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Author SHA1 Message Date
46f85865c0 Also install c10d headers with .h extension (#73422) (#73497)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73422

Fixes https://github.com/pytorch/pytorch/issues/73421
ghstack-source-id: 149978120

Test Plan: None

Reviewed By: cbalioglu

Differential Revision: D34475711

fbshipit-source-id: 9e4d1d57021cbff51f53762b32bbfffbf3f81c4c
2022-03-01 10:37:30 -05:00
a556333dfa scatter_reduce documentation (#73125) (#73365)
Summary:
Reland of https://github.com/pytorch/pytorch/issues/68580 (which were milestoned for 1.11) plus partial revert of https://github.com/pytorch/pytorch/pull/72543

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73125

Reviewed By: bdhirsh

Differential Revision: D34355217

Pulled By: malfet

fbshipit-source-id: 325ecdeaf53183d653b44ee5e6e8839ceefd9200
(cherry picked from commit 71db31748a8adcd8f95d5faf04aaa454e9c4c760)
(cherry picked from commit cfb6c942fed64dbb81ccc4f14b2a6650123af2e1)
2022-02-24 11:37:42 -08:00
3c14fe2151 Introduce an environment variable to change c10 log level (#71746) (#73357)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71746

This PR contains the following improvements:

- It exposes a new environment variable `TORCH_CPP_LOG_LEVEL` that enables users to set the log level of c10 logging facility (supports both GLOG and c10 loggers). Valid values are `INFO`, `WARNING`, `ERROR`, and `FATAL` or their numerical equivalents `0`, `1`, `2`, and `3`.
- It implements an `initLogging()` function and calls it as part of `torch._C` module import to ensure that the underlying logging facility is correctly initialized in Python.

With these changes a user can dynamically set the log level of c10 as in the following example:

```
$ TORCH_CPP_LOG_LEVEL=INFO python my_torch_script.py
```
ghstack-source-id: 149822703

Test Plan: Run existing tests.

Reviewed By: malfet

Differential Revision: D33756252

fbshipit-source-id: 7fd078c03a598595d992de0b474a23cec91838af
(cherry picked from commit 01d6ec6207faedf259ed1368730e9e197cb3e1c6)
2022-02-24 10:46:15 -08:00
055052bf64 Improvements to C10d log (#73358)
* Prefix c10d log messages with `[c10d]` for easier troubleshooting (#73144)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73144

This PR formats c10d log messages written by the `C10D_INFO/WARN/ERROR` macros by prefixing them with the `[c10d]` tag for easier troubleshooting. See #73121 for a specific customer request.

Note though that this is a temporary fix to unblock our users. Ideally our global logging facility should natively support component-based preambles.
ghstack-source-id: 149748943

Test Plan: N/A

Reviewed By: rohan-varma

Differential Revision: D34363975

fbshipit-source-id: 6b8096ac4b2fa344406c866a2e7665541cb60b34
(cherry picked from commit af14aef18d0239f04730545596a05536e0f9c857)

* Refactor TORCH_DISTRIBUTED_DEBUG implementation (#73166)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73166

This PR refactors, cleans up, and optimizes the implementation of `TORCH_DISTRIBUTED_DEBUG`. It also introduces three new user APIs: `get_debug_level()`, `set_debug_level()`, and `set_debug_level_from_env()` to retrieve and modify the debug level after a process has started.
ghstack-source-id: 149778566

Test Plan: Run the existing unit tests.

Reviewed By: rohan-varma

Differential Revision: D34371226

fbshipit-source-id: e18443b411adcbaf39b2ec999178c198052fcd5b
(cherry picked from commit 26d6bb1584b83a0490d8b766482656a5887fa21d)

* Introduce debug and trace log levels in c10d (#73167)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73167

This PR adds `C10D_DEBUG` and `C10D_TRACE` macros to enable fine grained logging in c10d. It also updates some log statements of `socket` to make its output less noisy.
ghstack-source-id: 149778567

Test Plan: Manual testing with different socket conditions.

Reviewed By: rohan-varma

Differential Revision: D34371426

fbshipit-source-id: a852b05ec353b18b0540ce5f803666c3da21ddd7
(cherry picked from commit 4519b06ac57f177dfc086bc10e8e1a746ba0870d)

* Make "server socket not listening" warning logs less noisy (#73149)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73149

This PR improves the handling of the "server socket not yet listening" warning log in c10d `socket`. Instead of outputting it after every failed attempt (meaning every second), it is now written every 20 seconds. Note though that if the log level is set to `INFO`, we keep writing a detailed message every second as before with additional `errno` information.

With log level set to `WARN` the output looks like:
```
[W socket.cpp:598] [c10d] No socket on (127.0.0.1, 29501) is listening yet, will retry.
[W socket.cpp:598] [c10d] No socket on (127.0.0.1, 29501) is listening yet, will retry.
...
[E socket.cpp:726] [c10d] The client socket has timed out after 300s while trying to connect to (127.0.0.1, 29501).
```

With log level set to `INFO` (a.k.a. verbose or debug level) the output looks like:
```
[I socket.cpp:515] [c10d] The client socket will attempt to connect to an IPv6 address of (127.0.0.1, 29501).
[I socket.cpp:582] [c10d] The client socket is attempting to connect to [localhost]:29501.
[I socket.cpp:643] [c10d] The server socket on [localhost]:29501 is not yet listening (errno: 111 - Connection refused), will retry.
[W socket.cpp:598] [c10d] No socket on (127.0.0.1, 29501) is listening yet, will retry.
[I socket.cpp:582] [c10d] The client socket is attempting to connect to [localhost]:29501.
[I socket.cpp:643] [c10d] The server socket on [localhost]:29501 is not yet listening (errno: 111 - Connection refused), will retry.
[I socket.cpp:582] [c10d] The client socket is attempting to connect to [localhost]:29501.
[I socket.cpp:643] [c10d] The server socket on [localhost]:29501 is not yet listening (errno: 111 - Connection refused), will retry.
[I socket.cpp:582] [c10d] The client socket is attempting to connect to [localhost]:29501.
[I socket.cpp:643] [c10d] The server socket on [localhost]:29501 is not yet listening (errno: 111 - Connection refused), will retry.
...
[W socket.cpp:598] [c10d] No socket on (127.0.0.1, 29501) is listening yet, will retry.
...
[E socket.cpp:726] [c10d] The client socket has timed out after 300s while trying to connect to (127.0.0.1, 29501).
```
ghstack-source-id: 149778565

Test Plan: Run manual tests to verify the correctness of the log message.

Reviewed By: rohan-varma

Differential Revision: D34365217

fbshipit-source-id: 296d01fa8b1ba803432903c10686d8a75145e539
(cherry picked from commit 8ae5aff0c5ffcc3e87d27d2deba6fedf8cef45cd)

* Rename `_get_debug_mode` to `get_debug_level` in distributed.py
2022-02-24 10:37:41 -08:00
68ef2a2188 Documenting cuda 11.5 windows issue (#73013) (#73312)
Summary:
Adding documentation about compiling extension with CUDA 11.5 and Windows

Example of failure: https://github.com/pytorch/pytorch/runs/4408796098?check_suite_focus=true

 Note: Don't use torch/extension.h In CUDA 11.5 under windows in your C++ code:
    Use aten instead of torch interface in all cuda 11.5 code under windows. It has been failing with errors, due to a bug in nvcc.
    Example use:
        >>> #include <ATen/ATen.h>
        >>> at::Tensor SigmoidAlphaBlendForwardCuda(....)
    Instead of:
        >>> #include <torch/extension.h>
        >>> torch::Tensor SigmoidAlphaBlendForwardCuda(...)
    Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460
    Complete Workaround code example: cb170ac024

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73013

Reviewed By: malfet, seemethere

Differential Revision: D34306134

Pulled By: atalman

fbshipit-source-id: 3c5b9d7a89c91bd1920dc63dbd356e45dc48a8bd
(cherry picked from commit 87098e7f17fca1b98c90fafe2dde1defb6633f49)
2022-02-24 10:34:39 -08:00
9647fb7d18 Use "large" macos for binary builds
Hopefully it will fix the timeout

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73089

(cherry picked from commit 99427654aa86d052420f18b03ee9aa9abcf7e6d0)
2022-02-24 09:55:15 -08:00
e4944871c8 stop sccache server after building (#72794) (#73122)
Summary:
This is to avoid the directory , where the sccache is installed, couldn't be deleted.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72794

Reviewed By: H-Huang

Differential Revision: D34222877

Pulled By: janeyx99

fbshipit-source-id: 2765d6f49b375d15598586ed83ae4c5e667e7226
(cherry picked from commit 551e21ca582c80d88a466b7bfe4eda9dee0c9a5f)

Co-authored-by: Yi Zhang <zhanyi@microsoft.com>
2022-02-21 11:08:08 -08:00
bbf2c0e3c6 Disable test history as it's fragile
Related to #73083

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73093

(cherry picked from commit 08510ba5e4ae0b53b67f0fbbc9f53b35aec9902c)
2022-02-18 15:01:40 -08:00
e6e8877bc2 avoiding adding some functions to the public python API before 1.11 release (#72543) (#72913)
cherry-picked for 1.11 release

(cherry picked from commit 6676a0c79a3b2bc1aa95e09e91eb92a6eca6b764)
2022-02-18 14:49:13 -08:00
eaa80c6fd8 [DataPipe] Adding usage examples for IterDataPipes (#73036)
Adding usage examples for IterDataPipes, with additional improvements for description of `groupby`, `IterDataPipe`, `MapDataPipe`.

Differential Revision: [D34313793](https://our.internmc.facebook.com/intern/diff/D34313793)
2022-02-18 14:38:05 -08:00
7fa092949e [NNC] TensorExprKernel state should not be modified on calls to run methods (#73029)
A typical use case for `TensorExprKernel` is to create the kernel once and call it multiple times, possibly in parallel. For the parallel calls to work, we need to ensure that the run() method calls do not change any state in `TensorExprKernel`.

Before this change, the `run()` method was modifying the sizes and strides vectors when dynamic shapes were present. This manifested as a data race when running a model with Static Runtime.
ghstack-source-id: 149398820

Differential Revision: [D34287960](https://our.internmc.facebook.com/intern/diff/D34287960/)

Co-authored-by: Raghavan Raman <raghavanr@fb.com>
2022-02-18 14:31:59 -08:00
74cd18623e Fix doc regressions for various modules and functional forms (#73014) (#73049)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73014

Fixes #72501
Fixes #72502
Fixes #72503
Fixes #72504
Fixes #72505
Fixes #72506
Fixes #72507
Fixes #72509
Fixes #72510

Test Plan: Imported from OSS

Reviewed By: albanD

Differential Revision: D34305640

Pulled By: jbschlosser

fbshipit-source-id: 62f341633fdb0316eaa346cf7247865290eb830a
(cherry picked from commit 8362d264e7b2c0c2bd5d688a87bf4f8f0bf60f0f)

Co-authored-by: Joel Schlosser <jbschlosser@fb.com>
2022-02-18 08:23:21 -08:00
dad4c2d032 Fix sequence_ops_test (#72844) (#73017) 2022-02-17 11:31:27 -08:00
565742cb63 [CircleCI] Re-enable nightly android builds (#73027)
A stop-gap measure to re-enable publishing of Android maven packages by
CI, see https://github.com/pytorch/pytorch/issues/72902

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72903

(cherry picked from commit 3493646f7636046c603921ef9a8b5c3fc635f39f)
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>

Co-authored-by: Nikita Shulga <nshulga@fb.com>
2022-02-17 14:27:55 -05:00
7acb591cf9 Add docstrings to native_channel_shuffle (#72954)
ghstack-source-id: 9288da6390b5e5702c250788a2644ec6ad32df3c
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72919
2022-02-17 08:07:08 -05:00
a5d5a6ad4f Set BLAS_LIBRARIES to ${MKL_LIBRARIES} for MKL case (#72806) (#72959)
This reverts [suggestion](https://github.com/pytorch/pytorch/pull/49647#discussion_r677737470) proposed to https://github.com/pytorch/pytorch/pull/49647

Which is somehow sufficient to workaround symptoms of https://github.com/pytorch/pytorch/issue/72653 

I.e. before this change, `BLAS_LIBRARIES` were set to `caffe2::mkl`
which is an interface library with link property set as follows:
59dd84cab6/cmake/public/mkl.cmake (L10-L12)
2022-02-17 07:54:33 -05:00
ea5089751f [JIT] API Changes for dynamic fusion (#72937)
* Move dyn fusion api to jit/api/module/

ghstack-source-id: 5597012c7381629ed478c10925b1b08eed1a32bf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72638

* Make fusion strategy api public

ghstack-source-id: b2ede61e046297f9f6132c3afd23e88b33d5b4eb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72639

Co-authored-by: Elias Ellison <eellison@devfair044.h1.fair>
2022-02-16 15:12:09 -08:00
d216c83667 [release/1.11] Create a CI workflow for XLA tests using the XLA test image (#72938)
* Create a CI workflow for XLA tests using the XLA test image (#72496)

Summary:
This PR resolves https://github.com/pytorch/pytorch/issues/72693

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72496

Reviewed By: H-Huang

Differential Revision: D34255441

Pulled By: seemethere

fbshipit-source-id: fdfd54fbd59ef7266a78c9f729c1d5b6ed25e9d6
(cherry picked from commit ba14f0ee6cfa2fe248784d2dc5d54e427aef6bf7)

* Update .github/workflows/generated-pytorch-xla-linux-bionic-py3.7-clang8.yml

Fixes lint

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2022-02-16 15:10:16 -08:00
f7e0ca546c add optional encoding argument to fileopener so users can open files in non-default encodings. (#72800)
Co-authored-by: Elijah Rippeth <elijah.rippeth@gmail.com>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
2022-02-16 13:17:11 -08:00
89ee69e173 Rename Typed/UntypedStorage to _Typed/_UntypedStorage (#72540) (#72914)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72540

Reviewed By: jbschlosser

Differential Revision: D34216823

Pulled By: bdhirsh

fbshipit-source-id: 1bc9930ab582771ebf02308e035576cd1a0dbe47
(cherry picked from commit 329238f612a9d92586bb0e5b33bcc45a0ec6936b)

Co-authored-by: Kurt Mohler <kmohler@quansight.com>
2022-02-16 12:24:21 -08:00
e0aad8e864 [quant][core][docs] Add docs for torch.quantize_per_tensor_dynamic (#72311) (#72929)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72311

att

Test Plan:
doc page in github

Imported from OSS

Reviewed By: bdhirsh

Differential Revision: D33996034

fbshipit-source-id: 797f7a55176e9219586d16142ca351c5c9cbe828
2022-02-16 12:22:12 -08:00
28ad47f553 [ONNX] Fix lstm reshape shape inference regression (#72734)
Fixes #72399
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72532

Co-authored-by: BowenBao <bowbao@microsoft.com>
2022-02-15 11:04:47 -08:00
2cc3c2ef38 [1.11][DataPipe] Docs Improvement (#72801)
* [DataPipe] Fixing MapDataPipe docstrings

[ghstack-poisoned]

* [DataPipe] Fixing IterDataPipe docstrings

[ghstack-poisoned]

* [DataPipe] Add docstrings for IterDataPipe and MapDataPipe, along with small doc changes for consistency

[ghstack-poisoned]
2022-02-15 08:24:38 -05:00
b0037f707f pad_sequence: fix regression - support tensor (#72436) (#72697)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/71365

Based on https://github.com/pytorch/pytorch/pull/72343

Thanks jbschlosser

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72436

Reviewed By: bdhirsh

Differential Revision: D34117724

Pulled By: jbschlosser

fbshipit-source-id: e5d6599d0791025e18ab36ae16c417a91554bf64
(cherry picked from commit ffe8a0e41b7906920e392a9588347215ac44f46f)

Co-authored-by: kshitij12345 <kshitijkalambarkar@gmail.com>
2022-02-14 08:44:45 -05:00
b6a3176c1c Cat shape analysis fix for -1 dim (#72678)
ghstack-source-id: b4e1e8b74889653d70b6111de71797c2e10f347d
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72616

Co-authored-by: Elias Ellison <eellison@devfair044.h1.fair>
2022-02-14 08:42:51 -05:00
5fac320809 Fix refcounting in access of saved for forward attribute (#72627) (#72656)
Summary:
fix https://github.com/pytorch/pytorch/issues/72612

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72627

Reviewed By: soulitzer

Differential Revision: D34119834

Pulled By: albanD

fbshipit-source-id: 893a1e88a738eb40072af2106527340aea1d0006
(cherry picked from commit 511a1f16c5e37f4946907bc89b246eb684b89428)

Co-authored-by: albanD <desmaison.alban@gmail.com>
2022-02-14 08:38:16 -05:00
1f406fe91d Pin builder repo for GHA builds to release/1.11 (#72739)
* Builder repo is not pinned in release branch

* Updated workflows
2022-02-11 15:29:26 -05:00
6a46b2e2aa Fix for builder repo not pinned in release branch (#72719) (#72732)
Summary:
Fixes https://github.com/pytorch/pytorch/issues/72655

Please note: Readme.md file change will be done after this change is performed and release specific change is done, so that I will reference the commit of the release specific change in the readme as an example

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72719

Reviewed By: seemethere

Differential Revision: D34177045

Pulled By: atalman

fbshipit-source-id: 2abb7af8cf1337704933c19c0d06022034ec77b4
(cherry picked from commit 31ff276d5e2cacc0e0592d624f3d486d5e8cfd1c)
2022-02-11 11:41:24 -08:00
503a0923d3 Fix tagged build detection for binary builds (#72628) (#72652)
Summary:
Should fix the following [error](https://github.com/pytorch/pytorch/runs/5058514346#step:13:88):
```
+ git --git-dir /pytorch/pytorch/.git describe --tags --match 'v[0-9]*.[0-9]*.[0-9]*' --exact
fatal: not a git repository: '/pytorch/pytorch/.git'
```
By setting `workdir` correctly for GHA linux and Windows builds

Also, abort `tagged_version` if GIT_DIR does not exist (as this script should only be executed in context of git folder.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72628

Reviewed By: atalman

Differential Revision: D34120721

Pulled By: malfet

fbshipit-source-id: 035e93e243e601f9c24659cd247f9c029210fba5
(cherry picked from commit 3a6c97b6ddb185d706494f64423a761fee8fce09)
(cherry picked from commit b6df02bbbb5b786b198938ffb5d90fa5251df3eb)
2022-02-10 07:17:32 -08:00
6641e9b75f Fix SVD error code handling for OpenBLAS 0.3.15+ and MKL 2022+ (again) (#72357) (#72513)
Summary:
This PR was opened as copy of https://github.com/pytorch/pytorch/pull/68812 by request https://github.com/pytorch/pytorch/pull/68812#issuecomment-1030215862.

-----

Fixes https://github.com/pytorch/pytorch/issues/67693.

Reference LAPACK (used in OpenBLAS) changed info error code for svd when inputs contain non-finite numbers. In PyTorch, we raise an internal assert error for negative `info` error codes because usually, it would indicate the wrong implementation. However, this is not the case with SVD now in newer versions of LAPACK. MKL (tried 2021.4.0) still gives a positive error code for this kind of input. This change aligns with the OpenBLAS and MKL behavior in our code.

MKL 2022 has uses the latest reference LAPACK behavior and returns the same `info` as OpenBLAS 0.3.15+
This PR also fixes https://github.com/pytorch/pytorch/issues/71645 that is due to the updated MKL version in CI.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72357

Reviewed By: albanD

Differential Revision: D34012245

Pulled By: ngimel

fbshipit-source-id: 2b66c173cc3458d8c766b542d0d569191cdce310
(cherry picked from commit fa29e65611ea5028bf6d2d3c151d79e6c9e4ffef)
2022-02-09 18:58:00 -05:00
4f9f0e7a13 Fix doc build for release branches (#72567) (#72635)
Summary:
Add "v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+" wildcard to tag triggers
Add similar `startsWith(github.event.ref, 'refs/tags/v1.')` for push
conditions

Fixes https://github.com/pytorch/pytorch/issues/72519

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72567

Reviewed By: atalman

Differential Revision: D34116048

Pulled By: malfet

fbshipit-source-id: 7ef6ae3972ff7eba213ae9c4eb4afea5a7e11827
(cherry picked from commit 3785553532ccf636e389c97713f2c5bbfec836ba)
2022-02-09 15:55:24 -08:00
0ea924fc98 Disable complex32 (#72604) 2022-02-09 15:51:37 -08:00
5a78725c29 Add missing entry for sampled_addmm in sparse.rst (#72312) (#72514)
Summary:
Let's make the documentation for `torch.sparse.sampled_addmm` searchable in the PyTorch documentation.
This PR shall be cherry-picked for the next 1.11 release.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72312

Reviewed By: davidberard98

Differential Revision: D34045230

Pulled By: cpuhrsch

fbshipit-source-id: c1b1dc907443284857f48c8ce1efab22c6701bbe
(cherry picked from commit 225929ecf20eb369f862b091818f5af16ee78f88)
2022-02-08 10:25:15 -08:00
f72151b900 [ONNX] Resolve attribute error in CI (#72350) (#72439)
Summary:
Tests under `test/onnx/test_models_onnxruntime.py` complains `AttributeError: 'TestModels' object has no attribute 'onnx_shape_inference'`.

This failure in CI appears suddenly without any code changes to related files. It is likely due to different test case run order. The test code was badly written such that test class `TestModels_new_jit_API`, if called first, will assign `TestModels.onnx_shape_inference = True`, circumventing this problem. On the other hand, if `TestModels` is called first, `AttributeError` will be raised.

Fixes https://github.com/pytorch/pytorch/issues/72337

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72350

Reviewed By: jbschlosser, seemethere, janeyx99

Differential Revision: D34010794

Pulled By: malfet

fbshipit-source-id: 816f7bee89ea0251bb5df8f482b68f8dc4823997
(cherry picked from commit b39b23bec5dfd3f2fd24a0d781757c20ff94b1db)

Co-authored-by: BowenBao <bowbao@microsoft.com>
2022-02-07 12:35:32 -08:00
8380187819 Pin librosa (#72440)
Should mitigate https://github.com/pytorch/pytorch/issues/72432
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72433

Co-authored-by: Jane Xu <janeyx@fb.com>
2022-02-07 10:06:57 -08:00
7cc129e60c Remove forcing CUDNN_STATIC when CAFFE2_STATIC_LINK_CUDA (#72290) (#72356)
Summary:
Remove forcing CUDNN_STATIC when CAFFE2_STATIC_LINK_CUDA is set
Since we are transitioning to using dynamic loading for multiple pytorch dependecies  and CUDNN is the first step in this transition,  hence we want to remove forcing CUDNN to statically load, and instead load it dynamically.

Tested using following workflow:
https://github.com/pytorch/pytorch/actions/runs/1790666862

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72290

Reviewed By: albanD

Differential Revision: D34003793

Pulled By: atalman

fbshipit-source-id: 41bda7ac019a612ee53ceb18d1e372b1bb3cb68e
(cherry picked from commit 4a01940e681f996017d924b08946188ef352ef41)

Co-authored-by: Andrey Talman <atalman@fb.com>
2022-02-04 14:56:08 -05:00
ff6c348762 Revert "Bump torch version to 1.12 (#72221)"
This reverts commit 0ca0e02685a9d033ac4f04e2fa5c8ba6dbc5ae50.
2022-02-04 11:38:35 -08:00
03a283b2b1 Fix persistent worker exits before pin_memory thread (#72269)
* release 1.11 Install torch from test channel, Pin builder and xla repo (#72217)

* Fix persistent worker exits before pin_memory thread

ghstack-source-id: 2d15b14df2e2d84b309081dffbedc4836495ae95
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71579

Co-authored-by: Andrey Talman <atalman@fb.com>
2022-02-04 11:31:16 -08:00
614e765575 [1.11] Make svd / svdvals fully functorch compatible (#72181) (#72274)
* release 1.11 Install torch from test channel, Pin builder and xla repo (#72217)

* Make svd / svdvals fully functorch compatible (#72181)

Summary:
This should (hopefully) make all the CI from `functorch` go green (including jvp's!) after changing `VARIADIC_BDIMS_BOXED(_svd_helper);` with `VARIADIC_BDIMS_BOXED(_linalg_svd);` and removing all the skip and xfails associated to `linalg.svdvals`.

Locally, there's just one test that started failing because of this, and that is `test_vmapjvpall_norm_nuc_cpu_float32`. I have no idea what's going on here, but it's a jvp product, so not a regression, and it might very well be caused by the jvp of other operation within `norm_nuc` as this is a composite operation.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72181

Reviewed By: ngimel

Differential Revision: D33952744

Pulled By: zou3519

fbshipit-source-id: 2a2510d97eed4a0bfc25615264ddd36e38856efe
(cherry picked from commit 5805fa107c3a91c58f8ecc9778cfc87aa7f64233)

Co-authored-by: Andrey Talman <atalman@fb.com>
Co-authored-by: lezcano <lezcano-93@hotmail.com>
2022-02-04 14:29:02 -05:00
7b0e140ecc [1.11] Remove torch.vmap (#65496) (#72275)
* release 1.11 Install torch from test channel, Pin builder and xla repo (#72217)

* [1.11] Remove torch.vmap (#65496)

torch.vmap is a prototype feature and should not be in the stable
binary. This PR:
- Removes the torch.vmap API
- Removes the documentation entry for torch.vmap
- Changes the vmap tests to use an internal API instead of torch.vmap.

Test Plan:
- Tested locally (test_torch, test_autograd, test_type_hints, test_vmap),
but also wait for CI.

Co-authored-by: Andrey Talman <atalman@fb.com>
2022-02-04 11:23:44 -08:00
3fab33e1c9 release 1.11 Install torch from test channel, Pin builder and xla repo (#72217) 2022-02-04 11:15:10 -08:00
7931 changed files with 448599 additions and 1207678 deletions

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# PyTorch CI Builds Pipeline on Azure DevOps
#
# This pipeline:
# 1) builds PyTorch on select configurations
# 2) runs only TestTorch unit tests.
stages:
- stage: 'Build'
displayName: 'Build PyTorch'
jobs:
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_CPU_docker
pool: 'PyTorch-Linux-CPU'
container_endpoint: pytorchms.azurecr.io
build_stage: True
is_ci_build: True
os: ubuntu
cuda: cpu
customMatrixes:
Py_38:
configuration: ubuntu_1804_py_38_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: 'PyTorch-Linux-GPU'
container_endpoint: pytorchms.azurecr.io
build_stage: True
is_ci_build: True
os: ubuntu
cuda: gpu
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
CUDA_VERSION: 112
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_CPU
pool: 'PyTorch-Win-CPU'
build_stage: True
is_ci_build: True
os: windows
cuda: cpu
customMatrixes:
Py_37:
configuration: windows_2019_py_37_cpu
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_GPU
pool: 'PyTorch-Win-GPU'
build_stage: True
is_ci_build: True
os: windows
cuda: gpu
customMatrixes:
Py_38_CUDA_102_cuDNN_765:
configuration: windows_2019_py_38_cuda_102_cudnn_765
CUDA_VERSION: 102

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# PyTorch Daily Builds Pipeline on Azure DevOps
#
# This pipeline:
# 1) builds PyTorch on all available configurations
# 2) runs all PyTorch unit tests
stages:
- stage: 'BuildTest'
displayName: 'Build and Test PyTorch'
jobs:
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_CPU_docker
pool: 'PyTorch-Linux-CPU'
container_endpoint: pytorchms.azurecr.io
build_stage: True
is_daily_build: True
os: ubuntu
cuda: cpu
customMatrixes:
Py_38:
configuration: ubuntu_1804_py_38_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
Py_37:
configuration: ubuntu_1804_py_37_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: 'PyTorch-Linux-GPU'
container_endpoint: pytorchms.azurecr.io
build_stage: True
is_daily_build: True
os: ubuntu
cuda: gpu
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_810:
configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_765:
configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
CUDA_VERSION: 101
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_CPU
pool: 'PyTorch-Win-CPU'
build_stage: True
is_daily_build: True
os: windows
cuda: cpu
customMatrixes:
Py_38:
configuration: windows_2019_py_38_cpu
Py_37:
configuration: windows_2019_py_37_cpu
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_GPU
pool: 'PyTorch-Win-GPU'
build_stage: True
is_daily_build: True
os: windows
cuda: gpu
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: windows_2019_py_39_cuda_112_cudnn_810
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_765:
configuration: windows_2019_py_38_cuda_102_cudnn_765
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_764:
configuration: windows_2019_py_37_cuda_101_cudnn_764
CUDA_VERSION: 101

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# PyTorch build steps template with Unix images Azure DevOps Instances
#
# This build depends on 3 parameters set as environment variables in the pipeline:
# - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
# - AZURE_DEVOPS_ARTIFACTS_ORGANIZATION: Azure Artifacts Organization name to publish artifacts
# - AZURE_DEVOPS_ARTIFACTS_PROJECT: Azure Artifacts Project name to publish artifacts
parameters:
name: ''
pool: ''
container_endpoint: ''
os: ''
cuda: ''
is_ci_build: False
is_official_build: False
is_daily_build: False
build_stage: False
verify_stage: False
publish_stage: False
customMatrixes: ''
jobs:
- job: ${{parameters.name}}
timeoutInMinutes: 300
strategy:
matrix:
${{ insert }}: ${{parameters.customMatrixes}}
pool:
name: ${{ parameters.pool}}
variables:
DECODE_PERCENTS: false
container:
image: $[variables['container_image']]
endpoint: ${{parameters.container_endpoint}}
steps:
# Build stage
- ${{ if eq(parameters.build_stage, 'True') }}:
# Set up environment variables for specific pipeline build
- template: set-environment-variables.yml
parameters:
os: ${{ parameters.os}}
cuda: ${{ parameters.cuda}}
is_official_build: ${{ parameters.is_official_build}}
# Sync and update PyTorch submodules
- bash: git submodule update --init --recursive --jobs 0
displayName: Update PyTorch submodules
# Build PyTorch and run unit tests - no packaging
- ${{ if or(eq(parameters.is_ci_build, 'True'), eq(parameters.is_daily_build, 'True')) }}:
# Build PyTorch from source in develop mode
- bash: python setup.py develop
displayName: Build PyTorch from source
- ${{ if eq(parameters.is_ci_build, 'True') }}:
# Run TestTorch unit tests to demonstrate successful PyTorch build
- bash: python test/test_torch.py TestTorch
displayName: Run TestTorch unit tests
- ${{ if eq(parameters.is_daily_build, 'True') }}:
# Run all unit tests to demonstrate successful PyTorch build
- bash: python test/run_test.py --continue-through-error --exclude-jit-executor --verbose
displayName: Run all unit tests
# Run ComponentGovernance
- task: ComponentGovernanceComponentDetection@0
inputs:
scanType: 'Register'
verbosity: 'Verbose'
alertWarningLevel: 'High'
# Build PyTorch and produce artifacts for verification stage
- ${{ if eq(parameters.is_official_build, 'True') }}:
# Build PyTorch from source in install mode and exclude test binaries
- bash: python setup.py install
displayName: Build PyTorch from source without test binaries
# Package PyTorch Wheel
- bash: python setup.py bdist_wheel
displayName: Package PyTorch Wheel
# Publish PyTorch Wheel
- task: PublishPipelineArtifact@1
inputs:
targetPath: $(Build.SourcesDirectory)/dist/
artifactName: Build_$(Build.BuildNumber)_$(configuration)
displayName: Publish PyTorch Wheel to Pipeline Artifacts
# Verification stage
- ${{ if eq(parameters.verify_stage, 'True') }}:
# Download PyTorch Wheel
- task: DownloadPipelineArtifact@2
inputs:
artifact: Build_$(Build.BuildNumber)_$(configuration)
path: $(Build.SourcesDirectory)/verify
displayName: Download PyTorch Wheel
# Install PyTorch Wheel on Windows
- bash: python -m pip install $(Build.SourcesDirectory)/verify/torch*linux*.whl
displayName: Install PyTorch Wheel
# Ensure PyTorch installed correctly from produced wheel
- bash: |
cd $(Build.SourcesDirectory)/verify
python -c "import torch; print('Installed Torch version: ' + torch.__version__)"
displayName: Check PyTorch correctly installed from wheel
# Publishing stage
- ${{ if eq(parameters.publish_stage, 'True') }}:
# Download PyTorch Wheel
- task: DownloadPipelineArtifact@2
inputs:
artifact: Build_$(Build.BuildNumber)_$(configuration)
path: $(Build.SourcesDirectory)/publish
displayName: Download PyTorch Wheel
# Publish wheel to Azure Artifacts
# The flag continueOnError=true is needed as the artifact to be published
# may already exist, because the artifact is differentiated based on the
# last commit date.
- bash: |
export TORCH_VERSION=$(head -c 5 ./version.txt)
export LAST_COMMIT=$(git rev-parse --short HEAD)
export LAST_COMMIT_DATE=$(git log -1 --pretty=%ad --date=format:%Y%m%d)
cd $(Build.SourcesDirectory)/publish
export TORCH_WHEEL=$(echo torch*linux*whl)
az extension add -n azure-devops
echo $ADOTOKEN | az devops login
az artifacts universal publish --organization $AZURE_DEVOPS_ARTIFACTS_ORGANIZATION --project $AZURE_DEVOPS_ARTIFACTS_PROJECT --scope project --feed "PyTorch" --name $TORCH_WHEEL --description "PyTorch Official Build Artifact" --version $TORCH_VERSION-$LAST_COMMIT_DATE-$LAST_COMMIT --path .
env:
ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
continueOnError: true
displayName: Upload PyTorch Official Build package to Azure Artifacts

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# PyTorch build steps template with Windows images Azure DevOps Instances
#
# This build depends on 3 parameters set as environment variables in the pipeline:
# - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
# - AZURE_DEVOPS_ARTIFACTS_ORGANIZATION: Azure Artifacts Organization name to publish artifacts
# - AZURE_DEVOPS_ARTIFACTS_PROJECT: Azure Artifacts Project name to publish artifacts
parameters:
name: ''
pool: ''
os: ''
cuda: ''
is_ci_build: False
is_official_build: False
is_daily_build: False
build_stage: False
verify_stage: False
publish_stage: False
customMatrixes: ''
jobs:
- job: ${{parameters.name}}
timeoutInMinutes: 300
strategy:
matrix:
${{ insert }}: ${{parameters.customMatrixes}}
pool:
name: ${{ parameters.pool}}
variables:
CMAKE_GENERATOR: Ninja
PACKAGE_PDBS: 0
steps:
# Prepare for PyTorch build on Windows
- template: prepare-build-template.yml
parameters:
configuration: $(configuration)
build_stage: ${{ parameters.build_stage}}
# Build Stage
- ${{ if eq(parameters.build_stage, 'True') }}:
# Set up environment variables for specific pipeline build
- template: set-environment-variables.yml
parameters:
os: ${{ parameters.os}}
cuda: ${{ parameters.cuda}}
is_official_build: ${{ parameters.is_official_build}}
# Sync and update PyTorch submodules
- script: git submodule update --init --recursive --jobs 0
displayName: Update PyTorch submodules
# Build PyTorch and run unit tests - no packaging
- ${{ if or(eq(parameters.is_ci_build, 'True'), eq(parameters.is_daily_build, 'True')) }}:
# Build PyTorch from source in develop mode with Ninja
- script: call activate $(configuration) && python setup.py develop
displayName: Build PyTorch from source
- ${{ if eq(parameters.is_ci_build, 'True') }}:
# Run TestTorch unit tests to demonstrate successful PyTorch build
- script: call activate $(configuration) && python test\test_torch.py TestTorch
displayName: Run TestTorch unit tests
- ${{ if eq(parameters.is_daily_build, 'True') }}:
# Run all unit tests to demonstrate successful PyTorch build
- script: call activate $(configuration) && python test/run_test.py --continue-through-error --exclude-jit-executor --verbose
displayName: Run all unit tests
# Run ComponentGovernance
- task: ComponentGovernanceComponentDetection@0
inputs:
scanType: 'Register'
verbosity: 'Verbose'
alertWarningLevel: 'High'
# Build PyTorch and produce artifacts for verification stage
- ${{ if eq(parameters.is_official_build, 'True') }}:
# Build PyTorch from source in install mode with Ninja and exclude test binaries
- script: call activate $(configuration) && python setup.py install
displayName: Build PyTorch from source without test binaries
# Package PyTorch Wheel
- script: call activate $(configuration) && python setup.py bdist_wheel
displayName: Package PyTorch Wheel
# Publish PyTorch Wheel
- task: PublishPipelineArtifact@1
inputs:
targetPath: $(Build.SourcesDirectory)\dist\
artifactName: Build_$(Build.BuildNumber)_$(configuration)
displayName: Publish PyTorch Wheel to Pipeline Artifacts
# Verification Stage
- ${{ if eq(parameters.verify_stage, 'True') }}:
# Download PyTorch Wheel
- task: DownloadPipelineArtifact@2
inputs:
artifact: Build_$(Build.BuildNumber)_$(configuration)
path: $(Build.SourcesDirectory)\verify
displayName: Download PyTorch Wheel
# Install PyTorch Wheel on Windows
- script: |
call activate $(configuration)
cd $(Build.SourcesDirectory)\verify
dir torch*win*.whl /b > whl.txt
set /p whl= < whl.txt
python -m pip install %whl%
displayName: Install PyTorch Wheel
# Ensure PyTorch installed correctly from produced wheel
- script: |
call activate $(configuration)
cd $(Build.SourcesDirectory)\verify
python -c "import torch; print('Installed Torch version: ' + torch.__version__)"
displayName: Check PyTorch correctly installed from wheel
# Publishing stage
- ${{ if eq(parameters.publish_stage, 'True') }}:
# Download PyTorch Wheel
- task: DownloadPipelineArtifact@2
inputs:
artifact: Build_$(Build.BuildNumber)_$(configuration)
path: $(Build.SourcesDirectory)\publish
displayName: Download PyTorch Wheel
# Set up Azure Artifacts for Windows
# The pip install --upgrade command is a bug fix for Azure CLI on Windows
# More info: https://github.com/Azure/azure-cli/issues/16858
- script: |
pip install --upgrade pip --target \opt\az\lib\python3.6\site-packages\
az extension add -n azure-devops
displayName: Set up Azure Artifacts download on Windows
# Publish wheel to Azure Artifacts
# The flag continueOnError=true is needed as the artifact to be published
# may already exist, because the artifact is differentiated based on the
# last commit date.
- script: |
set /p TORCH_VERSION= < version.txt
cd $(Build.SourcesDirectory)\publish
git rev-parse --short HEAD > last_commit.txt && set /p LAST_COMMIT= < last_commit.txt
git log -1 --pretty=%ad --date=format:%Y%m%d > last_commit_date.txt && set /p LAST_COMMIT_DATE= < last_commit_date.txt
dir torch*win*.whl /b > whl.txt && set /p TORCH_WHEEL= < whl.txt
echo %ADOTOKEN% | az devops login
az artifacts universal publish --organization %AZURE_DEVOPS_ARTIFACTS_ORGANIZATION% --project %AZURE_DEVOPS_ARTIFACTS_PROJECT% --scope project --feed "PyTorch" --name %TORCH_WHEEL% --description "PyTorch Official Build Artifact" --version %TORCH_VERSION:~0,5%-%LAST_COMMIT_DATE%-%LAST_COMMIT% --path .
env:
ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
continueOnError: true
displayName: Upload PyTorch nigthly package to Azure Artifacts

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dependencies:
- python=PYTHON_VERSION
- numpy
- ninja
- pyyaml
- mkl
- mkl-include
- setuptools
- cmake
- cffi
- typing_extensions
- future
- six
- requests
- dataclasses
- pip:
- -r ../../requirements.txt

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parameters:
name: ''
pool: ''
customMatrixes: ''
jobs:
- job: ${{parameters.name}}
timeoutInMinutes: 600
strategy:
matrix:
${{ insert }}: ${{parameters.customMatrixes}}
pool:
name: ${{ parameters.pool}}
steps:
# Clone PyTorch Tests repository
- bash: |
B64_PAT=$(echo -n ":$_ADOTOKEN" | base64)
git -c http.extraHeader="Authorization: Basic ${B64_PAT}" clone $(AZURE_DEVOPS_PYTORCH_TESTS_REPO_URL)
cd pytorch_tests
git checkout $(PYTORCH_TESTS_CHECKOUT_BRANCH)
env:
_ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
displayName: Clone PyTorch Tests repo
- bash: |
bash $(Build.SourcesDirectory)/pytorch_tests/webapp/notify_webapp.sh
displayName: Notify Webapp

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# Build prepare steps for PyTorch on Azure DevOps to build from source.
# These steps share between normal build process and semmle security scan tasks
parameters:
build_stage: False
configuration: ''
steps:
# End Python tasks that may be lingering over from previous runs
# Note: If python.exe isn't currently running, exit code becomes 128,
# which fails the run. Here exit code is set to 0 to avoid failed run.
- script: |
taskkill /f /im python.exe
IF %ERRORLEVEL% EQU 128 exit 0
displayName: End previous Python processes
# Clean up env directory in conda for fresh builds and set up conda environment YAML
- powershell: |
Remove-Item 'C:\Miniconda\envs' -Recurse -ErrorAction Ignore
$env:PYTHON_VERSION = $env:SYSTEM_JOBNAME.Substring(3,1) + '.' + $env:SYSTEM_JOBNAME.Substring(4,1)
(Get-Content .azure_pipelines\job_templates\common-packages.yml) -replace 'PYTHON_VERSION', $env:PYTHON_VERSION | Out-File -encoding ASCII .azure_pipelines\job_templates\common-packages.yml
displayName: Clean up previous environments and Set up conda environment YAML
# Make conda environment and install required packages
- script: |
call conda clean --all -y
call conda env create -n $(configuration) --file .azure_pipelines\job_templates\common-packages.yml
call activate $(configuration)
call conda install -c conda-forge libuv=1.39
displayName: Set up conda environment for building from source
- ${{ if eq(parameters.build_stage, 'True') }}:
# Install MKL
- script: |
rmdir /s /q mkl
del mkl_2020.2.254.7z
curl https://s3.amazonaws.com/ossci-windows/mkl_2020.2.254.7z -k -O
7z x -aoa mkl_2020.2.254.7z -omkl
displayName: Install MKL
# Install sccache and randomtemp
# Related PyTorch GitHub issue: https://github.com/pytorch/pytorch/issues/25393
# Related fix: https://github.com/pytorch/builder/pull/448/
- script: |
mkdir .\tmp_bin
curl -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output .\tmp_bin\sccache.exe
curl -k https://s3.amazonaws.com/ossci-windows/sccache-cl.exe --output .\tmp_bin\sccache-cl.exe
copy .\tmp_bin\sccache.exe .\tmp_bin\nvcc.exe
curl -kL https://github.com/peterjc123/randomtemp-rust/releases/download/v0.4/randomtemp.exe --output .\tmp_bin\randomtemp.exe
displayName: Install sccache and randomtemp
condition: not(eq(variables.CUDA_VERSION, ''))
# CUDA 11.2's CUB directory conflicts with CUDA 10.2 and 10.1
# builds, where CUDA 11.2's CUB is injected into non-CUDA
# 11.2 builds.
- powershell: Remove-Item "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include\cub" -Recurse -ErrorAction Ignore
displayName: Remove conflicting CUB from CUDA installation
condition: not(eq(variables.CUDA_VERSION, ''))
- powershell: Copy-Item -Path "F:\cuda_11_2\cub\" -Destination "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include" -Recurse
displayName: Copy CUDA CUB for CUDA 11.2 build
condition: eq(variables.CUDA_VERSION, '112')

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# PyTorch build steps template with Unix images Azure DevOps Instances
#
# This build depends on 5 parameters set as an environment variables in the pipeline:
# - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
# - AZURE_STORAGE_KEY: Secret var for authenticating to Azure Storage
# - _TS_CLONE_P, _TS_P, _TS_SM_P: Secret vars for specific unit tests
parameters:
name: ''
pool: ''
container_endpoint: ''
customMatrixes: ''
jobs:
- job: ${{parameters.name}}
timeoutInMinutes: 600
strategy:
matrix:
${{ insert }}: ${{parameters.customMatrixes}}
pool:
name: ${{ parameters.pool}}
variables:
DECODE_PERCENTS: false
steps:
# Don't checkout repo contents to save time and CPU compute. Environment variables
# related to checkout branch such as $(BUILD_SOURCEBRANCH) are still available.
- checkout: none
# Delete pytorch_tests repo from previous builds if exists
- bash: rm -rf pytorch_tests/
displayName: Delete pytorch_tests repo from previous builds if exists
# Clone PyTorch Tests repository
- bash: |
B64_PAT=$(echo -n ":$_ADOTOKEN" | base64)
git -c http.extraHeader="Authorization: Basic ${B64_PAT}" clone $(AZURE_DEVOPS_PYTORCH_TESTS_REPO_URL)
cd pytorch_tests
git checkout $(PYTORCH_TESTS_CHECKOUT_BRANCH)
env:
_ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
displayName: Clone PyTorch Tests repo
# Run PyTorch Unit Tests
- bash: bash $(Build.SourcesDirectory)/pytorch_tests/scripts/linux/run.sh
env:
_AZURE_STORAGE_KEY: $(AZURE_STORAGE_KEY)
_TS_CLONE_P: $(TS_CLONE_PASSWORD)
_TS_P: $(TS_PAT)
_TS_SM_P: $(TS_SM_PAT)
_AZUREML_CLONE_PASSWORD: $(AZUREML_CLONE_PASSWORD)
_SPPASSWORD: $(SPPASSWORD)
displayName: Run PyTorch Unit Tests
# Tests results are available outside the docker container since
# the current directory is mounted as a volume of the container.
- task: PublishTestResults@2
condition: always()
inputs:
testResultsFiles: '**/test-*.xml'
testRunTitle: 'Publish test results for Python'

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# PyTorch build steps template with Windows images Azure DevOps Instances
#
# This build depends on 5 parameters set as an environment variables in the pipeline:
# - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
# - AZURE_STORAGE_KEY: Secret var for authenticating to Azure Storage
# - _TS_CLONE_P, _TS_P, _TS_SM_P: Secret vars for specific unit tests
parameters:
name: ''
pool: ''
customMatrixes: ''
jobs:
- job: ${{parameters.name}}
timeoutInMinutes: 600
strategy:
matrix:
${{ insert }}: ${{parameters.customMatrixes}}
pool:
name: ${{ parameters.pool}}
steps:
# Don't checkout repo contents to save time and CPU compute. Environment variables
# related to checkout branch such as $(BUILD_SOURCEBRANCH) are still available.
- checkout: none
# Delete pytorch_tests repo from previous builds if exists
- script: if exist "pytorch_tests/" rmdir "pytorch_tests/" /q /s
displayName: Delete pytorch_tests repo from previous builds if exists
# Clone PyTorch Tests repository
- powershell: |
$env:B64Pat = [Convert]::ToBase64String([System.Text.Encoding]::UTF8.GetBytes(":$env:_ADOTOKEN"))
git -c http.extraHeader="Authorization: Basic $env:B64Pat" clone $env:AZURE_DEVOPS_pytorch_tests_REPO_URL
cd pytorch_tests
git checkout $(PYTORCH_TESTS_CHECKOUT_BRANCH)
env:
_ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
displayName: Clone PyTorch Tests repo
# Run PyTorch Unit Tests
- script: call $(Build.SourcesDirectory)\pytorch_tests\scripts\windows\run.bat
env:
_ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
_AZURE_STORAGE_KEY: $(AZURE_STORAGE_KEY)
_TS_CLONE_P: $(TS_CLONE_PASSWORD)
_TS_P: $(TS_PAT)
_TS_SM_P: $(TS_SM_PAT)
displayName: Run PyTorch Unit Tests
# Tests results are available outside the docker container since
# the current directory is mounted as a volume of the container.
- task: PublishTestResults@2
condition: always()
inputs:
testResultsFiles: '**\test-*.xml'
testRunTitle: 'Publish test results for Python'

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# Set environment variables for specific configurations
parameters:
is_official_build: False
os: ''
cuda: ''
steps:
# Environment configuration steps for Ubuntu builds
- ${{ if contains(parameters.os, 'ubuntu') }}:
# Set configuration specific build flags
- ${{ if eq(parameters.is_official_build, True) }}:
- bash: |
echo "##vso[task.setvariable variable=INSTALL_TEST;]0"
echo "##vso[task.setvariable variable=PYTORCH_BUILD_NUMBER;]1"
export PYTORCH_VERSION=$(head -c 5 ./version.txt)
echo "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$PYTORCH_VERSION.dev"
displayName: Set configuration-specific build flags
# Set PyTorch CPU/GPU build flags.
- ${{ if contains(parameters.cuda, 'cpu') }}:
- bash: |
echo "##vso[task.setvariable variable=USE_CUDA;]0"
echo "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cpu"
displayName: Set CUDA-specific build flag for CPU builds
- ${{ if contains(parameters.cuda, 'gpu') }}:
- bash: |
echo "##vso[task.setvariable variable=USE_CUDA;]1"
echo "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cu$(CUDA_VERSION)"
displayName: Set CUDA-specific build flag for GPU builds
# Set MKL environment variables
- bash: |
echo "##vso[task.setvariable variable=CMAKE_LIBRARY_PATH;]/opt/intel/lib:$CMAKE_LIBRARY_PATH"
echo "##vso[task.setvariable variable=CMAKE_INCLUDE_PATH;]/opt/intel/include:$CMAKE_INCLUDE_PATH"
displayName: Set MKL paths
# View current environment variables
- bash:
printenv
displayName: Show environment variables
# Environment configuration steps for Windows builds
- ${{ if contains(parameters.os, 'windows') }}:
# Set Conda Lib Path
- powershell: Write-Host "##vso[task.setvariable variable=CONDA_LIB_PATH;]C:\Miniconda\envs\$(configuration)\Library\bin"
displayName: Set Conda Lib Path
# Set configuration specific build flags
- ${{ if eq(parameters.is_official_build, True) }}:
- powershell: |
Write-Host "##vso[task.setvariable variable=INSTALL_TEST;]0"
Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_NUMBER;]1"
Set-Variable -Name PYTORCH_VERSION -Value (Get-Content .\version.txt).Substring(0,5)
Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$PYTORCH_VERSION.dev"
displayName: Set configuration-specific build flags
# Set PyTorch CPU/GPU build flags..
- ${{ if contains(parameters.cuda, 'cpu') }}:
- powershell: |
Write-Host "##vso[task.setvariable variable=USE_CUDA;]0"
Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cpu"
displayName: Set CUDA-specific build flag for CPU build
- ${{ if contains(parameters.cuda, 'gpu') }}:
- powershell: |
Write-Host "##vso[task.setvariable variable=USE_CUDA;]1"
Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cu$(CUDA_VERSION)"
displayName: Set CUDA-specific build flag for GPU build
# Set CUDA 11.2, 10.2 or 10.1 specific build flags
- ${{ if eq(parameters.cuda, 'gpu') }}:
- powershell: |
Write-Host "##vso[task.setvariable variable=TORCH_CUDA_ARCH_LIST;]3.7+PTX;5.0;6.0;6.1;7.0;7.5;8.0;8.6"
Write-Host "##vso[task.setvariable variable=CUDA_PATH;]C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\"
displayName: Set CUDA 11.2 specific build flags
condition: eq(variables.CUDA_VERSION, '112')
- powershell: |
Write-Host "##vso[task.setvariable variable=TORCH_CUDA_ARCH_LIST;]3.7+PTX;5.0;6.0;6.1;7.0;7.5"
Write-Host "##vso[task.setvariable variable=CUDA_PATH;]C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\"
displayName: Set CUDA 10.2 specific build flags
condition: eq(variables.CUDA_VERSION, '102')
- powershell: |
Write-Host "##vso[task.setvariable variable=TORCH_CUDA_ARCH_LIST;]3.7+PTX;5.0;6.0;6.1;7.0;7.5"
Write-Host "##vso[task.setvariable variable=CUDA_PATH;]C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\"
displayName: Set CUDA 10.1 specific build flags
condition: eq(variables.CUDA_VERSION, '101')
- powershell: |
Write-Host "##vso[task.setvariable variable=CUDA_BIN_PATH;]$env:CUDA_PATH\bin\"
Write-Host "##vso[task.setvariable variable=CUDNN_ROOT;]$env:CUDA_PATH"
Write-Host "##vso[task.setvariable variable=CUDNN_INCLUDE_DIR;]$env:CUDA_PATH\include\"
Write-Host "##vso[task.setvariable variable=CUDNN_LIBRARY;]$env:CUDA_PATH\lib\x64\"
Write-Host "##vso[task.prependpath]$env:CUDA_PATH\bin"
Write-Host "##vso[task.setvariable variable=TORCH_NVCC_FLAGS;]-Xfatbin -compress-all --no-host-device-move-forward"
Write-Host "##vso[task.setvariable variable=THRUST_IGNORE_CUB_VERSION_CHECK;]1"
Write-Host "##vso[task.setvariable variable=NVTOOLSEXT_PATH;]C:\Program Files\NVIDIA Corporation\NvToolsExt\"
displayName: Set CUDA environment variables
- powershell: |
copy "$(CUDA_BIN_PATH)\cusparse*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\cublas*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\cudart*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\curand*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\cufft*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\cusolver*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\cudnn*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CUDA_BIN_PATH)\nvrtc*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
copy "C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64\nvToolsExt64_1.dll*" $(Build.SourcesDirectory)\torch\lib
copy "$(CONDA_LIB_PATH)\libiomp*5md.dll" $(Build.SourcesDirectory)\torch\lib
copy "$(CONDA_LIB_PATH)\uv.dll" $(Build.SourcesDirectory)\torch\lib
displayName: Copy CUDA/cuDNN/libomp/libuv dlls to torch\lib
# Set MKL, sccache and randomtemp environment variables
- powershell: |
Write-Host "##vso[task.setvariable variable=CMAKE_INCLUDE_PATH;]$(Build.SourcesDirectory)\mkl\include"
Write-Host "##vso[task.setvariable variable=CMAKE_LIBRARY_PATH;]$(Build.SourcesDirectory)\mkl\lib;$env:CMAKE_LIBRARY_PATH"
Write-Host "##vso[task.setvariable variable=ADDITIONAL_PATH;]$(Build.SourcesDirectory)\tmp_bin"
Write-Host "##vso[task.setvariable variable=SCCACHE_IDLE_TIMEOUT;]1500"
Write-Host "##vso[task.setvariable variable=CMAKE_CUDA_COMPILER_LAUNCHER;]$(Build.SourcesDirectory)/tmp_bin/randomtemp.exe;$(Build.SourcesDirectory)/tmp_bin/sccache.exe"
displayName: Set MKL, sccache and randomtemp environment variables
# View current environment variables
- script:
set
displayName: Show environment variables

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# Main logic to initiate wait for PR artifact to be ready
steps:
- task: InvokeRESTAPI@1
displayName: 'Wait for job success and wheel ready'
timeoutInMinutes: 60
inputs:
connectionType: 'connectedServiceName'
serviceConnection: circleciconn
method: 'POST'
headers: '{"Content-Type":"application/json", "BranchName":"$(_TARGET_BRANCH_TO_CHECK)", "JobName":"$(TARGET_CIRCLECI_BUILD_PR)", "PRNumber":"$(_TARGET_PR_NUMBER)", "TargetCommit":"$(_TARGET_COMMIT)", "PlanUrl":"$(System.CollectionUri)", "ProjectId":"$(System.TeamProjectId)", "HubName":"$(System.HostType)", "PlanId":"$(System.PlanId)", "JobId":"$(System.JobId)", "TimelineId":"$(System.TimelineId)", "TaskInstanceId":"$(System.TaskInstanceId)", "AuthToken":"$(System.AccessToken)"}'
body: ''
urlSuffix: 'api/JobStatus'
waitForCompletion: true

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# Initiate 5 agentless-server waiting jobs to check on the
# status of PR artifact builds, for a maximum wait time of
# 11*60 min=660 mins. These jobs will pass immediately
# once targeted CircleCI build is ready.
jobs:
- job: checkjob1
pool: server
timeoutInMinutes: 60
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob2
pool: server
timeoutInMinutes: 60
dependsOn: checkjob1
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob3
pool: server
timeoutInMinutes: 60
dependsOn: checkjob2
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob4
pool: server
timeoutInMinutes: 60
dependsOn: checkjob3
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob5
pool: server
timeoutInMinutes: 60
dependsOn: checkjob4
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob6
pool: server
timeoutInMinutes: 60
dependsOn: checkjob5
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob7
pool: server
timeoutInMinutes: 60
dependsOn: checkjob6
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob8
pool: server
timeoutInMinutes: 60
dependsOn: checkjob7
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob9
pool: server
timeoutInMinutes: 60
dependsOn: checkjob8
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob10
pool: server
timeoutInMinutes: 60
dependsOn: checkjob9
continueOnError: true
steps:
- template: wheel-wait-job-template.yml
- job: checkjob11
pool: server
timeoutInMinutes: 60
dependsOn: checkjob10
continueOnError: true
steps:
- template: wheel-wait-job-template.yml

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# PyTorch Nightly PyTorch Tests Builds Pipeline on Azure DevOps
#
# This pipeline runs custom PyTorch unit-tests on nightly
# PyTorch wheels.
stages:
- stage: 'NightlyCustomTests'
displayName: 'Run custom unit tests on PyTorch wheels'
jobs:
- template: job_templates/pytorch-template-unix.yml
parameters:
name: ubuntu_1804_CPU_docker
pool: $(BUILD_POOL_LIN_1)
customMatrixes:
Nightly_Custom_Tests:
_DOCKER_IMAGE: $(DOCKER_IMAGE_LIN_1)
_PYTHON_VERSION: $(PYTHON_VERSION_LIN_1)
_CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_LIN_1)
_RUN_TESTS: $(RUN_TESTS_LIN)
- template: job_templates/pytorch-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: $(BUILD_POOL_LIN_2)
customMatrixes:
Nightly_Custom_Tests:
_DOCKER_IMAGE: $(DOCKER_IMAGE_LIN_2)
_PYTHON_VERSION: $(PYTHON_VERSION_LIN_2)
_CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_LIN_2)
_RUN_TESTS: $(RUN_TESTS_LIN)
- template: job_templates/pytorch-template-win.yml
parameters:
name: windows_2019_CPU
pool: $(BUILD_POOL_WIN_1)
customMatrixes:
Nightly_Custom_Tests:
_PYTHON_VERSION: $(PYTHON_VERSION_WIN_1)
_CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_WIN_1)
_RUN_TESTS: $(RUN_TESTS_WIN)
- template: job_templates/pytorch-template-win.yml
parameters:
name: windows_2019_GPU
pool: $(BUILD_POOL_WIN_2)
customMatrixes:
Nightly_Custom_Tests:
_PYTHON_VERSION: $(PYTHON_VERSION_WIN_2)
_CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_WIN_2)
_RUN_TESTS: $(RUN_TESTS_WIN)
- stage: 'NotifyWebapp'
displayName: 'Notify Webapp that pipeline is finished'
dependsOn: NightlyCustomTests
condition: succeededOrFailed()
jobs:
- template: job_templates/notify-webapp-template.yml
parameters:
name: ubuntu_1804_CPU
pool: $(BUILD_POOL_LIN_1)

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# PyTorch PR PyTorch Tests Builds Pipeline on Azure DevOps
#
# This pipeline:
# 1) ensures that CircleCI builds for a given PR
# have finished, and that its artifacts are
# ready for download
# 2) runs custom PyTorch unit-tests on PyTorch
# wheels generated during PR builds.
resources:
webhooks:
- webhook: GitHubPyTorchPRTrigger
connection: GitHubPyTorchPRTriggerConnection
filters:
- path: repositoryName
value: pytorch_tests
stages:
- stage: 'EnsureArtifactsReady'
displayName: 'Ensure PyTorch PR Artifacts are ready'
jobs:
- template: job_templates/wheel-wait-template.yml
variables:
_TARGET_BRANCH_TO_CHECK: ${{parameters.GitHubPyTorchPRTrigger.TARGET_BRANCH_TO_CHECK_AZ_DEVOPS_PR}}
_TARGET_PR_NUMBER: ${{parameters.GitHubPyTorchPRTrigger.PR_NUMBER}}
_TARGET_COMMIT: ${{parameters.GitHubPyTorchPRTrigger.TARGET_COMMIT}}
- stage: 'PRCustomTests'
displayName: 'Run custom unit tests on PyTorch wheels'
dependsOn: EnsureArtifactsReady
condition: succeeded()
jobs:
- template: job_templates/pytorch-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: $(BUILD_POOL_PR)
customMatrixes:
PR_Custom_Tests:
_PYTHON_VERSION: $(PYTHON_VERSION_PR)
_CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_PR)
_TARGET_CIRCLECI_BUILD: $(TARGET_CIRCLECI_BUILD_PR)
_TARGET_BRANCH_TO_CHECK: ${{parameters.GitHubPyTorchPRTrigger.TARGET_BRANCH_TO_CHECK_AZ_DEVOPS_PR}}
_TARGET_PR_NUMBER: ${{parameters.GitHubPyTorchPRTrigger.PR_NUMBER}}
_TARGET_COMMIT: ${{parameters.GitHubPyTorchPRTrigger.TARGET_COMMIT}}
_DOCKER_IMAGE: $(DOCKER_IMAGE_PR)
_RUN_TESTS: $(RUN_TESTS_PR)
- stage: 'NotifyWebapp'
displayName: 'Notify Webapp that pipeline is finished'
dependsOn: PRCustomTests
condition: succeededOrFailed()
jobs:
- template: job_templates/notify-webapp-template.yml
parameters:
name: ubuntu_1804_CPU
pool: $(BUILD_POOL_LIN_1)
customMatrixes:
PR_Notify_WebApp:
_TARGET_CIRCLECI_BUILD: $(TARGET_CIRCLECI_BUILD_PR)
_TARGET_BRANCH_TO_CHECK: ${{parameters.GitHubPyTorchPRTrigger.TARGET_BRANCH_TO_CHECK_AZ_DEVOPS_PR}}
_TARGET_PR_NUMBER: ${{parameters.GitHubPyTorchPRTrigger.PR_NUMBER}}
_TARGET_COMMIT: ${{parameters.GitHubPyTorchPRTrigger.TARGET_COMMIT}}

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# PyTorch Official Builds Pipeline on Azure DevOps
#
# This pipeline:
# 1) builds PyTorch on all available configurations
# 2) verifies PyTorch artifacts by installing them in a clean environment
# and checking torch.__version_
# 3) publishes official PyTorch artifacts to Azure DevOps Artifacts for consumption
stages:
- stage: 'Build'
displayName: 'Build PyTorch'
jobs:
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_CPU_docker
pool: 'PyTorch-Linux-CPU'
container_endpoint: pytorchms.azurecr.io
build_stage: True
is_official_build: True
os: ubuntu
cuda: cpu
customMatrixes:
Py_38:
configuration: ubuntu_1804_py_38_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
Py_37:
configuration: ubuntu_1804_py_37_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: 'PyTorch-Linux-GPU'
container_endpoint: pytorchms.azurecr.io
build_stage: True
is_official_build: True
os: ubuntu
cuda: gpu
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_810:
configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_765:
configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
CUDA_VERSION: 101
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_CPU
pool: 'PyTorch-Win-CPU'
build_stage: True
is_official_build: True
os: windows
cuda: cpu
customMatrixes:
Py_38:
configuration: windows_2019_py_38_cpu
Py_37:
configuration: windows_2019_py_37_cpu
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_GPU
pool: 'PyTorch-Win-GPU'
build_stage: True
is_official_build: True
os: windows
cuda: gpu
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: windows_2019_py_39_cuda_112_cudnn_810
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_765:
configuration: windows_2019_py_38_cuda_102_cudnn_765
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_764:
configuration: windows_2019_py_37_cuda_101_cudnn_764
CUDA_VERSION: 101
- stage: 'Verify'
displayName: 'Verify PyTorch wheels'
dependsOn: Build
condition: succeeded()
jobs:
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_CPU_docker
pool: 'PyTorch-Linux-CPU'
container_endpoint: pytorchms.azurecr.io
verify_stage: True
is_official_build: True
customMatrixes:
Py_38:
configuration: ubuntu_1804_py_38_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
Py_37:
configuration: ubuntu_1804_py_37_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: 'PyTorch-Linux-GPU'
container_endpoint: pytorchms.azurecr.io
verify_stage: True
is_official_build: True
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_810:
configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_765:
configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
CUDA_VERSION: 101
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_CPU
pool: 'PyTorch-Win-CPU'
verify_stage: True
is_official_build: True
customMatrixes:
Py_38:
configuration: windows_2019_py_38_cpu
Py_37:
configuration: windows_2019_py_37_cpu
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_GPU
pool: 'PyTorch-Win-GPU'
verify_stage: True
is_official_build: True
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: windows_2019_py_39_cuda_112_cudnn_810
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_765:
configuration: windows_2019_py_38_cuda_102_cudnn_765
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_764:
configuration: windows_2019_py_37_cuda_101_cudnn_764
CUDA_VERSION: 101
- stage: 'Publish'
displayName: 'Publish PyTorch wheels'
dependsOn: Verify
condition: succeeded()
jobs:
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_CPU_docker
pool: 'PyTorch-Linux-CPU'
container_endpoint: pytorchms.azurecr.io
publish_stage: True
is_official_build: True
customMatrixes:
Py_38:
configuration: ubuntu_1804_py_38_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
Py_37:
configuration: ubuntu_1804_py_37_cpu
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
- template: job_templates/build-verify-publish-template-unix.yml
parameters:
name: ubuntu_1804_GPU_docker
pool: 'PyTorch-Linux-GPU'
container_endpoint: pytorchms.azurecr.io
publish_stage: True
is_official_build: True
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_810:
configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_765:
configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
CUDA_VERSION: 101
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_CPU
pool: 'PyTorch-Win-CPU'
publish_stage: True
is_official_build: True
customMatrixes:
Py_38:
configuration: windows_2019_py_38_cpu
Py_37:
configuration: windows_2019_py_37_cpu
- template: job_templates/build-verify-publish-template-win.yml
parameters:
name: windows_2019_GPU
pool: 'PyTorch-Win-GPU'
publish_stage: True
is_official_build: True
customMatrixes:
Py_39_CUDA_112_cuDNN_810:
configuration: windows_2019_py_39_cuda_112_cudnn_810
CUDA_VERSION: 112
Py_38_CUDA_102_cuDNN_765:
configuration: windows_2019_py_38_cuda_102_cudnn_765
CUDA_VERSION: 102
Py_37_CUDA_101_cuDNN_764:
configuration: windows_2019_py_37_cuda_101_cudnn_764
CUDA_VERSION: 101

109
.bazelrc
View File

@ -1,11 +1,10 @@
build --cxxopt=--std=c++17
build --copt=--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. 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
build --experimental_ui_max_stdouterr_bytes=2048576
# Configuration to disable tty features for environments like CI
@ -13,103 +12,15 @@ 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)$'@-Werror=all
# The following warnings come from -Wall. We downgrade them from error
# to warnings here.
#
# sign-compare has a tremendous amount of violations in the
# codebase. It will be a lot of work to fix them, just disable it for
# now.
build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-sign-compare
# 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='//: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

View File

@ -1,25 +0,0 @@
[pt]
is_oss=1
[buildfile]
name = BUCK.oss
includes = //tools/build_defs/select.bzl
[repositories]
bazel_skylib = third_party/bazel-skylib/
ovr_config = .
[download]
in_build = true
[cxx]
cxxflags = -std=c++17
should_remap_host_platform = true
cpp = /usr/bin/clang
cc = /usr/bin/clang
cxx = /usr/bin/clang++
cxxpp = /usr/bin/clang++
ld = /usr/bin/clang++
[project]
default_flavors_mode=all

View File

@ -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

View File

@ -1,172 +0,0 @@
#!/bin/bash
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
pip install click mock tabulate networkx==2.0
pip -q install --user "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
fi
# Skip tests in environments where they are not built/applicable
if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
echo 'Skipping tests'
exit 0
fi
if [[ "${BUILD_ENVIRONMENT}" == *-rocm* ]]; then
# temporary to locate some kernel issues on the CI nodes
export HSAKMT_DEBUG_LEVEL=4
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 [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
# TODO(sdym@meta.com) remove this when the linked issue resolved.
# py is temporary until https://github.com/Teemu/pytest-sugar/issues/241 is fixed
pip install --user py==1.11.0
pip install --user pytest-sugar
# NB: Warnings are disabled because they make it harder to see what
# the actual erroring test is
"$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

View File

@ -1,392 +0,0 @@
#!/bin/bash
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" == *-bionic* ]]; then
UBUNTU_VERSION=18.04
elif [[ "$image" == *-focal* ]]; then
UBUNTU_VERSION=20.04
elif [[ "$image" == *-jammy* ]]; then
UBUNTU_VERSION=22.04
elif [[ "$image" == *ubuntu* ]]; then
extract_version_from_image_name ubuntu UBUNTU_VERSION
elif [[ "$image" == *centos* ]]; then
extract_version_from_image_name centos CENTOS_VERSION
fi
if [ -n "${UBUNTU_VERSION}" ]; then
OS="ubuntu"
elif [ -n "${CENTOS_VERSION}" ]; then
OS="centos"
else
echo "Unable to derive operating system base..."
exit 1
fi
DOCKERFILE="${OS}/Dockerfile"
# When using ubuntu - 22.04, start from Ubuntu docker image, instead of nvidia/cuda docker image.
if [[ "$image" == *cuda* && "$UBUNTU_VERSION" != "22.04" ]]; then
DOCKERFILE="${OS}-cuda/Dockerfile"
elif [[ "$image" == *rocm* ]]; then
DOCKERFILE="${OS}-rocm/Dockerfile"
elif [[ "$image" == *linter* ]]; then
# Use a separate Dockerfile for linter to keep a small image size
DOCKERFILE="linter/Dockerfile"
fi
# CMake 3.18 is needed to support CUDA17 language variant
CMAKE_VERSION=3.18.5
_UCX_COMMIT=31e74cac7bee0ef66bef2af72e7d86d9c282e5ab
_UCC_COMMIT=1c7a7127186e7836f73aafbd7697bbc274a77eee
# It's annoying to rename jobs every time you want to rewrite a
# configuration, so we hardcode everything here rather than do it
# from scratch
case "$image" in
pytorch-linux-bionic-cuda11.6-cudnn8-py3-gcc7)
CUDA_VERSION=11.6.2
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
;;
pytorch-linux-bionic-cuda11.7-cudnn8-py3-gcc7)
CUDA_VERSION=11.7.0
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
;;
pytorch-linux-bionic-cuda11.8-cudnn8-py3-gcc7)
CUDA_VERSION=11.8.0
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
;;
pytorch-linux-focal-py3-clang7-asan)
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
;;
pytorch-linux-focal-py3-clang10-onnx)
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
;;
pytorch-linux-focal-py3-clang7-android-ndk-r19c)
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=7
LLVMDEV=yes
PROTOBUF=yes
ANDROID=yes
ANDROID_NDK_VERSION=r19c
GRADLE_VERSION=6.8.3
NINJA_VERSION=1.9.0
;;
pytorch-linux-bionic-py3.8-clang9)
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
CONDA_CMAKE=yes
;;
pytorch-linux-bionic-py3.11-clang9)
ANACONDA_PYTHON_VERSION=3.11
CLANG_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
CONDA_CMAKE=yes
;;
pytorch-linux-bionic-py3.8-gcc9)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
;;
pytorch-linux-focal-rocm-n-1-py3)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=5.3
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=5.4.2
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
;;
pytorch-linux-focal-py3.8-gcc7)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
CONDA_CMAKE=yes
;;
pytorch-linux-jammy-cuda11.6-cudnn8-py3.8-clang12)
ANACONDA_PYTHON_VERSION=3.8
CUDA_VERSION=11.6
CUDNN_VERSION=8
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-jammy-cuda11.7-cudnn8-py3.8-clang12)
ANACONDA_PYTHON_VERSION=3.8
CUDA_VERSION=11.7
CUDNN_VERSION=8
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-jammy-cuda11.8-cudnn8-py3.8-clang12)
ANACONDA_PYTHON_VERSION=3.8
CUDA_VERSION=11.8
CUDNN_VERSION=8
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-focal-linter)
# TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
# We will need to update mypy version eventually, but that's for another day. The task
# would be to upgrade mypy to 1.0.0 with Python 3.11
ANACONDA_PYTHON_VERSION=3.9
CONDA_CMAKE=yes
;;
*)
# Catch-all for builds that are not hardcoded.
PROTOBUF=yes
DB=yes
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 [[ "$image" == *cuda* ]]; then
extract_version_from_image_name cuda CUDA_VERSION
extract_version_from_image_name cudnn CUDNN_VERSION
fi
if [[ "$image" == *rocm* ]]; then
extract_version_from_image_name rocm ROCM_VERSION
NINJA_VERSION=1.9.0
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 [[ "$image" == *glibc* ]]; then
extract_version_from_image_name glibc GLIBC_VERSION
fi
if [[ "$image" == *cmake* ]]; then
extract_version_from_image_name cmake CMAKE_VERSION
fi
;;
esac
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
#when using cudnn version 8 install it separately from cuda
if [[ "$image" == *cuda* && ${OS} == "ubuntu" ]]; then
IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
if [[ ${CUDNN_VERSION} == 8 ]]; then
IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
fi
fi
# Build image
# TODO: build-arg THRIFT is not turned on for any image, remove it once we confirm
# it's no longer needed.
docker build \
--no-cache \
--progress=plain \
--build-arg "BUILD_ENVIRONMENT=${image}" \
--build-arg "PROTOBUF=${PROTOBUF:-}" \
--build-arg "THRIFT=${THRIFT:-}" \
--build-arg "LLVMDEV=${LLVMDEV:-}" \
--build-arg "DB=${DB:-}" \
--build-arg "VISION=${VISION:-}" \
--build-arg "UBUNTU_VERSION=${UBUNTU_VERSION}" \
--build-arg "CENTOS_VERSION=${CENTOS_VERSION}" \
--build-arg "DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}" \
--build-arg "GLIBC_VERSION=${GLIBC_VERSION}" \
--build-arg "CLANG_VERSION=${CLANG_VERSION}" \
--build-arg "ANACONDA_PYTHON_VERSION=${ANACONDA_PYTHON_VERSION}" \
--build-arg "GCC_VERSION=${GCC_VERSION}" \
--build-arg "CUDA_VERSION=${CUDA_VERSION}" \
--build-arg "CUDNN_VERSION=${CUDNN_VERSION}" \
--build-arg "TENSORRT_VERSION=${TENSORRT_VERSION}" \
--build-arg "ANDROID=${ANDROID}" \
--build-arg "ANDROID_NDK=${ANDROID_NDK_VERSION}" \
--build-arg "GRADLE_VERSION=${GRADLE_VERSION}" \
--build-arg "VULKAN_SDK_VERSION=${VULKAN_SDK_VERSION}" \
--build-arg "SWIFTSHADER=${SWIFTSHADER}" \
--build-arg "CMAKE_VERSION=${CMAKE_VERSION:-}" \
--build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
--build-arg "KATEX=${KATEX:-}" \
--build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
--build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx906}" \
--build-arg "IMAGE_NAME=${IMAGE_NAME}" \
--build-arg "UCX_COMMIT=${UCX_COMMIT}" \
--build-arg "UCC_COMMIT=${UCC_COMMIT}" \
--build-arg "CONDA_CMAKE=${CONDA_CMAKE}" \
-f $(dirname ${DOCKERFILE})/Dockerfile \
-t "$tmp_tag" \
"$@" \
.
# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
# for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
# find the correct image. As a result, here we have to replace the
# "$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 !(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

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@ -1,111 +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 ${PYTORCH_ROCM_ARCH}
# Install required packages to build Caffe2
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Update CentOS git version
RUN yum -y remove git
RUN yum -y remove git-*
RUN yum -y install https://packages.endpoint.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm || \
(yum -y install https://packages.endpointdev.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm && \
sed -i "s/packages.endpoint/packages.endpointdev/" /etc/yum.repos.d/endpoint.repo)
RUN yum install -y git
# Install devtoolset
ARG DEVTOOLSET_VERSION
COPY ./common/install_devtoolset.sh install_devtoolset.sh
RUN bash ./install_devtoolset.sh && rm install_devtoolset.sh
ENV BASH_ENV "/etc/profile"
# (optional) Install non-default glibc version
ARG GLIBC_VERSION
COPY ./common/install_glibc.sh install_glibc.sh
RUN if [ -n "${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 CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV and ffmpeg
ARG VISION
COPY ./common/install_vision.sh install_vision.sh
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh
ENV INSTALLED_VISION ${VISION}
# Install rocm
ARG ROCM_VERSION
COPY ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh
RUN rm install_rocm_magma.sh
ENV PATH /opt/rocm/bin:$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 CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${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}
USER jenkins
CMD ["bash"]

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@ -1,32 +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
$SUDO -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$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" $*
}
conda_run() {
as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION --no-capture-output $*
}
pip_install() {
as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION pip install --progress-bar off $*
}

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@ -1,169 +0,0 @@
#!/bin/bash
set -ex
install_ubuntu() {
# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-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 check for
# "$UBUNTU_VERSION" == "18.04"*
# instead of
# "$UBUNTU_VERSION" == "18.04"
if [[ "$UBUNTU_VERSION" == "18.04"* ]]; then
cmake3="cmake=3.10*"
maybe_libiomp_dev="libiomp-dev"
elif [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
cmake3="cmake=3.16*"
maybe_libiomp_dev=""
elif [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
cmake3="cmake=3.22*"
maybe_libiomp_dev=""
else
cmake3="cmake=3.5*"
maybe_libiomp_dev="libiomp-dev"
fi
if [[ "$CLANG_VERSION" == 12 ]]; then
maybe_libomp_dev="libomp-12-dev"
elif [[ "$CLANG_VERSION" == 10 ]]; then
maybe_libomp_dev="libomp-10-dev"
else
maybe_libomp_dev=""
fi
# TODO: Remove this once nvidia package repos are back online
# Comment out nvidia repositories to prevent them from getting apt-get updated, see https://github.com/pytorch/pytorch/issues/74968
# shellcheck disable=SC2046
sed -i 's/.*nvidia.*/# &/' $(find /etc/apt/ -type f -name "*.list")
# Install common dependencies
apt-get update
# TODO: Some of these may not be necessary
ccache_deps="asciidoc docbook-xml docbook-xsl xsltproc"
deploy_deps="libffi-dev libbz2-dev libreadline-dev libncurses5-dev libncursesw5-dev libgdbm-dev libsqlite3-dev uuid-dev tk-dev"
numpy_deps="gfortran"
apt-get install -y --no-install-recommends \
$ccache_deps \
$numpy_deps \
${deploy_deps} \
${cmake3} \
apt-transport-https \
autoconf \
automake \
build-essential \
ca-certificates \
curl \
git \
libatlas-base-dev \
libc6-dbg \
${maybe_libiomp_dev} \
libyaml-dev \
libz-dev \
libjpeg-dev \
libasound2-dev \
libsndfile-dev \
${maybe_libomp_dev} \
software-properties-common \
wget \
sudo \
vim \
jq \
libtool \
vim \
unzip \
gdb
# Should resolve issues related to various apt package repository cert issues
# see: https://github.com/pytorch/pytorch/issues/65931
apt-get install -y libgnutls30
# cuda-toolkit does not work with gcc-11.2.0 which is default in Ubunutu 22.04
# see: https://github.com/NVlabs/instant-ngp/issues/119
if [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
apt-get install -y g++-10
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-10 30
update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-10 30
update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-10 30
# https://www.spinics.net/lists/libreoffice/msg07549.html
sudo rm -rf /usr/lib/gcc/x86_64-linux-gnu/11
wget https://github.com/gcc-mirror/gcc/commit/2b2d97fc545635a0f6aa9c9ee3b017394bc494bf.patch -O noexecpt.patch
sudo patch /usr/include/c++/10/bits/range_access.h noexecpt.patch
fi
# Cleanup package manager
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
}
install_centos() {
# Need EPEL for many packages we depend on.
# See http://fedoraproject.org/wiki/EPEL
yum --enablerepo=extras install -y epel-release
ccache_deps="asciidoc docbook-dtds docbook-style-xsl libxslt"
numpy_deps="gcc-gfortran"
# Note: protobuf-c-{compiler,devel} on CentOS are too old to be used
# for Caffe2. That said, we still install them to make sure the build
# system opts to build/use protoc and libprotobuf from third-party.
yum install -y \
$ccache_deps \
$numpy_deps \
autoconf \
automake \
bzip2 \
cmake \
cmake3 \
curl \
gcc \
gcc-c++ \
gflags-devel \
git \
glibc-devel \
glibc-headers \
glog-devel \
hiredis-devel \
libstdc++-devel \
libsndfile-devel \
make \
opencv-devel \
sudo \
wget \
vim \
unzip \
gdb
# Cleanup
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
}
# Install base packages depending on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
install_ubuntu
;;
centos)
install_centos
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
# Install Valgrind separately since the apt-get version is too old.
mkdir valgrind_build && cd valgrind_build
VALGRIND_VERSION=3.20.0
wget https://ossci-linux.s3.amazonaws.com/valgrind-${VALGRIND_VERSION}.tar.bz2
tar -xjf valgrind-${VALGRIND_VERSION}.tar.bz2
cd valgrind-${VALGRIND_VERSION}
./configure --prefix=/usr/local
make -j6
sudo make install
cd ../../
rm -rf valgrind_build
alias valgrind="/usr/local/bin/valgrind"

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@ -1,31 +0,0 @@
#!/bin/bash
set -ex
[ -n "$CMAKE_VERSION" ]
# Remove system cmake install so it won't get used instead
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
apt-get remove cmake -y
;;
centos)
yum remove cmake -y
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
# Turn 3.6.3 into v3.6
path=$(echo "${CMAKE_VERSION}" | sed -e 's/\([0-9].[0-9]\+\).*/v\1/')
file="cmake-${CMAKE_VERSION}-Linux-x86_64.tar.gz"
# Download and install specific CMake version in /usr/local
pushd /tmp
curl -Os --retry 3 "https://cmake.org/files/${path}/${file}"
tar -C /usr/local --strip-components 1 --no-same-owner -zxf cmake-*.tar.gz
rm -f cmake-*.tar.gz
popd

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@ -1,98 +0,0 @@
#!/bin/bash
set -ex
# Optionally install conda
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
BASE_URL="https://repo.anaconda.com/miniconda"
MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
case "$MAJOR_PYTHON_VERSION" in
2)
CONDA_FILE="Miniconda2-latest-Linux-x86_64.sh"
;;
3)
CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
;;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
mkdir -p /opt/conda
chown jenkins:jenkins /opt/conda
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
pushd /tmp
wget -q "${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:$PATH"
# Ensure we run conda in a directory that jenkins has write access to
pushd /opt/conda
# Prevent conda from updating to 4.14.0, which causes docker build failures
# See https://hud.pytorch.org/pytorch/pytorch/commit/754d7f05b6841e555cea5a4b2c505dd9e0baec1d
# Uncomment the below when resolved to track the latest conda update
# as_jenkins conda update -y -n base conda
# Install correct Python version
as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION"
# Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
# TODO: Stop using `-c malfet`
conda_install numpy=1.23.5 ${CONDA_COMMON_DEPS} llvmdev=8.0.0 -c malfet
elif [ "$ANACONDA_PYTHON_VERSION" = "3.10" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
elif [ "$ANACONDA_PYTHON_VERSION" = "3.9" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.19.2 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
elif [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.18.5 ${CONDA_COMMON_DEPS} llvmdev=8.0.0
else
# Install `typing-extensions` for 3.7
conda_install numpy=1.18.5 ${CONDA_COMMON_DEPS} typing-extensions
fi
# Use conda cmake in some cases. Conda cmake will be newer than our supported
# min version (3.5 for xenial and 3.10 for bionic), so we only do it in those
# following builds that we know should use conda. Specifically, Ubuntu bionic
# and focal cannot find conda mkl with stock cmake, so we need a cmake from conda
if [ -n "${CONDA_CMAKE}" ]; then
conda_install cmake
fi
# 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
if [ -n "$CUDA_VERSION" ]; then
conda_install magma-cuda$(TMP=${CUDA_VERSION/./};echo ${TMP%.*[0-9]}) -c pytorch
fi
# Install some other packages, including those needed for Python test reporting
pip_install -r /opt/conda/requirements-ci.txt
# Update scikit-learn to a python-3.8 compatible version
if [[ $(python -c "import sys; print(int(sys.version_info >= (3, 8)))") == "1" ]]; then
pip_install -U scikit-learn
else
# Pinned scikit-learn due to https://github.com/scikit-learn/scikit-learn/issues/14485 (affects gcc 5.5 only)
pip_install scikit-learn==0.20.3
fi
popd
fi

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@ -1,27 +0,0 @@
#!/bin/bash
if [[ ${CUDNN_VERSION} == 8 ]]; then
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
CUDNN_NAME="cudnn-linux-x86_64-8.3.2.44_cuda11.5-archive"
if [[ ${CUDA_VERSION:0:4} == "11.7" ]]; then
CUDNN_NAME="cudnn-linux-x86_64-8.5.0.96_cuda11-archive"
curl --retry 3 -OLs https://ossci-linux.s3.amazonaws.com/${CUDNN_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
CUDNN_NAME="cudnn-linux-x86_64-8.7.0.84_cuda11-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/redist/cudnn/v8.7.0/local_installers/11.8/${CUDNN_NAME}.tar.xz
else
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/redist/cudnn/v8.3.2/local_installers/11.5/${CUDNN_NAME}.tar.xz
fi
tar xf ${CUDNN_NAME}.tar.xz
cp -a ${CUDNN_NAME}/include/* /usr/include/
cp -a ${CUDNN_NAME}/include/* /usr/local/cuda/include/
cp -a ${CUDNN_NAME}/include/* /usr/include/x86_64-linux-gnu/
cp -a ${CUDNN_NAME}/lib/* /usr/local/cuda/lib64/
cp -a ${CUDNN_NAME}/lib/* /usr/lib/x86_64-linux-gnu/
cd ..
rm -rf tmp_cudnn
ldconfig
fi

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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_12.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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@ -1,29 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
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
conda_run 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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@ -1,146 +0,0 @@
#!/bin/bash
set -ex
ver() {
printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
}
# Map ROCm version to AMDGPU version
declare -A AMDGPU_VERSIONS=( ["5.0"]="21.50" ["5.1.1"]="22.10.1" ["5.2"]="22.20" )
install_ubuntu() {
apt-get update
if [[ $UBUNTU_VERSION == 18.04 ]]; then
# gpg-agent is not available by default on 18.04
apt-get install -y --no-install-recommends gpg-agent
fi
if [[ $UBUNTU_VERSION == 20.04 ]]; then
# gpg-agent is not available by default on 20.04
apt-get install -y --no-install-recommends gpg-agent
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
if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
# Add amdgpu repository
UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
local amdgpu_baseurl
if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
else
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/ubuntu"
fi
echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
fi
ROCM_REPO="ubuntu"
if [[ $(ver $ROCM_VERSION) -lt $(ver 4.2) ]]; then
ROCM_REPO="xenial"
fi
if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
ROCM_REPO="${UBUNTU_VERSION_NAME}"
fi
# Add rocm repository
wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
echo "deb [arch=amd64] ${rocm_baseurl} ${ROCM_REPO} main" > /etc/apt/sources.list.d/rocm.list
apt-get update --allow-insecure-repositories
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
rocm-dev \
rocm-utils \
rocm-libs \
rccl \
rocprofiler-dev \
roctracer-dev
# precompiled miopen kernels added in ROCm 3.5; search for all unversioned packages
# if search fails it will abort this script; use true to avoid case where search fails
MIOPENKERNELS=$(apt-cache search --names-only miopenkernels | awk '{print $1}' | grep -F -v . || true)
if [[ "x${MIOPENKERNELS}" = x ]]; then
echo "miopenkernels package not available"
else
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENKERNELS}
fi
# 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`
if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
# Add amdgpu repository
local amdgpu_baseurl
if [[ $OS_VERSION == 9 ]]; then
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/9.0/main/x86_64"
else
if [[ $(ver $ROCM_VERSION) -ge $(ver 5.3) ]]; then
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/7.9/main/x86_64"
else
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/7.9/main/x86_64"
fi
fi
echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
echo "baseurl=${amdgpu_baseurl}" >> /etc/yum.repos.d/amdgpu.repo
echo "enabled=1" >> /etc/yum.repos.d/amdgpu.repo
echo "gpgcheck=1" >> /etc/yum.repos.d/amdgpu.repo
echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/amdgpu.repo
fi
local rocm_baseurl="http://repo.radeon.com/rocm/yum/${ROCM_VERSION}"
echo "[ROCm]" > /etc/yum.repos.d/rocm.repo
echo "name=ROCm" >> /etc/yum.repos.d/rocm.repo
echo "baseurl=${rocm_baseurl}" >> /etc/yum.repos.d/rocm.repo
echo "enabled=1" >> /etc/yum.repos.d/rocm.repo
echo "gpgcheck=1" >> /etc/yum.repos.d/rocm.repo
echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/rocm.repo
yum update -y
yum install -y \
rocm-dev \
rocm-utils \
rocm-libs \
rccl \
rocprofiler-dev \
roctracer-dev
# Cleanup
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
}
# Install Python packages depending on the base OS
ID=$(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

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@ -1,29 +0,0 @@
#!/bin/bash
set -ex
# "install" hipMAGMA into /opt/rocm/magma by copying after build
git clone https://bitbucket.org/icl/magma.git
pushd magma
# Fixes memory leaks of magma found while executing linalg UTs
git checkout 5959b8783e45f1809812ed96ae762f38ee701972
cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib' >> make.inc
echo 'DEVCCFLAGS += --gpu-max-threads-per-block=256' >> make.inc
export PATH="${PATH}:/opt/rocm/bin"
if [[ -n "$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 $amdgpu_targets; do
echo "DEVCCFLAGS += --amdgpu-target=$arch" >> make.inc
done
# hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
sed -i 's/^FOPENMP/#FOPENMP/g' make.inc
make -f make.gen.hipMAGMA -j $(nproc)
LANG=C.UTF-8 make lib/libmagma.so -j $(nproc) MKLROOT=/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION
make testing/testing_dgemm -j $(nproc) MKLROOT=/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION
popd
mv magma /opt/rocm

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@ -1,48 +0,0 @@
#!/bin/bash
set -ex
if [[ -d "/usr/local/cuda/" ]]; then
with_cuda=/usr/local/cuda/
else
with_cuda=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 \
--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 ${UCC_COMMIT}
git submodule update --init --recursive
./autogen.sh
./configure --prefix=$UCC_HOME --with-ucx=$UCX_HOME --with-cuda=$with_cuda
time make -j
sudo make install
popd
rm -rf ucc
}
install_ucx
install_ucc

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@ -1,33 +0,0 @@
#!/bin/bash
set -ex
# 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

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@ -1,34 +0,0 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$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
# Note that Docker build forbids copying file outside the build context
COPY ./common/install_linter.sh install_linter.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_linter.sh
RUN rm install_linter.sh common_utils.sh
USER jenkins
CMD ["bash"]

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@ -1,260 +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==1.19.12
#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
#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:
expecttest==0.1.3
#Description: method for writing tests where test framework auto populates
# the expected output based on previous runs
#Pinned versions: 0.1.3
#test that import:
flatbuffers==2.0
#Description: cross platform serialization library
#Pinned versions: 2.0
#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: 3.44.6, 4.53.2
#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:
librosa>=0.6.2 ; python_version < "3.11"
#Description: A python package for music and audio analysis
#Pinned versions: >=0.6.2
#test that import: test_spectral_ops.py
#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 # breaks ci/circleci: docker-pytorch-linux-xenial-py3-clang5-android-ndk-r19c
#Description: A testing library that allows you to replace parts of your
#system under test with mock objects
#Pinned versions:
#test that import: test_module_init.py, 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==0.960
# Pin MyPy version because new errors are likely to appear with each release
#Description: linter
#Pinned versions: 0.960
#test that import: test_typing.py, test_type_hints.py
networkx==2.6.3
#Description: creation, manipulation, and study of
#the structure, dynamics, and functions of complex networks
#Pinned versions: 2.6.3 (latest version that works with Python 3.7+)
#test that import: functorch
#ninja
#Description: build system. Note that it install from
#here breaks things so it is commented out
#Pinned versions: 1.10.0.post1
#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
numba==0.49.0 ; python_version < "3.9"
numba==0.54.1 ; python_version == "3.9"
numba==0.55.2 ; python_version == "3.10"
#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
#numpy
#Description: Provides N-dimensional arrays and linear algebra
#Pinned versions: 1.20
#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
#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
#pillow
#Description: Python Imaging Library fork
#Pinned versions:
#test that import:
protobuf==3.20.2
#Description: Googles data interchange format
#Pinned versions: 3.20.1
#test that import: test_tensorboard.py
psutil
#Description: information on running processes and system utilization
#Pinned versions:
#test that import: test_profiler.py, test_openmp.py, test_dataloader.py
pytest
#Description: testing framework
#Pinned versions:
#test that import: test_typing.py, test_cpp_extensions_aot.py, run_test.py
pytest-xdist
#Description: plugin for running pytest in parallel
#Pinned versions:
#test that import:
pytest-shard
#Description: plugin spliting up tests in pytest
#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
#Description: plugin for rerunning failure tests in pytest
#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==2.12.0
#Description: support doctest highlighting
#Pinned versions: 2.12.0
#test that import: the doctests
#PyYAML
#Description: data serialization format
#Pinned versions:
#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: 10.9.0
#test that import:
scikit-image
#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==1.6.3 ; python_version < "3.10"
scipy==1.8.1 ; python_version == "3.10"
scipy==1.9.3 ; python_version == "3.11"
# Pin SciPy because of failing distribution tests (see #60347)
#Description: scientific python
#Pinned versions: 1.6.3
#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:
tb-nightly
#Description: TensorBoard
#Pinned versions:
#test that import:
#typing-extensions
#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==0.9.2
#Description: all about linters
#Pinned versions: 0.9.2
#test that import:
rockset==1.0.3
#Description: queries Rockset
#Pinned versions: 1.0.3
#test that import:
ghstack==0.7.1
#Description: ghstack tool
#Pinned versions: 0.7.1
#test that import:

View File

@ -1,132 +0,0 @@
ARG UBUNTU_VERSION
ARG CUDA_VERSION
ARG IMAGE_NAME
FROM ${IMAGE_NAME}
ARG UBUNTU_VERSION
ARG CUDA_VERSION
ENV DEBIAN_FRONTEND noninteractive
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
ARG CONDA_CMAKE
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 protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV and ffmpeg
ARG VISION
COPY ./common/install_vision.sh install_vision.sh
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh
ENV INSTALLED_VISION ${VISION}
# (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 "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
COPY ./common/install_openssl.sh install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
RUN bash ./install_openssl.sh
ENV OPENSSL_DIR /opt/openssl
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.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:$PATH
# See https://github.com/pytorch/pytorch/issues/82174
# TODO(sdym@fb.com):
# check if this is needed after full off Xenial migration
ENV CARGO_NET_GIT_FETCH_WITH_CLI true
RUN bash ./install_cache.sh && rm install_cache.sh
ENV CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
# Add jni.h for java host build
COPY ./common/install_jni.sh install_jni.sh
COPY ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
# Install Open MPI for CUDA
COPY ./common/install_openmpi.sh install_openmpi.sh
RUN if [ -n "${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 ${BUILD_ENVIRONMENT}
# AWS specific CUDA build guidance
ENV TORCH_CUDA_ARCH_LIST Maxwell
ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
ENV CUDA_PATH /usr/local/cuda
# Install LLVM dev version (Defined in the pytorch/builder github repository)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
# Install CUDNN
ARG CUDNN_VERSION
ARG CUDA_VERSION
COPY ./common/install_cudnn.sh install_cudnn.sh
RUN if [ "${CUDNN_VERSION}" -eq 8 ]; then bash install_cudnn.sh; fi
RUN rm install_cudnn.sh
# Delete /usr/local/cuda-11.X/cuda-11.X symlinks
RUN if [ -h /usr/local/cuda-11.6/cuda-11.6 ]; then rm /usr/local/cuda-11.6/cuda-11.6; fi
RUN if [ -h /usr/local/cuda-11.7/cuda-11.7 ]; then rm /usr/local/cuda-11.7/cuda-11.7; fi
USER jenkins
CMD ["bash"]

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@ -1,102 +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 ${PYTORCH_ROCM_ARCH}
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install clang
ARG LLVMDEV
ARG CLANG_VERSION
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$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
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV and ffmpeg
ARG VISION
COPY ./common/install_vision.sh install_vision.sh
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh
ENV INSTALLED_VISION ${VISION}
# Install rocm
ARG ROCM_VERSION
COPY ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh
RUN rm install_rocm_magma.sh
ENV PATH /opt/rocm/bin:$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 C.UTF-8
ENV LC_ALL C.UTF-8
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${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}
USER jenkins
CMD ["bash"]

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ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
ENV DEBIAN_FRONTEND noninteractive
ARG CLANG_VERSION
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install clang
ARG LLVMDEV
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# (optional) Install thrift.
ARG THRIFT
COPY ./common/install_thrift.sh install_thrift.sh
RUN if [ -n "${THRIFT}" ]; then bash ./install_thrift.sh; fi
RUN rm install_thrift.sh
ENV INSTALLED_THRIFT ${THRIFT}
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$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 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
RUN wget -q https://raw.githubusercontent.com/pytorch/builder/main/common/install_cuda.sh -O install_cuda.sh
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh
ENV DESIRED_CUDA ${CUDA_VERSION}
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
# (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 "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV and ffmpeg
ARG VISION
COPY ./common/install_vision.sh install_vision.sh
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh
ENV INSTALLED_VISION ${VISION}
# (optional) Install Android NDK
ARG ANDROID
ARG ANDROID_NDK
ARG GRADLE_VERSION
COPY ./common/install_android.sh install_android.sh
COPY ./android/AndroidManifest.xml AndroidManifest.xml
COPY ./android/build.gradle build.gradle
RUN if [ -n "${ANDROID}" ]; then bash ./install_android.sh; fi
RUN rm install_android.sh
RUN rm AndroidManifest.xml
RUN rm build.gradle
ENV INSTALLED_ANDROID ${ANDROID}
# (optional) Install Vulkan SDK
ARG VULKAN_SDK_VERSION
COPY ./common/install_vulkan_sdk.sh install_vulkan_sdk.sh
RUN if [ -n "${VULKAN_SDK_VERSION}" ]; then bash ./install_vulkan_sdk.sh; fi
RUN rm install_vulkan_sdk.sh
# (optional) Install swiftshader
ARG SWIFTSHADER
COPY ./common/install_swiftshader.sh install_swiftshader.sh
RUN if [ -n "${SWIFTSHADER}" ]; then bash ./install_swiftshader.sh; fi
RUN rm install_swiftshader.sh
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
COPY ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
ENV OPENSSL_DIR /opt/openssl
RUN rm install_openssl.sh
# Install ccache/sccache (do this last, so we get priority in PATH)
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH
RUN bash ./install_cache.sh && rm install_cache.sh
# 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)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
# AWS specific CUDA build guidance
ENV TORCH_CUDA_ARCH_LIST Maxwell
ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
ENV CUDA_PATH /usr/local/cuda
USER jenkins
CMD ["bash"]

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@ -1,14 +0,0 @@
# Jenkins
The scripts in this directory are the entrypoint for testing ONNX exporter.
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`.
Our Jenkins installation is located at https://ci.pytorch.org/jenkins/.

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set -ex
LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
TEST_DIR="$ROOT_DIR/test"
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
fi
mkdir -p "$pytest_reports_dir" || true

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#!/bin/bash
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
pip install click mock tabulate networkx==2.0
pip -q install --user "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
fi
# Skip tests in environments where they are not built/applicable
if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
echo 'Skipping tests'
exit 0
fi
if [[ "${BUILD_ENVIRONMENT}" == *-rocm* ]]; then
# temporary to locate some kernel issues on the CI nodes
export HSAKMT_DEBUG_LEVEL=4
fi
# These additional packages are needed for circleci ROCm builds.
if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
# Need networkx 2.0 because bellmand_ford was moved in 2.1 . Scikit-image by
# defaults installs the most recent networkx version, so we install this lower
# version explicitly before scikit-image pulls it in as a dependency
pip install networkx==2.0
# click - onnx
pip install --progress-bar off click protobuf tabulate virtualenv mock typing-extensions
fi
################################################################################
# Python tests #
################################################################################
if [[ "$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
$MAYBE_SUDO pip -q install hypothesis==4.57.1
##############
# ONNX tests #
##############
if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
pip install -q --user --no-use-pep517 "git+https://github.com/pytorch/vision.git@$(cat .github/ci_commit_pins/vision.txt)"
pip install -q --user transformers==4.25.1
pip install -q --user ninja flatbuffers==2.0 numpy==1.22.4 onnxruntime==1.14.0 beartype==0.10.4
# TODO: change this when onnx 1.13.1 is released.
pip install --no-use-pep517 'onnx @ git+https://github.com/onnx/onnx@e192ba01e438d22ca2dedd7956e28e3551626c91'
# TODO: change this when onnx-script is on testPypi
pip install 'onnx-script @ git+https://github.com/microsoft/onnx-script@a71e35bcd72537bf7572536ee57250a0c0488bf6'
# numba requires numpy <= 1.20, onnxruntime requires numpy >= 1.21.
# We don't actually need it for our tests, but it's imported if it's present, so uninstall.
pip uninstall -q --yes numba
# JIT C++ extensions require ninja, so put it into PATH.
export PATH="/var/lib/jenkins/.local/bin:$PATH"
"$ROOT_DIR/scripts/onnx/test.sh"
fi

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@ -1,42 +0,0 @@
This directory contains scripts for our continuous integration.
One important thing to keep in mind when reading the scripts here is
that they are all based off of Docker images, which we build for each of
the various system configurations we want to run on Jenkins. This means
it is very easy to run these tests yourself:
1. Figure out what Docker image you want. The general template for our
images look like:
``registry.pytorch.org/pytorch/pytorch-$BUILD_ENVIRONMENT:$DOCKER_VERSION``,
where ``$BUILD_ENVIRONMENT`` is one of the build environments
enumerated in
[pytorch-dockerfiles](https://github.com/pytorch/pytorch/blob/master/.ci/docker/build.sh). The dockerfile used by jenkins can be found under the `.ci` [directory](https://github.com/pytorch/pytorch/blob/master/.ci/docker)
2. Run ``docker run -it -u jenkins $DOCKER_IMAGE``, clone PyTorch and
run one of the scripts in this directory.
The Docker images are designed so that any "reasonable" build commands
will work; if you look in [build.sh](build.sh) you will see that it is a
very simple script. This is intentional. Idiomatic build instructions
should work inside all of our Docker images. You can tweak the commands
however you need (e.g., in case you want to rebuild with DEBUG, or rerun
the build with higher verbosity, etc.).
We have to do some work to make this so. Here is a summary of the
mechanisms we use:
- We install binaries to directories like `/usr/local/bin` which
are automatically part of your PATH.
- We add entries to the PATH using Docker ENV variables (so
they apply when you enter Docker) and `/etc/environment` (so they
continue to apply even if you sudo), instead of modifying
`PATH` in our build scripts.
- We use `/etc/ld.so.conf.d` to register directories containing
shared libraries, instead of modifying `LD_LIBRARY_PATH` in our
build scripts.
- We reroute well known paths like `/usr/bin/gcc` to alternate
implementations with `update-alternatives`, instead of setting
`CC` and `CXX` in our implementations.

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#!/bin/bash
# Required environment variable: $BUILD_ENVIRONMENT
# (This is set by default in the Docker images we build, so you don't
# need to set it yourself.
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
# shellcheck source=./common-build.sh
source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
echo "Clang version:"
clang --version
python tools/stats/export_test_times.py
# detect_leaks=0: Python is very leaky, so we need suppress it
# symbolize=1: Gives us much better errors when things go wrong
export ASAN_OPTIONS=detect_leaks=0:detect_stack_use_after_return=1:symbolize=1:detect_odr_violation=0
if [ -n "$(which conda)" ]; then
export CMAKE_PREFIX_PATH=/opt/conda
fi
# TODO: Make the ASAN flags a centralized env var and unify with USE_ASAN option
CC="clang" CXX="clang++" LDSHARED="clang --shared" \
CFLAGS="-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -fsanitize-address-use-after-scope -shared-libasan" \
USE_ASAN=1 USE_CUDA=0 USE_MKLDNN=0 \
python setup.py bdist_wheel
pip_install_whl "$(echo dist/*.whl)"
# Test building via the sdist source tarball
python setup.py sdist
mkdir -p /tmp/tmp
pushd /tmp/tmp
tar zxf "$(dirname "${BASH_SOURCE[0]}")/../../dist/"*.tar.gz
cd torch-*
python setup.py build --cmake-only
popd
print_sccache_stats
assert_git_not_dirty

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#!/bin/bash
# Required environment variable: $BUILD_ENVIRONMENT
# (This is set by default in the Docker images we build, so you don't
# need to set it yourself.
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
# shellcheck source=./common-build.sh
source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
echo "Clang version:"
clang --version
python tools/stats/export_test_times.py
if [ -n "$(which conda)" ]; then
export CMAKE_PREFIX_PATH=/opt/conda
fi
CC="clang" CXX="clang++" LDSHARED="clang --shared" \
CFLAGS="-fsanitize=thread" \
USE_TSAN=1 USE_CUDA=0 USE_MKLDNN=0 \
python setup.py bdist_wheel
pip_install_whl "$(echo dist/*.whl)"
print_sccache_stats
assert_git_not_dirty

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@ -1,318 +0,0 @@
#!/bin/bash
set -ex
# Required environment variable: $BUILD_ENVIRONMENT
# (This is set by default in the Docker images we build, so you don't
# need to set it yourself.
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
# shellcheck source=./common-build.sh
source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
if [[ "$BUILD_ENVIRONMENT" == *-clang7-asan* ]]; then
exec "$(dirname "${BASH_SOURCE[0]}")/build-asan.sh" "$@"
fi
if [[ "$BUILD_ENVIRONMENT" == *-clang7-tsan* ]]; then
exec "$(dirname "${BASH_SOURCE[0]}")/build-tsan.sh" "$@"
fi
if [[ "$BUILD_ENVIRONMENT" == *-mobile-*build* ]]; then
exec "$(dirname "${BASH_SOURCE[0]}")/build-mobile.sh" "$@"
fi
echo "Python version:"
python --version
echo "GCC version:"
gcc --version
echo "CMake version:"
cmake --version
echo "Environment variables:"
env
if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
echo "NVCC version:"
nvcc --version
fi
if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *cuda11.3* && "$BUILD_ENVIRONMENT" != *clang* ]]; then
# TODO: there is a linking issue when building with UCC using clang,
# disable it for now and to be fix later.
export USE_UCC=1
export USE_SYSTEM_UCC=1
fi
fi
if [[ ${BUILD_ENVIRONMENT} == *"caffe2"* ]]; then
echo "Caffe2 build is ON"
export BUILD_CAFFE2=ON
fi
if [[ ${BUILD_ENVIRONMENT} == *"paralleltbb"* ]]; then
export ATEN_THREADING=TBB
export USE_TBB=1
elif [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
export ATEN_THREADING=NATIVE
fi
# Enable LLVM dependency for TensorExpr testing
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
export USE_LLVM=/opt/rocm/llvm
export LLVM_DIR=/opt/rocm/llvm/lib/cmake/llvm
else
export USE_LLVM=/opt/llvm
export LLVM_DIR=/opt/llvm/lib/cmake/llvm
fi
if ! which conda; then
# In ROCm CIs, we are doing cross compilation on build machines with
# intel cpu and later run tests on machines with amd cpu.
# Also leave out two builds to make sure non-mkldnn builds still work.
if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
export USE_MKLDNN=1
else
export USE_MKLDNN=0
fi
else
export CMAKE_PREFIX_PATH=/opt/conda
fi
if [[ "$BUILD_ENVIRONMENT" == *libtorch* ]]; then
POSSIBLE_JAVA_HOMES=()
POSSIBLE_JAVA_HOMES+=(/usr/local)
POSSIBLE_JAVA_HOMES+=(/usr/lib/jvm/java-8-openjdk-amd64)
POSSIBLE_JAVA_HOMES+=(/Library/Java/JavaVirtualMachines/*.jdk/Contents/Home)
# Add the Windows-specific JNI
POSSIBLE_JAVA_HOMES+=("$PWD/.circleci/windows-jni/")
for JH in "${POSSIBLE_JAVA_HOMES[@]}" ; do
if [[ -e "$JH/include/jni.h" ]] ; then
# Skip if we're not on Windows but haven't found a JAVA_HOME
if [[ "$JH" == "$PWD/.circleci/windows-jni/" && "$OSTYPE" != "msys" ]] ; then
break
fi
echo "Found jni.h under $JH"
export JAVA_HOME="$JH"
export BUILD_JNI=ON
break
fi
done
if [ -z "$JAVA_HOME" ]; then
echo "Did not find jni.h"
fi
fi
# Use special scripts for Android builds
if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
export ANDROID_NDK=/opt/ndk
build_args=()
if [[ "${BUILD_ENVIRONMENT}" == *-arm-v7a* ]]; then
build_args+=("-DANDROID_ABI=armeabi-v7a")
elif [[ "${BUILD_ENVIRONMENT}" == *-arm-v8a* ]]; then
build_args+=("-DANDROID_ABI=arm64-v8a")
elif [[ "${BUILD_ENVIRONMENT}" == *-x86_32* ]]; then
build_args+=("-DANDROID_ABI=x86")
elif [[ "${BUILD_ENVIRONMENT}" == *-x86_64* ]]; then
build_args+=("-DANDROID_ABI=x86_64")
fi
if [[ "${BUILD_ENVIRONMENT}" == *vulkan* ]]; then
build_args+=("-DUSE_VULKAN=ON")
fi
build_args+=("-DUSE_LITE_INTERPRETER_PROFILER=OFF")
exec ./scripts/build_android.sh "${build_args[@]}" "$@"
fi
if [[ "$BUILD_ENVIRONMENT" != *android* && "$BUILD_ENVIRONMENT" == *vulkan* ]]; then
export USE_VULKAN=1
# shellcheck disable=SC1091
source /var/lib/jenkins/vulkansdk/setup-env.sh
fi
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
# hcc used to run out of memory, silently exiting without stopping
# the build process, leaving undefined symbols in the shared lib,
# causing undefined symbol errors when later running tests.
# We used to set MAX_JOBS to 4 to avoid, but this is no longer an issue.
if [ -z "$MAX_JOBS" ]; then
export MAX_JOBS=$(($(nproc) - 1))
fi
if [[ -n "$CI" && -z "$PYTORCH_ROCM_ARCH" ]]; then
# Set ROCM_ARCH to gfx906 for CI builds, if user doesn't override.
echo "Limiting PYTORCH_ROCM_ARCH to gfx906 for CI builds"
export PYTORCH_ROCM_ARCH="gfx906"
fi
# hipify sources
python tools/amd_build/build_amd.py
fi
# sccache will fail for CUDA builds if all cores are used for compiling
# gcc 7 with sccache seems to have intermittent OOM issue if all cores are used
if [ -z "$MAX_JOBS" ]; then
if { [[ "$BUILD_ENVIRONMENT" == *cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *gcc7* ]]; } && which sccache > /dev/null; then
export MAX_JOBS=$(($(nproc) - 1))
fi
fi
# TORCH_CUDA_ARCH_LIST must be passed from an environment variable
if [[ "$BUILD_ENVIRONMENT" == *cuda* && -z "$TORCH_CUDA_ARCH_LIST" ]]; then
echo "TORCH_CUDA_ARCH_LIST must be defined"
exit 1
fi
if [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
export CC=clang
export CXX=clang++
fi
if [[ "${BUILD_ENVIRONMENT}" == *no-ops* ]]; then
export USE_PER_OPERATOR_HEADERS=0
fi
if [[ "${BUILD_ENVIRONMENT}" == *-pch* ]]; then
export USE_PRECOMPILED_HEADERS=1
fi
if [[ "${BUILD_ENVIRONMENT}" == *linux-focal-py3.7-gcc7-build* ]]; then
export USE_GLOO_WITH_OPENSSL=ON
fi
if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* ]]; then
export BUILD_STATIC_RUNTIME_BENCHMARK=ON
fi
if [[ "$BUILD_ENVIRONMENT" == *-bazel-* ]]; then
set -e
get_bazel
# Leave 1 CPU free and use only up to 80% of memory to reduce the change of crashing
# the runner
BAZEL_MEM_LIMIT="--local_ram_resources=HOST_RAM*.8"
BAZEL_CPU_LIMIT="--local_cpu_resources=HOST_CPUS-1"
tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" //...
# Build torch, the Python module, and tests for CPU-only
tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" --config=cpu-only :torch :_C.so :all_tests
else
# check that setup.py would fail with bad arguments
echo "The next three invocations are expected to fail with invalid command error messages."
( ! get_exit_code python setup.py bad_argument )
( ! get_exit_code python setup.py clean] )
( ! get_exit_code python setup.py clean bad_argument )
if [[ "$BUILD_ENVIRONMENT" != *libtorch* ]]; then
# rocm builds fail when WERROR=1
# XLA test build fails when WERROR=1
# set only when building other architectures
# or building non-XLA tests.
if [[ "$BUILD_ENVIRONMENT" != *rocm* &&
"$BUILD_ENVIRONMENT" != *xla* ]]; then
WERROR=1 python setup.py bdist_wheel
else
python setup.py bdist_wheel
fi
pip_install_whl "$(echo dist/*.whl)"
# TODO: I'm not sure why, but somehow we lose verbose commands
set -x
assert_git_not_dirty
# Copy ninja build logs to dist folder
mkdir -p dist
if [ -f build/.ninja_log ]; then
cp build/.ninja_log dist
fi
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
# remove sccache wrappers post-build; runtime compilation of MIOpen kernels does not yet fully support them
sudo rm -f /opt/cache/bin/cc
sudo rm -f /opt/cache/bin/c++
sudo rm -f /opt/cache/bin/gcc
sudo rm -f /opt/cache/bin/g++
pushd /opt/rocm/llvm/bin
if [[ -d original ]]; then
sudo mv original/clang .
sudo mv original/clang++ .
fi
sudo rm -rf original
popd
fi
CUSTOM_TEST_ARTIFACT_BUILD_DIR=${CUSTOM_TEST_ARTIFACT_BUILD_DIR:-"build/custom_test_artifacts"}
CUSTOM_TEST_USE_ROCM=$([[ "$BUILD_ENVIRONMENT" == *rocm* ]] && echo "ON" || echo "OFF")
CUSTOM_TEST_MODULE_PATH="${PWD}/cmake/public"
mkdir -pv "${CUSTOM_TEST_ARTIFACT_BUILD_DIR}"
# Build custom operator tests.
CUSTOM_OP_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-op-build"
CUSTOM_OP_TEST="$PWD/test/custom_operator"
python --version
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
mkdir -p "$CUSTOM_OP_BUILD"
pushd "$CUSTOM_OP_BUILD"
cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
assert_git_not_dirty
# Build jit hook tests
JIT_HOOK_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/jit-hook-build"
JIT_HOOK_TEST="$PWD/test/jit_hooks"
python --version
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
mkdir -p "$JIT_HOOK_BUILD"
pushd "$JIT_HOOK_BUILD"
cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
assert_git_not_dirty
# Build custom backend tests.
CUSTOM_BACKEND_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-backend-build"
CUSTOM_BACKEND_TEST="$PWD/test/custom_backend"
python --version
mkdir -p "$CUSTOM_BACKEND_BUILD"
pushd "$CUSTOM_BACKEND_BUILD"
cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPYTHON_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
assert_git_not_dirty
else
# Test no-Python build
echo "Building libtorch"
# This is an attempt to mitigate flaky libtorch build OOM error. By default, the build parallelization
# is set to be the number of CPU minus 2. So, let's try a more conservative value here. A 4xlarge has
# 16 CPUs
MAX_JOBS=$(nproc --ignore=4)
export MAX_JOBS
# NB: Install outside of source directory (at the same level as the root
# pytorch folder) so that it doesn't get cleaned away prior to docker push.
BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
mkdir -p ../cpp-build/caffe2
pushd ../cpp-build/caffe2
WERROR=1 VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
popd
fi
fi
if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]]; then
# export test times so that potential sharded tests that'll branch off this build will use consistent data
# don't do this for libtorch as libtorch is C++ only and thus won't have python tests run on its build
python tools/stats/export_test_times.py
fi
print_sccache_stats

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@ -1,58 +0,0 @@
#!/bin/bash
# Required environment variables:
# $BUILD_ENVIRONMENT (should be set by your Docker image)
if [[ "$BUILD_ENVIRONMENT" != *win-* ]]; then
# Save the absolute path in case later we chdir (as occurs in the gpu perf test)
script_dir="$( cd "$(dirname "${BASH_SOURCE[0]}")" || exit ; pwd -P )"
if which sccache > /dev/null; then
# Save sccache logs to file
sccache --stop-server > /dev/null 2>&1 || true
rm -f ~/sccache_error.log || true
function sccache_epilogue() {
echo "::group::Sccache Compilation Log"
echo '=================== sccache compilation log ==================='
python "$script_dir/print_sccache_log.py" ~/sccache_error.log 2>/dev/null || true
echo '=========== If your build fails, please take a look at the log above for possible reasons ==========='
sccache --show-stats
sccache --stop-server || true
echo "::endgroup::"
}
# Register the function here so that the error log can be printed even when
# sccache fails to start, i.e. timeout error
trap_add sccache_epilogue EXIT
if [[ -n "${SKIP_SCCACHE_INITIALIZATION:-}" ]]; then
# sccache --start-server seems to hang forever on self hosted runners for GHA
# so let's just go ahead and skip the --start-server altogether since it seems
# as though sccache still gets used even when the sscache server isn't started
# explicitly
echo "Skipping sccache server initialization, setting environment variables"
export SCCACHE_IDLE_TIMEOUT=1200
export SCCACHE_ERROR_LOG=~/sccache_error.log
export RUST_LOG=sccache::server=error
elif [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
else
# increasing SCCACHE_IDLE_TIMEOUT so that extension_backend_test.cpp can build after this PR:
# https://github.com/pytorch/pytorch/pull/16645
SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=1200 RUST_LOG=sccache::server=error sccache --start-server
fi
# Report sccache stats for easier debugging
sccache --zero-stats
fi
if which ccache > /dev/null; then
# Report ccache stats for easier debugging
ccache --zero-stats
ccache --show-stats
function ccache_epilogue() {
ccache --show-stats
}
trap_add ccache_epilogue EXIT
fi
fi

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@ -1,28 +0,0 @@
#!/bin/bash
# Common setup for all Jenkins scripts
# shellcheck source=./common_utils.sh
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
set -ex
# Required environment variables:
# $BUILD_ENVIRONMENT (should be set by your Docker image)
# Figure out which Python to use for ROCm
if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
# HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
unset HIP_PLATFORM
export PYTORCH_TEST_WITH_ROCM=1
# temporary to locate some kernel issues on the CI nodes
export HSAKMT_DEBUG_LEVEL=4
# improve rccl performance for distributed tests
export HSA_FORCE_FINE_GRAIN_PCIE=1
fi
# TODO: Renable libtorch testing for MacOS, see https://github.com/pytorch/pytorch/issues/62598
# shellcheck disable=SC2034
BUILD_TEST_LIBTORCH=0
retry () {
"$@" || (sleep 1 && "$@") || (sleep 2 && "$@")
}

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@ -1,243 +0,0 @@
#!/bin/bash
# Common util **functions** that can be sourced in other scripts.
# note: printf is used instead of echo to avoid backslash
# processing and to properly handle values that begin with a '-'.
log() { printf '%s\n' "$*"; }
error() { log "ERROR: $*" >&2; }
fatal() { error "$@"; exit 1; }
retry () {
"$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
}
# compositional trap taken from https://stackoverflow.com/a/7287873/23845
# appends a command to a trap
#
# - 1st arg: code to add
# - remaining args: names of traps to modify
#
trap_add() {
trap_add_cmd=$1; shift || fatal "${FUNCNAME[0]} usage error"
for trap_add_name in "$@"; do
trap -- "$(
# helper fn to get existing trap command from output
# of trap -p
extract_trap_cmd() { printf '%s\n' "$3"; }
# print existing trap command with newline
eval "extract_trap_cmd $(trap -p "${trap_add_name}")"
# print the new trap command
printf '%s\n' "${trap_add_cmd}"
)" "${trap_add_name}" \
|| fatal "unable to add to trap ${trap_add_name}"
done
}
# set the trace attribute for the above function. this is
# required to modify DEBUG or RETURN traps because functions don't
# inherit them unless the trace attribute is set
declare -f -t trap_add
function assert_git_not_dirty() {
# TODO: we should add an option to `build_amd.py` that reverts the repo to
# an unmodified state.
if [[ "$BUILD_ENVIRONMENT" != *rocm* ]] && [[ "$BUILD_ENVIRONMENT" != *xla* ]] ; then
git_status=$(git status --porcelain)
if [[ $git_status ]]; then
echo "Build left local git repository checkout dirty"
echo "git status --porcelain:"
echo "${git_status}"
exit 1
fi
fi
}
function pip_install_whl() {
# This is used to install PyTorch and other build artifacts wheel locally
# without using any network connection
python3 -mpip install --no-index --no-deps "$@"
}
function pip_install() {
# retry 3 times
# old versions of pip don't have the "--progress-bar" flag
pip install --progress-bar off "$@" || pip install --progress-bar off "$@" || pip install --progress-bar off "$@" ||\
pip install "$@" || pip install "$@" || pip install "$@"
}
function pip_uninstall() {
# uninstall 2 times
pip uninstall -y "$@" || pip uninstall -y "$@"
}
function get_exit_code() {
set +e
"$@"
retcode=$?
set -e
return $retcode
}
function get_bazel() {
if [[ $(uname) == "Darwin" ]]; then
# download bazel version
retry curl https://github.com/bazelbuild/bazel/releases/download/4.2.1/bazel-4.2.1-darwin-x86_64 -Lo tools/bazel
# verify content
echo '74d93848f0c9d592e341e48341c53c87e3cb304a54a2a1ee9cff3df422f0b23c tools/bazel' | shasum -a 256 -c >/dev/null
else
# download bazel version
retry curl https://ossci-linux.s3.amazonaws.com/bazel-4.2.1-linux-x86_64 -o tools/bazel
# verify content
echo '1a4f3a3ce292307bceeb44f459883859c793436d564b95319aacb8af1f20557c tools/bazel' | shasum -a 256 -c >/dev/null
fi
chmod +x tools/bazel
}
function install_monkeytype {
# Install MonkeyType
pip_install MonkeyType
}
function get_pinned_commit() {
cat .github/ci_commit_pins/"${1}".txt
}
function install_torchtext() {
local commit
commit=$(get_pinned_commit text)
pip_install --no-use-pep517 --user "git+https://github.com/pytorch/text.git@${commit}"
}
function install_torchvision() {
local commit
commit=$(get_pinned_commit vision)
pip_install --no-use-pep517 --user "git+https://github.com/pytorch/vision.git@${commit}"
}
function clone_pytorch_xla() {
if [[ ! -d ./xla ]]; then
git clone --recursive -b r2.0 --quiet https://github.com/pytorch/xla.git
pushd xla
# pin the xla hash so that we don't get broken by changes to xla
git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"
git submodule sync
git submodule update --init --recursive
popd
fi
}
function install_filelock() {
pip_install filelock
}
function install_triton() {
local commit
commit=$(get_pinned_commit triton)
local short_hash
short_hash=$(echo "${commit}"|cut -c -10)
local index_url
index_url=https://download.pytorch.org/whl/nightly/cpu
if [[ "${TEST_CONFIG}" == *rocm* ]]; then
echo "skipping triton due to rocm"
elif pip install "pytorch-triton==2.0.0+${short_hash}" --index-url "${index_url}"; then
echo "Using prebuilt version ${short_hash}"
else
if [[ "${BUILD_ENVIRONMENT}" == *gcc7* ]]; then
# Trition needs gcc-9 to build
sudo apt-get install -y g++-9
CXX=g++-9 pip_install --user "git+https://github.com/openai/triton@${commit}#subdirectory=python"
elif [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
# Trition needs <filesystem> which surprisingly is not available with clang-9 toolchain
sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test
sudo apt-get install -y g++-9
CXX=g++-9 pip_install --user "git+https://github.com/openai/triton@${commit}#subdirectory=python"
else
pip_install --user "git+https://github.com/openai/triton@${commit}#subdirectory=python"
fi
pip_install --user jinja2
fi
}
function setup_torchdeploy_deps(){
conda install -y -n "py_${ANACONDA_PYTHON_VERSION}" "libpython-static=${ANACONDA_PYTHON_VERSION}"
local CC
local CXX
CC="$(which gcc)"
CXX="$(which g++)"
export CC
export CXX
pip install --upgrade pip
}
function checkout_install_torchdeploy() {
local commit
commit=$(get_pinned_commit multipy)
setup_torchdeploy_deps
pushd ..
git clone --recurse-submodules https://github.com/pytorch/multipy.git
pushd multipy
git checkout "${commit}"
python multipy/runtime/example/generate_examples.py
pip install -e . --install-option="--cudatests"
popd
popd
}
function test_torch_deploy(){
pushd ..
pushd multipy
./multipy/runtime/build/test_deploy
./multipy/runtime/build/test_deploy_gpu
popd
popd
}
function install_huggingface() {
local commit
commit=$(get_pinned_commit huggingface)
pip_install pandas
pip_install scipy
pip_install "git+https://github.com/huggingface/transformers.git@${commit}#egg=transformers"
}
function install_timm() {
local commit
commit=$(get_pinned_commit timm)
pip_install pandas
pip_install scipy
pip_install "git+https://github.com/rwightman/pytorch-image-models@${commit}"
}
function checkout_install_torchbench() {
git clone https://github.com/pytorch/benchmark torchbench
pushd torchbench
git checkout no_torchaudio
if [ "$1" ]; then
python install.py --continue_on_fail models "$@"
else
# Occasionally the installation may fail on one model but it is ok to continue
# to install and test other models
python install.py --continue_on_fail
fi
popd
}
function test_functorch() {
python test/run_test.py --functorch --verbose
}
function print_sccache_stats() {
echo 'PyTorch Build Statistics'
sccache --show-stats
if [[ -n "${OUR_GITHUB_JOB_ID}" ]]; then
sccache --show-stats --stats-format json | jq .stats \
> "sccache-stats-${BUILD_ENVIRONMENT}-${OUR_GITHUB_JOB_ID}.json"
else
echo "env var OUR_GITHUB_JOB_ID not set, will not write sccache stats to json"
fi
}

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#!/bin/bash
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
docker build -t pytorch .

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#!/bin/bash
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
echo "Testing pytorch docs"
cd docs
pip_install -r requirements.txt
make doctest

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#!/bin/bash
# Common prelude for macos-build.sh and macos-test.sh
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
sysctl -a | grep machdep.cpu
# These are required for both the build job and the test job.
# In the latter to test cpp extensions.
export MACOSX_DEPLOYMENT_TARGET=10.9
export CXX=clang++
export CC=clang

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#!/bin/bash
# shellcheck disable=SC2034
# shellcheck source=./macos-common.sh
source "$(dirname "${BASH_SOURCE[0]}")/macos-common.sh"
if [[ -n "$CONDA_ENV" ]]; then
# Use binaries under conda environment
export PATH="$CONDA_ENV/bin":$PATH
fi
# Test that OpenMP is enabled for non-arm64 build
if [[ ${BUILD_ENVIRONMENT} != *arm64* ]]; then
pushd test
if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available()))") == "1" ]]; then
echo "Build should have OpenMP enabled, but torch.backends.openmp.is_available() is False"
exit 1
fi
popd
fi
setup_test_python() {
# The CircleCI worker hostname doesn't resolve to an address.
# This environment variable makes ProcessGroupGloo default to
# using the address associated with the loopback interface.
export GLOO_SOCKET_IFNAME=lo0
echo "Ninja version: $(ninja --version)"
# Increase default limit on open file handles from 256 to 1024
ulimit -n 1024
}
test_python_all() {
setup_test_python
time python test/run_test.py --verbose --exclude-jit-executor
assert_git_not_dirty
}
test_python_shard() {
if [[ -z "$NUM_TEST_SHARDS" ]]; then
echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
exit 1
fi
setup_test_python
time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --shard "$1" "$NUM_TEST_SHARDS"
assert_git_not_dirty
}
test_libtorch() {
# C++ API
if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
# NB: Install outside of source directory (at the same level as the root
# pytorch folder) so that it doesn't get cleaned away prior to docker push.
# But still clean it before we perform our own build.
echo "Testing libtorch"
CPP_BUILD="$PWD/../cpp-build"
rm -rf "$CPP_BUILD"
mkdir -p "$CPP_BUILD"/caffe2
BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
pushd "$CPP_BUILD"/caffe2
VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
popd
python tools/download_mnist.py --quiet -d test/cpp/api/mnist
# Unfortunately it seems like the test can't load from miniconda3
# without these paths being set
export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$PWD/miniconda3/lib"
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/miniconda3/lib"
TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" "$CPP_BUILD"/caffe2/bin/test_api
assert_git_not_dirty
fi
}
print_cmake_info() {
CMAKE_EXEC=$(which cmake)
echo "$CMAKE_EXEC"
CONDA_INSTALLATION_DIR=$(dirname "$CMAKE_EXEC")
# Print all libraries under cmake rpath for debugging
ls -la "$CONDA_INSTALLATION_DIR/../lib"
export CMAKE_EXEC
# Explicitly add conda env lib folder to cmake rpath to address the flaky issue
# where cmake dependencies couldn't be found. This seems to point to how conda
# links $CMAKE_EXEC to its package cache when cloning a new environment
install_name_tool -add_rpath @executable_path/../lib "${CMAKE_EXEC}" || true
# Adding the rpath will invalidate cmake signature, so signing it again here
# to trust the executable. EXC_BAD_ACCESS (SIGKILL (Code Signature Invalid))
# with an exit code 137 otherwise
codesign -f -s - "${CMAKE_EXEC}" || true
}
test_custom_backend() {
print_cmake_info
echo "Testing custom backends"
pushd test/custom_backend
rm -rf build && mkdir build
pushd build
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
make VERBOSE=1
popd
# Run Python tests and export a lowered module.
python test_custom_backend.py -v
python backend.py --export-module-to=model.pt
# Run C++ tests using the exported module.
build/test_custom_backend ./model.pt
rm -f ./model.pt
popd
assert_git_not_dirty
}
test_custom_script_ops() {
print_cmake_info
echo "Testing custom script operators"
pushd test/custom_operator
# Build the custom operator library.
rm -rf build && mkdir build
pushd build
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
make VERBOSE=1
popd
# Run tests Python-side and export a script module.
python test_custom_ops.py -v
python model.py --export-script-module=model.pt
# Run tests C++-side and load the exported script module.
build/test_custom_ops ./model.pt
popd
assert_git_not_dirty
}
test_jit_hooks() {
print_cmake_info
echo "Testing jit hooks in cpp"
pushd test/jit_hooks
# Build the custom operator library.
rm -rf build && mkdir build
pushd build
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
make VERBOSE=1
popd
# Run tests Python-side and export a script module.
python model.py --export-script-module=model
# Run tests C++-side and load the exported script module.
build/test_jit_hooks ./model
popd
assert_git_not_dirty
}
if [[ "${TEST_CONFIG}" == *functorch* ]]; then
test_functorch
elif [[ $NUM_TEST_SHARDS -gt 1 ]]; then
test_python_shard "${SHARD_NUMBER}"
if [[ "${SHARD_NUMBER}" == 1 ]]; then
test_libtorch
test_custom_script_ops
elif [[ "${SHARD_NUMBER}" == 2 ]]; then
test_jit_hooks
test_custom_backend
fi
else
test_python_all
test_libtorch
test_custom_script_ops
test_jit_hooks
test_custom_backend
fi

View File

@ -1,49 +0,0 @@
#!/bin/bash
# Required environment variable: $BUILD_ENVIRONMENT
# (This is set by default in the Docker images we build, so you don't
# need to set it yourself.
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
echo "Testing pytorch"
# Disabling tests to see if they solve timeout issues; see https://github.com/pytorch/pytorch/issues/70015
# python tools/download_mnist.py --quiet -d test/cpp/api/mnist
# OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" build/bin/test_api
time python test/run_test.py --verbose -i distributed/test_c10d_common
time python test/run_test.py --verbose -i distributed/test_c10d_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_nccl
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
time python test/run_test.py --verbose -i distributed/test_store
time python test/run_test.py --verbose -i distributed/test_pg_wrapper
time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
# FSDP tests
for f in test/distributed/fsdp/*.py ; do time python test/run_test.py --verbose -i "${f#*/}" ; done
# ShardedTensor tests
time python test/run_test.py --verbose -i distributed/checkpoint/test_checkpoint
time python test/run_test.py --verbose -i distributed/checkpoint/test_file_system_checkpoint
time python test/run_test.py --verbose -i distributed/_shard/sharding_spec/test_sharding_spec
time python test/run_test.py --verbose -i distributed/_shard/sharding_plan/test_sharding_plan
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_megatron_prototype
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor_reshard
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_chunk
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_elementwise_ops
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_embedding
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_embedding_bag
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_binary_cmp
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_init
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_linear
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_math_ops
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_matrix_ops
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/ops/test_softmax
time python test/run_test.py --verbose -i distributed/_shard/sharded_optim/test_sharded_optim
time python test/run_test.py --verbose -i distributed/_shard/test_partial_tensor
time python test/run_test.py --verbose -i distributed/_shard/test_replicated_tensor
# Other tests
time python test/run_test.py --verbose -i test_cuda_primary_ctx
time python test/run_test.py --verbose -i test_optim -- -k optimizers_with_varying_tensors
assert_git_not_dirty

View File

@ -1,71 +0,0 @@
#!/bin/bash
SCRIPT_PARENT_DIR=$(dirname "${BASH_SOURCE[0]}")
# shellcheck source=.ci/pytorch/common.sh
source "$SCRIPT_PARENT_DIR/common.sh"
cd .ci/pytorch/perf_test
echo "Running CPU perf test for PyTorch..."
pip install -q awscli
# Set multipart_threshold to be sufficiently high, so that `aws s3 cp` is not a multipart read
# More info at https://github.com/aws/aws-cli/issues/2321
aws configure set default.s3.multipart_threshold 5GB
UPSTREAM_DEFAULT_BRANCH="$(git remote show https://github.com/pytorch/pytorch.git | awk '/HEAD branch/ {print $NF}')"
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
# Get current default branch commit hash
DEFAULT_BRANCH_COMMIT_ID=$(git log --format="%H" -n 1)
export DEFAULT_BRANCH_COMMIT_ID
fi
# Find the default branch commit to test against
git remote add upstream https://github.com/pytorch/pytorch.git
git fetch upstream
IFS=$'\n'
while IFS='' read -r commit_id; do
if aws s3 ls s3://ossci-perf-test/pytorch/cpu_runtime/"${commit_id}".json; then
LATEST_TESTED_COMMIT=${commit_id}
break
fi
done < <(git rev-list upstream/"$UPSTREAM_DEFAULT_BRANCH")
aws s3 cp s3://ossci-perf-test/pytorch/cpu_runtime/"${LATEST_TESTED_COMMIT}".json cpu_runtime.json
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
# Prepare new baseline file
cp cpu_runtime.json new_cpu_runtime.json
python update_commit_hash.py new_cpu_runtime.json "${DEFAULT_BRANCH_COMMIT_ID}"
fi
# Include tests
# shellcheck source=./perf_test/test_cpu_speed_mini_sequence_labeler.sh
. ./test_cpu_speed_mini_sequence_labeler.sh
# shellcheck source=./perf_test/test_cpu_speed_mnist.sh
. ./test_cpu_speed_mnist.sh
# shellcheck source=./perf_test/test_cpu_speed_torch.sh
. ./test_cpu_speed_torch.sh
# shellcheck source=./perf_test/test_cpu_speed_torch_tensor.sh
. ./test_cpu_speed_torch_tensor.sh
# Run tests
export TEST_MODE="compare_with_baseline"
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
export TEST_MODE="compare_and_update"
fi
# Operator tests
run_test test_cpu_speed_torch ${TEST_MODE}
run_test test_cpu_speed_torch_tensor ${TEST_MODE}
# Sample model tests
run_test test_cpu_speed_mini_sequence_labeler 20 ${TEST_MODE}
run_test test_cpu_speed_mnist 20 ${TEST_MODE}
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
# This could cause race condition if we are testing the same default branch commit twice,
# but the chance of them executing this line at the same time is low.
aws s3 cp new_cpu_runtime.json s3://ossci-perf-test/pytorch/cpu_runtime/"${DEFAULT_BRANCH_COMMIT_ID}".json --acl public-read
fi

View File

@ -1,76 +0,0 @@
#!/bin/bash
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
pushd .ci/pytorch/perf_test
echo "Running GPU perf test for PyTorch..."
# Trying to uninstall PyYAML can cause problem. Workaround according to:
# https://github.com/pypa/pip/issues/5247#issuecomment-415571153
pip install -q awscli --ignore-installed PyYAML
# Set multipart_threshold to be sufficiently high, so that `aws s3 cp` is not a multipart read
# More info at https://github.com/aws/aws-cli/issues/2321
aws configure set default.s3.multipart_threshold 5GB
UPSTREAM_DEFAULT_BRANCH="$(git remote show https://github.com/pytorch/pytorch.git | awk '/HEAD branch/ {print $NF}')"
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
# Get current default branch commit hash
DEFAULT_BRANCH_COMMIT_ID=$(git log --format="%H" -n 1)
export DEFAULT_BRANCH_COMMIT_ID
fi
# Find the default branch commit to test against
git remote add upstream https://github.com/pytorch/pytorch.git
git fetch upstream
IFS=$'\n'
while IFS='' read -r commit_id; do
if aws s3 ls s3://ossci-perf-test/pytorch/gpu_runtime/"${commit_id}".json; then
LATEST_TESTED_COMMIT=${commit_id}
break
fi
done < <(git rev-list upstream/"$UPSTREAM_DEFAULT_BRANCH")
aws s3 cp s3://ossci-perf-test/pytorch/gpu_runtime/"${LATEST_TESTED_COMMIT}".json gpu_runtime.json
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
# Prepare new baseline file
cp gpu_runtime.json new_gpu_runtime.json
python update_commit_hash.py new_gpu_runtime.json "${DEFAULT_BRANCH_COMMIT_ID}"
fi
# Include tests
# shellcheck source=./perf_test/test_gpu_speed_mnist.sh
. ./test_gpu_speed_mnist.sh
# shellcheck source=./perf_test/test_gpu_speed_word_language_model.sh
. ./test_gpu_speed_word_language_model.sh
# shellcheck source=./perf_test/test_gpu_speed_cudnn_lstm.sh
. ./test_gpu_speed_cudnn_lstm.sh
# shellcheck source=./perf_test/test_gpu_speed_lstm.sh
. ./test_gpu_speed_lstm.sh
# shellcheck source=./perf_test/test_gpu_speed_mlstm.sh
. ./test_gpu_speed_mlstm.sh
# Run tests
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
run_test test_gpu_speed_mnist 20 compare_and_update
run_test test_gpu_speed_word_language_model 20 compare_and_update
run_test test_gpu_speed_cudnn_lstm 20 compare_and_update
run_test test_gpu_speed_lstm 20 compare_and_update
run_test test_gpu_speed_mlstm 20 compare_and_update
else
run_test test_gpu_speed_mnist 20 compare_with_baseline
run_test test_gpu_speed_word_language_model 20 compare_with_baseline
run_test test_gpu_speed_cudnn_lstm 20 compare_with_baseline
run_test test_gpu_speed_lstm 20 compare_with_baseline
run_test test_gpu_speed_mlstm 20 compare_with_baseline
fi
if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
# This could cause race condition if we are testing the same default branch commit twice,
# but the chance of them executing this line at the same time is low.
aws s3 cp new_gpu_runtime.json s3://ossci-perf-test/pytorch/gpu_runtime/"${DEFAULT_BRANCH_COMMIT_ID}".json --acl public-read
fi
popd

File diff suppressed because it is too large Load Diff

View File

@ -1,19 +0,0 @@
call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
:: exit the batch once there's an error
if not errorlevel 0 (
echo "setup pytorch env failed"
echo %errorlevel%
exit /b
)
echo "Test functorch"
pushd test
python run_test.py --functorch --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
popd
if ERRORLEVEL 1 goto fail
:eof
exit /b 0
:fail
exit /b 1

View File

@ -1,26 +0,0 @@
if "%BUILD_ENVIRONMENT%"=="" (
set CONDA_PARENT_DIR=%CD%
) else (
set CONDA_PARENT_DIR=C:\Jenkins
)
:: Be conservative here when rolling out the new AMI with conda. This will try
:: to install conda as before if it couldn't find the conda installation. This
:: can be removed eventually after we gain enough confidence in the AMI
if not exist %CONDA_PARENT_DIR%\Miniconda3 (
set INSTALL_FRESH_CONDA=1
)
if "%INSTALL_FRESH_CONDA%"=="1" (
curl --retry 3 --retry-all-errors -k https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
if errorlevel 1 exit /b
if not errorlevel 0 exit /b
%TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
if errorlevel 1 exit /b
if not errorlevel 0 exit /b
)
:: Activate conda so that we can use its commands, i.e. conda, python, pip
call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3

View File

@ -1,18 +0,0 @@
mkdir %TMP_DIR_WIN%\bin
if "%REBUILD%"=="" (
:check_sccache
%TMP_DIR_WIN%\bin\sccache.exe --show-stats || (
taskkill /im sccache.exe /f /t || ver > nul
del %TMP_DIR_WIN%\bin\sccache.exe || ver > nul
del %TMP_DIR_WIN%\bin\sccache-cl.exe || ver > nul
if "%BUILD_ENVIRONMENT%"=="" (
curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output %TMP_DIR_WIN%\bin\sccache.exe
curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache-cl.exe --output %TMP_DIR_WIN%\bin\sccache-cl.exe
) else (
aws s3 cp s3://ossci-windows/sccache.exe %TMP_DIR_WIN%\bin\sccache.exe
aws s3 cp s3://ossci-windows/sccache-cl.exe %TMP_DIR_WIN%\bin\sccache-cl.exe
)
goto :check_sccache
)
)

View File

@ -1,37 +0,0 @@
call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
:: exit the batch once there's an error
if not errorlevel 0 (
echo "setup pytorch env failed"
echo %errorlevel%
exit /b
)
pushd test
set GFLAGS_EXE="C:\Program Files (x86)\Windows Kits\10\Debuggers\x64\gflags.exe"
if "%SHARD_NUMBER%" == "1" (
if exist %GFLAGS_EXE% (
echo Some smoke tests
%GFLAGS_EXE% /i python.exe +sls
python %SCRIPT_HELPERS_DIR%\run_python_nn_smoketests.py
if ERRORLEVEL 1 goto fail
%GFLAGS_EXE% /i python.exe -sls
if ERRORLEVEL 1 goto fail
)
)
echo Copying over test times file
copy /Y "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.pytorch-test-times.json" "%PROJECT_DIR_WIN%"
echo Run nn tests
python run_test.py --exclude-jit-executor --exclude-distributed-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
if ERRORLEVEL 1 goto fail
popd
:eof
exit /b 0
:fail
exit /b 1

View File

@ -1,86 +0,0 @@
#!/bin/bash
set -ex
SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
# shellcheck source=./common.sh
source "$SCRIPT_PARENT_DIR/common.sh"
IMAGE_COMMIT_ID=$(git rev-parse HEAD)
export IMAGE_COMMIT_ID
export IMAGE_COMMIT_TAG=${BUILD_ENVIRONMENT}-${IMAGE_COMMIT_ID}
if [[ ${JOB_NAME} == *"develop"* ]]; then
export IMAGE_COMMIT_TAG=develop-${IMAGE_COMMIT_TAG}
fi
export TMP_DIR="${PWD}/build/win_tmp"
TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
export TMP_DIR_WIN
export PROJECT_DIR="${PWD}"
PROJECT_DIR_WIN=$(cygpath -w "${PROJECT_DIR}")
export PROJECT_DIR_WIN
export TEST_DIR="${PWD}/test"
TEST_DIR_WIN=$(cygpath -w "${TEST_DIR}")
export TEST_DIR_WIN
export PYTORCH_FINAL_PACKAGE_DIR="${PYTORCH_FINAL_PACKAGE_DIR:-/c/users/circleci/workspace/build-results}"
PYTORCH_FINAL_PACKAGE_DIR_WIN=$(cygpath -w "${PYTORCH_FINAL_PACKAGE_DIR}")
export PYTORCH_FINAL_PACKAGE_DIR_WIN
mkdir -p "$TMP_DIR"/build/torch
# This directory is used only to hold "pytorch_env_restore.bat", called via "setup_pytorch_env.bat"
CI_SCRIPTS_DIR=$TMP_DIR/ci_scripts
mkdir -p "$CI_SCRIPTS_DIR"
if [ -n "$(ls "$CI_SCRIPTS_DIR"/*)" ]; then
rm "$CI_SCRIPTS_DIR"/*
fi
export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
if [[ "$TEST_CONFIG" = "force_on_cpu" ]]; then
# run the full test suite for force_on_cpu test
export USE_CUDA=0
fi
if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
# Used so that only cuda/rocm specific versions of tests are generated
# mainly used so that we're not spending extra cycles testing cpu
# devices on expensive gpu machines
export PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda"
fi
run_tests() {
# Run nvidia-smi if available
for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do
if [[ -x "$path" ]]; then
"$path" || echo "true";
break
fi
done
if [[ "${TEST_CONFIG}" == *functorch* ]]; then
"$SCRIPT_HELPERS_DIR"/install_test_functorch.bat
elif [[ $NUM_TEST_SHARDS -eq 1 ]]; then
"$SCRIPT_HELPERS_DIR"/test_python_shard.bat
"$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
"$SCRIPT_HELPERS_DIR"/test_custom_backend.bat
"$SCRIPT_HELPERS_DIR"/test_libtorch.bat
else
"$SCRIPT_HELPERS_DIR"/test_python_shard.bat
if [[ "${SHARD_NUMBER}" == 1 && $NUM_TEST_SHARDS -gt 1 ]]; then
"$SCRIPT_HELPERS_DIR"/test_libtorch.bat
if [[ "${USE_CUDA}" == "1" ]]; then
"$SCRIPT_HELPERS_DIR"/test_python_jit_legacy.bat
fi
elif [[ "${SHARD_NUMBER}" == 2 && $NUM_TEST_SHARDS -gt 1 ]]; then
"$SCRIPT_HELPERS_DIR"/test_custom_backend.bat
"$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
fi
fi
}
run_tests
assert_git_not_dirty
echo "TEST PASSED"

View File

@ -1,8 +1,3 @@
Warning
=======
Contents may be out of date. Our CircleCI workflows are gradually being migrated to Github actions.
Structure of CI
===============
@ -21,6 +16,8 @@ setup job:
not, even if there isn't a Git checkout.
CircleCI configuration generator
================================
@ -59,6 +56,7 @@ See comment [here](https://github.com/pytorch/pytorch/pull/17323#pullrequestrevi
In contrast with a full recursive tree traversal of configuration dimensions,
> in the future I think we actually want to decrease our matrix somewhat and have only a few mostly-orthogonal builds that taste as many different features as possible on PRs, plus a more complete suite on every PR and maybe an almost full suite nightly/weekly (we don't have this yet). Specifying PR jobs in the future might be easier to read with an explicit list when we come to this.
----------------
----------------
@ -73,9 +71,9 @@ A **binary configuration** is a collection of
* release or nightly
* releases are stable, nightlies are beta and built every night
* python version
* linux: 3.7m (mu is wide unicode or something like that. It usually doesn't matter but you should know that it exists)
* macos: 3.7, 3.8
* windows: 3.7, 3.8
* linux: 3.5m, 3.6m 3.7m (mu is wide unicode or something like that. It usually doesn't matter but you should know that it exists)
* macos: 3.6, 3.7, 3.8
* windows: 3.6, 3.7, 3.8
* cpu version
* cpu, cuda 9.0, cuda 10.0
* The supported cuda versions occasionally change
@ -190,6 +188,18 @@ binary_run_in_docker.sh is a way to share the docker start-up code between the b
We want all the nightly binary jobs to run on the exact same git commit, so we wrote our own checkout logic to ensure that the same commit was always picked. Later circleci changed that to use a single pytorch checkout and persist it through the workspace (they did this because our config file was too big, so they wanted to take a lot of the setup code into scripts, but the scripts needed the code repo to exist to be called, so they added a prereq step called 'setup' to checkout the code and persist the needed scripts to the workspace). The changes to the binary jobs were not properly tested, so they all broke from missing pytorch code no longer existing. We hotfixed the problem by adding the pytorch checkout back to binary_checkout, so now there's two checkouts of pytorch on the binary jobs. This problem still needs to be fixed, but it takes careful tracing of which code is being called where.
# Azure Pipelines structure of the binaries
TODO: fill in stuff
## How are the workflows structured?
TODO: fill in stuff
## How are the jobs structured?
TODO: fill in stuff
# Code structure of the binaries (circleci agnostic)
## Overview
@ -205,22 +215,28 @@ pytorch/pytorch
- config.yml # GENERATED file that actually controls all circleci behavior
- verbatim-sources # Used to generate job/workflow sections in ^
- scripts/ # Code needed to prepare circleci environments for binary build scripts
- setup.py # Builds pytorch. This is wrapped in pytorch/builder
- cmake files # used in normal building of pytorch
# All code needed to prepare a binary build, given an environment
# with all the right variables/packages/paths.
pytorch/builder
# Given an installed binary and a proper python env, runs some checks
# to make sure the binary was built the proper way. Checks things like
# the library dependencies, symbols present, etc.
- check_binary.sh
# Given an installed binary, runs python tests to make sure everything
# is in order. These should be de-duped. Right now they both run smoke
# tests, but are called from different places. Usually just call some
# import statements, but also has overlap with check_binary.sh above
- run_tests.sh
- smoke_test.sh
# Folders that govern how packages are built. See paragraphs below
- conda/
- build_pytorch.sh # Entrypoint. Delegates to proper conda build folder
- switch_cuda_version.sh # Switches activate CUDA installation in Docker
@ -349,28 +365,36 @@ Writing PRs that test the binaries is annoying, since the default circleci jobs
```sh
# Make your changes
touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
# Regenerate the yaml, has to be in python 3.7
.circleci/regenerate.sh
# Make a commit
git add .circleci *
git commit -m "My real changes"
git push origin my_branch
# Now hardcode the jobs that you want in the .circleci/config.yml workflows section
# Also eliminate ensure-consistency and should_run_job checks
# e.g. https://github.com/pytorch/pytorch/commit/2b3344bfed8772fe86e5210cc4ee915dee42b32d
# Make a commit you won't keep
git add .circleci
git commit -m "[DO NOT LAND] testing binaries for above changes"
git push origin my_branch
# Now you need to make some changes to the first commit.
git rebase -i HEAD~2 # mark the first commit as 'edit'
# Make the changes
touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
.circleci/regenerate.sh
# Ammend the commit and recontinue
git add .circleci
git commit --amend
git rebase --continue
# Update the PR, need to force since the commits are different now
git push origin my_branch --force
```
@ -399,12 +423,14 @@ docker run \
-v your/builder/repo:/builder \
-v where/you/want/packages/to/appear:/final_pkgs \
-it pytorch/conda-cuda /bin/bash
# Export whatever variables are important to you. All variables that you'd
# possibly need are in .circleci/scripts/binary_populate_env.sh
# You should probably always export at least these 3 variables
export PACKAGE_TYPE=conda
export DESIRED_PYTHON=3.7
export DESIRED_PYTHON=3.6
export DESIRED_CUDA=cpu
# Call the entrypoint
# `|& tee foo.log` just copies all stdout and stderr output to foo.log
# The builds generate lots of output so you probably need this when
@ -428,6 +454,7 @@ But if you want to try, then Id recommend
# Create a new terminal
# Clear your LD_LIBRARY_PATH and trim as much out of your PATH as you
# know how to do
# Install a new miniconda
# First remove any other python or conda installation from your PATH
# Always install miniconda 3, even if building for Python <3
@ -438,17 +465,20 @@ chmod +x "$conda_sh"
"$conda_sh" -b -p "$MINICONDA_ROOT"
rm -f "$conda_sh"
export PATH="~/my_new_conda/bin:$PATH"
# Create a clean python env
# All MacOS builds use conda to manage the python env and dependencies
# that are built with, even the pip packages
conda create -yn binary python=2.7
conda activate binary
# Export whatever variables are important to you. All variables that you'd
# possibly need are in .circleci/scripts/binary_populate_env.sh
# You should probably always export at least these 3 variables
export PACKAGE_TYPE=conda
export DESIRED_PYTHON=3.7
export DESIRED_PYTHON=3.6
export DESIRED_CUDA=cpu
# Call the entrypoint you want
path/to/builder/wheel/build_wheel.sh
```

View File

@ -31,6 +31,30 @@ def get_processor_arch_name(gpu_version):
)
CONFIG_TREE_DATA = OrderedDict(
macos=([None], OrderedDict(
wheel=dimensions.STANDARD_PYTHON_VERSIONS,
conda=dimensions.STANDARD_PYTHON_VERSIONS,
libtorch=[
"3.7",
],
)),
macos_arm64=([None], OrderedDict(
wheel=[
"3.8",
"3.9",
],
conda=[
"3.8",
"3.9",
],
)),
windows=(
# Stop building Win+CU102, see https://github.com/pytorch/pytorch/issues/65648
[v for v in dimensions.GPU_VERSIONS if v not in dimensions.ROCM_VERSION_LABELS and v != "cuda102"],
OrderedDict(
conda=dimensions.STANDARD_PYTHON_VERSIONS,
)
),
)
# GCC config variants:
@ -57,7 +81,7 @@ WINDOWS_LIBTORCH_CONFIG_VARIANTS = [
class TopLevelNode(ConfigNode):
def __init__(self, node_name, config_tree_data, smoke):
super().__init__(None, node_name)
super(TopLevelNode, self).__init__(None, node_name)
self.config_tree_data = config_tree_data
self.props["smoke"] = smoke
@ -68,7 +92,7 @@ class TopLevelNode(ConfigNode):
class OSConfigNode(ConfigNode):
def __init__(self, parent, os_name, gpu_versions, py_tree):
super().__init__(parent, os_name)
super(OSConfigNode, self).__init__(parent, os_name)
self.py_tree = py_tree
self.props["os_name"] = os_name
@ -80,7 +104,7 @@ class OSConfigNode(ConfigNode):
class PackageFormatConfigNode(ConfigNode):
def __init__(self, parent, package_format, python_versions):
super().__init__(parent, package_format)
super(PackageFormatConfigNode, self).__init__(parent, package_format)
self.props["python_versions"] = python_versions
self.props["package_format"] = package_format
@ -97,7 +121,7 @@ class PackageFormatConfigNode(ConfigNode):
class LinuxGccConfigNode(ConfigNode):
def __init__(self, parent, gcc_config_variant):
super().__init__(parent, "GCC_CONFIG_VARIANT=" + str(gcc_config_variant))
super(LinuxGccConfigNode, self).__init__(parent, "GCC_CONFIG_VARIANT=" + str(gcc_config_variant))
self.props["gcc_config_variant"] = gcc_config_variant
@ -122,7 +146,7 @@ class LinuxGccConfigNode(ConfigNode):
class WindowsLibtorchConfigNode(ConfigNode):
def __init__(self, parent, libtorch_config_variant):
super().__init__(parent, "LIBTORCH_CONFIG_VARIANT=" + str(libtorch_config_variant))
super(WindowsLibtorchConfigNode, self).__init__(parent, "LIBTORCH_CONFIG_VARIANT=" + str(libtorch_config_variant))
self.props["libtorch_config_variant"] = libtorch_config_variant
@ -132,7 +156,7 @@ class WindowsLibtorchConfigNode(ConfigNode):
class ArchConfigNode(ConfigNode):
def __init__(self, parent, gpu):
super().__init__(parent, get_processor_arch_name(gpu))
super(ArchConfigNode, self).__init__(parent, get_processor_arch_name(gpu))
self.props["gpu"] = gpu
@ -142,7 +166,7 @@ class ArchConfigNode(ConfigNode):
class PyVersionConfigNode(ConfigNode):
def __init__(self, parent, pyver):
super().__init__(parent, pyver)
super(PyVersionConfigNode, self).__init__(parent, pyver)
self.props["pyver"] = pyver
@ -158,7 +182,7 @@ class PyVersionConfigNode(ConfigNode):
class LinkingVariantConfigNode(ConfigNode):
def __init__(self, parent, linking_variant):
super().__init__(parent, linking_variant)
super(LinkingVariantConfigNode, self).__init__(parent, linking_variant)
def get_children(self):
return [DependencyInclusionConfigNode(self, v) for v in DEPS_INCLUSION_DIMENSIONS]
@ -166,6 +190,6 @@ class LinkingVariantConfigNode(ConfigNode):
class DependencyInclusionConfigNode(ConfigNode):
def __init__(self, parent, deps_variant):
super().__init__(parent, deps_variant)
super(DependencyInclusionConfigNode, self).__init__(parent, deps_variant)
self.props["libtorch_variant"] = "-".join([self.parent.get_label(), self.get_label()])

View File

@ -2,9 +2,9 @@ PHASES = ["build", "test"]
CUDA_VERSIONS = [
"102",
"111",
"113",
"116",
"117",
"115",
]
ROCM_VERSIONS = [

View File

@ -12,7 +12,7 @@ def get_major_pyver(dotted_version):
class TreeConfigNode(ConfigNode):
def __init__(self, parent, node_name, subtree):
super().__init__(parent, self.modify_label(node_name))
super(TreeConfigNode, self).__init__(parent, self.modify_label(node_name))
self.subtree = subtree
self.init2(node_name)
@ -28,7 +28,7 @@ class TreeConfigNode(ConfigNode):
class TopLevelNode(TreeConfigNode):
def __init__(self, node_name, subtree):
super().__init__(None, node_name, subtree)
super(TopLevelNode, self).__init__(None, node_name, subtree)
# noinspection PyMethodMayBeStatic
def child_constructor(self):
@ -71,11 +71,10 @@ class ExperimentalFeatureConfigNode(TreeConfigNode):
next_nodes = {
"asan": AsanConfigNode,
"xla": XlaConfigNode,
"mps": MPSConfigNode,
"mlc": MLCConfigNode,
"vulkan": VulkanConfigNode,
"parallel_tbb": ParallelTBBConfigNode,
"crossref": CrossRefConfigNode,
"dynamo": DynamoConfigNode,
"noarch": NoarchConfigNode,
"parallel_native": ParallelNativeConfigNode,
"onnx": ONNXConfigNode,
"libtorch": LibTorchConfigNode,
@ -117,12 +116,12 @@ class XlaConfigNode(TreeConfigNode):
def child_constructor(self):
return ImportantConfigNode
class MPSConfigNode(TreeConfigNode):
class MLCConfigNode(TreeConfigNode):
def modify_label(self, label):
return "MPS=" + str(label)
return "MLC=" + str(label)
def init2(self, node_name):
self.props["is_mps"] = node_name
self.props["is_mlc"] = node_name
def child_constructor(self):
return ImportantConfigNode
@ -172,17 +171,9 @@ class ParallelTBBConfigNode(TreeConfigNode):
return ImportantConfigNode
class CrossRefConfigNode(TreeConfigNode):
class NoarchConfigNode(TreeConfigNode):
def init2(self, node_name):
self.props["is_crossref"] = node_name
def child_constructor(self):
return ImportantConfigNode
class DynamoConfigNode(TreeConfigNode):
def init2(self, node_name):
self.props["is_dynamo"] = node_name
self.props["is_noarch"] = node_name
def child_constructor(self):
return ImportantConfigNode

View File

@ -185,7 +185,7 @@ def gen_docs_configs(xenial_parent_config):
HiddenConf(
"pytorch_python_doc_build",
parent_build=xenial_parent_config,
filters=gen_filter_dict(branches_list=["master", "main", "nightly"],
filters=gen_filter_dict(branches_list=["master", "nightly"],
tags_list=RC_PATTERN),
)
)
@ -201,7 +201,7 @@ def gen_docs_configs(xenial_parent_config):
HiddenConf(
"pytorch_cpp_doc_build",
parent_build=xenial_parent_config,
filters=gen_filter_dict(branches_list=["master", "main", "nightly"],
filters=gen_filter_dict(branches_list=["master", "nightly"],
tags_list=RC_PATTERN),
)
)
@ -239,8 +239,7 @@ def instantiate_configs(only_slow_gradcheck):
compiler_version = fc.find_prop("compiler_version")
is_xla = fc.find_prop("is_xla") or False
is_asan = fc.find_prop("is_asan") or False
is_crossref = fc.find_prop("is_crossref") or False
is_dynamo = fc.find_prop("is_dynamo") or False
is_noarch = fc.find_prop("is_noarch") or False
is_onnx = fc.find_prop("is_onnx") or False
is_pure_torch = fc.find_prop("is_pure_torch") or False
is_vulkan = fc.find_prop("is_vulkan") or False
@ -284,11 +283,8 @@ def instantiate_configs(only_slow_gradcheck):
python_version = fc.find_prop("pyver")
parms_list[0] = fc.find_prop("abbreviated_pyver")
if is_crossref:
parms_list_ignored_for_docker_image.append("crossref")
if is_dynamo:
parms_list_ignored_for_docker_image.append("dynamo")
if is_noarch:
parms_list_ignored_for_docker_image.append("noarch")
if is_onnx:
parms_list.append("onnx")
@ -338,12 +334,13 @@ def instantiate_configs(only_slow_gradcheck):
build_only=build_only,
)
# run docs builds on "pytorch-linux-xenial-py3.7-gcc5.4". Docs builds
# run docs builds on "pytorch-linux-xenial-py3.6-gcc5.4". Docs builds
# should run on a CPU-only build that runs on all PRs.
# XXX should this be updated to a more modern build?
# XXX should this be updated to a more modern build? Projects are
# beginning to drop python3.6
if (
distro_name == "xenial"
and fc.find_prop("pyver") == "3.7"
and fc.find_prop("pyver") == "3.6"
and cuda_version is None
and parallel_backend is None
and not is_vulkan

View File

@ -0,0 +1,103 @@
import cimodel.data.simple.util.branch_filters as branch_filters
from cimodel.data.simple.util.docker_constants import (
DOCKER_IMAGE_NDK, DOCKER_REQUIREMENT_NDK
)
class AndroidJob:
def __init__(self,
variant,
template_name,
is_master_only=True):
self.variant = variant
self.template_name = template_name
self.is_master_only = is_master_only
def gen_tree(self):
base_name_parts = [
"pytorch",
"linux",
"xenial",
"py3",
"clang5",
"android",
"ndk",
"r19c",
] + self.variant + [
"build",
]
full_job_name = "_".join(base_name_parts)
build_env_name = "-".join(base_name_parts)
props_dict = {
"name": full_job_name,
"build_environment": "\"{}\"".format(build_env_name),
"docker_image": "\"{}\"".format(DOCKER_IMAGE_NDK),
"requires": [DOCKER_REQUIREMENT_NDK]
}
if self.is_master_only:
props_dict["filters"] = branch_filters.gen_filter_dict(branch_filters.NON_PR_BRANCH_LIST)
return [{self.template_name: props_dict}]
class AndroidGradleJob:
def __init__(self,
job_name,
template_name,
dependencies,
is_master_only=True,
is_pr_only=False,
extra_props=tuple()):
self.job_name = job_name
self.template_name = template_name
self.dependencies = dependencies
self.is_master_only = is_master_only
self.is_pr_only = is_pr_only
self.extra_props = dict(extra_props)
def gen_tree(self):
props_dict = {
"name": self.job_name,
"requires": self.dependencies,
}
if self.is_master_only:
props_dict["filters"] = branch_filters.gen_filter_dict(branch_filters.NON_PR_BRANCH_LIST)
elif self.is_pr_only:
props_dict["filters"] = branch_filters.gen_filter_dict(branch_filters.PR_BRANCH_LIST)
if self.extra_props:
props_dict.update(self.extra_props)
return [{self.template_name: props_dict}]
WORKFLOW_DATA = [
AndroidJob(["x86_32"], "pytorch_linux_build", is_master_only=False),
AndroidJob(["x86_64"], "pytorch_linux_build"),
AndroidJob(["arm", "v7a"], "pytorch_linux_build"),
AndroidJob(["arm", "v8a"], "pytorch_linux_build"),
AndroidGradleJob(
"pytorch-linux-xenial-py3-clang5-android-ndk-r19c-gradle-build-x86_32",
"pytorch_android_gradle_build-x86_32",
["pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build"],
is_master_only=False,
is_pr_only=True),
AndroidGradleJob(
"pytorch-linux-xenial-py3-clang5-android-ndk-r19c-gradle-build",
"pytorch_android_gradle_build",
["pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build",
"pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_64_build",
"pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v7a_build",
"pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v8a_build"]),
]
def get_workflow_jobs():
return [item.gen_tree() for item in WORKFLOW_DATA]

View File

@ -0,0 +1,193 @@
"""
TODO: Refactor circleci/cimodel/data/binary_build_data.py to generate this file
instead of doing one offs here
Binary builds (subset, to smoke test that they'll work)
NB: If you modify this file, you need to also modify
the binary_and_smoke_tests_on_pr variable in
pytorch-ci-hud to adjust the allowed build list
at https://github.com/ezyang/pytorch-ci-hud/blob/master/src/BuildHistoryDisplay.js
Note:
This binary build is currently broken, see https://github_com/pytorch/pytorch/issues/16710
- binary_linux_conda_3_6_cu90_devtoolset7_build
- binary_linux_conda_3_6_cu90_devtoolset7_test
TODO
we should test a libtorch cuda build, but they take too long
- binary_linux_libtorch_3_6m_cu90_devtoolset7_static-without-deps_build
"""
import cimodel.lib.miniutils as miniutils
import cimodel.data.simple.util.branch_filters
class SmoketestJob:
def __init__(self,
template_name,
build_env_parts,
docker_image,
job_name,
is_master_only=False,
requires=None,
has_libtorch_variant=False,
extra_props=None):
self.template_name = template_name
self.build_env_parts = build_env_parts
self.docker_image = docker_image
self.job_name = job_name
self.is_master_only = is_master_only
self.requires = requires or []
self.has_libtorch_variant = has_libtorch_variant
self.extra_props = extra_props or {}
def gen_tree(self):
props_dict = {
"build_environment": " ".join(self.build_env_parts),
"name": self.job_name,
"requires": self.requires,
}
if self.docker_image:
props_dict["docker_image"] = self.docker_image
if self.is_master_only:
props_dict["filters"] = cimodel.data.simple.util.branch_filters.gen_filter_dict()
if self.has_libtorch_variant:
props_dict["libtorch_variant"] = "shared-with-deps"
props_dict.update(self.extra_props)
return [{self.template_name: props_dict}]
WORKFLOW_DATA = [
SmoketestJob(
"binary_linux_build",
["manywheel", "3.7m", "cu102", "devtoolset7"],
"pytorch/manylinux-cuda102",
"binary_linux_manywheel_3_7m_cu102_devtoolset7_build",
is_master_only=True,
),
SmoketestJob(
"binary_linux_build",
["libtorch", "3.7m", "cpu", "devtoolset7"],
"pytorch/manylinux-cuda102",
"binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_build",
is_master_only=True,
has_libtorch_variant=True,
),
SmoketestJob(
"binary_linux_build",
["libtorch", "3.7m", "cpu", "gcc5.4_cxx11-abi"],
"pytorch/pytorch-binary-docker-image-ubuntu16.04:latest",
"binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build",
is_master_only=False,
has_libtorch_variant=True,
),
SmoketestJob(
"binary_mac_build",
["wheel", "3.7", "cpu"],
None,
"binary_macos_wheel_3_7_cpu_build",
is_master_only=True,
),
# This job has an average run time of 3 hours o.O
# Now only running this on master to reduce overhead
SmoketestJob(
"binary_mac_build",
["libtorch", "3.7", "cpu"],
None,
"binary_macos_libtorch_3_7_cpu_build",
is_master_only=True,
),
SmoketestJob(
"binary_windows_build",
["libtorch", "3.7", "cpu", "debug"],
None,
"binary_windows_libtorch_3_7_cpu_debug_build",
is_master_only=True,
),
SmoketestJob(
"binary_windows_build",
["libtorch", "3.7", "cpu", "release"],
None,
"binary_windows_libtorch_3_7_cpu_release_build",
is_master_only=True,
),
SmoketestJob(
"binary_windows_build",
["wheel", "3.7", "cu113"],
None,
"binary_windows_wheel_3_7_cu113_build",
is_master_only=True,
),
SmoketestJob(
"binary_windows_test",
["libtorch", "3.7", "cpu", "debug"],
None,
"binary_windows_libtorch_3_7_cpu_debug_test",
is_master_only=True,
requires=["binary_windows_libtorch_3_7_cpu_debug_build"],
),
SmoketestJob(
"binary_windows_test",
["libtorch", "3.7", "cpu", "release"],
None,
"binary_windows_libtorch_3_7_cpu_release_test",
is_master_only=False,
requires=["binary_windows_libtorch_3_7_cpu_release_build"],
),
SmoketestJob(
"binary_windows_test",
["wheel", "3.7", "cu113"],
None,
"binary_windows_wheel_3_7_cu113_test",
is_master_only=True,
requires=["binary_windows_wheel_3_7_cu113_build"],
extra_props={
"executor": "windows-with-nvidia-gpu",
},
),
SmoketestJob(
"binary_linux_test",
["manywheel", "3.7m", "cu102", "devtoolset7"],
"pytorch/manylinux-cuda102",
"binary_linux_manywheel_3_7m_cu102_devtoolset7_test",
is_master_only=True,
requires=["binary_linux_manywheel_3_7m_cu102_devtoolset7_build"],
extra_props={
"resource_class": "gpu.medium",
"use_cuda_docker_runtime": miniutils.quote((str(1))),
},
),
SmoketestJob(
"binary_linux_test",
["libtorch", "3.7m", "cpu", "devtoolset7"],
"pytorch/manylinux-cuda102",
"binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_test",
is_master_only=True,
requires=["binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_build"],
has_libtorch_variant=True,
),
SmoketestJob(
"binary_linux_test",
["libtorch", "3.7m", "cpu", "gcc5.4_cxx11-abi"],
"pytorch/pytorch-binary-docker-image-ubuntu16.04:latest",
"binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_test",
is_master_only=True,
requires=["binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build"],
has_libtorch_variant=True,
),
]
def get_workflow_jobs():
return [item.gen_tree() for item in WORKFLOW_DATA]

View File

@ -26,7 +26,7 @@ def get_workflow_jobs(images=IMAGE_NAMES, only_slow_gradcheck=False):
"name": quote(f"docker-{image_name}"),
"image_name": quote(image_name),
})
if image_name == "pytorch-linux-xenial-py3.7-gcc5.4":
if image_name == "pytorch-linux-xenial-py3.6-gcc5.4":
# pushing documentation on tags requires CircleCI to also
# build all the dependencies on tags, including this docker image
parameters['filters'] = gen_filter_dict(branches_list=r"/.*/",

View File

@ -1,5 +1,4 @@
from cimodel.data.simple.util.versions import MultiPartVersion
from cimodel.data.simple.util.branch_filters import gen_filter_dict_exclude
import cimodel.lib.miniutils as miniutils
XCODE_VERSION = MultiPartVersion([12, 5, 1])
@ -12,7 +11,7 @@ class ArchVariant:
def render(self):
extra_parts = [self.custom_build_name] if len(self.custom_build_name) > 0 else []
return "-".join([self.name] + extra_parts).replace("_", "-")
return "_".join([self.name] + extra_parts)
def get_platform(arch_variant_name):
@ -26,25 +25,30 @@ class IOSJob:
self.is_org_member_context = is_org_member_context
self.extra_props = extra_props
def gen_name_parts(self):
version_parts = self.xcode_version.render_dots_or_parts("-")
build_variant_suffix = self.arch_variant.render()
def gen_name_parts(self, with_version_dots):
version_parts = self.xcode_version.render_dots_or_parts(with_version_dots)
build_variant_suffix = "_".join([self.arch_variant.render(), "build"])
return [
"pytorch",
"ios",
] + version_parts + [
build_variant_suffix,
]
def gen_job_name(self):
return "-".join(self.gen_name_parts())
return "_".join(self.gen_name_parts(False))
def gen_tree(self):
platform_name = get_platform(self.arch_variant.name)
props_dict = {
"name": self.gen_job_name(),
"build_environment": self.gen_job_name(),
"build_environment": "-".join(self.gen_name_parts(True)),
"ios_arch": self.arch_variant.name,
"ios_platform": platform_name,
"name": self.gen_job_name(),
}
if self.is_org_member_context:
@ -53,28 +57,30 @@ class IOSJob:
if self.extra_props:
props_dict.update(self.extra_props)
props_dict["filters"] = gen_filter_dict_exclude()
return [{"pytorch_ios_build": props_dict}]
WORKFLOW_DATA = [
IOSJob(XCODE_VERSION, ArchVariant("x86_64"), is_org_member_context=False, extra_props={
"lite_interpreter": miniutils.quote(str(int(True)))}),
# IOSJob(XCODE_VERSION, ArchVariant("arm64"), extra_props={
# "lite_interpreter": miniutils.quote(str(int(True)))}),
# IOSJob(XCODE_VERSION, ArchVariant("arm64", "metal"), extra_props={
# "use_metal": miniutils.quote(str(int(True))),
# "lite_interpreter": miniutils.quote(str(int(True)))}),
# IOSJob(XCODE_VERSION, ArchVariant("arm64", "custom-ops"), extra_props={
# "op_list": "mobilenetv2.yaml",
# "lite_interpreter": miniutils.quote(str(int(True)))}),
IOSJob(XCODE_VERSION, ArchVariant("x86_64", "full_jit"), is_org_member_context=False, extra_props={
"lite_interpreter": miniutils.quote(str(int(False)))}),
IOSJob(XCODE_VERSION, ArchVariant("arm64"), extra_props={
"lite_interpreter": miniutils.quote(str(int(True)))}),
IOSJob(XCODE_VERSION, ArchVariant("arm64", "metal"), extra_props={
"use_metal": miniutils.quote(str(int(True))),
"lite_interpreter": miniutils.quote(str(int(True)))}),
IOSJob(XCODE_VERSION, ArchVariant("arm64", "full_jit"), extra_props={
"lite_interpreter": miniutils.quote(str(int(False)))}),
IOSJob(XCODE_VERSION, ArchVariant("arm64", "custom"), extra_props={
"op_list": "mobilenetv2.yaml",
"lite_interpreter": miniutils.quote(str(int(True)))}),
IOSJob(XCODE_VERSION, ArchVariant("x86_64", "coreml"), is_org_member_context=False, extra_props={
"use_coreml": miniutils.quote(str(int(True))),
"lite_interpreter": miniutils.quote(str(int(True)))}),
# IOSJob(XCODE_VERSION, ArchVariant("arm64", "coreml"), extra_props={
# "use_coreml": miniutils.quote(str(int(True))),
# "lite_interpreter": miniutils.quote(str(int(True)))}),
IOSJob(XCODE_VERSION, ArchVariant("arm64", "coreml"), extra_props={
"use_coreml": miniutils.quote(str(int(True))),
"lite_interpreter": miniutils.quote(str(int(True)))}),
]

View File

@ -11,14 +11,10 @@ class MacOsJob:
non_phase_parts = ["pytorch", "macos", self.os_version, "py3"]
extra_name_list = [name for name, exist in self.extra_props.items() if exist]
full_job_name_list = (
non_phase_parts
+ extra_name_list
+ [
"build" if self.is_build else None,
"test" if self.is_test else None,
]
)
full_job_name_list = non_phase_parts + extra_name_list + [
'build' if self.is_build else None,
'test' if self.is_test else None,
]
full_job_name = "_".join(list(filter(None, full_job_name_list)))
@ -45,8 +41,10 @@ WORKFLOW_DATA = [
"10_13",
is_build=True,
is_test=True,
extra_props=tuple({"lite_interpreter": True}.items()),
),
extra_props=tuple({
"lite_interpreter": True
}.items()),
)
]

View File

@ -0,0 +1,77 @@
from cimodel.data.simple.util.docker_constants import (
DOCKER_IMAGE_NDK,
DOCKER_REQUIREMENT_NDK
)
class AndroidNightlyJob:
def __init__(self,
variant,
template_name,
extra_props=None,
with_docker=True,
requires=None,
no_build_suffix=False):
self.variant = variant
self.template_name = template_name
self.extra_props = extra_props or {}
self.with_docker = with_docker
self.requires = requires
self.no_build_suffix = no_build_suffix
def gen_tree(self):
base_name_parts = [
"pytorch",
"linux",
"xenial",
"py3",
"clang5",
"android",
"ndk",
"r19c",
] + self.variant
build_suffix = [] if self.no_build_suffix else ["build"]
full_job_name = "_".join(["nightly"] + base_name_parts + build_suffix)
build_env_name = "-".join(base_name_parts)
props_dict = {
"name": full_job_name,
"requires": self.requires,
"filters": {"branches": {"only": "nightly"}},
}
props_dict.update(self.extra_props)
if self.with_docker:
props_dict["docker_image"] = DOCKER_IMAGE_NDK
props_dict["build_environment"] = build_env_name
return [{self.template_name: props_dict}]
BASE_REQUIRES = [DOCKER_REQUIREMENT_NDK]
WORKFLOW_DATA = [
AndroidNightlyJob(["x86_32"], "pytorch_linux_build", requires=BASE_REQUIRES),
AndroidNightlyJob(["x86_64"], "pytorch_linux_build", requires=BASE_REQUIRES),
AndroidNightlyJob(["arm", "v7a"], "pytorch_linux_build", requires=BASE_REQUIRES),
AndroidNightlyJob(["arm", "v8a"], "pytorch_linux_build", requires=BASE_REQUIRES),
AndroidNightlyJob(["android_gradle"], "pytorch_android_gradle_build",
with_docker=False,
requires=[
"nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build",
"nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_64_build",
"nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v7a_build",
"nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v8a_build"]),
AndroidNightlyJob(["x86_32_android_publish_snapshot"], "pytorch_android_publish_snapshot",
extra_props={"context": "org-member"},
with_docker=False,
requires=["nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_android_gradle_build"],
no_build_suffix=True),
]
def get_workflow_jobs():
return [item.gen_tree() for item in WORKFLOW_DATA]

View File

@ -15,7 +15,7 @@ class IOSNightlyJob:
def get_phase_name(self):
return "upload" if self.is_upload else "build"
def get_common_name_pieces(self, sep):
def get_common_name_pieces(self, with_version_dots):
extra_name_suffix = [self.get_phase_name()] if self.is_upload else []
@ -24,7 +24,7 @@ class IOSNightlyJob:
common_name_pieces = [
"ios",
] + extra_name + [
] + ios_definitions.XCODE_VERSION.render_dots_or_parts(sep) + [
] + ios_definitions.XCODE_VERSION.render_dots_or_parts(with_version_dots) + [
"nightly",
self.variant,
"build",
@ -33,14 +33,14 @@ class IOSNightlyJob:
return common_name_pieces
def gen_job_name(self):
return "_".join(["pytorch"] + self.get_common_name_pieces(None))
return "_".join(["pytorch"] + self.get_common_name_pieces(False))
def gen_tree(self):
build_configs = BUILD_CONFIGS_FULL_JIT if self.is_full_jit else BUILD_CONFIGS
extra_requires = [x.gen_job_name() for x in build_configs] if self.is_upload else []
props_dict = {
"build_environment": "-".join(["libtorch"] + self.get_common_name_pieces(".")),
"build_environment": "-".join(["libtorch"] + self.get_common_name_pieces(True)),
"requires": extra_requires,
"context": "org-member",
"filters": {"branches": {"only": "nightly"}},

View File

@ -1,5 +1,4 @@
NON_PR_BRANCH_LIST = [
"main",
"master",
r"/ci-all\/.*/",
r"/release\/.*/",
@ -12,9 +11,6 @@ PR_BRANCH_LIST = [
RC_PATTERN = r"/v[0-9]+(\.[0-9]+)*-rc[0-9]+/"
MAC_IOS_EXCLUSION_LIST = ["nightly", "postnightly"]
def gen_filter_dict(
branches_list=NON_PR_BRANCH_LIST,
tags_list=None
@ -29,11 +25,3 @@ def gen_filter_dict(
if tags_list is not None:
filter_dict["tags"] = {"only": tags_list}
return filter_dict
def gen_filter_dict_exclude(branches_list=MAC_IOS_EXCLUSION_LIST):
return {
"branches": {
"ignore": branches_list,
},
}

View File

@ -1,6 +1,3 @@
from typing import Optional
class MultiPartVersion:
def __init__(self, parts, prefix=""):
self.parts = parts
@ -16,11 +13,14 @@ class MultiPartVersion:
else:
return [self.prefix]
def render_dots_or_parts(self, sep: Optional[str] = None):
if sep is None:
return self.prefixed_parts()
def render_dots(self):
return ".".join(self.prefixed_parts())
def render_dots_or_parts(self, with_dots):
if with_dots:
return [self.render_dots()]
else:
return [sep.join(self.prefixed_parts())]
return self.prefixed_parts()
class CudaVersion(MultiPartVersion):

2628
.circleci/config.yml generated

File diff suppressed because it is too large Load Diff

View File

@ -53,7 +53,7 @@ dependencies {
implementation 'androidx.appcompat:appcompat:1.0.0'
implementation 'com.facebook.fbjni:fbjni-java-only:0.2.2'
implementation 'com.google.code.findbugs:jsr305:3.0.1'
implementation 'com.facebook.soloader:nativeloader:0.10.4'
implementation 'com.facebook.soloader:nativeloader:0.10.1'
implementation 'junit:junit:' + rootProject.junitVersion
implementation 'androidx.test:core:' + rootProject.coreVersion

406
.circleci/docker/build.sh Executable file
View File

@ -0,0 +1,406 @@
#!/bin/bash
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${name}" = xpy ]; then
vername=ANACONDA_PYTHON_VERSION
fi
# skip non-conforming fields such as "pytorch", "linux" or "xenial" without version string
if [ -n "${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" == *-xenial* ]]; then
UBUNTU_VERSION=16.04
elif [[ "$image" == *-artful* ]]; then
UBUNTU_VERSION=17.10
elif [[ "$image" == *-bionic* ]]; then
UBUNTU_VERSION=18.04
elif [[ "$image" == *-focal* ]]; then
UBUNTU_VERSION=20.04
elif [[ "$image" == *ubuntu* ]]; then
extract_version_from_image_name ubuntu UBUNTU_VERSION
elif [[ "$image" == *centos* ]]; then
extract_version_from_image_name centos CENTOS_VERSION
fi
if [ -n "${UBUNTU_VERSION}" ]; then
OS="ubuntu"
elif [ -n "${CENTOS_VERSION}" ]; then
OS="centos"
else
echo "Unable to derive operating system base..."
exit 1
fi
DOCKERFILE="${OS}/Dockerfile"
if [[ "$image" == *cuda* ]]; then
DOCKERFILE="${OS}-cuda/Dockerfile"
elif [[ "$image" == *rocm* ]]; then
DOCKERFILE="${OS}-rocm/Dockerfile"
fi
TRAVIS_DL_URL_PREFIX="https://s3.amazonaws.com/travis-python-archives/binaries/ubuntu/14.04/x86_64"
# 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 "$image" in
pytorch-linux-xenial-py3.8)
ANACONDA_PYTHON_VERSION=3.8
CMAKE_VERSION=3.10.3
GCC_VERSION=7
# Do not install PROTOBUF, DB, and VISION as a test
;;
pytorch-linux-xenial-py3.7-gcc5.4)
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=5
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
;;
pytorch-linux-xenial-py3.7-gcc7.2)
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=7
# Do not install PROTOBUF, DB, and VISION as a test
;;
pytorch-linux-xenial-py3.7-gcc7)
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7)
CUDA_VERSION=10.2
CUDNN_VERSION=7
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
;;
pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7)
CUDA_VERSION=11.1
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
;;
pytorch-linux-xenial-cuda11.3-cudnn8-py3-gcc7)
CUDA_VERSION=11.3.0 # Deviating from major.minor to conform to nvidia's Docker image names
CUDNN_VERSION=8
TENSORRT_VERSION=8.0.1.6
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
;;
pytorch-linux-bionic-cuda11.5-cudnn8-py3-gcc7)
CUDA_VERSION=11.5.0
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
;;
pytorch-linux-xenial-py3-clang5-asan)
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=5.0
CMAKE_VERSION=3.13.5
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-xenial-py3-clang7-asan)
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=7
CMAKE_VERSION=3.10.3
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-xenial-py3-clang7-onnx)
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=7
CMAKE_VERSION=3.10.3
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-xenial-py3-clang5-android-ndk-r19c)
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=5.0
CMAKE_VERSION=3.13.5
LLVMDEV=yes
PROTOBUF=yes
ANDROID=yes
ANDROID_NDK_VERSION=r19c
GRADLE_VERSION=6.8.3
NINJA_VERSION=1.9.0
;;
pytorch-linux-xenial-py3.7-clang7)
ANACONDA_PYTHON_VERSION=3.7
CMAKE_VERSION=3.10.3
CLANG_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-bionic-py3.7-clang9)
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
;;
pytorch-linux-bionic-py3.8-gcc9)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-bionic-cuda10.2-cudnn7-py3.7-clang9)
CUDA_VERSION=10.2
CUDNN_VERSION=7
ANACONDA_PYTHON_VERSION=3.7
CLANG_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-bionic-cuda10.2-cudnn7-py3.9-gcc7)
CUDA_VERSION=10.2
CUDNN_VERSION=7
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=7
PROTOBUF=yes
DB=yes
VISION=yes
;;
pytorch-linux-bionic-cuda11.0-cudnn8-py3.7-gcc9)
CUDA_VERSION=11.0
CUDNN_VERSION=8
ANACONDA_PYTHON_VERSION=3.7
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=3.9
;;
pytorch-linux-bionic-rocm4.3.1-py3.7)
ANACONDA_PYTHON_VERSION=3.7
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=4.3.1
;;
pytorch-linux-bionic-rocm4.5-py3.7)
ANACONDA_PYTHON_VERSION=3.7
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=4.5.2
;;
*)
# Catch-all for builds that are not hardcoded.
PROTOBUF=yes
DB=yes
VISION=yes
echo "image '$image' did not match an existing build configuration"
if [[ "$image" == *xenial* ]]; then
CMAKE_VERSION=3.10.3
fi
if [[ "$image" == *py* ]]; then
extract_version_from_image_name py ANACONDA_PYTHON_VERSION
fi
if [[ "$image" == *cuda* ]]; then
extract_version_from_image_name cuda CUDA_VERSION
extract_version_from_image_name cudnn CUDNN_VERSION
fi
if [[ "$image" == *rocm* ]]; then
extract_version_from_image_name rocm ROCM_VERSION
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
if [[ "$image" == *cmake* ]]; then
extract_version_from_image_name cmake CMAKE_VERSION
fi
;;
esac
# Set Jenkins UID and GID if running Jenkins
if [ -n "${JENKINS:-}" ]; then
JENKINS_UID=$(id -u jenkins)
JENKINS_GID=$(id -g jenkins)
fi
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
# Build image
# TODO: build-arg THRIFT is not turned on for any image, remove it once we confirm
# it's no longer needed.
docker build \
--no-cache \
--progress=plain \
--build-arg "TRAVIS_DL_URL_PREFIX=${TRAVIS_DL_URL_PREFIX}" \
--build-arg "BUILD_ENVIRONMENT=${image}" \
--build-arg "PROTOBUF=${PROTOBUF:-}" \
--build-arg "THRIFT=${THRIFT:-}" \
--build-arg "LLVMDEV=${LLVMDEV:-}" \
--build-arg "DB=${DB:-}" \
--build-arg "VISION=${VISION:-}" \
--build-arg "EC2=${EC2:-}" \
--build-arg "JENKINS=${JENKINS:-}" \
--build-arg "JENKINS_UID=${JENKINS_UID:-}" \
--build-arg "JENKINS_GID=${JENKINS_GID:-}" \
--build-arg "UBUNTU_VERSION=${UBUNTU_VERSION}" \
--build-arg "CENTOS_VERSION=${CENTOS_VERSION}" \
--build-arg "DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}" \
--build-arg "GLIBC_VERSION=${GLIBC_VERSION}" \
--build-arg "CLANG_VERSION=${CLANG_VERSION}" \
--build-arg "ANACONDA_PYTHON_VERSION=${ANACONDA_PYTHON_VERSION}" \
--build-arg "GCC_VERSION=${GCC_VERSION}" \
--build-arg "CUDA_VERSION=${CUDA_VERSION}" \
--build-arg "CUDNN_VERSION=${CUDNN_VERSION}" \
--build-arg "TENSORRT_VERSION=${TENSORRT_VERSION}" \
--build-arg "ANDROID=${ANDROID}" \
--build-arg "ANDROID_NDK=${ANDROID_NDK_VERSION}" \
--build-arg "GRADLE_VERSION=${GRADLE_VERSION}" \
--build-arg "VULKAN_SDK_VERSION=${VULKAN_SDK_VERSION}" \
--build-arg "SWIFTSHADER=${SWIFTSHADER}" \
--build-arg "CMAKE_VERSION=${CMAKE_VERSION:-}" \
--build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
--build-arg "KATEX=${KATEX:-}" \
--build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
--build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx900;gfx906}" \
-f $(dirname ${DOCKERFILE})/Dockerfile \
-t "$tmp_tag" \
"$@" \
.
# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
# for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
# find the correct image. As a result, here we have to replace the
# "$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 !(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

View File

@ -35,6 +35,9 @@ if [[ -z "${GITHUB_ACTIONS}" ]]; then
trap "docker logout ${registry}" EXIT
fi
# export EC2=1
# export JENKINS=1
# Try to pull the previous image (perhaps we can reuse some layers)
# if [ -n "${last_tag}" ]; then
# docker pull "${image}:${last_tag}" || true
@ -43,15 +46,7 @@ fi
# Build new image
./build.sh ${IMAGE_NAME} -t "${image}:${tag}"
# Only push if `DOCKER_SKIP_PUSH` = false
if [ "${DOCKER_SKIP_PUSH:-true}" = "false" ]; then
# Only push if docker image doesn't exist already.
# ECR image tags are immutable so this will avoid pushing if only just testing if the docker jobs work
# NOTE: The only workflow that should push these images should be the docker-builds.yml workflow
if ! docker manifest inspect "${image}:${tag}" >/dev/null 2>/dev/null; then
docker push "${image}:${tag}"
fi
fi
docker push "${image}:${tag}"
if [ -z "${DOCKER_SKIP_S3_UPLOAD:-}" ]; then
trap "rm -rf ${IMAGE_NAME}:${tag}.tar" EXIT

View File

@ -0,0 +1,103 @@
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 ${PYTORCH_ROCM_ARCH}
# Install required packages to build Caffe2
# Install common dependencies (so that this step can be cached separately)
ARG EC2
ADD ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Update CentOS git version
RUN yum -y remove git
RUN yum -y remove git-*
RUN yum -y install https://packages.endpoint.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm
RUN yum install -y git
# Install devtoolset
ARG DEVTOOLSET_VERSION
ADD ./common/install_devtoolset.sh install_devtoolset.sh
RUN bash ./install_devtoolset.sh && rm install_devtoolset.sh
ENV BASH_ENV "/etc/profile"
# (optional) Install non-default glibc version
ARG GLIBC_VERSION
ADD ./common/install_glibc.sh install_glibc.sh
RUN if [ -n "${GLIBC_VERSION}" ]; then bash ./install_glibc.sh; fi
RUN rm install_glibc.sh
# Install user
ADD ./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)
ENV PATH /opt/conda/bin:$PATH
ARG ANACONDA_PYTHON_VERSION
ADD ./common/install_conda.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
# (optional) Install protobuf for ONNX
ARG PROTOBUF
ADD ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
ADD ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV and ffmpeg
ARG VISION
ADD ./common/install_vision.sh install_vision.sh
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh
ENV INSTALLED_VISION ${VISION}
# Install rocm
ARG ROCM_VERSION
ADD ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh
ENV PATH /opt/rocm/bin:$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 CMake version
ARG CMAKE_VERSION
ADD ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
ADD ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${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)
ADD ./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"]

View File

@ -0,0 +1,129 @@
#!/bin/bash
set -ex
install_ubuntu() {
# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-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 check for
# "$UBUNTU_VERSION" == "18.04"*
# instead of
# "$UBUNTU_VERSION" == "18.04"
if [[ "$UBUNTU_VERSION" == "18.04"* ]]; then
cmake3="cmake=3.10*"
maybe_libiomp_dev="libiomp-dev"
elif [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
cmake3="cmake=3.16*"
maybe_libiomp_dev=""
else
cmake3="cmake=3.5*"
maybe_libiomp_dev="libiomp-dev"
fi
# Install common dependencies
apt-get update
# TODO: Some of these may not be necessary
ccache_deps="asciidoc docbook-xml docbook-xsl xsltproc"
numpy_deps="gfortran"
apt-get install -y --no-install-recommends \
$ccache_deps \
$numpy_deps \
${cmake3} \
apt-transport-https \
autoconf \
automake \
build-essential \
ca-certificates \
curl \
git \
libatlas-base-dev \
libc6-dbg \
${maybe_libiomp_dev} \
libyaml-dev \
libz-dev \
libjpeg-dev \
libasound2-dev \
libsndfile-dev \
software-properties-common \
sudo \
wget \
vim
# Should resolve issues related to various apt package repository cert issues
# see: https://github.com/pytorch/pytorch/issues/65931
apt-get install -y libgnutls30
# Cleanup package manager
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
}
install_centos() {
# Need EPEL for many packages we depend on.
# See http://fedoraproject.org/wiki/EPEL
yum --enablerepo=extras install -y epel-release
ccache_deps="asciidoc docbook-dtds docbook-style-xsl libxslt"
numpy_deps="gcc-gfortran"
# Note: protobuf-c-{compiler,devel} on CentOS are too old to be used
# for Caffe2. That said, we still install them to make sure the build
# system opts to build/use protoc and libprotobuf from third-party.
yum install -y \
$ccache_deps \
$numpy_deps \
autoconf \
automake \
bzip2 \
cmake \
cmake3 \
curl \
gcc \
gcc-c++ \
gflags-devel \
git \
glibc-devel \
glibc-headers \
glog-devel \
hiredis-devel \
libstdc++-devel \
libsndfile-devel \
make \
opencv-devel \
sudo \
wget \
vim
# Cleanup
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
}
# Install base packages depending on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
install_ubuntu
;;
centos)
install_centos
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
# Install Valgrind separately since the apt-get version is too old.
mkdir valgrind_build && cd valgrind_build
VALGRIND_VERSION=3.16.1
wget https://ossci-linux.s3.amazonaws.com/valgrind-${VALGRIND_VERSION}.tar.bz2
tar -xjf valgrind-${VALGRIND_VERSION}.tar.bz2
cd valgrind-${VALGRIND_VERSION}
./configure --prefix=/usr/local
make -j6
sudo make install
cd ../../
rm -rf valgrind_build
alias valgrind="/usr/local/bin/valgrind"

View File

@ -5,9 +5,7 @@ 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
apt-get install -y cargo pkg-config libssl-dev
echo "Checking out sccache repo"
git clone https://github.com/pytorch/sccache
cd sccache
@ -48,9 +46,7 @@ fi
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
printf "#!/bin/sh\nif [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then\n exec sccache $(which $1) \"\$@\"\nelse\n exec $(which $1) \"\$@\"\nfi" > "/opt/cache/bin/$1"
printf "#!/bin/sh\nif [ \$(ps -p \$PPID -o comm=) != sccache ]; then\n exec sccache $(which $1) \"\$@\"\nelse\n exec $(which $1) \"\$@\"\nfi" > "/opt/cache/bin/$1"
chmod a+x "/opt/cache/bin/$1"
}

View File

@ -13,9 +13,6 @@ if [ -n "$CLANG_VERSION" ]; then
sudo apt-get install -y --no-install-recommends gpg-agent
wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
apt-add-repository "deb http://apt.llvm.org/bionic/ llvm-toolchain-bionic-${CLANG_VERSION} main"
elif [[ $UBUNTU_VERSION == 22.04 ]]; then
# work around ubuntu apt-get conflicts
sudo apt-get -y -f install
fi
sudo apt-get update

View File

@ -0,0 +1,19 @@
#!/bin/bash
set -ex
[ -n "$CMAKE_VERSION" ]
# Remove system cmake install so it won't get used instead
apt-get remove cmake -y
# Turn 3.6.3 into v3.6
path=$(echo "${CMAKE_VERSION}" | sed -e 's/\([0-9].[0-9]\+\).*/v\1/')
file="cmake-${CMAKE_VERSION}-Linux-x86_64.tar.gz"
# Download and install specific CMake version in /usr/local
pushd /tmp
curl -Os --retry 3 "https://cmake.org/files/${path}/${file}"
tar -C /usr/local --strip-components 1 --no-same-owner -zxf cmake-*.tar.gz
rm -f cmake-*.tar.gz
popd

View File

@ -0,0 +1,137 @@
#!/bin/bash
set -ex
# Optionally install conda
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
BASE_URL="https://repo.anaconda.com/miniconda"
MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
case "$MAJOR_PYTHON_VERSION" in
2)
CONDA_FILE="Miniconda2-latest-Linux-x86_64.sh"
;;
3)
if [ "$ANACONDA_PYTHON_VERSION" = "3.6" ]; then
# Latest release of Conda that still supports python-3.6
CONDA_FILE="Miniconda3-py37_4.10.3-Linux-x86_64.sh"
else
CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
fi
;;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
mkdir /opt/conda
chown jenkins:jenkins /opt/conda
# Work around bug where devtoolset replaces sudo and breaks it.
if [ -n "$DEVTOOLSET_VERSION" ]; then
SUDO=/bin/sudo
else
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
$SUDO -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
}
pushd /tmp
wget -q "${BASE_URL}/${CONDA_FILE}"
chmod +x "${CONDA_FILE}"
as_jenkins ./"${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:$PATH"
# Ensure we run conda in a directory that jenkins has write access to
pushd /opt/conda
# Track latest conda update
if [ "$ANACONDA_PYTHON_VERSION" != "3.6" ]; then
as_jenkins conda update -y -n base conda
fi
# Install correct Python version
as_jenkins conda install -y python="$ANACONDA_PYTHON_VERSION"
conda_install() {
# Ensure that the install command don't upgrade/downgrade Python
# This should be called as
# conda_install pkg1 pkg2 ... [-c channel]
as_jenkins conda install -q -y python="$ANACONDA_PYTHON_VERSION" $*
}
# Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
# DO NOT install cmake here as it would install a version newer than 3.10, but
# we want to pin to version 3.10.
SCIPY_VERSION=1.1.0
if [ "$ANACONDA_PYTHON_VERSION" = "3.9" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.19.2 astunparse pyyaml mkl mkl-include setuptools cffi future six llvmdev=8.0.0 -c conda-forge
SCIPY_VERSION=1.6.0
elif [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
conda_install numpy=1.18.5 astunparse pyyaml mkl mkl-include setuptools cffi future six llvmdev=8.0.0
elif [ "$ANACONDA_PYTHON_VERSION" = "3.7" ]; then
# DO NOT install dataclasses if installing python-3.7, since its part of python-3.7 core packages
conda_install numpy=1.18.5 astunparse pyyaml mkl mkl-include setuptools cffi future six typing_extensions
else
conda_install numpy=1.18.5 astunparse pyyaml mkl mkl-include setuptools cffi future six dataclasses typing_extensions
fi
# 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
if [ -n "$CUDA_VERSION" ]; then
conda_install magma-cuda$(TMP=${CUDA_VERSION/./};echo ${TMP%.*[0-9]}) -c pytorch
fi
# TODO: This isn't working atm
conda_install nnpack -c killeent
# Install some other packages, including those needed for Python test reporting
# TODO: Why is scipy pinned
# Pin MyPy version because new errors are likely to appear with each release
# Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
as_jenkins pip install --progress-bar off pytest \
scipy==$SCIPY_VERSION \
scikit-image \
psutil \
unittest-xml-reporting \
boto3==1.16.34 \
hypothesis==4.53.2 \
expecttest==0.1.3 \
mypy==0.812 \
tb-nightly
# Install numba only on python-3.8 or below
# For numba issue see https://github.com/pytorch/pytorch/issues/51511
if [[ $(python -c "import sys; print(int(sys.version_info < (3, 9)))") == "1" ]]; then
as_jenkins pip install --progress-bar off numba==0.54.1 "librosa>=0.6.2,<0.9.0"
else
as_jenkins pip install --progress-bar off numba==0.49.0 "librosa>=0.6.2,<0.9.0"
fi
# Update scikit-learn to a python-3.8 compatible version
if [[ $(python -c "import sys; print(int(sys.version_info >= (3, 8)))") == "1" ]]; then
as_jenkins pip install --progress-bar off -U scikit-learn
else
# Pinned scikit-learn due to https://github.com/scikit-learn/scikit-learn/issues/14485 (affects gcc 5.5 only)
as_jenkins pip install --progress-bar off scikit-learn==0.20.3
fi
popd
fi

View File

@ -0,0 +1,20 @@
#!/bin/bash
set -ex
if [ -n "$KATEX" ]; then
curl -sL https://deb.nodesource.com/setup_12.x | sudo -E bash -
sudo apt-get install -y nodejs
curl -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
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
fi

View File

@ -10,7 +10,5 @@ cd "${OPENSSL}"
./config --prefix=/opt/openssl -d '-Wl,--enable-new-dtags,-rpath,$(LIBRPATH)'
# NOTE: openssl install errors out when built with the -j option
make -j6; 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}"

View File

@ -12,7 +12,7 @@ install_protobuf_317() {
# g++: error: ./../lib64/crti.o: No such file or directory
ln -s /usr/lib64 "$pb_dir/lib64"
curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz" --retry 3
curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz"
tar -xvz -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
# -j6 to balance memory usage and speed.
# naked `-j` seems to use too much memory.

View File

@ -0,0 +1,160 @@
#!/bin/bash
set -ex
install_magma() {
# "install" hipMAGMA into /opt/rocm/magma by copying after build
git clone https://bitbucket.org/icl/magma.git
pushd magma
# fix for magma_queue memory leak issue
git checkout c62d700d880c7283b33fb1d615d62fc9c7f7ca21
cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib' >> make.inc
echo 'DEVCCFLAGS += --gpu-max-threads-per-block=256' >> make.inc
export PATH="${PATH}:/opt/rocm/bin"
if [[ -n "$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 $amdgpu_targets; do
echo "DEVCCFLAGS += --amdgpu-target=$arch" >> make.inc
done
# hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
sed -i 's/^FOPENMP/#FOPENMP/g' make.inc
make -f make.gen.hipMAGMA -j $(nproc)
LANG=C.UTF-8 make lib/libmagma.so -j $(nproc) MKLROOT=/opt/conda
make testing/testing_dgemm -j $(nproc) MKLROOT=/opt/conda
popd
mv magma /opt/rocm
}
ver() {
printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
}
# Map ROCm version to AMDGPU version
declare -A AMDGPU_VERSIONS=( ["4.5.2"]="21.40.2" )
install_ubuntu() {
apt-get update
if [[ $UBUNTU_VERSION == 18.04 ]]; then
# gpg-agent is not available by default on 18.04
apt-get install -y --no-install-recommends gpg-agent
fi
if [[ $UBUNTU_VERSION == 20.04 ]]; then
# gpg-agent is not available by default on 20.04
apt-get install -y --no-install-recommends gpg-agent
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
if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
# Add amdgpu repository
UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
local amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/ubuntu"
echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
fi
ROCM_REPO="ubuntu"
if [[ $(ver $ROCM_VERSION) -lt $(ver 4.2) ]]; then
ROCM_REPO="xenial"
fi
# Add rocm repository
wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
echo "deb [arch=amd64] ${rocm_baseurl} ${ROCM_REPO} main" > /etc/apt/sources.list.d/rocm.list
apt-get update --allow-insecure-repositories
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
rocm-dev \
rocm-utils \
rocm-libs \
rccl \
rocprofiler-dev \
roctracer-dev
# precompiled miopen kernels added in ROCm 3.5; search for all unversioned packages
# if search fails it will abort this script; use true to avoid case where search fails
MIOPENKERNELS=$(apt-cache search --names-only miopenkernels | awk '{print $1}' | grep -F -v . || true)
if [[ "x${MIOPENKERNELS}" = x ]]; then
echo "miopenkernels package not available"
else
DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENKERNELS}
fi
install_magma
# 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`
if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
# Add amdgpu repository
local amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/7.9/main/x86_64"
echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
echo "baseurl=${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
fi
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
install_magma
# 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

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