CURL_DOWNLOAD_URL=https://curl.se/download
AUTOCONF_DOWNLOAD_URL=https://ftp.gnu.org/gnu/autoconf
function check_var {
if [ -z "$1" ]; then
echo "required variable not defined"
exit 1
fi
}
function do_openssl_build {
./config no-ssl2 no-shared -fPIC --prefix=/usr/local/ssl > /dev/null
make > /dev/null
make install > /dev/null
}
function check_sha256sum {
local fname=$1
check_var ${fname}
local sha256=$2
check_var ${sha256}
echo "${sha256} ${fname}" > ${fname}.sha256
sha256sum -c ${fname}.sha256
rm -f ${fname}.sha256
}
function build_openssl {
local openssl_fname=$1
check_var ${openssl_fname}
local openssl_sha256=$2
check_var ${openssl_sha256}
check_var ${OPENSSL_DOWNLOAD_URL}
curl -sLO ${OPENSSL_DOWNLOAD_URL}/${openssl_fname}.tar.gz
check_sha256sum ${openssl_fname}.tar.gz ${openssl_sha256}
tar -xzf ${openssl_fname}.tar.gz
(cd ${openssl_fname} && do_openssl_build)
rm -rf ${openssl_fname} ${openssl_fname}.tar.gz
}
function do_curl_build {
LIBS=-ldl ./configure --with-ssl --disable-shared > /dev/null
make > /dev/null
make install > /dev/null
}
function build_curl {
local curl_fname=$1
check_var ${curl_fname}
local curl_sha256=$2
check_var ${curl_sha256}
check_var ${CURL_DOWNLOAD_URL}
curl -sLO ${CURL_DOWNLOAD_URL}/${curl_fname}.tar.bz2
check_sha256sum ${curl_fname}.tar.bz2 ${curl_sha256}
tar -jxf ${curl_fname}.tar.bz2
(cd ${curl_fname} && do_curl_build)
rm -rf ${curl_fname} ${curl_fname}.tar.bz2
}
function do_standard_install {
./configure > /dev/null
make > /dev/null
make install > /dev/null
}
function build_autoconf {
local autoconf_fname=$1
check_var ${autoconf_fname}
local autoconf_sha256=$2
check_var ${autoconf_sha256}
check_var ${AUTOCONF_DOWNLOAD_URL}
curl -sLO ${AUTOCONF_DOWNLOAD_URL}/${autoconf_fname}.tar.gz
check_sha256sum ${autoconf_fname}.tar.gz ${autoconf_sha256}
tar -zxf ${autoconf_fname}.tar.gz
(cd ${autoconf_fname} && do_standard_install)
rm -rf ${autoconf_fname} ${autoconf_fname}.tar.gz
}

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@ -1,60 +0,0 @@
# Logic copied from PEP 513
def is_manylinux1_compatible():
# Only Linux, and only x86-64 / i686
from distutils.util import get_platform
if get_platform() not in ["linux-x86_64", "linux-i686", "linux-s390x"]:
return False
# Check for presence of _manylinux module
try:
import _manylinux
return bool(_manylinux.manylinux1_compatible)
except (ImportError, AttributeError):
# Fall through to heuristic check below
pass
# Check glibc version. CentOS 5 uses glibc 2.5.
return have_compatible_glibc(2, 5)
def have_compatible_glibc(major, minimum_minor):
import ctypes
process_namespace = ctypes.CDLL(None)
try:
gnu_get_libc_version = process_namespace.gnu_get_libc_version
except AttributeError:
# Symbol doesn't exist -> therefore, we are not linked to
# glibc.
return False
# Call gnu_get_libc_version, which returns a string like "2.5".
gnu_get_libc_version.restype = ctypes.c_char_p
version_str = gnu_get_libc_version()
# py2 / py3 compatibility:
if not isinstance(version_str, str):
version_str = version_str.decode("ascii")
# Parse string and check against requested version.
version = [int(piece) for piece in version_str.split(".")]
assert len(version) == 2
if major != version[0]:
return False
if minimum_minor > version[1]:
return False
return True
import sys
if is_manylinux1_compatible():
print(f"{sys.executable} is manylinux1 compatible")
sys.exit(0)
else:
print(f"{sys.executable} is NOT manylinux1 compatible")
sys.exit(1)

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@ -1,31 +0,0 @@
# cf. https://github.com/pypa/manylinux/issues/53
import sys
from urllib.request import urlopen
GOOD_SSL = "https://google.com"
BAD_SSL = "https://self-signed.badssl.com"
print("Testing SSL certificate checking for Python:", sys.version)
if sys.version_info[:2] < (2, 7) or sys.version_info[:2] < (3, 4):
print("This version never checks SSL certs; skipping tests")
sys.exit(0)
EXC = OSError
print(f"Connecting to {GOOD_SSL} should work")
urlopen(GOOD_SSL)
print("...it did, yay.")
print(f"Connecting to {BAD_SSL} should fail")
try:
urlopen(BAD_SSL)
# If we get here then we failed:
print("...it DIDN'T!!!!!11!!1one!")
sys.exit(1)
except EXC:
print("...it did, yay.")

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@ -1,390 +0,0 @@
# Python dependencies required for unit tests
#awscli==1.6 #this breaks some platforms
#Description: AWS command line interface
#Pinned versions: 1.6
#test that import:
boto3==1.35.42
#Description: AWS SDK for python
#Pinned versions: 1.19.12, 1.16.34
#test that import:
click
#Description: Command Line Interface Creation Kit
#Pinned versions:
#test that import:
coremltools==5.0b5 ; python_version < "3.12"
coremltools==8.3 ; python_version == "3.12"
#Description: Apple framework for ML integration
#Pinned versions: 5.0b5
#test that import:
#dataclasses #this breaks some platforms
#Description: Provides decorators for auto adding special methods to user classes
#Pinned versions:
#test that import:
dill==0.3.7
#Description: dill extends pickle with serializing and de-serializing for most built-ins
#Pinned versions: 0.3.7
#test that import: dynamo/test_replay_record.py test_dataloader.py test_datapipe.py test_serialization.py
expecttest==0.3.0
#Description: method for writing tests where test framework auto populates
# the expected output based on previous runs
#Pinned versions: 0.3.0
#test that import:
fbscribelogger==0.1.7
#Description: write to scribe from authenticated jobs on CI
#Pinned versions: 0.1.6
#test that import:
flatbuffers==24.12.23
#Description: cross platform serialization library
#Pinned versions: 24.12.23
#test that import:
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: 5.35.1
#test that import: test_xnnpack_integration.py, test_pruning_op.py, test_nn.py
junitparser==2.1.1
#Description: unitparser handles JUnit/xUnit Result XML files
#Pinned versions: 2.1.1
#test that import:
lark==0.12.0
#Description: parser
#Pinned versions: 0.12.0
#test that import:
librosa>=0.6.2 ; python_version < "3.11" and platform_machine != "s390x"
librosa==0.10.2 ; python_version == "3.12" and platform_machine != "s390x"
#Description: A python package for music and audio analysis
#Pinned versions: >=0.6.2
#test that import: test_spectral_ops.py
#librosa depends on numba; disable it for s390x while numba is disabled too
#mkl #this breaks linux-bionic-rocm4.5-py3.7
#Description: Intel oneAPI Math Kernel Library
#Pinned versions:
#test that import: test_profiler.py, test_public_bindings.py, test_testing.py,
#test_nn.py, test_mkldnn.py, test_jit.py, test_fx_experimental.py,
#test_autograd.py
#mkl-devel
# see mkl
#mock
#Description: A testing library that allows you to replace parts of your
#system under test with mock objects
#Pinned versions:
#test that import: test_modules.py, test_nn.py,
#test_testing.py
#MonkeyType # breaks pytorch-xla-linux-bionic-py3.7-clang8
#Description: collects runtime types of function arguments and return
#values, and can automatically generate stub files
#Pinned versions:
#test that import:
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
#Pinned versions: 1.16.0
#test that import: test_typing.py, test_type_hints.py
networkx==2.8.8
#Description: creation, manipulation, and study of
#the structure, dynamics, and functions of complex networks
#Pinned versions: 2.8.8
#test that import: functorch
ninja==1.11.1.3
#Description: build system. Used in some tests. Used in build to generate build
#time tracing information
#Pinned versions: 1.11.1.3
#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
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
#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
#test that import: test_numba_integration.py
#For numba issue see https://github.com/pytorch/pytorch/issues/51511
#Need release > 0.61.2 for s390x due to https://github.com/numba/numba/pull/10073
#numpy
#Description: Provides N-dimensional arrays and linear algebra
#Pinned versions: 1.26.2
#test that import: test_view_ops.py, test_unary_ufuncs.py, test_type_promotion.py,
#test_type_info.py, test_torch.py, test_tensorexpr_pybind.py, test_tensorexpr.py,
#test_tensorboard.py, test_tensor_creation_ops.py, test_static_runtime.py,
#test_spectral_ops.py, test_sort_and_select.py, test_shape_ops.py,
#test_segment_reductions.py, test_reductions.py, test_pruning_op.py,
#test_overrides.py, test_numpy_interop.py, test_numba_integration.py
#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==1.26.2; python_version == "3.11" or python_version == "3.12"
numpy==2.1.2; python_version >= "3.13"
pandas==2.0.3; python_version < "3.13"
pandas==2.2.3; python_version >= "3.13"
#onnxruntime
#Description: scoring engine for Open Neural Network Exchange (ONNX) models
#Pinned versions: 1.9.0
#test that import:
opt-einsum==3.3
#Description: Python library to optimize tensor contraction order, used in einsum
#Pinned versions: 3.3
#test that import: test_linalg.py
optree==0.13.0
#Description: A library for tree manipulation
#Pinned versions: 0.13.0
#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
#test_pytree.py, test_ops.py, test_control_flow.py, test_modules.py,
#common_utils.py, test_eager_transforms.py, test_python_dispatch.py,
#test_expanded_weights.py, test_decomp.py, test_overrides.py, test_masked.py,
#test_ops.py, test_prims.py, test_subclass.py, test_functionalization.py,
#test_schema_check.py, test_profiler_tree.py, test_meta.py, test_torchxla_num_output.py,
#test_utils.py, test_proxy_tensor.py, test_memory_profiler.py, test_view_ops.py,
#test_pointwise_ops.py, test_dtensor_ops.py, test_torchinductor.py, test_fx.py,
#test_fake_tensor.py, test_mps.py
pillow==11.0.0
#Description: Python Imaging Library fork
#Pinned versions: 10.3.0
#test that import:
protobuf==5.29.4
#Description: Google's data interchange format
#Pinned versions: 5.29.4
#test that import: test_tensorboard.py, test/onnx/*
psutil
#Description: information on running processes and system utilization
#Pinned versions:
#test that import: test_profiler.py, test_openmp.py, test_dataloader.py
pytest==7.3.2
#Description: testing framework
#Pinned versions:
#test that import: test_typing.py, test_cpp_extensions_aot.py, run_test.py
pytest-xdist==3.3.1
#Description: plugin for running pytest in parallel
#Pinned versions:
#test that import:
pytest-flakefinder==1.1.0
#Description: plugin for rerunning tests a fixed number of times in pytest
#Pinned versions: 1.1.0
#test that import:
pytest-rerunfailures>=10.3
#Description: plugin for rerunning failure tests in pytest
#Pinned versions:
#test that import:
pytest-subtests==0.13.1
#Description: plugin for subtest support
#Pinned versions:
#test that import:
#pytest-benchmark
#Description: fixture for benchmarking code
#Pinned versions: 3.2.3
#test that import:
#pytest-sugar
#Description: shows failures and errors instantly
#Pinned versions:
#test that import:
xdoctest==1.1.0
#Description: runs doctests in pytest
#Pinned versions: 1.1.0
#test that import:
pygments==2.15.0
#Description: support doctest highlighting
#Pinned versions: 2.12.0
#test that import: the doctests
#pyyaml
#Description: data serialization format
#Pinned versions: 6.0.2
#test that import:
#requests
#Description: HTTP library
#Pinned versions:
#test that import: test_type_promotion.py
#rich
#Description: rich text and beautiful formatting in the terminal
#Pinned versions: 14.1.0
#test that import:
scikit-image==0.19.3 ; python_version < "3.10"
scikit-image==0.22.0 ; python_version >= "3.10"
#Description: image processing routines
#Pinned versions:
#test that import: test_nn.py
#scikit-learn
#Description: machine learning package
#Pinned versions: 0.20.3
#test that import:
scipy==1.10.1 ; python_version <= "3.11"
scipy==1.14.1 ; python_version >= "3.12"
# Pin SciPy because of failing distribution tests (see #60347)
#Description: scientific python
#Pinned versions: 1.10.1
#test that import: test_unary_ufuncs.py, test_torch.py,test_tensor_creation_ops.py
#test_spectral_ops.py, test_sparse_csr.py, test_reductions.py,test_nn.py
#test_linalg.py, test_binary_ufuncs.py
#tabulate
#Description: Pretty-print tabular data
#Pinned versions:
#test that import:
# needed by torchgen utils
typing-extensions>=4.10.0
#Description: type hints for python
#Pinned versions:
#test that import:
#virtualenv
#Description: virtual environment for python
#Pinned versions:
#test that import:
unittest-xml-reporting<=3.2.0,>=2.0.0
#Description: saves unit test results to xml
#Pinned versions:
#test that import:
#lintrunner is supported on aarch64-linux only from 0.12.4 version
lintrunner==0.12.7
#Description: all about linters!
#Pinned versions: 0.12.7
#test that import:
redis>=4.0.0
#Description: redis database
#test that import: anything that tests OSS caching/mocking (inductor/test_codecache.py, inductor/test_max_autotune.py)
ghstack==0.8.0
#Description: ghstack tool
#Pinned versions: 0.8.0
#test that import:
jinja2==3.1.6
#Description: jinja2 template engine
#Pinned versions: 3.1.4
#test that import:
pytest-cpp==2.3.0
#Description: This is used by pytest to invoke C++ tests
#Pinned versions: 2.3.0
#test that import:
z3-solver==4.15.1.0 ; platform_machine != "s390x"
#Description: The Z3 Theorem Prover Project
#Pinned versions:
#test that import:
tensorboard==2.13.0 ; python_version < "3.13"
tensorboard==2.18.0 ; python_version >= "3.13"
#Description: Also included in .ci/docker/requirements-docs.txt
#Pinned versions:
#test that import: test_tensorboard
pywavelets==1.4.1 ; python_version < "3.12"
pywavelets==1.7.0 ; python_version >= "3.12"
#Description: This is a requirement of scikit-image, we need to pin
# it here because 1.5.0 conflicts with numpy 1.21.2 used in CI
#Pinned versions: 1.4.1
#test that import:
lxml==5.3.0
#Description: This is a requirement of unittest-xml-reporting
PyGithub==2.3.0
sympy==1.13.3
#Description: Required by coremltools, also pinned in .github/requirements/pip-requirements-macOS.txt
#Pinned versions:
#test that import:
onnx==1.18.0
#Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
onnxscript==0.4.0
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
parameterized==0.8.1
#Description: Parameterizes unittests, both the tests themselves and the entire testing class
#Pinned versions:
#test that import:
#Description: required for testing torch/distributed/_tools/sac_estimator.py
#Pinned versions: 1.24.0
#test that import: test_sac_estimator.py
pwlf==2.2.1
#Description: required for testing torch/distributed/_tools/sac_estimator.py
#Pinned versions: 2.2.1
#test that import: test_sac_estimator.py
# To build PyTorch itself
pyyaml
pyzstd
setuptools>=70.1.0
six
scons==4.5.2 ; platform_machine == "aarch64"
pulp==2.9.0
#Description: required for testing ilp formulaiton under torch/distributed/_tools
#Pinned versions: 2.9.0
#test that import: test_sac_ilp.py
dataclasses_json==0.6.7
#Description: required for data pipeline and scripts under tools/stats
#Pinned versions: 0.6.7
#test that import:
cmake==4.0.0
#Description: required for building
tlparse==0.4.0
#Description: required for log parsing
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
setuptools-git-versioning==2.1.0
scikit-build==0.18.1
pyre-extensions==0.0.32
tabulate==0.9.0
#Description: These package are needed to build FBGEMM and torchrec on PyTorch CI

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@ -1,61 +0,0 @@
sphinx==5.3.0
#Description: This is used to generate PyTorch docs
#Pinned versions: 5.3.0
-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@d53b0ffb9b1cda68260693ea98f3483823c88d8e#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.
sphinxcontrib.katex==0.8.6
#Description: This is used to generate PyTorch docs
#Pinned versions: 0.8.6
sphinxext-opengraph==0.9.1
#Description: This is used to generate PyTorch docs
#Pinned versions: 0.9.1
sphinx_sitemap==2.6.0
#Description: This is used to generate sitemap for PyTorch docs
#Pinned versions: 2.6.0
matplotlib==3.5.3 ; python_version < "3.13"
matplotlib==3.6.3 ; python_version >= "3.13"
#Description: This is used to generate PyTorch docs
#Pinned versions: 3.6.3 if python > 3.12. Otherwise 3.5.3.
tensorboard==2.13.0 ; python_version < "3.13"
tensorboard==2.18.0 ; python_version >= "3.13"
#Description: This is used to generate PyTorch docs
#Pinned versions: 2.13.0
breathe==4.34.0
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 4.34.0
exhale==0.2.3
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 0.2.3
docutils==0.16
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 0.16
bs4==0.0.1
#Description: This is used to generate PyTorch C++ docs
#Pinned versions: 0.0.1
IPython==8.12.0
#Description: This is used to generate PyTorch functorch docs
#Pinned versions: 8.12.0
myst-nb==0.17.2
#Description: This is used to generate PyTorch functorch and torch.compile docs.
#Pinned versions: 0.17.2
# The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
python-etcd==0.4.5
sphinx-copybutton==0.5.0
sphinx-design==0.4.0
sphinxcontrib-mermaid==1.0.0
myst-parser==0.18.1

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@ -1 +0,0 @@
3.5.0

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3.5.0

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@ -1,155 +0,0 @@
# Cross-compilation Docker container for RISC-V architecture
ARG UBUNTU_VERSION
FROM --platform=linux/amd64 ubuntu:${UBUNTU_VERSION} as base
ARG UBUNTU_VERSION
ENV GCC_VERSION=14
ENV PYTHON_VERSION=3.12.3
ENV DEBIAN_FRONTEND=noninteractive
ENV CC=riscv64-linux-gnu-gcc-${GCC_VERSION}
ENV CXX=riscv64-linux-gnu-g++-${GCC_VERSION}
ENV QEMU_LD_PREFIX=/usr/riscv64-linux-gnu/
ENV SYSROOT=/opt/sysroot
# Install basic dependencies
RUN apt-get update && apt-get install -y \
ninja-build \
autoconf \
automake \
libtool \
patchelf \
ccache \
git \
wget \
python3-pip \
python3-venv \
python-is-python3 \
cmake \
sudo \
lsb-release \
gcc-${GCC_VERSION}-riscv64-linux-gnu \
g++-${GCC_VERSION}-riscv64-linux-gnu \
pkg-config \
&& rm -rf /var/lib/apt/lists/*
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
FROM base as python
ARG ZLIB_VERSION=1.3.1
ARG FFI_VERSION=3.4.6
ARG BZ2_VERSION=1.0.8
ARG XZ_VERSION=5.4.6
ARG OPENSSL_VERSION=3.2.1
# Set up sysroot directory for dependencies
ENV PKG_CONFIG_PATH=${SYSROOT}/lib/pkgconfig
ENV PKG_CONFIG_SYSROOT_DIR=${SYSROOT}
WORKDIR /opt
# Build zlib (for compression)
RUN echo "--- Building zlib ---" \
&& wget -c https://www.zlib.net/zlib-${ZLIB_VERSION}.tar.gz \
&& tar -xf zlib-${ZLIB_VERSION}.tar.gz --no-same-permissions --no-same-owner \
&& cd zlib-${ZLIB_VERSION}/ \
&& mkdir build && cd build \
&& ../configure --prefix=${SYSROOT} \
&& make -j$(nproc) && make install \
&& cd ../..
# Build libffi (for ctypes module)
RUN echo "--- Building libffi ---" \
&& wget -c https://github.com/libffi/libffi/releases/download/v${FFI_VERSION}/libffi-${FFI_VERSION}.tar.gz \
&& tar -xf libffi-${FFI_VERSION}.tar.gz --no-same-permissions --no-same-owner \
&& cd libffi-${FFI_VERSION}/ \
&& mkdir build && cd build \
&& ../configure --prefix=${SYSROOT} --host=riscv64-linux-gnu --build=x86_64-linux-gnu \
&& make -j$(nproc) && make install \
&& cd ../..
# Build bzip2 (for bz2 module)
RUN echo "--- Building bzip2 ---" \
&& wget -c https://sourceware.org/pub/bzip2/bzip2-${BZ2_VERSION}.tar.gz \
&& tar -xf bzip2-${BZ2_VERSION}.tar.gz --no-same-permissions --no-same-owner \
&& cd bzip2-${BZ2_VERSION}/ \
&& make CC=riscv64-linux-gnu-gcc-${GCC_VERSION} bzip2 bzip2recover libbz2.a \
&& make CC=riscv64-linux-gnu-gcc-${GCC_VERSION} -f Makefile-libbz2_so \
&& make install PREFIX=${SYSROOT} \
&& cp libbz2.so.${BZ2_VERSION} ${SYSROOT}/lib/ \
&& cd ${SYSROOT}/lib/ \
&& ln -sf libbz2.so.${BZ2_VERSION} libbz2.so.1.0 \
&& ln -sf libbz2.so.1.0 libbz2.so \
&& cd /opt/
# Build xz (for lzma module)
RUN echo "--- Building xz ---" \
&& wget -c https://github.com/tukaani-project/xz/releases/download/v${XZ_VERSION}/xz-${XZ_VERSION}.tar.gz \
&& tar -xf xz-${XZ_VERSION}.tar.gz --no-same-permissions --no-same-owner \
&& cd xz-${XZ_VERSION} \
&& mkdir build && cd build \
&& ../configure --prefix=${SYSROOT} --host=riscv64-linux-gnu --build=x86_64-linux-gnu \
&& make -j$(nproc) && make install \
&& cd ../..
# Build OpenSSL (for ssl module)
RUN echo "--- Building OpenSSL ---" \
&& wget -c https://www.openssl.org/source/openssl-${OPENSSL_VERSION}.tar.gz \
&& tar -xf openssl-${OPENSSL_VERSION}.tar.gz --no-same-permissions --no-same-owner \
&& cd openssl-${OPENSSL_VERSION}/ \
&& mkdir build && cd build \
&& ../Configure linux64-riscv64 --prefix=${SYSROOT} \
&& make -j$(nproc) && make install_sw \
&& cd ../..
# Build SQLite3 (for sqlite3 module)
RUN echo "--- Building SQLite3 ---" \
&& wget -c https://www.sqlite.org/2024/sqlite-autoconf-3450200.tar.gz \
&& tar -xf sqlite-autoconf-3450200.tar.gz --no-same-permissions --no-same-owner \
&& cd sqlite-autoconf-3450200 \
&& mkdir build && cd build \
&& ../configure --prefix=${SYSROOT} --host=riscv64-linux-gnu --build=x86_64-linux-gnu \
&& make -j$(nproc) && make install \
&& cd ../..
# Build and install RISC-V Python with all modules
RUN wget -c https://www.python.org/ftp/python/${PYTHON_VERSION}/Python-${PYTHON_VERSION}.tgz \
&& tar -xf Python-${PYTHON_VERSION}.tgz --no-same-permissions --no-same-owner \
&& cd Python-${PYTHON_VERSION} \
&& mkdir build && cd build \
&& ../configure \
--host=riscv64-linux-gnu \
--build=x86_64-linux-gnu \
--prefix=${SYSROOT} \
--enable-shared \
--disable-ipv6 \
--with-build-python=/usr/bin/python3 \
--with-ensurepip=no \
ac_cv_file__dev_ptmx=yes \
ac_cv_file__dev_ptc=no \
&& make -j$(nproc) \
&& make install
FROM base as final
COPY --from=python /opt/sysroot /opt/sysroot
# Install crossenv and cmake
RUN pip install crossenv cmake==4.0.0 --break-system-packages \
&& /usr/bin/python3 -m crossenv ${SYSROOT}/bin/python3 /opt/riscv-cross-env
# Add pip-installed cmake binaries to PATH
ENV PATH="/usr/local/bin:${PATH}"
# Set up cross Python environment
SHELL ["/bin/bash", "-c"]
RUN source /opt/riscv-cross-env/bin/activate \
&& pip install setuptools pyyaml typing_extensions wheel
# Set default environment variables for PyTorch build
ENV Python_ROOT_DIR=${SYSROOT}
ENV OPENSSL_ROOT_DIR=${SYSROOT}
USER jenkins
CMD ["bash"]

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@ -1,144 +0,0 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
# Set AMD gpu targets to build for
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG BUILD_ENVIRONMENT
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# Install gcc
ARG GCC_VERSION
COPY ./common/install_gcc.sh install_gcc.sh
RUN bash ./install_gcc.sh && rm install_gcc.sh
# Install clang
ARG CLANG_VERSION
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
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
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
RUN rm install_rocm_magma.sh
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh
ENV ROCM_PATH /opt/rocm
ENV PATH /opt/rocm/bin:$PATH
ENV PATH /opt/rocm/hcc/bin:$PATH
ENV PATH /opt/rocm/hip/bin:$PATH
ENV PATH /opt/rocm/opencl/bin:$PATH
ENV PATH /opt/rocm/llvm/bin:$PATH
ENV MAGMA_HOME /opt/rocm/magma
ENV LANG C.UTF-8
ENV LC_ALL C.UTF-8
# Install amdsmi
COPY ./common/install_amdsmi.sh install_amdsmi.sh
RUN bash ./install_amdsmi.sh
RUN rm install_amdsmi.sh
# (optional) Install UCC
ARG UCX_COMMIT
ARG UCC_COMMIT
ENV UCX_COMMIT $UCX_COMMIT
ENV UCC_COMMIT $UCC_COMMIT
ENV UCX_HOME /usr
ENV UCC_HOME /usr
ADD ./common/install_ucc.sh install_ucc.sh
RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
COPY ./common/install_openssl.sh install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
RUN bash ./install_openssl.sh
ENV OPENSSL_DIR /opt/openssl
ARG INDUCTOR_BENCHMARKS
ARG ANACONDA_PYTHON_VERSION
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/timm.txt timm.txt
COPY ci_commit_pins/torchbench.txt torchbench.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
ARG TRITON
# Install triton, this needs to be done before sccache because the latter will
# try to reach out to S3, which docker build runners don't have access
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
# Install ccache/sccache (do this last, so we get priority in PATH)
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH
RUN bash ./install_cache.sh && rm install_cache.sh
# Install Open MPI for ROCm
COPY ./common/install_openmpi.sh install_openmpi.sh
RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
RUN rm install_openmpi.sh
# Include BUILD_ENVIRONMENT environment variable in image
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
# Install LLVM dev version (Defined in the pytorch/builder github repository)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
USER jenkins
CMD ["bash"]

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@ -1,105 +0,0 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
ARG CLANG_VERSION
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install clang
ARG LLVMDEV
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG DOCS
ARG BUILD_ENVIRONMENT
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
ENV DOCS=$DOCS
COPY requirements-ci.txt requirements-docs.txt /opt/conda/
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
# Install gcc
ARG GCC_VERSION
COPY ./common/install_gcc.sh install_gcc.sh
RUN bash ./install_gcc.sh && rm install_gcc.sh
# Install lcov for C++ code coverage
COPY ./common/install_lcov.sh install_lcov.sh
RUN bash ./install_lcov.sh && rm install_lcov.sh
COPY ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
ENV OPENSSL_DIR /opt/openssl
RUN rm install_openssl.sh
ARG INDUCTOR_BENCHMARKS
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/timm.txt timm.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt
# Install XPU Dependencies
ARG XPU_VERSION
COPY ./common/install_xpu.sh install_xpu.sh
RUN bash ./install_xpu.sh && rm install_xpu.sh
ARG TRITON
# Install triton, this needs to be done before sccache because the latter will
# try to reach out to S3, which docker build runners don't have access
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton-xpu.txt triton-xpu.txt
COPY triton_xpu_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton-xpu.txt triton_version.txt
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
ENV INSTALLED_VISION ${VISION}
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
# Install ccache/sccache (do this last, so we get priority in PATH)
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH
RUN bash ./install_cache.sh && rm install_cache.sh
# Include BUILD_ENVIRONMENT environment variable in image
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
# Install LLVM dev version (Defined in the pytorch/builder github repository)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
USER jenkins
CMD ["bash"]

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@ -1,189 +0,0 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION} as base
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
ARG CLANG_VERSION
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install clang
ARG LLVMDEV
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG DOCS
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
ENV DOCS=$DOCS
COPY requirements-ci.txt requirements-docs.txt /opt/conda/
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ./common/install_magma_conda.sh install_magma_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh install_magma_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
RUN if [ -n "${UNINSTALL_DILL}" ]; then pip uninstall -y dill; fi
# Install gcc
ARG GCC_VERSION
COPY ./common/install_gcc.sh install_gcc.sh
RUN bash ./install_gcc.sh && rm install_gcc.sh
# Install lcov for C++ code coverage
COPY ./common/install_lcov.sh install_lcov.sh
RUN bash ./install_lcov.sh && rm install_lcov.sh
# Install cuda and cudnn
ARG CUDA_VERSION
COPY ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu* install_cusparselt.sh
ENV DESIRED_CUDA ${CUDA_VERSION}
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
# No effect if cuda not installed
ENV USE_SYSTEM_NCCL=1
ENV NCCL_INCLUDE_DIR="/usr/local/cuda/include/"
ENV NCCL_LIB_DIR="/usr/local/cuda/lib64/"
# (optional) Install UCC
ARG UCX_COMMIT
ARG UCC_COMMIT
ARG CUDA_VERSION
ENV UCX_COMMIT $UCX_COMMIT
ENV UCC_COMMIT $UCC_COMMIT
ENV UCX_HOME /usr
ENV UCC_HOME /usr
ADD ./common/install_ucc.sh install_ucc.sh
RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
ENV INSTALLED_VISION ${VISION}
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
COPY ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
ENV OPENSSL_DIR /opt/openssl
RUN rm install_openssl.sh
ARG INDUCTOR_BENCHMARKS
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/timm.txt timm.txt
COPY ci_commit_pins/torchbench.txt torchbench.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
ARG TRITON
ARG TRITON_CPU
# Create a separate stage for building Triton and Triton-CPU. install_triton
# will check for the presence of env vars
FROM base as triton-builder
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY ci_commit_pins/triton-cpu.txt triton-cpu.txt
RUN bash ./install_triton.sh
FROM base as final
COPY --from=triton-builder /opt/triton /opt/triton
RUN if [ -n "${TRITON}" ] || [ -n "${TRITON_CPU}" ]; then pip install /opt/triton/*.whl; chown -R jenkins:jenkins /opt/conda; fi
RUN rm -rf /opt/triton
ARG EXECUTORCH
# Build and install executorch
COPY ./common/install_executorch.sh install_executorch.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/executorch.txt executorch.txt
RUN if [ -n "${EXECUTORCH}" ]; then bash ./install_executorch.sh; fi
RUN rm install_executorch.sh common_utils.sh executorch.txt
ARG HALIDE
# Build and install halide
COPY ./common/install_halide.sh install_halide.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/halide.txt halide.txt
RUN if [ -n "${HALIDE}" ]; then bash ./install_halide.sh; fi
RUN rm install_halide.sh common_utils.sh halide.txt
ARG ONNX
# Install ONNX dependencies
COPY ./common/install_onnx.sh ./common/common_utils.sh ./
RUN if [ -n "${ONNX}" ]; then bash ./install_onnx.sh; fi
RUN rm install_onnx.sh common_utils.sh
# (optional) Build ACL
ARG ACL
COPY ./common/install_acl.sh install_acl.sh
RUN if [ -n "${ACL}" ]; then bash ./install_acl.sh; fi
RUN rm install_acl.sh
ENV INSTALLED_ACL ${ACL}
ARG OPENBLAS
COPY ./common/install_openblas.sh install_openblas.sh
RUN if [ -n "${OPENBLAS}" ]; then bash ./install_openblas.sh; fi
RUN rm install_openblas.sh
ENV INSTALLED_OPENBLAS ${OPENBLAS}
# Install ccache/sccache (do this last, so we get priority in PATH)
ARG SKIP_SCCACHE_INSTALL
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH
RUN if [ -z "${SKIP_SCCACHE_INSTALL}" ]; then bash ./install_cache.sh; fi
RUN rm install_cache.sh
# Add jni.h for java host build
COPY ./common/install_jni.sh install_jni.sh
COPY ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
# Install Open MPI for CUDA
COPY ./common/install_openmpi.sh install_openmpi.sh
RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
RUN rm install_openmpi.sh
# Include BUILD_ENVIRONMENT environment variable in image
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
# Install LLVM dev version (Defined in the pytorch/builder github repository)
ARG SKIP_LLVM_SRC_BUILD_INSTALL
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
RUN if [ -n "${SKIP_LLVM_SRC_BUILD_INSTALL}" ]; then set -eu; rm -rf /opt/llvm; fi
# AWS specific CUDA build guidance
ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
ENV CUDA_PATH /usr/local/cuda
USER jenkins
CMD ["bash"]

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@ -1,10 +0,0 @@
#!/usr/bin/env bash
# This is mostly just a shim to manywheel/build.sh
# TODO: Make this a dedicated script to build just libtorch
set -ex
SCRIPTPATH="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
USE_NVSHMEM=0 USE_CUSPARSELT=0 BUILD_PYTHONLESS=1 DESIRED_PYTHON="3.10" ${SCRIPTPATH}/../manywheel/build.sh

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@ -1,31 +0,0 @@
# 🔧 Lumen_cli
A Python CLI tool for building and testing PyTorch-based components, using a YAML configuration file for structured, repeatable workflows.
## Features
- **Build**
- external projects (e.g. vLLM)
## 📦 Installation
at the root of the pytorch repo
```bash
pip install -e .ci/lumen_cli
```
## Run the cli tool
The cli tool must be used at root of pytorch repo, as example to run build external vllm:
```bash
python -m cli.run build external vllm
```
this will run the build steps with default behaviour for vllm project.
to see help messages, run
```bash
python3 -m cli.run --help
```
## Add customized external build logics
To add a new external build, for instance, add a new external build logics:
1. create the build function in cli/lib folder
2. register your target and the main build function at EXTERNAL_BUILD_TARGET_DISPATCH in `cli/build_cli/register_build.py`
3. [optional] create your ci config file in .github/ci_configs/${EXTERNAL_PACKAGE_NAME}.yaml

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@ -1,37 +0,0 @@
import argparse
import logging
from cli.lib.common.cli_helper import register_targets, RichHelp, TargetSpec
from cli.lib.core.vllm.vllm_build import VllmBuildRunner
logger = logging.getLogger(__name__)
# Maps targets to their argparse configuration and runner
# it adds new target to path python -m cli.run build external {target} with buildrunner
_TARGETS: dict[str, TargetSpec] = {
"vllm": {
"runner": VllmBuildRunner,
"help": "Build vLLM using docker buildx.",
}
# add yours ...
}
def register_build_commands(subparsers: argparse._SubParsersAction) -> None:
build_parser = subparsers.add_parser(
"build",
help="Build related commands",
formatter_class=RichHelp,
)
build_subparsers = build_parser.add_subparsers(dest="build_command", required=True)
overview = "\n".join(
f" {name:12} {spec.get('help', '')}" for name, spec in _TARGETS.items()
)
external_parser = build_subparsers.add_parser(
"external",
help="Build external targets",
description="Build third-party targets.\n\nAvailable targets:\n" + overview,
formatter_class=RichHelp,
)
register_targets(external_parser, _TARGETS)

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@ -1,71 +0,0 @@
"""
Cli Argparser Utility helpers for CLI tasks.
"""
import argparse
from abc import ABC, abstractmethod
try:
from typing import Any, Callable, Required, TypedDict # Python 3.11+
except ImportError:
from typing import Any, Callable, TypedDict
from typing_extensions import Required # Fallback for Python <3.11
class BaseRunner(ABC):
def __init__(self, args: Any) -> None:
self.args = args
@abstractmethod
def run(self) -> None:
"""runs main logics, required"""
# Pretty help: keep newlines + show defaults
class RichHelp(
argparse.ArgumentDefaultsHelpFormatter, argparse.RawDescriptionHelpFormatter
):
pass
class TargetSpec(TypedDict, total=False):
"""CLI subcommand specification with bA."""
runner: Required[type[BaseRunner]]
help: str
description: str
add_arguments: Callable[[argparse.ArgumentParser], None]
def register_targets(
parser: argparse.ArgumentParser,
target_specs: dict[str, TargetSpec],
common_args: Callable[[argparse.ArgumentParser], None] = lambda _: None,
) -> None:
"""Register target subcommands."""
targets = parser.add_subparsers(
dest="target",
required=True,
metavar="{" + ",".join(target_specs.keys()) + "}",
)
for name, spec in target_specs.items():
desc = spec.get("description") or spec["runner"].__doc__ or ""
p = targets.add_parser(
name,
help=spec.get("help", ""),
description=desc.strip(),
formatter_class=RichHelp,
)
p.set_defaults(
func=lambda args, cls=spec["runner"]: cls(args).run(),
_runner_class=spec["runner"],
)
if "add_arguments" in spec and callable(spec["add_arguments"]):
spec["add_arguments"](p)
if common_args:
common_args(p)

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@ -1,42 +0,0 @@
"""
Docker Utility helpers for CLI tasks.
"""
import logging
from typing import Optional
import docker
from docker.errors import APIError, NotFound
logger = logging.getLogger(__name__)
# lazy singleton so we don't reconnect every call
_docker_client: Optional[docker.DockerClient] = None
def _get_client() -> docker.DockerClient:
global _docker_client
if _docker_client is None:
_docker_client = docker.from_env()
return _docker_client
def local_image_exists(
image_name: str, client: Optional[docker.DockerClient] = None
) -> bool:
"""Return True if a local Docker image exists."""
if not image_name:
return False
client = client or _get_client()
try:
client.images.get(image_name)
return True
except (NotFound, APIError) as e:
logger.error(
"Error when checking Docker image '%s': %s",
image_name,
e.explanation if hasattr(e, "explanation") else str(e),
)
return False

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@ -1,110 +0,0 @@
"""
Environment Variables and Dataclasses Utility helpers for CLI tasks.
"""
import os
from dataclasses import field, fields, is_dataclass, MISSING
from pathlib import Path
from textwrap import indent
from typing import Optional, Union
from cli.lib.common.utils import str2bool
def get_env(name: str, default: str = "") -> str:
"""Get environment variable with default fallback."""
return os.environ.get(name) or default
def env_path_optional(
name: str,
default: Optional[Union[str, Path]] = None,
resolve: bool = True,
) -> Optional[Path]:
"""Get environment variable as optional Path."""
val = get_env(name) or default
if not val:
return None
path = Path(val)
return path.resolve() if resolve else path
def env_path(
name: str,
default: Optional[Union[str, Path]] = None,
resolve: bool = True,
) -> Path:
"""Get environment variable as Path, raise if missing."""
path = env_path_optional(name, default, resolve)
if not path:
raise ValueError(f"Missing path value for {name}")
return path
def env_bool(
name: str,
default: bool = False,
) -> bool:
val = get_env(name)
if not val:
return default
return str2bool(val)
def env_bool_field(
name: str,
default: bool = False,
):
return field(default_factory=lambda: env_bool(name, default))
def env_path_field(
name: str,
default: Union[str, Path] = "",
*,
resolve: bool = True,
) -> Path:
return field(default_factory=lambda: env_path(name, default, resolve=resolve))
def env_str_field(
name: str,
default: str = "",
) -> str:
return field(default_factory=lambda: get_env(name, default))
def generate_dataclass_help(cls) -> str:
"""Auto-generate help text for dataclass fields."""
if not is_dataclass(cls):
raise TypeError(f"{cls} is not a dataclass")
def get_value(f):
if f.default is not MISSING:
return f.default
if f.default_factory is not MISSING:
try:
return f.default_factory()
except Exception as e:
return f"<error: {e}>"
return "<required>"
lines = [f"{f.name:<22} = {repr(get_value(f))}" for f in fields(cls)]
return indent("\n".join(lines), " ")
def with_params_help(params_cls: type, title: str = "Parameter defaults"):
"""
Class decorator that appends a help table generated from another dataclass
(e.g., VllmParameters) to the decorated class's docstring.
"""
if not is_dataclass(params_cls):
raise TypeError(f"{params_cls} must be a dataclass")
def _decorator(cls: type) -> type:
block = generate_dataclass_help(params_cls)
cls.__doc__ = (cls.__doc__ or "") + f"\n\n{title}:\n{block}"
return cls
return _decorator

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@ -1,143 +0,0 @@
from __future__ import annotations
import logging
import os
import textwrap
from pathlib import Path
from typing import TYPE_CHECKING
from cli.lib.common.utils import get_wheels
from jinja2 import Template
if TYPE_CHECKING:
from collections.abc import Iterable, Mapping
logger = logging.getLogger(__name__)
_TPL_CONTENT = Template(
textwrap.dedent("""\
## {{ title }}
```{{ lang }}
{{ content }}
```
""")
)
_TPL_LIST_ITEMS = Template(
textwrap.dedent("""\
## {{ title }}
{% for it in items %}
- {{ it.pkg }}: {{ it.relpath }}
{% else %}
_(no item found)_
{% endfor %}
""")
)
_TPL_TABLE = Template(
textwrap.dedent("""\
{%- if rows %}
| {{ cols | join(' | ') }} |
|{%- for _ in cols %} --- |{%- endfor %}
{%- for r in rows %}
| {%- for c in cols %} {{ r.get(c, "") }} |{%- endfor %}
{%- endfor %}
{%- else %}
_(no data)_
{%- endif %}
""")
)
def gh_summary_path() -> Path | None:
"""Return the Path to the GitHub step summary file, or None if not set."""
p = os.environ.get("GITHUB_STEP_SUMMARY")
return Path(p) if p else None
def write_gh_step_summary(md: str, *, append_content: bool = True) -> bool:
"""
Write Markdown content to the GitHub Step Summary file if GITHUB_STEP_SUMMARY is set.
append_content: default true, if True, append to the end of the file, else overwrite the whole file
Returns:
True if written successfully (in GitHub Actions environment),
False if skipped (e.g., running locally where the variable is not set).
"""
sp = gh_summary_path()
if not sp:
logger.info("[gh-summary] GITHUB_STEP_SUMMARY not set, skipping write.")
return False
md_clean = textwrap.dedent(md).strip() + "\n"
mode = "a" if append_content else "w"
with sp.open(mode, encoding="utf-8") as f:
f.write(md_clean)
return True
def md_heading(text: str, level: int = 2) -> str:
"""Generate a Markdown heading string with the given level (1-6)."""
return f"{'#' * max(1, min(level, 6))} {text}\n"
def md_details(summary: str, content: str) -> str:
"""Generate a collapsible <details> block with a summary and inner content."""
return f"<details>\n<summary>{summary}</summary>\n\n{content}\n\n</details>\n"
def summarize_content_from_file(
output_dir: Path,
freeze_file: str,
title: str = "Content from file",
code_lang: str = "", # e.g. "text" or "ini"
) -> bool:
f = Path(output_dir) / freeze_file
if not f.exists():
return False
content = f.read_text(encoding="utf-8").strip()
md = render_content(content, title=title, lang=code_lang)
return write_gh_step_summary(md)
def summarize_wheels(path: Path, title: str = "Wheels", max_depth: int = 3):
items = get_wheels(path, max_depth=max_depth)
if not items:
return False
md = render_list(items, title=title)
return write_gh_step_summary(md)
def md_kv_table(rows: Iterable[Mapping[str, str | int | float]]) -> str:
"""
Render a list of dicts as a Markdown table using Jinja template.
"""
rows = list(rows)
cols = list({k for r in rows for k in r.keys()})
md = _TPL_TABLE.render(cols=cols, rows=rows).strip() + "\n"
return md
def render_list(
items: Iterable[str],
*,
title: str = "List",
) -> str:
tpl = _TPL_LIST_ITEMS
md = tpl.render(title=title, items=items)
return md
def render_content(
content: str,
*,
title: str = "Content",
lang: str = "text",
) -> str:
tpl = _TPL_CONTENT
md = tpl.render(title=title, content=content, lang=lang)
return md

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@ -1,69 +0,0 @@
"""
Git Utility helpers for CLI tasks.
"""
import logging
from pathlib import Path
from cli.lib.common.path_helper import remove_dir
from git import GitCommandError, RemoteProgress, Repo
logger = logging.getLogger(__name__)
class PrintProgress(RemoteProgress):
"""Simple progress logger for git operations."""
def __init__(self, interval: int = 5):
super().__init__()
self._last_percent = -1
self._interval = interval
def update(self, op_code, cur, max=None, message=""):
msg = self._cur_line or message
if max and cur:
percent = int(cur / max * 100)
if percent != self._last_percent and percent % self._interval == 0:
self._last_percent = percent
logger.info("Progress: %d%% - %s", percent, msg)
elif msg:
logger.info(msg)
def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules=False):
"""Clone repository with pinned commit and optional submodules."""
dst = dst or target
try:
logger.info("Cloning %s to %s", target, dst)
# Clone and fetch
remove_dir(dst)
r = Repo.clone_from(repo, dst, progress=PrintProgress())
r.git.fetch("--all", "--tags")
# Checkout pinned commit
commit = get_post_build_pinned_commit(target)
logger.info("Checking out pinned %s commit %s", target, commit)
r.git.checkout(commit)
# Update submodules if requested
if update_submodules and r.submodules:
logger.info("Updating %d submodule(s)", len(r.submodules))
for sm in r.submodules:
sm.update(init=True, recursive=True, progress=PrintProgress())
logger.info("Successfully cloned %s", target)
return r, commit
except GitCommandError as e:
logger.error("Git operation failed: %s", e)
raise
def get_post_build_pinned_commit(name: str, prefix=".github/ci_commit_pins") -> str:
path = Path(prefix) / f"{name}.txt"
if not path.exists():
raise FileNotFoundError(f"Pin file not found: {path}")
return path.read_text(encoding="utf-8").strip()

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