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Author SHA1 Message Date
ee1b680438 [Doc] Fix rendering of the unicode characters (#134695)
* [Doc] Fix rendering of the unicode characters (#134597)

https://github.com/pytorch/pytorch/pull/124771 introduced unicode escape sequences inside raw strings, which were not rendered correctly. Also fix typo in `\uue0 ` escape sequence (should have been `\u00e0`)
Fix it by relying on [string literal concatenation](https://docs.python.org/3/reference/lexical_analysis.html#string-literal-concatenation) to join raw and regular strings together during lexical analysis stage

Fixes https://github.com/pytorch/pytorch/issues/134422

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134597
Approved by: https://github.com/aorenste, https://github.com/Skylion007

(cherry picked from commit 534f43ddce24ab6bafa3aed42ee3d68947073d3f)

* Fix lint

---------

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2024-08-28 17:25:42 -07:00
79c88679b9 Fix docstring for torch.signal.windows.nuttall (#134704)
Fix docstring for torch.signal.windows.nuttall (#134512)

This partially fixes regression introduced by https://github.com/pytorch/pytorch/pull/124771 but also just improves `z_n` rendering, by using MathML
In 2.3 it was [rendered](https://pytorch.org/docs/2.3/generated/torch.signal.windows.nuttall.html#torch.signal.windows.nuttall)
as
<img width="177" alt="image" src="https://github.com/user-attachments/assets/2c15d1f9-13ad-483f-bb66-41fa3fa4ba9c">

With this change it'll be [rendered](https://docs-preview.pytorch.org/pytorch/pytorch/134512/generated/torch.signal.windows.nuttall.html#torch.signal.windows.nuttall) as
```math
z_n = \frac{2 \pi n}{M}
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134512
Approved by: https://github.com/kit1980, https://github.com/aorenste, https://github.com/atalman

(cherry picked from commit 79b7fff18820e4f62a02534a961d5930040e3475)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2024-08-28 11:54:28 -07:00
38b96d3399 Do not use <filesystem> on Linux (#134494) (#134604)
Because right now it leads to symbol conflict from binary builds.
Use of `std::filesystem::file_exists` was introduced by https://github.com/pytorch/pytorch/pull/126601 and in this PR it is replaced with a very straightforward implementation that calls `stat` on the given path, which is a classic C-way of checking for the file existence.

This PR should be reverted once one figures out how to keep `std::filesystem` methods linked into the binary private

Fixes symptoms of https://github.com/pytorch/pytorch/issues/133437

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134494
Approved by: https://github.com/atalman, https://github.com/d4l3k

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2024-08-27 15:50:37 -04:00
b84e8c6848 Move module_tracker to logging for confused hierarchy (#134467) (#134501)
* Move module_tracker to logging for confused hierarchy (#134467)

Fixes https://github.com/pytorch/pytorch/issues/134242

Make sure to never raise an error when confused. Logs for confusion can be enabled with `TORCH_LOGS="torch.utils.module_tracker"` or the usual python systems.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134467
Approved by: https://github.com/malfet

* Fix bad merge conflict resolution
2024-08-27 07:37:59 -04:00
6a79d4afcd [ROCm] Prevent accidental enablement of efficient attention. (#134531)
[ROCm] Prevent accidental enablement of efficient attention. (#133331)

Currently Efficient attention and Flash attention share the same set of GPU
kernels on ROCM and have common limitations on head sizes.

Fixes https://github.com/pytorch/pytorch/issues/132004

Pull Request resolved: https://github.com/pytorch/pytorch/pull/133331
Approved by: https://github.com/malfet, https://github.com/jithunnair-amd

(cherry picked from commit 46ecc673ae048cc31a919a1d01dfdff51e3d2869)

Co-authored-by: Xinya Zhang <Xinya.Zhang@amd.com>
2024-08-27 07:36:18 -04:00
e0ddbffbc3 [Release Only] Disable flaky failing tests in release. Pin optree. Pin sympy (#134489)
* [Release Only] Disable failing tests in release

* fix

* skip_xla_op_test

* ping_optree_win

* Pin sympy to 1.13.1 (#133235)

Sympy 1.13.2 release yesterday, and it results in test failures on windows and mac

454713fe9d/1

Hopefully these are the places it needs to get pinned
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133235
Approved by: https://github.com/atalman, https://github.com/ZainRizvi

* fix sympy

* Revert "fix sympy"

This reverts commit 864cd32b1e54bffdd371320e2c98c74ba24c2510.

Revert "Pin sympy to 1.13.1 (#133235)"

This reverts commit cf77a5ecfc217da6643ffcd49a605b3434f9551f.

pin sympy win test

---------

Co-authored-by: Catherine Lee <csl@fb.com>
2024-08-27 07:29:02 -04:00
314f033e65 Use ephemeral runners for windows nightly builds (#134463) (#134496)
This is definition of windows.4xlarge:

```
  windows.4xlarge:
    disk_size: 256
    instance_type: c5d.4xlarge
    is_ephemeral: true
    max_available: 420
    os: windows
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134463
Approved by: https://github.com/jeanschmidt
2024-08-26 14:07:04 -07:00
9c1f78e018 [CD] Use ephemeral arm64 runners for nightly and docker builds (#134473) (#134493)
* [CD] Use ephemeral arm64 runners for nightly and docker builds (#134473)

Follow up after adding linux arm64 ephemeral instances: https://github.com/pytorch/pytorch/pull/134469
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134473
Approved by: https://github.com/malfet

* actionlint
2024-08-26 14:04:28 -07:00
3675fc52ae Use ephemeral runners for linux nightly builds (#134367) (#134492)
* Use ephemeral runners for linux nightly builds (#134367)

Should be landed with https://github.com/pytorch/test-infra/pull/5590
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134367
Approved by: https://github.com/kit1980, https://github.com/malfet, https://github.com/seemethere

* fix_conda_nightly

* regenerate
2024-08-26 14:03:59 -07:00
920c023664 docker: Use miniforge, install from pip (#134497)
docker: Use miniforge, install from pip (#134274)

Switch installation of the pytorch package to be installed from our download.pytorch.org sources which are better maintained.

As well, switching over the miniconda installation to a miniforge installation in order to ensure backwards compat for users expecting to have the conda package manager installed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134274
Approved by: https://github.com/malfet, https://github.com/atalman

Co-authored-by: atalman <atalman@fb.com>
(cherry picked from commit b2eb0e8c6a817f304277dfed39c25853ff301d90)

Co-authored-by: Eli Uriegas <eliuriegas@meta.com>
2024-08-26 14:03:09 -07:00
592038351e [Release only] Use amazon linux 2 runners for CI (#134350) 2024-08-24 08:39:08 -04:00
847a042a0d [Release only] Disable triton build workflows (#134347) 2024-08-23 13:39:13 -04:00
c469d14a14 [NJT+SDPA]Fix flash_attention output when batch_size=1 and seq_len=1 (#133595)
* [NJT+SDPA]Fix flash_attention output when batch_size=1 and seq_len=1 (#130652)

fix issue  #130196

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130652
Approved by: https://github.com/Skylion007, https://github.com/drisspg, https://github.com/jbschlosser

(cherry picked from commit 0e79e1f95841041ef689e8a94c8be1e92702b873)

* resolve conflict by using old the NT API

* fix lint

---------

Co-authored-by: yuqingj <yuqingj@meta.com>
2024-08-22 13:42:22 -04:00
bd92fa2cf2 Update conda-env-iOS.txt (#134239)
Update conda-env-iOS.txt (#134068)

Followup after https://github.com/pytorch/pytorch/pull/133814 To fix periodic build failures update `typing-extensions` to 4.11.0, as 4.10 is missing in conda
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134068
Approved by: https://github.com/wdvr, https://github.com/atalman, https://github.com/Skylion007

(cherry picked from commit 18aaceb7be552ccdcb65f485d5f82be9af8e2898)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2024-08-22 10:39:30 -07:00
eba4d08497 [MPS] Gather sliced inputs to batch norm (#134121)
[MPS] Gather sliced inputs to batch norm (#133610)

This PR removes the `executeGatherOp` flag from batch norm in favor of relying on the logic in 4aa66f68a8/aten/src/ATen/native/mps/OperationUtils.mm (L372) to decide if gathering is necessary.

It's not the most efficient way to solve this issue, but it assures correctness for sliced inputs.

### Performance impact

#### With fix

```
python -m timeit -n 100 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x)"
100 loops, best of 5: 282 usec per loop

python -m timeit -n 100 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x[5:])"
100 loops, best of 5: 448 usec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x)"
1000 loops, best of 5: 705 usec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x[5:])"
1000 loops, best of 5: 1.11 msec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(1000, 100, 35, 45).to('mps')" "bn(x)"
1000 loops, best of 5: 7.16 msec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(1000, 100, 35, 45).to('mps')" "bn(x[5:])"
1000 loops, best of 5: 11.7 msec per loop
```

#### Without fix

```
python -m timeit -n 100 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x)"
100 loops, best of 5: 284 usec per loop

python -m timeit -n 100 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x[5:])"
100 loops, best of 5: 265 usec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x)"
1000 loops, best of 5: 715 usec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(100, 100, 35, 45).to('mps')" "bn(x[5:])"
1000 loops, best of 5: 675 usec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(1000, 100, 35, 45).to('mps')" "bn(x)"
1000 loops, best of 5: 7.19 msec per loop

python -m timeit -n 1000 -s "import torch; import torch.nn as nn; bn = nn.BatchNorm2d(100, affine=False, device='mps');x = torch.randn(1000, 100, 35, 45).to('mps')" "bn(x[5:])"
1000 loops, best of 5: 7.13 msec per loop
```

Please feel free to push back or request changes.

Fixes #133520
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133610
Approved by: https://github.com/malfet

(cherry picked from commit 43f78bf37a0432af4c048e1f1363838f26ef3295)

Co-authored-by: Roy Hvaara <roy@lightyear.no>
2024-08-22 13:39:10 -04:00
2213c07dcd [CpuInductor] Enable NEON ISA detection on Linux ARM (#134165)
* [CpuInductor] Enable NEON ISA detection on Linux ARM (#129075)

Also, cleanup code a bit to use `x in [y, z]` instead of `x == y or x == z`

And do not redefine `at_align`, but instead use `alignas(64)` as was suggested in https://github.com/pytorch/pytorch/pull/128686/files#r1639365978

Test plan: `python3 -c "import torch._inductor.codecache as cc; isa = cc.valid_vec_isa_list()[0];print(str(isa), bool(isa))"`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129075
Approved by: https://github.com/jansel

* Fix merge mistakes

---------

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2024-08-21 16:30:01 -07:00
4ebe5b7cf4 Avoid autocast deprecation warning in DataParallel (#130660) (#134057)
Fixes #130659

Co-authored-by: Yu, Guangye <106960996+guangyey@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130660
Approved by: https://github.com/guangyey, https://github.com/fegin, https://github.com/albanD

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
2024-08-21 12:43:28 -04:00
346e0f605f [Inductor] short-term fix for needs_fixed_stride_order silent incorrectness (#133452) (#133888)
This is a low-risk short-term fix for
https://github.com/pytorch/pytorch/issues/128084, for the purposes of
2.4.1. The actual fix for that issue is more risky and we'll target 2.5.

needs_fixed_stride_order is silently incorrect with args that are
mutable because it creates clones of those args, writes into them, and
doesn't update the original args.

This PR makes it so that needs_fixed_stride_order doesn't apply to
inputs that are being mutated.

This PR doesn't completely fix the problem, but it makes it less
incorrect: most of the time the input already has the correct strides
but inductor fails to recognize it, and in those cases writing directly
to the input is fine.

Test Plan:
- new test

Pull Request resolved: https://github.com/pytorch/pytorch/pull/133452
Approved by: https://github.com/eellison
2024-08-21 11:16:20 -04:00
362a6ca99a Add xpu_cmake_macros.h to xpu build (#133649)
Add xpu_cmake_macros.h to xpu build (#132847)

# Motivation

fix https://github.com/pytorch/pytorch/issues/132971

Pull Request resolved: https://github.com/pytorch/pytorch/pull/132847
Approved by: https://github.com/EikanWang

(cherry picked from commit 9c5e0d47fe3373f3c468e59877f71c4999cca227)

Co-authored-by: Yu, Guangye <guangye.yu@intel.com>
2024-08-21 11:14:13 -04:00
dab239be1f [cpu][flash attention] fix nan issue (#133598)
[cpu][flash attention] fix nan issue (#130014)

Fixes #127055.

NaNs are generated in flash attention because the computation of `std::exp((-inf) - (-inf))` and `+/-inf * 0` in lazy softmax. We fix the issue by avoiding the related calculation.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130014
Approved by: https://github.com/jgong5, https://github.com/drisspg

(cherry picked from commit 868d9a4f129a0a146c06f730592bead27058efd8)

Co-authored-by: Valentine233 <xuan.liao@intel.com>
2024-08-21 08:54:29 -04:00
30faa177c4 Fix warning when pickle.load torch.Storage (#133597)
Fix warning when pickle.load torch.Storage (#130246)

Fixes https://github.com/pytorch/pytorch/issues/130242

Since `torch.save` does not use pickle for storages, the `torch.load` in `_load_from_bytes` should not ever be called when `torch.load`-ing a checkpoint. Setting weights_only=False explicitly in `_load_from_bytes` to avoid the weights_only warning when using the pickle module

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130246
Approved by: https://github.com/albanD

(cherry picked from commit dfd1d1971ea1265c597124b7e75fe5c8dd5a45b4)

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
2024-08-21 08:52:42 -04:00
18736d2b55 [inductor] parallel compile: Create new pipes for subproc communicati… (#134042)
* [inductor] parallel compile: Create new pipes for subproc communication (#131194)

Summary: Rather then using stdin/stdout for IPC, we can create new pipes and pass the descriptors to the subproc via the cmd line. https://github.com/pytorch/pytorch/issues/131070 reports an issue where the combination of deepspeed and onnxruntime-training causes _something_ in the subproc to write to stdout and corrupt the IPC. The current implementation was already brittle; we can just create new pipes specifically for the IPC.

Test Plan: I was able to repro the MemoryError in https://github.com/pytorch/pytorch/issues/131070 by installing deepspeed and onnxruntime-training. Verified this PR fixes.

Differential Revision: [D59968362](https://our.internmc.facebook.com/intern/diff/D59968362)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131194
Approved by: https://github.com/malfet, https://github.com/eellison, https://github.com/atalman

* add_catch_statement

* log_fix

---------

Co-authored-by: Sam Larsen <slarsen@meta.com>
2024-08-21 07:53:15 -04:00
1002815f17 Pass torch.load(weights_only=) internally to avoid FutureWarning (#133594)
Pass `torch.load(weights_only=)` internally to avoid FutureWarning (#130663)

Fixes #130658

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130663
Approved by: https://github.com/malfet, https://github.com/LucasLLC

(cherry picked from commit ad314a2f055dbd28bba07cdb585769e6b7b6654e)

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
2024-08-20 15:35:04 -04:00
d3d93a897b Replace [[unlikely]] with unlikely(x) (#133583)
Replace [[unlikely]] with unlikely(x) (#130816)

Do not use `[[unlikely]]` as its c++20 language features, see https://en.cppreference.com/w/cpp/language/attributes/likely

Fixes https://github.com/pytorch/pytorch/issues/130815

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130816
Approved by: https://github.com/jgong5, https://github.com/jansel, https://github.com/malfet

(cherry picked from commit 32f9a809c780d0dda2d5de8ff6348721e9f644e2)

Co-authored-by: Danielmic <30855238+Danielmic@users.noreply.github.com>
2024-08-20 15:26:48 -04:00
24e04f3bfd Remove QNNPACK reference from setup.py (#133526)
Remove QNNPACK reference from setup.py (#133177)

QNNPACK has been removed from third party
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133177
Approved by: https://github.com/albanD

(cherry picked from commit e76f0e0646eb5de838ba1410bcda6925d1ed58c3)

Co-authored-by: cyy <cyyever@outlook.com>
2024-08-15 14:27:54 -07:00
7e0ef343b0 [ROCm] Check supported archs before setting preferred blas backend to hipblasLT (#133359)
[ROCm] Check supported archs before setting preferred blas backend to hipblasLT (#128753)

This PR is needed to resolve usability issues with PyTorch ROCm nightly wheels on non-gfx90a/gf94x architectures as a result of https://github.com/pytorch/pytorch/pull/127944.

Addresses https://github.com/pytorch/pytorch/issues/119081#issuecomment-2166504992

### With this PR's changes, I get the following on a gfx908 (unsupported by hipblasLT) architecture:
_Using setter function:_
```
>>> torch.backends.cuda.preferred_blas_library(backend="cublaslt")
[W617 19:58:58.286088851 Context.cpp:280] Warning: torch.backends.cuda.preferred_blas_library is an experimental feature. If you see any error or unexpected behavior when this flag is set please file an issue on GitHub. (function operator())
[W617 19:59:02.125161985 Context.cpp:291] Warning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (function operator())
<_BlasBackend.Cublas: 0>
```

_Using `TORCH_BLAS_PREFER_HIPBLASLT` env var:_
```
root@9d47bf40d4d4:/tmp/pytorch# TORCH_BLAS_PREFER_CUBLASLT=1 python
>>> import torch
>>> torch.backends.cuda.preferred_blas_library()
[W619 06:14:11.627715807 Context.cpp:274] Warning: Attempting to use hipBLASLt on an unsupported architecture! Overriding blas backend to hipblas (function operator())
<_BlasBackend.Cublas: 0>
```

### and the following on a gfx90a (supported by hipblasLT) architecture:
_Using setter function:_
```
>>> import torch
>>> torch.backends.cuda.preferred_blas_library()
<_BlasBackend.Cublaslt: 1>
>>> torch.backends.cuda.preferred_blas_library(backend="cublas")
<_BlasBackend.Cublas: 0>
>>> torch.backends.cuda.preferred_blas_library(backend="cublaslt")
[W620 18:38:29.404265518 Context.cpp:293] Warning: torch.backends.cuda.preferred_blas_library is an experimental feature. If you see any error or unexpected behavior when this flag is set please file an issue on GitHub. (function operator())
<_BlasBackend.Cublaslt: 1>
```

_Using `TORCH_BLAS_PREFER_HIPBLASLT` env var:_
```
root@9d47bf40d4d4:/tmp/pytorch# TORCH_BLAS_PREFER_HIPBLASLT=1 python
>>> import torch
>>> torch.backends.cuda.preferred_blas_library()
<_BlasBackend.Cublaslt: 1>
```
(Same result for _Using `TORCH_BLAS_PREFER_CUBLASLT` env var:_)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128753
Approved by: https://github.com/malfet

(cherry picked from commit e16276b9bf9e7c5cfcfd8242d336b26eb7dd182f)

Co-authored-by: Jithun Nair <37884920+jithunnair-amd@users.noreply.github.com>
2024-08-15 15:33:34 -04:00
58ab993dcc Fix recent build error on ppc64le (#133416)
Fix recent build error on ppc64le  (#129736)

This PR will fix the recent build issue observed on ppc64le.
Fixes #128130

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129736
Approved by: https://github.com/albanD, https://github.com/malfet

(cherry picked from commit 69cbf0552932c9159fcf8557ea595e00b9f3f9d3)

Co-authored-by: pratiklp00 <pratikp@linux.ibm.com>
2024-08-14 10:21:21 -07:00
6ba64be950 fix for launching kernel invalid config error when calling embedding … (#133346)
fix for launching kernel invalid config error when calling embedding … (#130994)

…with large index

Fixes #130806
When an output size of 2147483648 (=131072*16384) is expected in the above issue, it throwed out the following error:
RuntimeError: HIP error: invalid configuration argument

What happened was that the second parameter passed to hipLaunchKernel was crazy {2147483648,1,1}.
Found two issues in the Indexing.cu:

1: ptrdiff_t was used but it is signed int,  outTotalSize >= 2147483648 can cause overflow when doing [this](39493aa934/aten/src/ATen/native/cuda/Indexing.cu (L1367)):
2: On ROCm, std::min -> ::min did not work as expected when outTotalSize>=2147483648

As the result, 2147483648 was sent to hipLaunchKernel which the GPU does not support such a huge number since this number specifies the number of threads per block. The original code intended to set 128 threads per block, though this is debatable as the perf would not good for latest powerful GPUs (a TODO item to update for perf maybe?) , but at least it would not cause `invalid configuration argument` error.

[Test]
Run the same code snippet in the [issue](https://github.com/pytorch/pytorch/issues/130806), and print the output, its dim and numel(), which looks like below now:
```
output=tensor([[ 0.4044, -0.0244, -0.6865,  ..., -0.7800,  0.1175,  1.6726],
        [-1.0866, -0.1609,  0.3538,  ...,  1.9105,  0.7882,  1.1583],
        [-2.2079,  0.3736,  0.3610,  ..., -0.2658, -0.0459,  1.3077],
        ...,
        [ 0.8753, -0.7482, -0.1978,  ...,  0.9016,  1.1501, -0.5178],
        [-1.5845, -0.6277,  1.4520,  ...,  0.5733, -2.1198, -0.0915],
        [-0.6310, -1.0239, -0.1910,  ...,  0.4309,  0.1630,  0.3239]],
       device='cuda:0'), dim=2, numel=2147483648
```

Added a large tensor unit test too.
```
/pytorch# pytest test/nn/test_embedding.py -k test_large_tensors
================================================================================== test session starts ===================================================================================
platform linux -- Python 3.9.19, pytest-7.3.2, pluggy-1.4.0
rootdir: /dockerx/development/pytorch
configfile: pytest.ini
plugins: flakefinder-1.1.0, rerunfailures-14.0, xdist-3.3.1, xdoctest-1.1.0, cpp-2.3.0, hypothesis-5.35.1
collected 288 items / 287 deselected / 1 selected
Running 1 items in this shard

test/nn/test_embedding.py .                                                                                                                                                        [100%]

=========================================================================== 1 passed, 287 deselected in 3.16s ============================================================================
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130994
Approved by: https://github.com/jeffdaily, https://github.com/xw285cornell

(cherry picked from commit 637ab85e7ff0ae6119cd39c4a554e21da901b45e)

Co-authored-by: hongxyan <hongxyan@amd.com>
2024-08-14 10:09:31 -04:00
d61995868c [Doc] update guide install mkl-static from conda to pip (#133328)
[Doc] update guide install mkl-static from conda to pip (#130026)

<img width="619" alt="image" src="https://github.com/pytorch/pytorch/assets/8433590/4ac3ca68-57dc-42c7-ac7a-876dc377ebcf">

Conda intel channel is not avaliable now.
Use `pip` install instead of `conda`.

`Windows` and `Linux` are avaliable:
Binary list: https://pypi.org/project/mkl-static/#files

`MacOS` is avaliable for old version:
https://pypi.org/project/mkl-static/2021.3.0/#files

TODO:
1. cherry-pick to `release/2.4` branch, @atalman .
2. fix it also in `release/2.3` branch: https://github.com/pytorch/pytorch/pull/131853

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130026
Approved by: https://github.com/jgong5, https://github.com/atalman

(cherry picked from commit 484852c02b94e894bde23b5c07d7ab2cb4d98b9b)

Co-authored-by: Xu Han <xu.han@outlook.com>
2024-08-14 10:08:02 -04:00
26735e7364 [MPS][TYPE_PROMOTION] Fix Clamp (#133260)
[MPS][TYPE_PROMOTION] Fix Clamp (#130226)

Summary:
1. Fixed #130201 by adding type promotion.
2. Added proper tests.
3. Found torch's type promotion is different from numpy as follows:

```python
import torch
import numpy as np
np.clip(np.array([1], dtype=np.float32), np.array([1], dtype=np.int32), None).dtype  # dtype('float64')
torch.clamp(torch.tensor([1], dtype=torch.float32), torch.tensor([1], dtype=torch.int32)).dtype  # torch.float32
```

~Not sure the proper way to handle it, it causes numpy ref tests to fail.~
Reason here, so think I'm gonna xfail it:
3c1cf03fde/test/test_ops.py (L260-L264)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130226
Approved by: https://github.com/malfet

(cherry picked from commit 99967e11199a624a852864354acf7ebccb9bb677)

Co-authored-by: Li-Huai (Allan) Lin <qqaatw@gmail.com>
2024-08-13 10:03:02 -07:00
f6fb80b0f9 Fix mkl-static issue for Windows. (#132401)
Fix mkl-static issue for Windows. (#130697)

Background:
We found the pytorch Windows release/2.4 performance regression: https://github.com/pytorch/pytorch/issues/130619

After some debug works, I found the pytorch Windows static mkl build options are wrong:
<img width="1049" alt="image" src="https://github.com/user-attachments/assets/38692142-bfca-4c98-8092-6e105c82bb13">
1. Thread lib is wrong.
2. Miss `openmp` lib and config.
> Debug history: https://github.com/pytorch/pytorch/issues/130619#issuecomment-2226782504 and https://github.com/pytorch/pytorch/issues/130619#issuecomment-2226418611

This PR will fix `mkl-static` build options issue.
<img width="863" alt="image" src="https://github.com/user-attachments/assets/834f6cee-7e6d-4d74-b2bc-8a270f05e429">

Reference:
<img width="482" alt="image" src="https://github.com/user-attachments/assets/8184dadb-f230-4062-a49f-51df1d7285f5">

https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-link-line-advisor.html#gs.c6izlg

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130697
Approved by: https://github.com/jgong5, https://github.com/atalman

(cherry picked from commit f1456c74a09ec33d7739814e13e5745408bf8b6c)

Co-authored-by: Xu Han <xu.han@outlook.com>
2024-08-01 09:55:45 -04:00
14ab5b5059 Add single Python 3.10, single Cuda 12.1 build with dependencies included (#132094)
* Add single Python 3.10, single Cuda 12.1 build with dependencies included (#130349)

Build large wheel for Python 3.10, CUDA 12.1 that will be used in Colab. Build name: ``manywheel-py3_11-cuda12_1-full-build``

We still have all code to support the full build in builder repo, here:
https://github.com/pytorch/builder/blob/main/manywheel/build_cuda.sh#L151

Test:
```
import sys
import torch
sys.version_info
print(torch.__version__)
sys.version_info

2.3.0+cu121
sys.version_info(major=3, minor=10, micro=12, releaselevel='final', serial=0)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130349
Approved by: https://github.com/malfet

(cherry picked from commit a1590e16dff4a24753235a4976c88164c66efc3a)

* fix cherry-pick

* lint

---------

Co-authored-by: atalman <atalman@fb.com>
2024-07-29 19:50:22 -04:00
d990dada86 [CMAKE] Look for Development.Module instead of Development (#129729)
Based on the [cmake issue](https://gitlab.kitware.com/cmake/cmake/-/issues/23716) and [manylinux issue](https://github.com/pypa/manylinux/issues/1347), when building a python module, it should find the `Development.Module` module, not `Development`, which includes `Development.Module` and `Development.Embed`, and will expect the shared python library only. After this PR and before #124613, pytorch could be built with a static libpython (e.g. in manylinux).

Cherry-pick of 953c6476bd75e3fa1d558204bb30ff5fc90ce4f1 into release/2.4
2024-07-09 11:17:43 -07:00
e4ee3be406 [Release only] use triton 3.0.x from pypi (#130336) 2024-07-09 11:06:52 -04:00
9afe4ec096 Update torchbench model expected accuracy values after pinning numpy (#129986)
* Update torchbench model expected accuracy values after pinning numpy (#129213)

After pinning numpy on torchbench, we need to move torchbench inductor benchmark jobs out of unstable state asap, so that more failures don't sneak it.  I'm updating the expected values here to make trunk green.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129213
Approved by: https://github.com/xuzhao9, https://github.com/malfet, https://github.com/desertfire

(cherry picked from commit b72ef9df0d09b9c020af8ca7930da5ca4728b7e7)

* No change to yolov3

---------

Co-authored-by: Huy Do <huydhn@gmail.com>
2024-07-03 22:44:58 -04:00
499621e7bb [CherryPick][FSDP2+TP] Disable 2D state_dict (#129519) (#129923)
[FSDP2+TP] Disable 2D state_dict (#129519)

Fixes #ISSUE_NUMBER

Gonna fill in the RFC but just want to run CI to see if anything else breaks.

Test:
```
python test/distributed/_composable/fsdp/test_fully_shard_training.py -k test_raise_not_implemented_state_dict_if_2d
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129519
Approved by: https://github.com/awgu

(cherry picked from commit 83e6ec2ccdf1333b946baf8b749b345addf641e8)
2024-07-02 10:51:39 -04:00
e5bda62849 [CherryPick][DCP] Fix Optimizer Learning Rate not being loaded correctly (#129398) (#129683)
[DCP] Fix Optimizer Learning Rate not being loaded correctly (#129398)

Fixes #129079

Currently, the tensor object is loading correctly in-place, but the non-tensor object such as learning rate is not load correctly after f518cf811d, which is a regression introduced in 2.3.

This PR replaces tree_map_only and manual replacement of the state dict items with _tree_map_only and fixes the regression of non-tensor loading.

Test:
```
python3 test/distributed/checkpoint/e2e/test_e2e_save_and_load.py -k test_init_state_dict
python3 test/distributed/checkpoint/test_tp_checkpoint.py -k test_tp_checkpoint_load_on_meta_device
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129398
Approved by: https://github.com/fegin

(cherry picked from commit 8b8e2fcdda4eb2d15a57496b7b5eddd27966854f)
2024-07-02 10:41:41 -04:00
705e3ae420 Improve error message for weights_only load (#129783)
* Improve error message for weights_only load (#129705)

As @vmoens pointed out, the current error message does not make the "either/or" between setting `weights_only=False` and using `add_safe_globals` clear enough, and should print the code for the user to call `add_safe_globals`

New formatting looks like such

In the case that `add_safe_globals` can be used

```python
>>> import torch
>>> from torch.testing._internal.two_tensor import TwoTensor
>>> torch.save(TwoTensor(torch.randn(2), torch.randn(2)), "two_tensor.pt")
>>> torch.load("two_tensor.pt", weights_only=True)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/data/users/mg1998/pytorch/torch/serialization.py", line 1225, in load
    raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
_pickle.UnpicklingError: Weights only load failed. This file can still be loaded, to do so you have two options
        (1) Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
        (2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.
        WeightsUnpickler error: Unsupported global: GLOBAL torch.testing._internal.two_tensor.TwoTensor was not an allowed global by default. Please use `torch.serialization.add_safe_globals([TwoTensor])` to allowlist this global if you trust this class/function.

Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
```

For other issues (unsupported bytecode)
```python
>>> import torch
>>> t = torch.randn(2, 3)
>>> torch.save(t, "protocol_5.pt", pickle_protocol=5)
>>> torch.load("protocol_5.pt", weights_only=True)
/data/users/mg1998/pytorch/torch/_weights_only_unpickler.py:359: UserWarning: Detected pickle protocol 5 in the checkpoint, which was not the default pickle protocol used by `torch.load` (2). The weights_only Unpickler might not support all instructions implemented by this protocol, please file an issue for adding support if you encounter this.
  warnings.warn(
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/data/users/mg1998/pytorch/torch/serialization.py", line 1225, in load
    raise pickle.UnpicklingError(_get_wo_message(str(e))) from None
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
 Please file an issue with the following so that we can make `weights_only=True` compatible with your use case: WeightsUnpickler error: Unsupported operand 149

Check the documentation of torch.load to learn more about types accepted by default with weights_only https://pytorch.org/docs/stable/generated/torch.load.html.
```

Old formatting would have been like:
```python
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/data/users/mg1998/pytorch/torch/serialization.py", line 1203, in load
    raise pickle.UnpicklingError(UNSAFE_MESSAGE + str(e)) from None
_pickle.UnpicklingError: Weights only load failed. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you get the file from a trusted source. Alternatively, to load with `weights_only` please check the recommended steps in the following error message. WeightsUnpickler error: Unsupported global: GLOBAL torch.testing._internal.two_tensor.TwoTensor was not an allowed global by default. Please use `torch.serialization.add_safe_globals` to allowlist this global if you trust this class/function.
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129705
Approved by: https://github.com/albanD, https://github.com/vmoens
ghstack dependencies: #129239, #129396, #129509

(cherry picked from commit 45f3e20527c0cc27a4d6c3b93f2fa529b80556bb)

* Fix pickle import when rebase onto release/2.4

* Update torch/serialization.py

fix bad rebase again

---------

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
2024-06-29 13:01:36 -04:00
b26cde49b6 [Windows] remove mkl shared library dependency. (#129740)
[Windows] remove mkl shared library dependency. (#129493)

# Background
I have fixed pytorch Windows missing mkl shared library dependency issue: https://github.com/pytorch/pytorch/issues/124009
The solution is change torch_cpu module static link mkl library:
1. pytorch static link mkl PR: https://github.com/pytorch/pytorch/pull/124925
2. builder install mkl static library: https://github.com/pytorch/builder/pull/1790

Double confirmed current build is using mkl static link: https://github.com/pytorch/pytorch/issues/124009#issuecomment-2160941802

# Goal
Remove setup.py `install_requires` will install mkl shared lib on pytorch Windows. It is not required now, due to we have static linked it.
It will reduce the pytorch install network traffic and avoid install useless mkl shared library package.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129493
Approved by: https://github.com/malfet

(cherry picked from commit 424068d0d22908294f2e0705d7227c37244b9319)

Co-authored-by: Xu Han <xu.han@outlook.com>
2024-06-28 14:59:08 -04:00
12ad767daf [distributed] NCCL result code update (#129704)
[distributed] NCCL result code update (#128777)

The nccl result codes are outdated. This PR fixes #128756.

Fixes #128756

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128777
Approved by: https://github.com/Skylion007

(cherry picked from commit c027c8935b25cdb99fce5595fa1a980df8cdb4ab)

Co-authored-by: Myungjin Lee <myungjle@cisco.com>
2024-06-28 08:25:26 -04:00
1164d3cb9c Add threadfence to 2-stage reduction for correct writes visibility (#129701)
Add threadfence to 2-stage reduction for correct writes visibility (#128455)

Final block accumulating 2-stage reduction result has to complete acquire pattern to make sure the writes of all other blocks are visible to it, see https://docs.nvidia.com/cuda/parallel-thread-execution/index.html?highlight=atom#release-and-acquire-patterns
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128455
Approved by: https://github.com/eqy, https://github.com/ezyang

(cherry picked from commit 77a0ca66e4eb6919ed14a9491fa7579d06a29f3c)

Co-authored-by: Natalia Gimelshein <ngimel@meta.com>
2024-06-28 08:23:40 -04:00
9533637daa Inductor to fail gracefully on Voltas for bf16 tensors (#129699)
Inductor to fail gracefully on Voltas for bf16 tensors (#129288)

Volta(sm_7x) do not have a HW support for bfloat16 datatype, and while it is is emulated to ted in software, so PyTorch eager can use bfloat16 tensors, but not in Triton. So if graph with either CUDA bf16 input or output tensors is used, raise warnings and skip the frame.

Add optional parameter `including_emulation` to `torch.cuda.is_bf16_supported` method and call it from `torch._inductor.compile_fx. _check_triton_bf16_support`.

Test plan: Modify `is_bf16_supported` to return False and see that warning is generated

Fixes https://github.com/pytorch/pytorch/issues/118122 and https://github.com/pytorch/pytorch/issues/118581

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129288
Approved by: https://github.com/eqy, https://github.com/jansel

(cherry picked from commit 14dc08ddc7dc3d8d2a66d15e4df0eec626a17fcd)

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2024-06-28 08:22:31 -04:00
fadd3cc4ab [MacOS] Improve libomp packaging (#129697)
[MacOS] Improve libomp packaging (#129473)

Instead of replacing `@rpath/libomp.dylib` with `@loadper_path/libomp.dylib`, keep it in place and add `@loadper_path` as new rpath

This should prevent double-loading of OpenMP runtime, because in case of `@rpath` loader is allowed to reuse other libraries, but `loadper_path` directive forces it to load it from the location relative to the executable

Test plan:
- Prepare the environment
```shell
conda create -n py310-cf python=3.10 numpy pip -c conda-forge
conda activate py310-cf
pip install torch --index-url https://download.pytorch.org/whl/test/cpu
```
- Verify that OpenMP is loaded twice and than crashes
```shell
KMP_VERSION=true python -c "import numpy as np; import torch; print(torch.__version__, torch.backends.openmp.is_available()); print(torch.rand(300, 300).abs().max())"
```
output:
```
LLVM OMP version: 5.0.20140926
LLVM OMP library type: performance
LLVM OMP link type: dynamic
LLVM OMP build time: no_timestamp
LLVM OMP build compiler: Clang 16.0
LLVM OMP alternative compiler support: yes
LLVM OMP API version: 5.0 (201611)
LLVM OMP dynamic error checking: no
LLVM OMP thread affinity support: no
LLVM OMP version: 5.0.20140926
LLVM OMP library type: performance
LLVM OMP link type: dynamic
LLVM OMP build time: no_timestamp
LLVM OMP build compiler: Clang 12.0
LLVM OMP alternative compiler support: yes
LLVM OMP API version: 5.0 (201611)
LLVM OMP dynamic error checking: no
LLVM OMP thread affinity support: no
2.4.0 True
zsh: segmentation fault  KMP_VERSION=true python -c
```
- Install artifact from this PR and make sure it passes the same test
```shell
python -mpip install ~/Downloads/torch-2.5.0.dev20240625-cp310-none-macosx_11_0_arm64.whl
KMP_VERSION=true python -c "import numpy as np; import torch; print(torch.__version__, torch.backends.openmp.is_available()); print(torch.rand(300, 300).abs().max())"
```
output
```
LLVM OMP version: 5.0.20140926
LLVM OMP library type: performance
LLVM OMP link type: dynamic
LLVM OMP build time: no_timestamp
LLVM OMP build compiler: Clang 16.0
LLVM OMP alternative compiler support: yes
LLVM OMP API version: 5.0 (201611)
LLVM OMP dynamic error checking: no
LLVM OMP thread affinity support: no
2.5.0.dev20240625 True
tensor(1.0000)
```
- Make sure it still uses bundled OpenMP if none is available in the environment
```
conda uninstall numpy -c conda-forge
KMP_VERSION=true python -c "from ctypes import cdll, c_char_p, c_uint32; import torch; from ctypes import cdll, c_char_p, c_uint32; libdyld = cdll.LoadLibrary('libSystem.dylib'); libdyld._dyld_image_count.restype = c_uint32; libdyld._dyld_get_image_name.restype = c_char_p; libdyld._dyld_get_image_name.argtypes = [c_uint32]; print(torch.rand(300, 300).abs().max()); libs = [libdyld._dyld_get_image_name(i).decode('ascii') for i in range(libdyld._dyld_image_count())]; print([l for l in libs if 'libomp.dylib' in l])"
```

Fixes https://github.com/pytorch/pytorch/issues/124497 and https://github.com/pytorch/pytorch/issues/126385
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129473
Approved by: https://github.com/atalman

(cherry picked from commit 816e8a3f2171aa2b7350bcd2106fe556de7db7a1)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2024-06-28 08:19:45 -04:00
80277a50bc Remove cuda check in the CUDAGraph destructor (#129696)
Remove cuda check in the CUDAGraph destructor (#127382)

Fixes #125804

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127382
Approved by: https://github.com/eqy, https://github.com/eellison

(cherry picked from commit d3e8b8bf47206c27b6c5fdc021f7c2c3a8009521)

Co-authored-by: Frank Lin <eee4017@gmail.com>
2024-06-28 08:18:07 -04:00
d0831d65aa Tunableop hotfix2 unit tests, release/2.4 (#129607)
TunableOp hotfix, unit test follow-up

PR #129281 was landed to fix critical issues but did not contain unit
tests to exercise those issues.  This is a follow-up set of unit tests
that would exercise the problems seen previously.
2024-06-27 16:53:43 -04:00
ca8d4d1751 TunableOp hotfix (#129499)
TunableOp hotfix (#129281)

Fixes.
- PYTORCH_TUNABLEOP_NUMERICAL_CHECK=1 had a memory leak.
- The strided batched gemm size calculation for buffer rotation was incorrect resulting in a mem fault.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129281
Approved by: https://github.com/xw285cornell, https://github.com/eqy, https://github.com/mxz297

(cherry picked from commit e68ee2cadb76f84c3bda788fc3b9c8194e8d921e)

Co-authored-by: Jeff Daily <jeff.daily@amd.com>
2024-06-27 16:51:40 -04:00
3d7d7927ca Upload release tag source code to s3 (#129600)
Upload release tag source code to s3 (#128842)

Upload tarball containing source code to s3 for release tags

Can be found here https://us-east-1.console.aws.amazon.com/s3/buckets/pytorch?region=us-east-1&bucketType=general&prefix=source_code/test/&showversions=false

D58695048 for adding permissions to allow uploading to the s3 folder
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128842
Approved by: https://github.com/atalman, https://github.com/malfet

(cherry picked from commit 795db8097558e4679784f403e278d38e30c6583d)

Co-authored-by: Catherine Lee <csl@fb.com>
2024-06-27 12:32:41 -04:00
5f7de217cb Add warning for weights_only (#129572)
ghstack-source-id: 1098e33fad7c0d38688912c2edb375d77976bbc7
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129239
2024-06-27 12:31:21 -04:00
072d9e8ac9 Add example for torch.serialization.add_safe_globals (#129573)
ghstack-source-id: e23d66f6ab317274e36519141474a6010db54e59
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129396
2024-06-27 12:30:13 -04:00
1f84579407 Cherry pick #129244 #129251 #129509 (#129574)
* Fix allowlisting of builtins for weights_only unpickler (#129244)

Since we use [`DEFAULT_PROTOCOL=2`](https://github.com/pytorch/pytorch/blob/main/torch/serialization.py#L62), some functions/classes that were renamed from python 2-->3 will be pickled with their python2 name. This PR ensures that when a mod `GLOBAL <python2_mod>.<python2_name> ` is encountered, [following the strategy used by pickle](https://github.com/python/cpython/blob/main/Lib/pickle.py#L1590C13-L1593C63) it is properly mapped to `<python3_mod>.<python3_name>`.

This fix ensures that `add_safe_globals` works properly for such functions/classes (i.e. users will allowlist the python3 func and the weights_only unpickler will do the appropriate translation when checking whether a class was allowlisted).

An example is as follows:
`__builtin__` was named to `builtins`, see the [release notes for Python 3.0](https://docs.python.org/3/whatsnew/3.0.html)

> Renamed module `__builtin__` to [`builtins`](https://docs.python.org/3/library/builtins.html#module-builtins) (removing the underscores, adding an ‘s’). The __builtins__ variable found in most global namespaces is unchanged. To modify a builtin, you should use [builtins](https://docs.python.org/3/library/builtins.html#module-builtins), not `__builtins__`!

However, since we use [`DEFAULT_PROTOCOL=2`](https://github.com/pytorch/pytorch/blob/main/torch/serialization.py#L62), builtins will be pickled with their module string as `__builtin__`.

```python
>>> import pickle
>>> import pickletools
>>> print.__module__
'builtins'
>>> with open('print.pkl', 'wb') as f:
>>>      pickle.dump(print, f, protocol=2) # 2 because this is the default protocol used by pytorch
>>> with open('print.pkl', 'rb') as f:
>>>     pickletools.dis(f)
0: \x80 PROTO      2
2: c    GLOBAL     '__builtin__ print' # pickle saves the module string as __builtin__ !!! :(
21: q    BINPUT     0
23: .    STOP
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129244
Approved by: https://github.com/albanD

* Allow BUILD/NEWOBJ instruction for items added via torch.serialization.add_safe_globals (#129251)

Previously, allowlisting functions/classes via `torch.serialization.add_safe_globals(obj)` for the `weights_only` Unpickler had the following effect:

- For a [`GLOBAL`](https://github.com/python/cpython/blob/3.12/Lib/pickletools.py#L1926-L1939) instruction, `GLOBAL obj.__module__ obj.__name__` would be allowed and translated back to obj to be pushed back to the stack.
- For a [`REDUCE`](https://github.com/python/cpython/blob/3.12/Lib/pickletools.py#L1926-L1982) instruction where we expect the stack to contain `func` and `args`, `func` is allowed if it was added via `add_safe_globals`

However, it did not have an effect on `BUILD` and `NEWOBJ` instructions

Some classes may be rebuilt via [`NEWOBJ`](https://github.com/python/cpython/blob/3.12/Lib/pickletools.py#L2091-L2104) instruction, which indicates that their constructor should be used to rebuild the class.

Further, a [`BUILD`](https://github.com/python/cpython/blob/3.12/Lib/pickletools.py#L1984-L2007) instruction might be used if an object's `__reduce__`/`__reduce_ex__` returns a non-None value for `state`. Which indicates a `__setstate__` or `__dict__.update`.

**This PR makes sure that adding objects to the allowlist will also allow `NEWOBJ` and `BUILD` instructions for them.**

In particular, the update for `NEWOBJ` should unblock allowlisting of [`ScaledMMConfig`](d4ade877df/float8_experimental/float8_tensor.py (L26-L30)) in float8_experimental @drisspg

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129251
Approved by: https://github.com/albanD
ghstack dependencies: #129244

* Remove dependency on private _compat_pickle in CPython

ghstack-source-id: 7d6ee402dd0acbaa23c362475b96367f90447cc8
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129509
2024-06-27 12:29:21 -04:00
4d83bca8d8 Revert "Cherry pick #129244, #129251, #129239, 129396 into release/2.4" (#129571)
Revert "Cherry pick #129244, #129251, #129239, 129396 into release/2.4 (#129478)"

This reverts commit 22a4d46e2b4d5404e7df374e8ecb21026feb373e.
2024-06-26 10:56:24 -04:00
04339eec05 [Inductor][Intel GPU] Support reduction split. (#129120) (#129337)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129120
Approved by: https://github.com/EikanWang, https://github.com/jansel, https://github.com/desertfire
ghstack dependencies: #129124

(cherry picked from commit b0ae0db8156f186eaed69b0332d8698a8dfc799a)
2024-06-26 10:48:42 -04:00
22a4d46e2b Cherry pick #129244, #129251, #129239, 129396 into release/2.4 (#129478)
* Fix allowlisting of builtins for weights_only unpickler

ghstack-source-id: de329c75af5e022f1a4517cbfca2bd7f02baef4e
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129244

(cherry picked from commit cc99c015b7f74dcec5d77ea69c75aa3c3e152512)

* Allow NEWOBJ instruction for items added via torch.serialization.add_safe_globals

ghstack-source-id: 34a8fc32d256aa5fe68d4da8ea8f54e536a7ee31
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129251

(cherry picked from commit 50b888dc236b3bcc9dfe224ade311e66892fb64b)

* Add warning for weights_only

ghstack-source-id: ffa772cce121418ec96e13d1d93b6654f75f1c7d
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129239

(cherry picked from commit b3f9aa3f8f4c03b40fed53423d4a0a9340e3bd09)

* Add example for torch.serialization.add_safe_globals

ghstack-source-id: 6dc3275b4e58393813ab43b82fe5683e0d4559af
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129396

(cherry picked from commit ed8c36eda0f4dcf7b1d9c5eb2fb1cdccdf3fee6e)
2024-06-26 10:46:20 -04:00
560869918d Documentations for XPU functionality to PyTorch (#129266)
* Adding a note for Getting Started with PyTorch on Intel GPUs (#127872)

Adding a note for Getting Started with PyTorch on Intel GPUs

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127872
Approved by: https://github.com/svekars

* update amp example to device-agnostic (#127278)

As support for Intel GPU has been upstreamed, this PR is to make the AMP example doc device-agnostic.

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127278
Approved by: https://github.com/dvrogozh, https://github.com/EikanWang, https://github.com/svekars

* add xpu to torch.compile (#127279)

As support for Intel GPU has been upstreamed, this PR is to add the XPU-related contents to torch.compile doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127279
Approved by: https://github.com/dvrogozh, https://github.com/svekars

* add xpu to torch.tensors (#127280)

As support for Intel GPU has been upstreamed, this PR is to add the XPU-related contents to torch.tensors doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127280
Approved by: https://github.com/svekars

* add xpu for amp (#127276)

As support for Intel GPU has been upstreamed, this PR is to add the XPU-related contents to AMP doc.

Co-authored-by: Yu, Guangye <guangye.yu@intel.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127276
Approved by: https://github.com/dvrogozh, https://github.com/albanD, https://github.com/malfet

---------

Co-authored-by: Zheng, Zhaoqiong <zhaoqiong.zheng@intel.com>
2024-06-26 10:33:09 -04:00
2bf37985b1 Support HSDP + Monolith Checkpointing (#128446) (#129254)
Fixes #128444. Rank 0 check should be in the same group as the broadcast

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128446
Approved by: https://github.com/fegin

(cherry picked from commit 153362fbc9e8642fb851a4de3b99e3871a2cc714)

Co-authored-by: Mihir Patel <mihir.v.patel7@gmail.com>
2024-06-26 10:31:15 -04:00
491e9e2d4a [DSD] Add unittest to verify HSDP1 + broadcast_from_rank0 (#128755) (#129255)
HSDP1 + broadcast_from_rank0 actually behaves differently from FSDP1 + broadcast_from_rank0. So we need an unittest to cover this use case.

This test relies on the fix from https://github.com/pytorch/pytorch/pull/128446.

Differential Revision: [D58621436](https://our.internmc.facebook.com/intern/diff/D58621436/)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128755
Approved by: https://github.com/Skylion007, https://github.com/wz337
ghstack dependencies: #128685

(cherry picked from commit fe8558b7aa4ce55d06893c48d5cb00b7a7eb7dae)
2024-06-26 10:30:44 -04:00
ec19059347 [DSD] Correctly handle shared parameters for optimizer state_dict (#1… (#129252)
[DSD] Correctly handle shared parameters for optimizer state_dict (#128685)

*
Fixes https://github.com/pytorch/pytorch/issues/128011

See the discussion in https://github.com/pytorch/pytorch/pull/128076

Current implementation of `set_optimizer_state_dict()` assumes that all the fqns returned by `_get_fqns()` must exist in the optimizer state_dict. This is not true if the model has shared parameters. In such a case, only one fqn of the shared parameters will appear in the optimizer state_dict. This PR addresses the issue.

Differential Revision: [D58573487](https://our.internmc.facebook.com/intern/diff/D58573487/)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128685
Approved by: https://github.com/LucasLLC

(cherry picked from commit 1a527915a64b8e5f60951715b09fa294b1a8844f)
2024-06-26 10:27:36 -04:00
04e98d3d0e [cpp_extension][inductor] Fix sleef windows depends. (#128770) (#128811)
# Issue:
During I'm working on enable inductor on PyTorch Windows, I found the sleef lib dependency issue.
<img width="1011" alt="image" src="https://github.com/pytorch/pytorch/assets/8433590/423bd854-3c5f-468f-9a64-a392d9b514e3">

# Analysis:
After we enabled SIMD on PyTorch Windows(https://github.com/pytorch/pytorch/pull/118980 ), the sleef functions are called from VEC headers. It bring the sleef to the dependency.

Here is a different between Windows and Linux OS.
## Linux :
Linux is default export its functions, so libtorch_cpu.so static link to sleef.a, and then It also export sleef's functions.
<img width="647" alt="image" src="https://github.com/pytorch/pytorch/assets/8433590/00ac536c-33fc-4943-a435-25590508840d">

## Windows:
Windows is by default not export its functions, and have many limitation to export functions, reference: https://github.com/pytorch/pytorch/issues/80604
We can't package sleef functions via torch_cpu.dll like Linux.

# Solution:
Acturally, we also packaged sleef static lib as a part of release. We just need to help user link to sleef.lib, it should be fine.
1. Add sleef to cpp_builder for inductor.
2. Add sleef to cpp_extension for C++ extesion.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128770
Approved by: https://github.com/jgong5, https://github.com/jansel
2024-06-26 10:24:17 -04:00
699c056479 [ROCm] Include hsa headers for rocm-triton whl (#129235)
* Include hsa headers for rocm-triton whl

* Update triton pin to release/3.0.x tip

* Update .ci/docker/ci_commit_pins/triton-rocm.txt

---------

Co-authored-by: Andrey Talman <atalman@fb.com>
2024-06-21 13:19:23 -04:00
49d2eec960 [custom ops] Switch out references from old landing page to new landi… (#129237)
[custom ops] Switch out references from old landing page to new landing page (#129178)

Test Plan:
- existing tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129178
Approved by: https://github.com/albanD
ghstack dependencies: #129177
2024-06-21 09:18:50 -07:00
165e09874b [docs] Redirect custom ops landing page to the correct place (#129177) (#129236)
I'm moving it to pytorch/tutorials
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129177
Approved by: https://github.com/albanD
2024-06-21 09:18:13 -07:00
93c51dc84b Re-enable py3.12 nightly wheel builds and add triton dependency for ROCm (#129161)
* Re-enable py3.12 nightly wheel builds and add triton dependency for ROCm  (#128525)

The llnl-hatchet developers have published the py3.12 binaries on [PyPI](https://pypi.org/project/llnl-hatchet/#files). In fact, looking [here](https://download.pytorch.org/whl/nightly/llnl-hatchet), it seems we already have the py3.12 wheels mirrored. This should allow us to re-enable py3.12 binaries for ROCm.

This PR reverts commit 9d849d4312cd1e62d97b9e9d58979ec78d36c95f.

It also adds the pytorch-triton-rocm dependency for torch wheels on ROCm since pytorch-triton-rocm py3.12 wheels are available now

Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128525
Approved by: https://github.com/malfet

(cherry picked from commit a6ac6447b55bcf910dee5f925c2c17673f162a36)

* Regenerate workflows

* regenerate-2

---------

Co-authored-by: Jithun Nair <jithun.nair@amd.com>
Co-authored-by: atalman <atalman@fb.com>
2024-06-21 10:28:06 -04:00
67a815abd2 [Release only] Temporary change to depend on pytorch-triton (#129232)
[Release only] Temporary change to depend ot pytorch-triton
2024-06-21 09:58:07 -04:00
d2e4cc71f1 [inductor][ci] Fix torchbench dependency issue with numpy (#129074)
[inductor][ci] Fix torchbench dependency issue with numpy (#128968)

For some reason, pip will always upgrade the numpy version even when an older version has been installed.
We have to lock numpy version to the old version to make this constraint explicit.

Torchbench commit: 23512dbebd

Second attempt to fix #128845

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128968
Approved by: https://github.com/eellison

(cherry picked from commit 118f9ceb7c9ec608a845b40c2142f1a1720b73c9)

Co-authored-by: Xu Zhao <xzhao9@meta.com>
2024-06-21 09:19:22 -04:00
0233f8df5b [ROCm] [Triton] - Include roctracer headers in triton whl (#129227)
Include roctracer header
2024-06-21 09:18:47 -04:00
434bf9559f [Release 2.4] Release only changes for triton 3.0.x build (#129143)
* [Release only changes] Release changes for triton 3.0

* fix
2024-06-20 11:22:10 -04:00
50e57d4f3f Revert "[Release 2.4] Release only changes - use pinned triton." (#129139)
Revert "[Release 2.4] Release only changes - use pinned triton. (#128388)"

This reverts commit 1cd41997e99ae1722be3fe88e1867af5f6779433.
2024-06-20 10:15:27 -04:00
edcc77dadb Remove leftover warning causing log spew (#128837)
Original PR: #128688

This warning was left by mistake, and is uninformative (the user is doing nothing wrong) and causing log spew in trainings. See #120750 (comment)
2024-06-19 12:06:47 -04:00
0e0a9c5a5c [Inductor] Fix the High Order Op layout issue (#128275) (#128834)
Fix the issue: https://github.com/pytorch/pytorch/issues/127995

- In current implementation of creating `FallbackKernel`, the `device` of the `NoneLayout` is set to `None` when `example_output` returns from `cls.process_kernel` is `None`. 921aa194c7/torch/_inductor/ir.py (L5632-L5649)
- If a `ExternalKernel schedulerNode` has None device, the previous buffer will not flush before codegen this `ExternalKernel schedulerNode`  which causes the wrong generated code.
ef2b5ed500/torch/_inductor/scheduler.py (L2701-L2709)

**Test Plan**
```
python -u -m pytest -s -v test/higher_order_ops/test_with_effects.py -k test_compile_inductor_external_op_return_none
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128275
Approved by: https://github.com/eellison

Co-authored-by: leslie-fang-intel <leslie.fang@intel.com>
2024-06-19 12:05:13 -04:00
4af5000bff [Port][Quant][Inductor] Bug fix: mutation nodes not handled correctly for QLinearPointwiseBinaryPT2E (#128591)
[Quant][Inductor] Bug fix: mutation nodes not handled correctly for QLinearPointwiseBinaryPT2E (#127592)

Fixes #127402

- Revert some changes to `ir.MutationOutput` and inductor/test_flex_attention.py
- Add checks of mutation for QLinearPointwiseBinaryPT2E

Pull Request resolved: https://github.com/pytorch/pytorch/pull/127592
Approved by: https://github.com/leslie-fang-intel, https://github.com/Chillee
2024-06-19 11:46:53 -04:00
562cdc2084 [tp] refactor and fix PrepareModuleInput for DTensor inputs (#128431) (#128719)
as titled, this PR refactors the PrepareModuleInput style to have common
method prepare_input_arg, allow both args/kwargs to reuse this logic

This also fixes https://github.com/pytorch/pytorch/issues/128365

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128431
Approved by: https://github.com/awgu

(cherry picked from commit 7775fee10f31ee683bd7beee9a5a9829c6574637)
2024-06-19 11:35:06 -04:00
b1d53f07b2 [inductor] fix compile time regression by caching get_gpu_type (#128363) (#128717)
We observed signficant compile time regression in torchtitan when turning
on 2D parallel + torch.compile recently. So I decided to get a deeper
understanding why.

It turns out this is affecting **all the trainings** that have functional collectives
captured in the graph, not only 2D parallel (2D parallel was just the
job that happen to have collectives captured in the TP region).

The root cause is because when doing inductor lowering, we are calling
the comm analysis pass to get a estimated collective time for each
collective node in the graph, for each call to check the collective
node, we are calling `get_gpu_type()`, which under the hood calls a
`torch.utils.collect_env.run` to get the GPU info. However, this call is
super expensive! The reason is that this call effectively spawns a new
process and call `nvidia-smi` to get the GPU info, so the cost is **linear**
to the number of collective nodes in the graph.

see https://github.com/pytorch/pytorch/blob/main/torch/utils/collect_env.py#L75

The fix is to add a lru cache to the function, so that we only call this
once and reuse the cached results afterwards

torchtitan benchmark shows:
* before this fix: 2D parallel + fp8 compile time: 6min +
* after this fix: 2D parallel + fp8 compile time: 2min 48s (more than 100% improvement)

There're more room to improve the compile time, but this PR is trying to fix the biggest regression I found so far.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128363
Approved by: https://github.com/yf225

(cherry picked from commit 8a09940a543d4c2fd23a5c78edbf1ac24d481b45)
2024-06-19 11:31:16 -04:00
86271445d6 [Inductor] Update Intel GPU Triton commit pin. (#124842) (#128615)
Update Intel triton for Pytorch 2.4 release.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124842
Approved by: https://github.com/EikanWang

(cherry picked from commit cf7adc2fa1c5c3b8e8cc5464a03823b6752958ad)
2024-06-19 10:31:08 -04:00
d71de3c95c Revert "Make torch_geometric models compatible with export (#123403)"… (#128511)
Revert "Make torch_geometric models compatible with export (#123403)" (#128377)

This reverts commit d78991a7381adb3df5e9b63c365db4506643edce.

This PR reverts https://github.com/pytorch/pytorch/pull/123403 to fix the performance regression as discussed in https://github.com/pytorch/pytorch/issues/127513#issuecomment-2158835653.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128377
Approved by: https://github.com/jgong5, https://github.com/angelayi, https://github.com/desertfire

(cherry picked from commit 5ef70faaa76364a73cd7f9da2d3f8e23da218b02)
2024-06-19 10:28:01 -04:00
e7dde73d43 [custom_op] stop using nonlocals to store information (#128547) (#128616)
Fixes https://github.com/pytorch/pytorch/issues/128544
Fixes https://github.com/pytorch/pytorch/issues/128535

We had a problem with multithreading where the nonlocals were being
clobbered. In the first place, we stored these nonlocals because we
wanted to ferry information from an autograd.Function.apply to
autograd.Function.forward.

Our new approach is:
- pass the information directly as an input to the
  autograd.Function.apply. This means that the autograd.Function.forward
  will receive the information too.
- this messes up ctx.needs_input_grad, which has an element per input to
  forward. The user should not see the additional information we passed.
  We fix this by temporarily overriding ctx.needs_input_grad to the
  right thing.
- this exposed a bug in that ctx.needs_input_grad wasn't correct for
  TensorList inputs. This PR fixes that too.

Test Plan:
- existing and new tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128547
Approved by: https://github.com/williamwen42, https://github.com/soulitzer
2024-06-19 10:23:12 -04:00
9ad8a5b657 Clean up xpu ut to make CI happy (#128383) (#128614)
# Motivation
Before #127611 merged, the xpu-specific UT `test/test_xpu.py` was skipped temporarily. This PR aims to fix the UT bug introduced by #127741.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128383
Approved by: https://github.com/EikanWang

(cherry picked from commit 88974fedd06889bde8d1da297aa2bd10106f7c24)

Co-authored-by: Yu, Guangye <guangye.yu@intel.com>
2024-06-19 09:03:30 -04:00
ed624a0483 Change Dynamo's custom ops warning message to be less spammy (#128456) (#128581)
This is a short-term fix (for 2.4). In the longer term we should
fix https://github.com/pytorch/pytorch/issues/128430

The problem is that warnings.warn that are inside Dynamo print
all the time. Python warnings are supposed to print once, unless their
cache is reset: Dynamo ends up resetting that cache everytime it runs.

As a workaround we provide our own warn_once cache that is keyed on the
warning msg. I am not worried about this increasing memory usage because
that's effectively what python's warnings.warn cache does.

Test Plan:
- fix tests.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128456
Approved by: https://github.com/anijain2305
2024-06-19 08:56:23 -04:00
082c4f7e64 [inductor] fix linear add bias pattern (#128473) (#128577)
Fix https://github.com/pytorch/pytorch/issues/128287.
Previous the assertion in `linear_add_bias` are pretty bad
```
assert packed_weight_node.name == "_reorder_linear_weight"
assert transpose_weight_node.name == "permute_default"
```
because the `name` can be changed to `_reorder_linear_weight_id, permute_default_id` if we have more than 1 reorder/permute.

Check `target` instead `name` can solve this issue.

UT is also updated to have match more than 1 `linear_add_bias` pattern to cover this case.

Co-authored-by: Jiong Gong <jiong.gong@intel.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128473
Approved by: https://github.com/jgong5

(cherry picked from commit c53d65b3d3d5897c50d622acdd604ddfa8f57687)
2024-06-19 08:55:02 -04:00
459e2aa454 Revert "[cuDNN][SDPA] Remove TORCH_CUDNN_SDPA_ENABLED=1, enable cuDNN SDPA by default on H100 and 2nd on other archs >= sm80 (#125343)" (#128539)
This reverts commit 4c971932e839fc5da2b91906ad028d4654932bca.
2024-06-18 18:07:58 -07:00
6be0234f07 Revert "Deprecate torch._utils.is_compiling() and torch._dynamo.external_utils.is_compiling() (#127690)" (#128542)
This reverts commit 348b181a97abc2e636a6c18e5880a78e5d1dab94.
2024-06-18 18:07:35 -07:00
24a3885ef6 Revert "Set simdlen based on ATEN_CPU_CAPABILITY (#123514)" (#128541)
This reverts commit b66e3f0957b96b058c9b632ca60833d9717a9d8a because it was reverted on main.
2024-06-18 18:07:07 -07:00
62417c6ca9 [dynamo] Fix for #127696 (#128530)
[dynamo] Fix for #127696 (#128358)

Test Plan:
`buck2 test @//mode/dev-nosan //executorch/exir/backend/...`
https://www.internalfb.com/intern/testinfra/testrun/12666373989243932

Differential Revision: D58384518

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128358
Approved by: https://github.com/ydwu4

(cherry picked from commit 4345d98663d31f23492cafc0062f515a47d96a78)

Co-authored-by: Angela Yi <angelayi@meta.com>
2024-06-18 18:54:20 -04:00
1cd41997e9 [Release 2.4] Release only changes - use pinned triton. (#128388)
[Release 2.4] Release only changes - use pinned triton version
2024-06-10 23:19:21 -04:00
c85e2cacd3 [Release 2.4] Release only changes (#128347)
* Release 2.4 - release only changes

* more required changes

* fix

* temp changes for triton release

* fix_lint
2024-06-10 18:37:41 -04:00
5746 changed files with 170223 additions and 294327 deletions

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@ -1,4 +1,4 @@
# Docker images for GitHub CI and CD
# Docker images for GitHub CI
This directory contains everything needed to build the Docker images
that are used in our CI.
@ -12,7 +12,7 @@ each image as the `BUILD_ENVIRONMENT` environment variable.
See `build.sh` for valid build environments (it's the giant switch).
## Docker CI builds
## Contents
* `build.sh` -- dispatch script to launch all builds
* `common` -- scripts used to execute individual Docker build stages
@ -21,12 +21,6 @@ See `build.sh` for valid build environments (it's the giant switch).
* `ubuntu-rocm` -- Dockerfile for Ubuntu image with ROCm support
* `ubuntu-xpu` -- Dockerfile for Ubuntu image with XPU support
### Docker CD builds
* `conda` - Dockerfile and build.sh to build Docker images used in nightly conda builds
* `manywheel` - Dockerfile and build.sh to build Docker images used in nightly manywheel builds
* `libtorch` - Dockerfile and build.sh to build Docker images used in nightly libtorch builds
## Usage
```bash

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@ -1,5 +1,5 @@
0.7b
0.6b
manylinux_2_17
rocm6.2
9be04068c3c0857a4cfd17d7e39e71d0423ebac2
3e9e1959d23b93d78a08fcc5f868125dc3854dece32fd9458be9ef4467982291
rocm6
04b5df8c8123f90cba3ede7e971e6fbc6040d506
3db6ecbc915893ff967abd6e1b43bd5f54949868873be60dc802086c3863e648

View File

@ -92,7 +92,7 @@ _UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
# from scratch
case "$image" in
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDA_VERSION=12.4.0
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
@ -120,7 +120,7 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks)
CUDA_VERSION=12.4.1
CUDA_VERSION=12.4.0
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
@ -165,7 +165,7 @@ case "$image" in
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3.12-gcc9-inductor-benchmarks)
CUDA_VERSION=12.4.1
CUDA_VERSION=12.4.0
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=9
@ -194,7 +194,7 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDA_VERSION=12.4.0
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
@ -222,7 +222,7 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDA_VERSION=12.4.0
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
@ -236,7 +236,7 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-py3-clang10-onnx)
ANACONDA_PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
@ -245,7 +245,7 @@ case "$image" in
ONNX=yes
;;
pytorch-linux-focal-py3-clang9-android-ndk-r21e)
ANACONDA_PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=9
LLVMDEV=yes
PROTOBUF=yes
@ -254,8 +254,8 @@ case "$image" in
GRADLE_VERSION=6.8.3
NINJA_VERSION=1.9.0
;;
pytorch-linux-focal-py3.9-clang10)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-focal-py3.8-clang10)
ANACONDA_PYTHON_VERSION=3.8
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
@ -276,8 +276,8 @@ case "$image" in
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-py3.9-gcc9)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-focal-py3.8-gcc9)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
@ -286,7 +286,18 @@ case "$image" in
TRITON=yes
;;
pytorch-linux-focal-rocm-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.0
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=9
PROTOBUF=yes
DB=yes
@ -296,19 +307,8 @@ case "$image" in
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.2
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-xpu-2024.0-py3)
ANACONDA_PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=11
PROTOBUF=yes
DB=yes
@ -318,8 +318,8 @@ case "$image" in
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-jammy-py3.8-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=11
PROTOBUF=yes
DB=yes
@ -330,8 +330,8 @@ case "$image" in
DOCS=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-clang12)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-jammy-cuda11.8-cudnn9-py3.8-clang12)
ANACONDA_PYTHON_VERSION=3.8
CUDA_VERSION=11.8
CUDNN_VERSION=9
CLANG_VERSION=12
@ -355,8 +355,8 @@ case "$image" in
CONDA_CMAKE=yes
VISION=yes
;;
pytorch-linux-jammy-py3.9-gcc11)
ANACONDA_PYTHON_VERSION=3.9
pytorch-linux-jammy-py3.8-gcc11)
ANACONDA_PYTHON_VERSION=3.8
GCC_VERSION=11
PROTOBUF=yes
DB=yes
@ -373,14 +373,6 @@ case "$image" in
CONDA_CMAKE=yes
EXECUTORCH=yes
;;
pytorch-linux-jammy-py3.12-halide)
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
CONDA_CMAKE=yes
HALIDE=yes
TRITON=yes
;;
pytorch-linux-focal-linter)
# TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
# We will need to update mypy version eventually, but that's for another day. The task
@ -408,22 +400,6 @@ case "$image" in
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
;;
pytorch-linux-jammy-aarch64-py3.10-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
ACL=yes
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
# snadampal: skipping sccache due to the following issue
# https://github.com/pytorch/pytorch/issues/121559
SKIP_SCCACHE_INSTALL=yes
# snadampal: skipping llvm src build install because the current version
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
INDUCTOR_BENCHMARKS=yes
;;
*)
# Catch-all for builds that are not hardcoded.
PROTOBUF=yes
@ -514,7 +490,6 @@ docker build \
--build-arg "DOCS=${DOCS}" \
--build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
--build-arg "EXECUTORCH=${EXECUTORCH}" \
--build-arg "HALIDE=${HALIDE}" \
--build-arg "XPU_VERSION=${XPU_VERSION}" \
--build-arg "ACL=${ACL:-}" \
--build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \

View File

@ -108,10 +108,10 @@ ENV CMAKE_C_COMPILER cc
ENV CMAKE_CXX_COMPILER c++
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
# Install AOTriton (Early fail)
COPY ./aotriton_version.txt aotriton_version.txt

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@ -1 +1 @@
cd1c833b079adb324871dcbbe75b43d42ffc0ade
d4b3e5cc607e97afdba79dc90f8ef968142f347c

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@ -1 +0,0 @@
461c12871f336fe6f57b55d6a297f13ef209161b

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@ -1 +1 @@
ac3470188b914c5d7a5058a7e28b9eb685a62427
730b907b4d45a4713cbc425cbf224c46089fd514

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@ -0,0 +1 @@
21eae954efa5bf584da70324b640288c3ee7aede

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@ -1 +1 @@
91b14bf5593cf58a8541f3e6b9125600a867d4ef
aac14a3b93f11d781d1d5ebc5400b15ae8df5185

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@ -1 +1 @@
5fe38ffd73c2ac6ed6323b554205186696631c6f
45fff310c891f5a92d55445adf8cc9d29df5841e

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@ -4,12 +4,12 @@ set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
TARBALL='aotriton.tar.gz'
TARBALL='aotriton.tar.bz2'
# This read command alwasy returns with exit code 1
read -d "\n" VER MANYLINUX ROCMBASE PINNED_COMMIT SHA256 < aotriton_version.txt || true
ARCH=$(uname -m)
AOTRITON_INSTALL_PREFIX="$1"
AOTRITON_URL="https://github.com/ROCm/aotriton/releases/download/${VER}/aotriton-${VER}-${MANYLINUX}_${ARCH}-${ROCMBASE}-shared.tar.gz"
AOTRITON_URL="https://github.com/ROCm/aotriton/releases/download/${VER}/aotriton-${VER}-${MANYLINUX}_${ARCH}-${ROCMBASE}.tar.bz2"
cd "${AOTRITON_INSTALL_PREFIX}"
# Must use -L to follow redirects

View File

@ -5,22 +5,32 @@ set -ex
# Optionally install conda
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
BASE_URL="https://repo.anaconda.com/miniconda"
CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
if [[ $(uname -m) == "aarch64" ]] || [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
CONDA_FILE="Miniforge3-Linux-$(uname -m).sh"
fi
MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
MINOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
if [[ $(uname -m) == "aarch64" ]]; then
BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
case "$MAJOR_PYTHON_VERSION" in
3);;
3)
CONDA_FILE="Miniforge3-Linux-aarch64.sh"
;;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
else
case "$MAJOR_PYTHON_VERSION" in
3)
CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
;;
*)
echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
exit 1
;;
esac
fi
mkdir -p /opt/conda
chown jenkins:jenkins /opt/conda
@ -68,20 +78,19 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
CONDA_COMMON_DEPS="astunparse pyyaml setuptools openblas==0.3.25=*openmp* ninja==1.11.1 scons==4.5.2"
if [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
NUMPY_VERSION=1.24.4
conda_install numpy=1.24.4 ${CONDA_COMMON_DEPS}
else
NUMPY_VERSION=1.26.2
conda_install numpy=1.26.2 ${CONDA_COMMON_DEPS}
fi
else
CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.12" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.13" ]; then
NUMPY_VERSION=1.26.0
if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
conda_install numpy=1.26.0 ${CONDA_COMMON_DEPS}
else
NUMPY_VERSION=1.21.2
conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS}
fi
fi
conda_install ${CONDA_COMMON_DEPS}
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
# and libpython-static for torch deploy
@ -103,7 +112,7 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
# Install some other packages, including those needed for Python test reporting
pip_install -r /opt/conda/requirements-ci.txt
pip_install numpy=="$NUMPY_VERSION"
pip_install -U scikit-learn
if [ -n "$DOCS" ]; then

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

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@ -1,112 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -uex -o pipefail
PYTHON_DOWNLOAD_URL=https://www.python.org/ftp/python
PYTHON_DOWNLOAD_GITHUB_BRANCH=https://github.com/python/cpython/archive/refs/heads
GET_PIP_URL=https://bootstrap.pypa.io/get-pip.py
# Python versions to be installed in /opt/$VERSION_NO
CPYTHON_VERSIONS=${CPYTHON_VERSIONS:-"3.8.1 3.9.0 3.10.1 3.11.0 3.12.0 3.13.0 3.13.0t"}
function check_var {
if [ -z "$1" ]; then
echo "required variable not defined"
exit 1
fi
}
function do_cpython_build {
local py_ver=$1
local py_folder=$2
check_var $py_ver
check_var $py_folder
tar -xzf Python-$py_ver.tgz
local additional_flags=""
if [ "$py_ver" == "3.13.0t" ]; then
additional_flags=" --disable-gil"
mv cpython-3.13/ cpython-3.13t/
fi
pushd $py_folder
local prefix="/opt/_internal/cpython-${py_ver}"
mkdir -p ${prefix}/lib
if [[ -n $(which patchelf) ]]; then
local shared_flags="--enable-shared"
else
local shared_flags="--disable-shared"
fi
if [[ -z "${WITH_OPENSSL+x}" ]]; then
local openssl_flags=""
else
local openssl_flags="--with-openssl=${WITH_OPENSSL} --with-openssl-rpath=auto"
fi
# -Wformat added for https://bugs.python.org/issue17547 on Python 2.6
CFLAGS="-Wformat" ./configure --prefix=${prefix} ${openssl_flags} ${shared_flags} ${additional_flags} > /dev/null
make -j40 > /dev/null
make install > /dev/null
if [[ "${shared_flags}" == "--enable-shared" ]]; then
patchelf --set-rpath '$ORIGIN/../lib' ${prefix}/bin/python3
fi
popd
rm -rf $py_folder
# Some python's install as bin/python3. Make them available as
# bin/python.
if [ -e ${prefix}/bin/python3 ]; then
ln -s python3 ${prefix}/bin/python
fi
${prefix}/bin/python get-pip.py
if [ -e ${prefix}/bin/pip3 ] && [ ! -e ${prefix}/bin/pip ]; then
ln -s pip3 ${prefix}/bin/pip
fi
# install setuptools since python 3.12 is required to use distutils
${prefix}/bin/pip install wheel==0.34.2 setuptools==68.2.2
local abi_tag=$(${prefix}/bin/python -c "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag; print('{0}{1}-{2}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag()))")
ln -s ${prefix} /opt/python/${abi_tag}
}
function build_cpython {
local py_ver=$1
check_var $py_ver
check_var $PYTHON_DOWNLOAD_URL
local py_ver_folder=$py_ver
if [ "$py_ver" = "3.13.0t" ]; then
PY_VER_SHORT="3.13"
PYT_VER_SHORT="3.13t"
check_var $PYTHON_DOWNLOAD_GITHUB_BRANCH
wget $PYTHON_DOWNLOAD_GITHUB_BRANCH/$PY_VER_SHORT.tar.gz -O Python-$py_ver.tgz
do_cpython_build $py_ver cpython-$PYT_VER_SHORT
elif [ "$py_ver" = "3.13.0" ]; then
PY_VER_SHORT="3.13"
check_var $PYTHON_DOWNLOAD_GITHUB_BRANCH
wget $PYTHON_DOWNLOAD_GITHUB_BRANCH/$PY_VER_SHORT.tar.gz -O Python-$py_ver.tgz
do_cpython_build $py_ver cpython-$PY_VER_SHORT
else
wget -q $PYTHON_DOWNLOAD_URL/$py_ver_folder/Python-$py_ver.tgz
do_cpython_build $py_ver Python-$py_ver
fi
rm -f Python-$py_ver.tgz
}
function build_cpythons {
check_var $GET_PIP_URL
curl -sLO $GET_PIP_URL
for py_ver in $@; do
build_cpython $py_ver
done
rm -f get-pip.py
}
mkdir -p /opt/python
mkdir -p /opt/_internal
build_cpythons $CPYTHON_VERSIONS

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@ -1,250 +0,0 @@
#!/bin/bash
set -ex
NCCL_VERSION=v2.21.5-1
CUDNN_VERSION=9.1.0.70
function install_cusparselt_040 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.4.0.7-archive.tar.xz
tar xf libcusparse_lt-linux-x86_64-0.4.0.7-archive.tar.xz
cp -a libcusparse_lt-linux-x86_64-0.4.0.7-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-x86_64-0.4.0.7-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_cusparselt_052 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.5.2.1-archive.tar.xz
tar xf libcusparse_lt-linux-x86_64-0.5.2.1-archive.tar.xz
cp -a libcusparse_lt-linux-x86_64-0.5.2.1-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-x86_64-0.5.2.1-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_cusparselt_062 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.6.2.3-archive.tar.xz
tar xf libcusparse_lt-linux-x86_64-0.6.2.3-archive.tar.xz
cp -a libcusparse_lt-linux-x86_64-0.6.2.3-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-x86_64-0.6.2.3-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_118 {
echo "Installing CUDA 11.8 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.4.0"
rm -rf /usr/local/cuda-11.8 /usr/local/cuda
# install CUDA 11.8.0 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
chmod +x cuda_11.8.0_520.61.05_linux.run
./cuda_11.8.0_520.61.05_linux.run --toolkit --silent
rm -f cuda_11.8.0_520.61.05_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-11.8 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive.tar.xz
tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive.tar.xz
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda11-archive/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
# NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
# Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_040
ldconfig
}
function install_121 {
echo "Installing CUDA 12.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.5.2"
rm -rf /usr/local/cuda-12.1 /usr/local/cuda
# install CUDA 12.1.0 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda_12.1.1_530.30.02_linux.run
chmod +x cuda_12.1.1_530.30.02_linux.run
./cuda_12.1.1_530.30.02_linux.run --toolkit --silent
rm -f cuda_12.1.1_530.30.02_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.1 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
# NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
# Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_052
ldconfig
}
function install_124 {
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.5.2"
rm -rf /usr/local/cuda-12.4 /usr/local/cuda
# install CUDA 12.4.1 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux.run
chmod +x cuda_12.4.1_550.54.15_linux.run
./cuda_12.4.1_550.54.15_linux.run --toolkit --silent
rm -f cuda_12.4.1_550.54.15_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.4 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
# NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
# Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_062
ldconfig
}
function prune_118 {
echo "Pruning CUDA 11.8 and cuDNN"
#####################################################################################
# CUDA 11.8 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-11.8/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-11.8/lib64"
export GENCODE="-gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_35,code=sm_35 -gencode arch=compute_37,code=sm_37 -gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
# all CUDA libs except CuDNN and CuBLAS (cudnn and cublas need arch 3.7 included)
ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis" \
| xargs -I {} bash -c \
"echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"
# prune CuDNN and CuBLAS
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a
#####################################################################################
# CUDA 11.8 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-11.8/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2022.3.0 $CUDA_BASE/nsight-systems-2022.4.2/
}
function prune_121 {
echo "Pruning CUDA 12.1"
#####################################################################################
# CUDA 12.1 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.1/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.1/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
# all CUDA libs except CuDNN and CuBLAS
ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis" \
| xargs -I {} bash -c \
"echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"
# prune CuDNN and CuBLAS
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a
#####################################################################################
# CUDA 12.1 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-12.1/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2023.1.0 $CUDA_BASE/nsight-systems-2023.1.2/
}
function prune_124 {
echo "Pruning CUDA 12.4"
#####################################################################################
# CUDA 12.4 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.4/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.4/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
if [[ -n "$OVERRIDE_GENCODE_CUDNN" ]]; then
export GENCODE_CUDNN=$OVERRIDE_GENCODE_CUDNN
fi
# all CUDA libs except CuDNN and CuBLAS
ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis" \
| xargs -I {} bash -c \
"echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"
# prune CuDNN and CuBLAS
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a
#####################################################################################
# CUDA 12.1 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-12.4/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.1.0 $CUDA_BASE/nsight-systems-2023.4.4/
}
# idiomatic parameter and option handling in sh
while test $# -gt 0
do
case "$1" in
11.8) install_118; prune_118
;;
12.1) install_121; prune_121
;;
12.4) install_124; prune_124
;;
*) echo "bad argument $1"; exit 1
;;
esac
shift
done

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@ -1,93 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
NCCL_VERSION=v2.21.5-1
function install_cusparselt_052 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-sbsa/libcusparse_lt-linux-sbsa-0.5.2.1-archive.tar.xz
tar xf libcusparse_lt-linux-sbsa-0.5.2.1-archive.tar.xz
cp -a libcusparse_lt-linux-sbsa-0.5.2.1-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-sbsa-0.5.2.1-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_124 {
echo "Installing CUDA 12.4.1 and cuDNN 9.1 and NCCL ${NCCL_VERSION} and cuSparseLt-0.5.2"
rm -rf /usr/local/cuda-12.4 /usr/local/cuda
# install CUDA 12.4.1 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux_sbsa.run
chmod +x cuda_12.4.1_550.54.15_linux_sbsa.run
./cuda_12.4.1_550.54.15_linux_sbsa.run --toolkit --silent
rm -f cuda_12.4.1_550.54.15_linux_sbsa.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.4 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-9.1.0.70_cuda12-archive.tar.xz -O cudnn-linux-sbsa-9.1.0.70_cuda12-archive.tar.xz
tar xf cudnn-linux-sbsa-9.1.0.70_cuda12-archive.tar.xz
cp -a cudnn-linux-sbsa-9.1.0.70_cuda12-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-sbsa-9.1.0.70_cuda12-archive/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
# NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
# Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
git clone -b ${NCCL_VERSION} --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_052
ldconfig
}
function prune_124 {
echo "Pruning CUDA 12.4"
#####################################################################################
# CUDA 12.4 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.4/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.4/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
# all CUDA libs except CuDNN and CuBLAS
ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis" \
| xargs -I {} bash -c \
"echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"
# prune CuDNN and CuBLAS
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a
#####################################################################################
# CUDA 12.1 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-12.4/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.1.0 $CUDA_BASE/nsight-systems-2023.4.4/
}
# idiomatic parameter and option handling in sh
while test $# -gt 0
do
case "$1" in
12.4) install_124; prune_124
;;
*) echo "bad argument $1"; exit 1
;;
esac
shift
done

View File

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

View File

@ -5,15 +5,7 @@ set -ex
# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && cd tmp_cusparselt
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[2-6]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.6.2.3-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[1-4]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then

View File

@ -37,9 +37,6 @@ install_conda_dependencies() {
install_pip_dependencies() {
pushd executorch/.ci/docker
# Install PyTorch CPU build beforehand to avoid installing the much bigger CUDA
# binaries later, ExecuTorch only needs CPU
pip_install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
# Install all Python dependencies
pip_install -r requirements-ci.txt
popd
@ -47,14 +44,13 @@ install_pip_dependencies() {
setup_executorch() {
pushd executorch
# Setup swiftshader and Vulkan SDK which are required to build the Vulkan delegate
as_jenkins bash .ci/scripts/setup-vulkan-linux-deps.sh
source .ci/scripts/utils.sh
export PYTHON_EXECUTABLE=python
export EXECUTORCH_BUILD_PYBIND=ON
export CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
install_flatc_from_source
pip_install .
as_jenkins .ci/scripts/setup-linux.sh cmake
# Make sure that all the newly generate files are owned by Jenkins
chown -R jenkins .
popd
}

View File

@ -1,46 +0,0 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
COMMIT=$(get_pinned_commit halide)
test -n "$COMMIT"
# activate conda to populate CONDA_PREFIX
test -n "$ANACONDA_PYTHON_VERSION"
eval "$(conda shell.bash hook)"
conda activate py_$ANACONDA_PYTHON_VERSION
if [ -n "${UBUNTU_VERSION}" ];then
apt update
apt-get install -y lld liblld-15-dev libpng-dev libjpeg-dev libgl-dev \
libopenblas-dev libeigen3-dev libatlas-base-dev libzstd-dev
fi
conda_install numpy scipy imageio cmake ninja
git clone --depth 1 --branch release/16.x --recursive https://github.com/llvm/llvm-project.git
cmake -DCMAKE_BUILD_TYPE=Release \
-DLLVM_ENABLE_PROJECTS="clang" \
-DLLVM_TARGETS_TO_BUILD="X86;NVPTX" \
-DLLVM_ENABLE_TERMINFO=OFF -DLLVM_ENABLE_ASSERTIONS=ON \
-DLLVM_ENABLE_EH=ON -DLLVM_ENABLE_RTTI=ON -DLLVM_BUILD_32_BITS=OFF \
-S llvm-project/llvm -B llvm-build -G Ninja
cmake --build llvm-build
cmake --install llvm-build --prefix llvm-install
export LLVM_ROOT=`pwd`/llvm-install
export LLVM_CONFIG=$LLVM_ROOT/bin/llvm-config
git clone https://github.com/halide/Halide.git
pushd Halide
git checkout ${COMMIT} && git submodule update --init --recursive
pip_install -r requirements.txt
cmake -G Ninja -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake --build build
test -e ${CONDA_PREFIX}/lib/python3 || ln -s python${ANACONDA_PYTHON_VERSION} ${CONDA_PREFIX}/lib/python3
cmake --install build --prefix ${CONDA_PREFIX}
chown -R jenkins ${CONDA_PREFIX}
popd
rm -rf Halide llvm-build llvm-project llvm-install
python -c "import halide" # check for errors

View File

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

View File

@ -1,29 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eou pipefail
MAGMA_VERSION="2.5.2"
function do_install() {
cuda_version=$1
cuda_version_nodot=${1/./}
MAGMA_VERSION="2.6.1"
magma_archive="magma-cuda${cuda_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
cuda_dir="/usr/local/cuda-${cuda_version}"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://anaconda.org/pytorch/magma-cuda${cuda_version_nodot}/${MAGMA_VERSION}/download/linux-64/${magma_archive}
tar -xvf "${magma_archive}"
mkdir -p "${cuda_dir}/magma"
mv include "${cuda_dir}/magma/include"
mv lib "${cuda_dir}/magma/lib"
popd
)
}
do_install $1

View File

@ -1,172 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
ROCM_VERSION=$1
if [[ -z $ROCM_VERSION ]]; then
echo "missing ROCM_VERSION"
exit 1;
fi
IS_UBUNTU=0
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
IS_UBUNTU=1
;;
centos)
IS_UBUNTU=0
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac
# To make version comparison easier, create an integer representation.
save_IFS="$IFS"
IFS=. ROCM_VERSION_ARRAY=(${ROCM_VERSION})
IFS="$save_IFS"
if [[ ${#ROCM_VERSION_ARRAY[@]} == 2 ]]; then
ROCM_VERSION_MAJOR=${ROCM_VERSION_ARRAY[0]}
ROCM_VERSION_MINOR=${ROCM_VERSION_ARRAY[1]}
ROCM_VERSION_PATCH=0
elif [[ ${#ROCM_VERSION_ARRAY[@]} == 3 ]]; then
ROCM_VERSION_MAJOR=${ROCM_VERSION_ARRAY[0]}
ROCM_VERSION_MINOR=${ROCM_VERSION_ARRAY[1]}
ROCM_VERSION_PATCH=${ROCM_VERSION_ARRAY[2]}
else
echo "Unhandled ROCM_VERSION ${ROCM_VERSION}"
exit 1
fi
ROCM_INT=$(($ROCM_VERSION_MAJOR * 10000 + $ROCM_VERSION_MINOR * 100 + $ROCM_VERSION_PATCH))
# Install custom MIOpen + COMgr for ROCm >= 4.0.1
if [[ $ROCM_INT -lt 40001 ]]; then
echo "ROCm version < 4.0.1; will not install custom MIOpen"
exit 0
fi
# Function to retry functions that sometimes timeout or have flaky failures
retry () {
$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
}
# Build custom MIOpen to use comgr for offline compilation.
## Need a sanitized ROCM_VERSION without patchlevel; patchlevel version 0 must be added to paths.
ROCM_DOTS=$(echo ${ROCM_VERSION} | tr -d -c '.' | wc -c)
if [[ ${ROCM_DOTS} == 1 ]]; then
ROCM_VERSION_NOPATCH="${ROCM_VERSION}"
ROCM_INSTALL_PATH="/opt/rocm-${ROCM_VERSION}.0"
else
ROCM_VERSION_NOPATCH="${ROCM_VERSION%.*}"
ROCM_INSTALL_PATH="/opt/rocm-${ROCM_VERSION}"
fi
# MIOPEN_USE_HIP_KERNELS is a Workaround for COMgr issues
MIOPEN_CMAKE_COMMON_FLAGS="
-DMIOPEN_USE_COMGR=ON
-DMIOPEN_BUILD_DRIVER=OFF
"
# Pull MIOpen repo and set DMIOPEN_EMBED_DB based on ROCm version
if [[ $ROCM_INT -ge 60300 ]]; then
echo "ROCm 6.3+ MIOpen does not need any patches, do not build from source"
exit 0
elif [[ $ROCM_INT -ge 60200 ]] && [[ $ROCM_INT -lt 60300 ]]; then
MIOPEN_BRANCH="release/rocm-rel-6.2-staging"
elif [[ $ROCM_INT -ge 60100 ]] && [[ $ROCM_INT -lt 60200 ]]; then
echo "ROCm 6.1 MIOpen does not need any patches, do not build from source"
exit 0
elif [[ $ROCM_INT -ge 60000 ]] && [[ $ROCM_INT -lt 60100 ]]; then
echo "ROCm 6.0 MIOpen does not need any patches, do not build from source"
exit 0
elif [[ $ROCM_INT -ge 50700 ]] && [[ $ROCM_INT -lt 60000 ]]; then
echo "ROCm 5.7 MIOpen does not need any patches, do not build from source"
exit 0
elif [[ $ROCM_INT -ge 50600 ]] && [[ $ROCM_INT -lt 50700 ]]; then
MIOPEN_BRANCH="release/rocm-rel-5.6-staging"
elif [[ $ROCM_INT -ge 50500 ]] && [[ $ROCM_INT -lt 50600 ]]; then
MIOPEN_BRANCH="release/rocm-rel-5.5-gfx11"
elif [[ $ROCM_INT -ge 50400 ]] && [[ $ROCM_INT -lt 50500 ]]; then
MIOPEN_CMAKE_DB_FLAGS="-DMIOPEN_EMBED_DB=gfx900_56;gfx906_60;gfx90878;gfx90a6e;gfx1030_36 -DMIOPEN_USE_MLIR=Off"
MIOPEN_BRANCH="release/rocm-rel-5.4-staging"
elif [[ $ROCM_INT -ge 50300 ]] && [[ $ROCM_INT -lt 50400 ]]; then
MIOPEN_CMAKE_DB_FLAGS="-DMIOPEN_EMBED_DB=gfx900_56;gfx906_60;gfx90878;gfx90a6e;gfx1030_36 -DMIOPEN_USE_MLIR=Off"
MIOPEN_BRANCH="release/rocm-rel-5.3-staging"
elif [[ $ROCM_INT -ge 50200 ]] && [[ $ROCM_INT -lt 50300 ]]; then
MIOPEN_CMAKE_DB_FLAGS="-DMIOPEN_EMBED_DB=gfx900_56;gfx906_60;gfx90878;gfx90a6e;gfx1030_36 -DMIOPEN_USE_MLIR=Off"
MIOPEN_BRANCH="release/rocm-rel-5.2-staging"
elif [[ $ROCM_INT -ge 50100 ]] && [[ $ROCM_INT -lt 50200 ]]; then
MIOPEN_CMAKE_DB_FLAGS="-DMIOPEN_EMBED_DB=gfx900_56;gfx906_60;gfx90878;gfx90a6e;gfx1030_36"
MIOPEN_BRANCH="release/rocm-rel-5.1-staging"
elif [[ $ROCM_INT -ge 50000 ]] && [[ $ROCM_INT -lt 50100 ]]; then
MIOPEN_CMAKE_DB_FLAGS="-DMIOPEN_EMBED_DB=gfx900_56;gfx906_60;gfx90878;gfx90a6e;gfx1030_36"
MIOPEN_BRANCH="release/rocm-rel-5.0-staging"
else
echo "Unhandled ROCM_VERSION ${ROCM_VERSION}"
exit 1
fi
if [[ ${IS_UBUNTU} == 1 ]]; then
apt-get remove -y miopen-hip
else
yum remove -y miopen-hip
fi
git clone https://github.com/ROCm/MIOpen -b ${MIOPEN_BRANCH}
pushd MIOpen
# remove .git to save disk space since CI runner was running out
rm -rf .git
# Don't build CK to save docker build time
if [[ $ROCM_INT -ge 60200 ]]; then
sed -i '/composable_kernel/d' requirements.txt
fi
# Don't build MLIR to save docker build time
# since we are disabling MLIR backend for MIOpen anyway
if [[ $ROCM_INT -ge 50400 ]] && [[ $ROCM_INT -lt 50500 ]]; then
sed -i '/rocMLIR/d' requirements.txt
elif [[ $ROCM_INT -ge 50200 ]] && [[ $ROCM_INT -lt 50400 ]]; then
sed -i '/llvm-project-mlir/d' requirements.txt
fi
## MIOpen minimum requirements
cmake -P install_deps.cmake --minimum
# clean up since CI runner was running out of disk space
rm -rf /tmp/*
if [[ ${IS_UBUNTU} == 1 ]]; then
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
else
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
fi
## Build MIOpen
mkdir -p build
cd build
PKG_CONFIG_PATH=/usr/local/lib/pkgconfig CXX=${ROCM_INSTALL_PATH}/llvm/bin/clang++ cmake .. \
${MIOPEN_CMAKE_COMMON_FLAGS} \
${MIOPEN_CMAKE_DB_FLAGS} \
-DCMAKE_PREFIX_PATH="${ROCM_INSTALL_PATH}/hip;${ROCM_INSTALL_PATH}"
make MIOpen -j $(nproc)
# Build MIOpen package
make -j $(nproc) package
# clean up since CI runner was running out of disk space
rm -rf /usr/local/cget
if [[ ${IS_UBUNTU} == 1 ]]; then
sudo dpkg -i miopen-hip*.deb
else
yum install -y miopen-*.rpm
fi
popd
rm -rf MIOpen

View File

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

View File

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

View File

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

View File

@ -15,7 +15,7 @@ pip_install \
flatbuffers==2.0 \
mock==5.0.1 \
ninja==1.10.2 \
networkx==2.5 \
networkx==2.0 \
numpy==1.24.2
# ONNXRuntime should be installed before installing
@ -30,11 +30,10 @@ pip_install \
pip_install coloredlogs packaging
pip_install onnxruntime==1.18.1
pip_install onnx==1.16.2
pip_install onnxscript==0.1.0.dev20240831 --no-deps
# required by onnxscript
pip_install ml_dtypes
pip_install onnxruntime==1.18
pip_install onnx==1.16.0
# pip_install "onnxscript@git+https://github.com/microsoft/onnxscript@3e869ef8ccf19b5ebd21c10d3e9c267c9a9fa729" --no-deps
pip_install onnxscript==0.1.0.dev20240523 --no-deps
# Cache the transformers model to be used later by ONNX tests. We need to run the transformers
# package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/

View File

@ -1,22 +0,0 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
cd /
git clone https://github.com/OpenMathLib/OpenBLAS.git -b v0.3.25 --depth 1 --shallow-submodules
OPENBLAS_BUILD_FLAGS="
NUM_THREADS=128
USE_OPENMP=1
NO_SHARED=0
DYNAMIC_ARCH=1
TARGET=ARMV8
CFLAGS=-O3
"
OPENBLAS_CHECKOUT_DIR="OpenBLAS"
make -j8 ${OPENBLAS_BUILD_FLAGS} -C ${OPENBLAS_CHECKOUT_DIR}
make -j8 ${OPENBLAS_BUILD_FLAGS} install -C ${OPENBLAS_CHECKOUT_DIR}

View File

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

View File

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

View File

@ -1,11 +1,7 @@
#!/bin/bash
# Script used in CI and CD pipeline
set -ex
MKLROOT=${MKLROOT:-/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION}
# "install" hipMAGMA into /opt/rocm/magma by copying after build
git clone https://bitbucket.org/icl/magma.git
pushd magma
@ -15,10 +11,7 @@ git checkout a1625ff4d9bc362906bd01f805dbbe12612953f6
cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
if [[ -f "${MKLROOT}/lib/libmkl_core.a" ]]; then
echo 'LIB = -Wl,--start-group -lmkl_gf_lp64 -lmkl_gnu_thread -lmkl_core -Wl,--end-group -lpthread -lstdc++ -lm -lgomp -lhipblas -lhipsparse' >> make.inc
fi
echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib -ldl' >> make.inc
echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(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
@ -32,7 +25,7 @@ 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 $(nproc) MKLROOT="${MKLROOT}"
make testing/testing_dgemm -j $(nproc) MKLROOT="${MKLROOT}"
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

View File

@ -12,7 +12,10 @@ conda_reinstall() {
as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y --force-reinstall $*
}
if [ -n "${XPU_VERSION}" ]; then
if [ -n "${ROCM_VERSION}" ]; then
TRITON_REPO="https://github.com/openai/triton"
TRITON_TEXT_FILE="triton-rocm"
elif [ -n "${XPU_VERSION}" ]; then
TRITON_REPO="https://github.com/intel/intel-xpu-backend-for-triton"
TRITON_TEXT_FILE="triton-xpu"
else
@ -38,33 +41,19 @@ if [ -z "${MAX_JOBS}" ]; then
export MAX_JOBS=$(nproc)
fi
# Git checkout triton
mkdir /var/lib/jenkins/triton
chown -R jenkins /var/lib/jenkins/triton
chgrp -R jenkins /var/lib/jenkins/triton
pushd /var/lib/jenkins/
as_jenkins git clone ${TRITON_REPO} triton
cd triton
as_jenkins git checkout ${TRITON_PINNED_COMMIT}
cd python
# TODO: remove patch setup.py once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
as_jenkins sed -i -e 's/https:\/\/tritonlang.blob.core.windows.net\/llvm-builds/https:\/\/oaitriton.blob.core.windows.net\/public\/llvm-builds/g' setup.py
if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}" == "7" ]]; then
# Triton needs at least gcc-9 to build
apt-get install -y g++-9
CXX=g++-9 pip_install -e .
CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
# Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
add-apt-repository -y ppa:ubuntu-toolchain-r/test
apt-get install -y g++-9
CXX=g++-9 pip_install -e .
CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
else
pip_install -e .
pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
fi
if [ -n "${CONDA_CMAKE}" ]; then

View File

@ -1,6 +1,6 @@
#!/bin/bash
set -xe
# Script used in CI and CD pipeline
# Intel® software for general purpose GPU capabilities.
# Refer to https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
@ -8,23 +8,19 @@ set -xe
# Users should update to the latest version as it becomes available
function install_ubuntu() {
. /etc/os-release
if [[ ! " jammy " =~ " ${VERSION_CODENAME} " ]]; then
echo "Ubuntu version ${VERSION_CODENAME} not supported"
exit
fi
apt-get update -y
apt-get install -y gpg-agent wget
# To add the online network package repository for the GPU Driver
# Set up the repository. To do this, download the key to the system keyring
wget -qO - https://repositories.intel.com/gpu/intel-graphics.key \
| gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
| gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
wget -qO - https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
| gpg --dearmor --output /usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg
# Add the signed entry to APT sources and configure the APT client to use the Intel repository
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] \
https://repositories.intel.com/gpu/ubuntu ${VERSION_CODENAME}${XPU_DRIVER_VERSION} unified" \
| tee /etc/apt/sources.list.d/intel-gpu-${VERSION_CODENAME}.list
# To add the online network network package repository for the Intel Support Packages
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
| gpg --dearmor > /usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg
https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" \
| tee /etc/apt/sources.list.d/intel-gpu-jammy.list
echo "deb [signed-by=/usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg] \
https://apt.repos.intel.com/intel-for-pytorch-gpu-dev all main" \
| tee /etc/apt/sources.list.d/intel-for-pytorch-gpu-dev.list
@ -45,9 +41,9 @@ function install_ubuntu() {
apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
# Install Intel Support Packages
if [ -n "$XPU_VERSION" ]; then
apt-get install -y intel-for-pytorch-gpu-dev-${XPU_VERSION} intel-pti-dev
apt-get install -y intel-for-pytorch-gpu-dev-${XPU_VERSION}
else
apt-get install -y intel-for-pytorch-gpu-dev intel-pti-dev
apt-get install -y intel-for-pytorch-gpu-dev
fi
# Cleanup
@ -55,49 +51,44 @@ function install_ubuntu() {
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
}
function install_rhel() {
. /etc/os-release
if [[ "${ID}" == "rhel" ]]; then
if [[ ! " 8.6 8.8 8.9 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
echo "RHEL version ${VERSION_ID} not supported"
exit
fi
elif [[ "${ID}" == "almalinux" ]]; then
# Workaround for almalinux8 which used by quay.io/pypa/manylinux_2_28_x86_64
VERSION_ID="8.6"
fi
function install_centos() {
dnf install -y 'dnf-command(config-manager)'
# To add the online network package repository for the GPU Driver
dnf config-manager --add-repo \
https://repositories.intel.com/gpu/rhel/${VERSION_ID}${XPU_DRIVER_VERSION}/unified/intel-gpu-${VERSION_ID}.repo
# To add the online network network package repository for the Intel Support Packages
tee > /etc/yum.repos.d/intel-for-pytorch-gpu-dev.repo << EOF
[intel-for-pytorch-gpu-dev]
name=Intel for Pytorch GPU dev repository
baseurl=https://yum.repos.intel.com/intel-for-pytorch-gpu-dev
https://repositories.intel.com/gpu/rhel/8.6/production/2328/unified/intel-gpu-8.6.repo
# To add the EPEL repository needed for DKMS
dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
# https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
# Create the YUM repository file in the /temp directory as a normal user
tee > /tmp/oneAPI.repo << EOF
[oneAPI]
name=Intel® oneAPI repository
baseurl=https://yum.repos.intel.com/oneapi
enabled=1
gpgcheck=1
repo_gpgcheck=1
gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
EOF
# Move the newly created oneAPI.repo file to the YUM configuration directory /etc/yum.repos.d
mv /tmp/oneAPI.repo /etc/yum.repos.d
# The xpu-smi packages
dnf install -y xpu-smi
dnf install -y flex bison xpu-smi
# Compute and Media Runtimes
dnf install --skip-broken -y \
dnf install -y \
intel-opencl intel-media intel-mediasdk libmfxgen1 libvpl2\
level-zero intel-level-zero-gpu mesa-dri-drivers mesa-vulkan-drivers \
mesa-vdpau-drivers libdrm mesa-libEGL mesa-libgbm mesa-libGL \
mesa-libxatracker libvpl-tools intel-metrics-discovery \
intel-metrics-library intel-igc-core intel-igc-cm \
libva libva-utils intel-gmmlib libmetee intel-gsc intel-ocloc
libva libva-utils intel-gmmlib libmetee intel-gsc intel-ocloc hwinfo clinfo
# Development packages
dnf install -y --refresh \
intel-igc-opencl-devel level-zero-devel intel-gsc-devel libmetee-devel \
level-zero-devel
# Install Intel Support Packages
yum install -y intel-for-pytorch-gpu-dev intel-pti-dev
# Install Intel® oneAPI Base Toolkit
dnf install intel-basekit -y
# Cleanup
dnf clean all
@ -106,41 +97,6 @@ EOF
rm -rf /var/lib/yum/history
}
function install_sles() {
. /etc/os-release
VERSION_SP=${VERSION_ID//./sp}
if [[ ! " 15sp4 15sp5 " =~ " ${VERSION_SP} " ]]; then
echo "SLES version ${VERSION_ID} not supported"
exit
fi
# To add the online network package repository for the GPU Driver
zypper addrepo -f -r \
https://repositories.intel.com/gpu/sles/${VERSION_SP}${XPU_DRIVER_VERSION}/unified/intel-gpu-${VERSION_SP}.repo
rpm --import https://repositories.intel.com/gpu/intel-graphics.key
# To add the online network network package repository for the Intel Support Packages
zypper addrepo https://yum.repos.intel.com/intel-for-pytorch-gpu-dev intel-for-pytorch-gpu-dev
rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
# The xpu-smi packages
zypper install -y lsb-release flex bison xpu-smi
# Compute and Media Runtimes
zypper install -y intel-level-zero-gpu level-zero intel-gsc intel-opencl intel-ocloc \
intel-media-driver libigfxcmrt7 libvpl2 libvpl-tools libmfxgen1 libmfx1
# Development packages
zypper install -y libigdfcl-devel intel-igc-cm libigfxcmrt-devel level-zero-devel
# Install Intel Support Packages
zypper install -y intel-for-pytorch-gpu-dev intel-pti-dev
}
# Default use GPU driver LTS releases
XPU_DRIVER_VERSION="/lts/2350"
if [[ "${XPU_DRIVER_TYPE,,}" == "rolling" ]]; then
# Use GPU driver rolling releases
XPU_DRIVER_VERSION=""
fi
# The installation depends on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
@ -148,11 +104,8 @@ case "$ID" in
ubuntu)
install_ubuntu
;;
rhel|almalinux)
install_rhel
;;
sles)
install_sles
centos)
install_centos
;;
*)
echo "Unable to determine OS..."

View File

@ -1,100 +0,0 @@
ARG CUDA_VERSION=10.2
ARG BASE_TARGET=cuda${CUDA_VERSION}
FROM centos:7 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=9
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum update -y
RUN yum install -y wget curl perl util-linux xz bzip2 git patch which unzip
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
RUN yum install -y yum-utils centos-release-scl
RUN yum-config-manager --enable rhel-server-rhscl-7-rpms
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum install -y devtoolset-${DEVTOOLSET_VERSION}-gcc devtoolset-${DEVTOOLSET_VERSION}-gcc-c++ devtoolset-${DEVTOOLSET_VERSION}-gcc-gfortran devtoolset-${DEVTOOLSET_VERSION}-binutils
# EPEL for cmake
RUN yum --enablerepo=extras install -y epel-release
# cmake
RUN yum install -y cmake3 && \
ln -s /usr/bin/cmake3 /usr/bin/cmake
ENV PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
RUN yum install -y autoconf aclocal automake make sudo
RUN rm -rf /usr/local/cuda-*
FROM base as patchelf
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh && cp $(which patchelf) /patchelf
FROM base as openssl
# Install openssl
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
FROM base as conda
# Install Anaconda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
# Install CUDA
FROM base as cuda
ARG CUDA_VERSION=10.2
RUN rm -rf /usr/local/cuda-*
ADD ./common/install_cuda.sh install_cuda.sh
ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
# Preserve CUDA_VERSION for the builds
ENV CUDA_VERSION=${CUDA_VERSION}
# Make things in our path by default
ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:$PATH
FROM cuda as cuda11.8
RUN bash ./install_cuda.sh 11.8
ENV DESIRED_CUDA=11.8
FROM cuda as cuda12.1
RUN bash ./install_cuda.sh 12.1
ENV DESIRED_CUDA=12.1
FROM cuda as cuda12.4
RUN bash ./install_cuda.sh 12.4
ENV DESIRED_CUDA=12.4
# Install MNIST test data
FROM base as mnist
ADD ./common/install_mnist.sh install_mnist.sh
RUN bash ./install_mnist.sh
FROM base as all_cuda
COPY --from=cuda11.8 /usr/local/cuda-11.8 /usr/local/cuda-11.8
COPY --from=cuda12.1 /usr/local/cuda-12.1 /usr/local/cuda-12.1
COPY --from=cuda12.4 /usr/local/cuda-12.4 /usr/local/cuda-12.4
# Final step
FROM ${BASE_TARGET} as final
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=patchelf /patchelf /usr/local/bin/patchelf
COPY --from=conda /opt/conda /opt/conda
# Add jni.h for java host build.
COPY ./common/install_jni.sh install_jni.sh
COPY ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
ENV PATH /opt/conda/bin:$PATH
COPY --from=mnist /usr/local/mnist /usr/local/mnist
RUN rm -rf /usr/local/cuda
RUN chmod o+rw /usr/local
RUN touch /.condarc && \
chmod o+rw /.condarc && \
chmod -R o+rw /opt/conda

View File

@ -1,82 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eou pipefail
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGE"
exit 1
fi
DOCKER_IMAGE_NAME="pytorch/${image}"
export DOCKER_BUILDKIT=1
TOPDIR=$(git rev-parse --show-toplevel)
CUDA_VERSION=${CUDA_VERSION:-12.1}
case ${CUDA_VERSION} in
cpu)
BASE_TARGET=base
DOCKER_TAG=cpu
;;
all)
BASE_TARGET=all_cuda
DOCKER_TAG=latest
;;
*)
BASE_TARGET=cuda${CUDA_VERSION}
DOCKER_TAG=cuda${CUDA_VERSION}
;;
esac
(
set -x
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
docker build \
--target final \
--progress plain \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
--build-arg "CUDA_VERSION=${CUDA_VERSION}" \
--build-arg "DEVTOOLSET_VERSION=9" \
-t ${DOCKER_IMAGE_NAME} \
$@ \
-f "${TOPDIR}/.ci/docker/conda/Dockerfile" \
${TOPDIR}/.ci/docker/
)
if [[ "${DOCKER_TAG}" =~ ^cuda* ]]; then
# Test that we're using the right CUDA compiler
(
set -x
docker run --rm "${DOCKER_IMAGE_NAME}" nvcc --version | grep "cuda_${CUDA_VERSION}"
)
fi
GITHUB_REF=${GITHUB_REF:-$(git symbolic-ref -q HEAD || git describe --tags --exact-match)}
GIT_BRANCH_NAME=${GITHUB_REF##*/}
GIT_COMMIT_SHA=${GITHUB_SHA:-$(git rev-parse HEAD)}
DOCKER_IMAGE_BRANCH_TAG=${DOCKER_IMAGE_NAME}-${GIT_BRANCH_NAME}
DOCKER_IMAGE_SHA_TAG=${DOCKER_IMAGE_NAME}-${GIT_COMMIT_SHA}
if [[ "${WITH_PUSH:-}" == true ]]; then
(
set -x
docker push "${DOCKER_IMAGE_NAME}"
if [[ -n ${GITHUB_REF} ]]; then
docker tag ${DOCKER_IMAGE_NAME} ${DOCKER_IMAGE_BRANCH_TAG}
docker tag ${DOCKER_IMAGE_NAME} ${DOCKER_IMAGE_SHA_TAG}
docker push "${DOCKER_IMAGE_BRANCH_TAG}"
docker push "${DOCKER_IMAGE_SHA_TAG}"
fi
)
fi

View File

@ -1,107 +0,0 @@
ARG BASE_TARGET=base
ARG GPU_IMAGE=ubuntu:20.04
FROM ${GPU_IMAGE} as base
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get clean && apt-get update
RUN apt-get install -y curl locales g++ git-all autoconf automake make cmake wget unzip sudo
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
RUN locale-gen en_US.UTF-8
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
# Install openssl
FROM base as openssl
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# Install python
FROM base as python
ADD common/install_cpython.sh install_cpython.sh
RUN apt-get update -y && \
apt-get install build-essential gdb lcov libbz2-dev libffi-dev \
libgdbm-dev liblzma-dev libncurses5-dev libreadline6-dev \
libsqlite3-dev libssl-dev lzma lzma-dev tk-dev uuid-dev zlib1g-dev -y && \
bash ./install_cpython.sh && \
rm install_cpython.sh && \
apt-get clean
FROM base as conda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
FROM base as cpu
# Install Anaconda
COPY --from=conda /opt/conda /opt/conda
# Install python
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
ENV PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH
# Install MKL
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM cpu as cuda
ADD ./common/install_cuda.sh install_cuda.sh
ADD ./common/install_magma.sh install_magma.sh
ENV CUDA_HOME /usr/local/cuda
FROM cuda as cuda11.8
RUN bash ./install_cuda.sh 11.8
RUN bash ./install_magma.sh 11.8
RUN ln -sf /usr/local/cuda-11.8 /usr/local/cuda
FROM cuda as cuda12.1
RUN bash ./install_cuda.sh 12.1
RUN bash ./install_magma.sh 12.1
RUN ln -sf /usr/local/cuda-12.1 /usr/local/cuda
FROM cuda as cuda12.4
RUN bash ./install_cuda.sh 12.4
RUN bash ./install_magma.sh 12.4
RUN ln -sf /usr/local/cuda-12.4 /usr/local/cuda
FROM cpu as rocm
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ENV MKLROOT /opt/intel
# Adding ROCM_PATH env var so that LoadHip.cmake (even with logic updated for ROCm6.0)
# find HIP works for ROCm5.7. Not needed for ROCm6.0 and above.
# Remove below when ROCm5.7 is not in support matrix anymore.
ENV ROCM_PATH /opt/rocm
# No need to install ROCm as base docker image should have full ROCm install
#ADD ./common/install_rocm.sh install_rocm.sh
ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
ADD ./common/install_rocm_magma.sh install_rocm_magma.sh
# gfortran and python needed for building magma from source for ROCm
RUN apt-get update -y && \
apt-get install gfortran -y && \
apt-get install python -y && \
apt-get clean
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
RUN bash ./install_rocm_magma.sh && rm install_rocm_magma.sh
# Install AOTriton
COPY ./common/common_utils.sh common_utils.sh
COPY ./aotriton_version.txt aotriton_version.txt
COPY ./common/install_aotriton.sh install_aotriton.sh
RUN bash ./install_aotriton.sh /opt/rocm && rm install_aotriton.sh aotriton_version.txt
ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
FROM ${BASE_TARGET} as final
COPY --from=openssl /opt/openssl /opt/openssl
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh
# Install Anaconda
COPY --from=conda /opt/conda /opt/conda
# Install python
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
ENV PATH=/opt/conda/bin:/usr/local/cuda/bin:$PATH

View File

@ -1,93 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eou pipefail
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGE"
exit 1
fi
DOCKER_IMAGE="pytorch/${image}"
TOPDIR=$(git rev-parse --show-toplevel)
GPU_ARCH_TYPE=${GPU_ARCH_TYPE:-cpu}
GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
WITH_PUSH=${WITH_PUSH:-}
DOCKER=${DOCKER:-docker}
case ${GPU_ARCH_TYPE} in
cpu)
BASE_TARGET=cpu
DOCKER_TAG=cpu
GPU_IMAGE=ubuntu:20.04
DOCKER_GPU_BUILD_ARG=""
;;
cuda)
BASE_TARGET=cuda${GPU_ARCH_VERSION}
DOCKER_TAG=cuda${GPU_ARCH_VERSION}
GPU_IMAGE=ubuntu:20.04
DOCKER_GPU_BUILD_ARG=""
;;
rocm)
BASE_TARGET=rocm
DOCKER_TAG=rocm${GPU_ARCH_VERSION}
GPU_IMAGE=rocm/dev-ubuntu-20.04:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx1030;gfx1100"
ROCM_REGEX="([0-9]+)\.([0-9]+)[\.]?([0-9]*)"
if [[ $GPU_ARCH_VERSION =~ $ROCM_REGEX ]]; then
ROCM_VERSION_INT=$((${BASH_REMATCH[1]}*10000 + ${BASH_REMATCH[2]}*100 + ${BASH_REMATCH[3]:-0}))
else
echo "ERROR: rocm regex failed"
exit 1
fi
if [[ $ROCM_VERSION_INT -ge 60000 ]]; then
PYTORCH_ROCM_ARCH+=";gfx942"
fi
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
;;
*)
echo "ERROR: Unrecognized GPU_ARCH_TYPE: ${GPU_ARCH_TYPE}"
exit 1
;;
esac
(
set -x
DOCKER_BUILDKIT=1 ${DOCKER} build \
--target final \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
-t "${DOCKER_IMAGE}" \
$@ \
-f "${TOPDIR}/.ci/docker/libtorch/Dockerfile" \
"${TOPDIR}/.ci/docker/"
)
GITHUB_REF=${GITHUB_REF:-$(git symbolic-ref -q HEAD || git describe --tags --exact-match)}
GIT_BRANCH_NAME=${GITHUB_REF##*/}
GIT_COMMIT_SHA=${GITHUB_SHA:-$(git rev-parse HEAD)}
DOCKER_IMAGE_BRANCH_TAG=${DOCKER_IMAGE}-${GIT_BRANCH_NAME}
DOCKER_IMAGE_SHA_TAG=${DOCKER_IMAGE}-${GIT_COMMIT_SHA}
if [[ "${WITH_PUSH}" == true ]]; then
(
set -x
${DOCKER} push "${DOCKER_IMAGE}"
if [[ -n ${GITHUB_REF} ]]; then
${DOCKER} tag ${DOCKER_IMAGE} ${DOCKER_IMAGE_BRANCH_TAG}
${DOCKER} tag ${DOCKER_IMAGE} ${DOCKER_IMAGE_SHA_TAG}
${DOCKER} push "${DOCKER_IMAGE_BRANCH_TAG}"
${DOCKER} push "${DOCKER_IMAGE_SHA_TAG}"
fi
)
fi

View File

@ -29,7 +29,7 @@ RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/re
# Install cuda and cudnn
ARG CUDA_VERSION
COPY ./common/install_cuda.sh install_cuda.sh
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

View File

@ -1,203 +0,0 @@
# syntax = docker/dockerfile:experimental
ARG ROCM_VERSION=3.7
ARG BASE_CUDA_VERSION=11.8
ARG GPU_IMAGE=centos:7
FROM centos:7 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=9
# Note: This is required patch since CentOS have reached EOL
# otherwise any yum install setp will fail
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum install -y wget curl perl util-linux xz bzip2 git patch which perl zlib-devel
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
RUN yum install -y yum-utils centos-release-scl
RUN yum-config-manager --enable rhel-server-rhscl-7-rpms
# Note: After running yum-config-manager --enable rhel-server-rhscl-7-rpms
# patch is required once again. Somehow this steps adds mirror.centos.org
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum install -y devtoolset-${DEVTOOLSET_VERSION}-gcc devtoolset-${DEVTOOLSET_VERSION}-gcc-c++ devtoolset-${DEVTOOLSET_VERSION}-gcc-gfortran devtoolset-${DEVTOOLSET_VERSION}-binutils
ENV PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
RUN yum --enablerepo=extras install -y epel-release
# cmake-3.18.4 from pip
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -s /usr/local/bin/cmake /usr/bin/cmake
RUN yum install -y autoconf aclocal automake make sudo
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# EPEL for cmake
FROM base as patchelf
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh
RUN cp $(which patchelf) /patchelf
FROM patchelf as python
# build python
COPY manywheel/build_scripts /build_scripts
ADD ./common/install_cpython.sh /build_scripts/install_cpython.sh
RUN bash build_scripts/build.sh && rm -r build_scripts
FROM base as cuda
ARG BASE_CUDA_VERSION=10.2
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh
FROM base as intel
# MKL
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM base as magma
ARG BASE_CUDA_VERSION=10.2
# Install magma
ADD ./common/install_magma.sh install_magma.sh
RUN bash ./install_magma.sh ${BASE_CUDA_VERSION} && rm install_magma.sh
FROM base as jni
# Install java jni header
ADD ./common/install_jni.sh install_jni.sh
ADD ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
FROM base as libpng
# Install libpng
ADD ./common/install_libpng.sh install_libpng.sh
RUN bash ./install_libpng.sh && rm install_libpng.sh
FROM ${GPU_IMAGE} as common
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
RUN yum install -y \
aclocal \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm
RUN yum install -y \
https://repo.ius.io/ius-release-el7.rpm \
https://ossci-linux.s3.amazonaws.com/epel-release-7-14.noarch.rpm
RUN yum swap -y git git236-core
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
# Install LLVM version
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
COPY --from=python /opt/python/cp39-cp39/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=intel /opt/intel /opt/intel
COPY --from=patchelf /usr/local/bin/patchelf /usr/local/bin/patchelf
COPY --from=jni /usr/local/include/jni.h /usr/local/include/jni.h
COPY --from=libpng /usr/local/bin/png* /usr/local/bin/
COPY --from=libpng /usr/local/bin/libpng* /usr/local/bin/
COPY --from=libpng /usr/local/include/png* /usr/local/include/
COPY --from=libpng /usr/local/include/libpng* /usr/local/include/
COPY --from=libpng /usr/local/lib/libpng* /usr/local/lib/
COPY --from=libpng /usr/local/lib/pkgconfig /usr/local/lib/pkgconfig
FROM common as cpu_final
ARG BASE_CUDA_VERSION=10.1
ARG DEVTOOLSET_VERSION=9
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum install -y yum-utils centos-release-scl
RUN yum-config-manager --enable rhel-server-rhscl-7-rpms
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum install -y devtoolset-${DEVTOOLSET_VERSION}-gcc devtoolset-${DEVTOOLSET_VERSION}-gcc-c++ devtoolset-${DEVTOOLSET_VERSION}-gcc-gfortran devtoolset-${DEVTOOLSET_VERSION}-binutils
ENV PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# cmake is already installed inside the rocm base image, so remove if present
RUN rpm -e cmake || true
# cmake-3.18.4 from pip
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -s /usr/local/bin/cmake /usr/bin/cmake
# ninja
RUN yum install -y ninja-build
FROM cpu_final as cuda_final
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=magma /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
ENV PATH=/usr/local/cuda/bin:$PATH
FROM cpu_final as rocm_final
ARG ROCM_VERSION=3.7
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
# Adding ROCM_PATH env var so that LoadHip.cmake (even with logic updated for ROCm6.0)
# find HIP works for ROCm5.7. Not needed for ROCm6.0 and above.
# Remove below when ROCm5.7 is not in support matrix anymore.
ENV ROCM_PATH /opt/rocm
ENV MKLROOT /opt/intel
# No need to install ROCm as base docker image should have full ROCm install
#ADD ./common/install_rocm.sh install_rocm.sh
#RUN ROCM_VERSION=${ROCM_VERSION} bash ./install_rocm.sh && rm install_rocm.sh
ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
# cmake3 is needed for the MIOpen build
RUN ln -sf /usr/local/bin/cmake /usr/bin/cmake3
ADD ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh && rm install_rocm_magma.sh
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh
# Install AOTriton
COPY ./common/common_utils.sh common_utils.sh
COPY ./aotriton_version.txt aotriton_version.txt
COPY ./common/install_aotriton.sh install_aotriton.sh
RUN bash ./install_aotriton.sh /opt/rocm && rm install_aotriton.sh aotriton_version.txt
ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton

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@ -1,153 +0,0 @@
# syntax = docker/dockerfile:experimental
ARG ROCM_VERSION=3.7
ARG BASE_CUDA_VERSION=10.2
ARG GPU_IMAGE=nvidia/cuda:${BASE_CUDA_VERSION}-devel-centos7
FROM quay.io/pypa/manylinux2014_x86_64 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
RUN yum install -y wget curl perl util-linux xz bzip2 git patch which perl zlib-devel
RUN yum install -y yum-utils centos-release-scl sudo
RUN yum-config-manager --enable rhel-server-rhscl-7-rpms
RUN yum install -y devtoolset-7-gcc devtoolset-7-gcc-c++ devtoolset-7-gcc-gfortran devtoolset-7-binutils
ENV PATH=/opt/rh/devtoolset-7/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/devtoolset-7/root/usr/lib64:/opt/rh/devtoolset-7/root/usr/lib:$LD_LIBRARY_PATH
# cmake
RUN yum install -y cmake3 && \
ln -s /usr/bin/cmake3 /usr/bin/cmake
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# remove unncessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
FROM base as cuda
ARG BASE_CUDA_VERSION=10.2
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh
FROM base as intel
# MKL
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM base as magma
ARG BASE_CUDA_VERSION=10.2
# Install magma
ADD ./common/install_magma.sh install_magma.sh
RUN bash ./install_magma.sh ${BASE_CUDA_VERSION} && rm install_magma.sh
FROM base as jni
# Install java jni header
ADD ./common/install_jni.sh install_jni.sh
ADD ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
FROM base as libpng
# Install libpng
ADD ./common/install_libpng.sh install_libpng.sh
RUN bash ./install_libpng.sh && rm install_libpng.sh
FROM ${GPU_IMAGE} as common
RUN sed -i s/mirror.centos.org/vault.centos.org/g /etc/yum.repos.d/*.repo
RUN sed -i s/^#.*baseurl=http/baseurl=http/g /etc/yum.repos.d/*.repo
RUN sed -i s/^mirrorlist=http/#mirrorlist=http/g /etc/yum.repos.d/*.repo
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
RUN yum install -y \
aclocal \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm
RUN yum install -y \
https://repo.ius.io/ius-release-el7.rpm \
https://ossci-linux.s3.amazonaws.com/epel-release-7-14.noarch.rpm
RUN yum swap -y git git236-core
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
# Install LLVM version
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=base /opt/python /opt/python
COPY --from=base /opt/_internal /opt/_internal
COPY --from=base /usr/local/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=intel /opt/intel /opt/intel
COPY --from=base /usr/local/bin/patchelf /usr/local/bin/patchelf
COPY --from=libpng /usr/local/bin/png* /usr/local/bin/
COPY --from=libpng /usr/local/bin/libpng* /usr/local/bin/
COPY --from=libpng /usr/local/include/png* /usr/local/include/
COPY --from=libpng /usr/local/include/libpng* /usr/local/include/
COPY --from=libpng /usr/local/lib/libpng* /usr/local/lib/
COPY --from=libpng /usr/local/lib/pkgconfig /usr/local/lib/pkgconfig
COPY --from=jni /usr/local/include/jni.h /usr/local/include/jni.h
FROM common as cpu_final
ARG BASE_CUDA_VERSION=10.2
RUN yum install -y yum-utils centos-release-scl
RUN yum-config-manager --enable rhel-server-rhscl-7-rpms
RUN yum install -y devtoolset-7-gcc devtoolset-7-gcc-c++ devtoolset-7-gcc-gfortran devtoolset-7-binutils
ENV PATH=/opt/rh/devtoolset-7/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/devtoolset-7/root/usr/lib64:/opt/rh/devtoolset-7/root/usr/lib:$LD_LIBRARY_PATH
# cmake
RUN yum install -y cmake3 && \
ln -s /usr/bin/cmake3 /usr/bin/cmake
# ninja
RUN yum install -y http://repo.okay.com.mx/centos/7/x86_64/release/okay-release-1-1.noarch.rpm
RUN yum install -y ninja-build
FROM cpu_final as cuda_final
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=magma /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
FROM common as rocm_final
ARG ROCM_VERSION=3.7
# Install ROCm
ADD ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh ${ROCM_VERSION} && rm install_rocm.sh
# cmake is already installed inside the rocm base image, but both 2 and 3 exist
# cmake3 is needed for the later MIOpen custom build, so that step is last.
RUN yum install -y cmake3 && \
rm -f /usr/bin/cmake && \
ln -s /usr/bin/cmake3 /usr/bin/cmake
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh

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@ -1,157 +0,0 @@
# syntax = docker/dockerfile:experimental
ARG ROCM_VERSION=3.7
ARG BASE_CUDA_VERSION=11.8
ARG GPU_IMAGE=amd64/almalinux:8
FROM quay.io/pypa/manylinux_2_28_x86_64 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=11
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel yum-utils gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# cmake-3.18.4 from pip
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -s /usr/local/bin/cmake /usr/bin/cmake3
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# remove unncessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
FROM base as cuda
ARG BASE_CUDA_VERSION=11.8
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh
FROM base as intel
# MKL
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM base as magma
ARG BASE_CUDA_VERSION=10.2
# Install magma
ADD ./common/install_magma.sh install_magma.sh
RUN bash ./install_magma.sh ${BASE_CUDA_VERSION} && rm install_magma.sh
FROM base as jni
# Install java jni header
ADD ./common/install_jni.sh install_jni.sh
ADD ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
FROM base as libpng
# Install libpng
ADD ./common/install_libpng.sh install_libpng.sh
RUN bash ./install_libpng.sh && rm install_libpng.sh
FROM ${GPU_IMAGE} as common
ARG DEVTOOLSET_VERSION=11
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
gcc-toolset-${DEVTOOLSET_VERSION}-toolchain \
glibc-langpack-en
RUN yum install -y \
https://repo.ius.io/ius-release-el7.rpm \
https://ossci-linux.s3.amazonaws.com/epel-release-7-14.noarch.rpm
RUN yum swap -y git git236-core
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
# Install LLVM version
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=base /opt/python /opt/python
COPY --from=base /opt/_internal /opt/_internal
COPY --from=base /usr/local/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=intel /opt/intel /opt/intel
COPY --from=base /usr/local/bin/patchelf /usr/local/bin/patchelf
COPY --from=libpng /usr/local/bin/png* /usr/local/bin/
COPY --from=libpng /usr/local/bin/libpng* /usr/local/bin/
COPY --from=libpng /usr/local/include/png* /usr/local/include/
COPY --from=libpng /usr/local/include/libpng* /usr/local/include/
COPY --from=libpng /usr/local/lib/libpng* /usr/local/lib/
COPY --from=libpng /usr/local/lib/pkgconfig /usr/local/lib/pkgconfig
COPY --from=jni /usr/local/include/jni.h /usr/local/include/jni.h
FROM common as cpu_final
ARG BASE_CUDA_VERSION=11.8
ARG DEVTOOLSET_VERSION=11
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# cmake-3.18.4 from pip
RUN yum install -y python3-pip && \
python3 -mpip install cmake==3.18.4 && \
ln -s /usr/local/bin/cmake /usr/bin/cmake3
FROM cpu_final as cuda_final
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=magma /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
FROM common as rocm_final
ARG ROCM_VERSION=3.7
# Install ROCm
ADD ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh ${ROCM_VERSION} && rm install_rocm.sh
# cmake is already installed inside the rocm base image, but both 2 and 3 exist
# cmake3 is needed for the later MIOpen custom build, so that step is last.
RUN yum install -y cmake3 && \
rm -f /usr/bin/cmake && \
ln -s /usr/bin/cmake3 /usr/bin/cmake
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh
FROM cpu_final as xpu_final
# XPU CD use rolling driver
ENV XPU_DRIVER_TYPE ROLLING
# cmake-3.28.4 from pip
RUN python3 -m pip install --upgrade pip && \
python3 -mpip install cmake==3.28.4
# Install setuptools and wheel for python 3.13
RUN /opt/python/cp313-cp313/bin/python -m pip install setuptools wheel
ADD ./common/install_xpu.sh install_xpu.sh
RUN bash ./install_xpu.sh && rm install_xpu.sh
RUN pushd /opt/_internal && tar -xJf static-libs-for-embedding-only.tar.xz && popd

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@ -1,57 +0,0 @@
FROM quay.io/pypa/manylinux_2_28_aarch64 as base
# Graviton needs GCC 10 or above for the build. GCC12 is the default version in almalinux-8.
ARG GCCTOOLSET_VERSION=11
# Language variabes
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
less \
libffi-devel \
libgomp \
make \
openssl-devel \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm \
zstd \
sudo \
gcc-toolset-${GCCTOOLSET_VERSION}-toolchain
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
FROM base as final
# remove unncessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6

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FROM quay.io/pypa/manylinux2014_aarch64 as base
# Graviton needs GCC 10 for the build
ARG DEVTOOLSET_VERSION=10
# Language variabes
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm \
less \
zstd \
libgomp \
sudo \
devtoolset-${DEVTOOLSET_VERSION}-gcc \
devtoolset-${DEVTOOLSET_VERSION}-gcc-c++ \
devtoolset-${DEVTOOLSET_VERSION}-gcc-gfortran \
devtoolset-${DEVTOOLSET_VERSION}-binutils
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/devtoolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
###############################################################################
# libglfortran.a hack
#
# libgfortran.a from quay.io/pypa/manylinux2014_aarch64 is not compiled with -fPIC.
# This causes __stack_chk_guard@@GLIBC_2.17 on pytorch build. To solve, get
# ubuntu's libgfortran.a which is compiled with -fPIC
# NOTE: Need a better way to get this library as Ubuntu's package can be removed by the vender, or changed
###############################################################################
RUN cd ~/ \
&& curl -L -o ~/libgfortran-10-dev.deb http://ports.ubuntu.com/ubuntu-ports/pool/universe/g/gcc-10/libgfortran-10-dev_10.5.0-1ubuntu1_arm64.deb \
&& ar x ~/libgfortran-10-dev.deb \
&& tar --use-compress-program=unzstd -xvf data.tar.zst -C ~/ \
&& cp -f ~/usr/lib/gcc/aarch64-linux-gnu/10/libgfortran.a /opt/rh/devtoolset-10/root/usr/lib/gcc/aarch64-redhat-linux/10/
# install cmake
RUN yum install -y cmake3 && \
ln -s /usr/bin/cmake3 /usr/bin/cmake
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
FROM base as openblas
# Install openblas
ADD ./common/install_openblas.sh install_openblas.sh
RUN bash ./install_openblas.sh && rm install_openblas.sh
FROM openssl as final
# remove unncessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
COPY --from=openblas /opt/OpenBLAS/ /opt/OpenBLAS/
ENV LD_LIBRARY_PATH=/opt/OpenBLAS/lib:$LD_LIBRARY_PATH

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@ -1,91 +0,0 @@
FROM quay.io/pypa/manylinux_2_28_aarch64 as base
# Cuda ARM build needs gcc 11
ARG DEVTOOLSET_VERSION=11
# Language variables
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
RUN yum -y update
RUN yum install -y \
autoconf \
automake \
bison \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz \
yasm \
less \
zstd \
libgomp \
sudo \
gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
FROM openssl as final
# remove unncessary python versions
RUN rm -rf /opt/python/cp26-cp26m /opt/_internal/cpython-2.6.9-ucs2
RUN rm -rf /opt/python/cp26-cp26mu /opt/_internal/cpython-2.6.9-ucs4
RUN rm -rf /opt/python/cp33-cp33m /opt/_internal/cpython-3.3.6
RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
FROM base as cuda
ARG BASE_CUDA_VERSION
# Install CUDA
ADD ./common/install_cuda_aarch64.sh install_cuda_aarch64.sh
RUN bash ./install_cuda_aarch64.sh ${BASE_CUDA_VERSION} && rm install_cuda_aarch64.sh
FROM base as magma
ARG BASE_CUDA_VERSION
# Install magma
ADD ./common/install_magma.sh install_magma.sh
RUN bash ./install_magma.sh ${BASE_CUDA_VERSION} && rm install_magma.sh
FROM base as nvpl
# Install nvpl
ADD ./common/install_nvpl.sh install_nvpl.sh
RUN bash ./install_nvpl.sh && rm install_nvpl.sh
FROM final as cuda_final
ARG BASE_CUDA_VERSION
RUN rm -rf /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=cuda /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=magma /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda-${BASE_CUDA_VERSION}
COPY --from=nvpl /opt/nvpl/lib/ /usr/local/lib/
COPY --from=nvpl /opt/nvpl/include/ /usr/local/include/
RUN ln -sf /usr/local/cuda-${BASE_CUDA_VERSION} /usr/local/cuda
ENV PATH=/usr/local/cuda/bin:$PATH

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

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@ -1,73 +0,0 @@
FROM --platform=linux/s390x docker.io/ubuntu:24.04 as base
# Language variables
ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8
ENV LANGUAGE=C.UTF-8
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN apt update ; apt upgrade -y
RUN apt install -y \
build-essential \
autoconf \
automake \
bzip2 \
curl \
diffutils \
file \
git \
make \
patch \
perl \
unzip \
util-linux \
wget \
which \
xz-utils \
less \
zstd \
cmake \
python3 \
python3-dev \
python3-setuptools \
python3-yaml \
python3-typing-extensions \
libblas-dev \
libopenblas-dev \
liblapack-dev \
libatlas-base-dev
# git236+ would refuse to run git commands in repos owned by other users
# Which causes version check to fail, as pytorch repo is bind-mounted into the image
# Override this behaviour by treating every folder as safe
# For more details see https://github.com/pytorch/pytorch/issues/78659#issuecomment-1144107327
RUN git config --global --add safe.directory "*"
FROM base as openssl
# Install openssl (this must precede `build python` step)
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
# EPEL for cmake
FROM base as patchelf
# Install patchelf
ADD ./common/install_patchelf.sh install_patchelf.sh
RUN bash ./install_patchelf.sh && rm install_patchelf.sh
RUN cp $(which patchelf) /patchelf
FROM patchelf as python
# build python
COPY manywheel/build_scripts /build_scripts
ADD ./common/install_cpython.sh /build_scripts/install_cpython.sh
RUN bash build_scripts/build.sh && rm -r build_scripts
FROM openssl as final
COPY --from=python /opt/python /opt/python
COPY --from=python /opt/_internal /opt/_internal
COPY --from=python /opt/python/cp39-cp39/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=patchelf /usr/local/bin/patchelf /usr/local/bin/patchelf

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@ -1,161 +0,0 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eou pipefail
TOPDIR=$(git rev-parse --show-toplevel)
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGE"
exit 1
fi
DOCKER_IMAGE="pytorch/${image}"
DOCKER_REGISTRY="${DOCKER_REGISTRY:-docker.io}"
GPU_ARCH_TYPE=${GPU_ARCH_TYPE:-cpu}
GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
MANY_LINUX_VERSION=${MANY_LINUX_VERSION:-}
DOCKERFILE_SUFFIX=${DOCKERFILE_SUFFIX:-}
WITH_PUSH=${WITH_PUSH:-}
case ${GPU_ARCH_TYPE} in
cpu)
TARGET=cpu_final
DOCKER_TAG=cpu
GPU_IMAGE=centos:7
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
;;
cpu-manylinux_2_28)
TARGET=cpu_final
DOCKER_TAG=cpu
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
cpu-aarch64)
TARGET=final
DOCKER_TAG=cpu-aarch64
GPU_IMAGE=arm64v8/centos:7
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=10"
MANY_LINUX_VERSION="aarch64"
;;
cpu-aarch64-2_28)
TARGET=final
DOCKER_TAG=cpu-aarch64
GPU_IMAGE=arm64v8/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28_aarch64"
;;
cpu-cxx11-abi)
TARGET=final
DOCKER_TAG=cpu-cxx11-abi
GPU_IMAGE=""
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
MANY_LINUX_VERSION="cxx11-abi"
;;
cpu-s390x)
TARGET=final
DOCKER_TAG=cpu-s390x
GPU_IMAGE=redhat/ubi9
DOCKER_GPU_BUILD_ARG=""
MANY_LINUX_VERSION="s390x"
;;
cuda)
TARGET=cuda_final
DOCKER_TAG=cuda${GPU_ARCH_VERSION}
# Keep this up to date with the minimum version of CUDA we currently support
GPU_IMAGE=centos:7
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=9"
;;
cuda-manylinux_2_28)
TARGET=cuda_final
DOCKER_TAG=cuda${GPU_ARCH_VERSION}
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
cuda-aarch64)
TARGET=cuda_final
DOCKER_TAG=cuda${GPU_ARCH_VERSION}
GPU_IMAGE=arm64v8/centos:7
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="aarch64"
DOCKERFILE_SUFFIX="_cuda_aarch64"
;;
rocm)
TARGET=rocm_final
DOCKER_TAG=rocm${GPU_ARCH_VERSION}
GPU_IMAGE=rocm/dev-centos-7:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx1030;gfx1100"
ROCM_REGEX="([0-9]+)\.([0-9]+)[\.]?([0-9]*)"
if [[ $GPU_ARCH_VERSION =~ $ROCM_REGEX ]]; then
ROCM_VERSION_INT=$((${BASH_REMATCH[1]}*10000 + ${BASH_REMATCH[2]}*100 + ${BASH_REMATCH[3]:-0}))
else
echo "ERROR: rocm regex failed"
exit 1
fi
if [[ $ROCM_VERSION_INT -ge 60000 ]]; then
PYTORCH_ROCM_ARCH+=";gfx942"
fi
DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=9"
;;
xpu)
TARGET=xpu_final
DOCKER_TAG=xpu
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
*)
echo "ERROR: Unrecognized GPU_ARCH_TYPE: ${GPU_ARCH_TYPE}"
exit 1
;;
esac
IMAGES=''
if [[ -n ${MANY_LINUX_VERSION} && -z ${DOCKERFILE_SUFFIX} ]]; then
DOCKERFILE_SUFFIX=_${MANY_LINUX_VERSION}
fi
(
set -x
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
DOCKER_BUILDKIT=1 docker build \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--target "${TARGET}" \
-t "${DOCKER_IMAGE}" \
$@ \
-f "${TOPDIR}/.ci/docker/manywheel/Dockerfile${DOCKERFILE_SUFFIX}" \
"${TOPDIR}/.ci/docker/"
)
GITHUB_REF=${GITHUB_REF:-$(git symbolic-ref -q HEAD || git describe --tags --exact-match)}
GIT_BRANCH_NAME=${GITHUB_REF##*/}
GIT_COMMIT_SHA=${GITHUB_SHA:-$(git rev-parse HEAD)}
DOCKER_IMAGE_BRANCH_TAG=${DOCKER_IMAGE}-${GIT_BRANCH_NAME}
DOCKER_IMAGE_SHA_TAG=${DOCKER_IMAGE}-${GIT_COMMIT_SHA}
if [[ "${WITH_PUSH}" == true ]]; then
(
set -x
docker push "${DOCKER_IMAGE}"
if [[ -n ${GITHUB_REF} ]]; then
docker tag ${DOCKER_IMAGE} ${DOCKER_IMAGE_BRANCH_TAG}
docker tag ${DOCKER_IMAGE} ${DOCKER_IMAGE_SHA_TAG}
docker push "${DOCKER_IMAGE_BRANCH_TAG}"
docker push "${DOCKER_IMAGE_SHA_TAG}"
fi
)
fi

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@ -1,131 +0,0 @@
#!/bin/bash
# Top-level build script called from Dockerfile
# Script used only in CD pipeline
# Stop at any error, show all commands
set -ex
# openssl version to build, with expected sha256 hash of .tar.gz
# archive
OPENSSL_ROOT=openssl-1.1.1l
OPENSSL_HASH=0b7a3e5e59c34827fe0c3a74b7ec8baef302b98fa80088d7f9153aa16fa76bd1
DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
CURL_ROOT=curl-7.73.0
CURL_HASH=cf34fe0b07b800f1c01a499a6e8b2af548f6d0e044dca4a29d88a4bee146d131
AUTOCONF_ROOT=autoconf-2.69
AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969
# Get build utilities
MY_DIR=$(dirname "${BASH_SOURCE[0]}")
source $MY_DIR/build_utils.sh
if [ "$(uname -m)" != "s390x" ] ; then
# Dependencies for compiling Python that we want to remove from
# the final image after compiling Python
PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel libffi-devel"
# Libraries that are allowed as part of the manylinux1 profile
MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel mesa-libGL-devel libICE-devel libSM-devel ncurses-devel"
# Development tools and libraries
yum -y install bzip2 make git patch unzip bison yasm diffutils \
automake which file cmake28 \
kernel-devel-`uname -r` \
${PYTHON_COMPILE_DEPS}
else
# Dependencies for compiling Python that we want to remove from
# the final image after compiling Python
PYTHON_COMPILE_DEPS="zlib1g-dev libbz2-dev libncurses-dev libsqlite3-dev libdb-dev libpcap-dev liblzma-dev libffi-dev"
# Libraries that are allowed as part of the manylinux1 profile
MANYLINUX1_DEPS="libglib2.0-dev libX11-dev libncurses-dev"
# Development tools and libraries
apt install -y bzip2 make git patch unzip diffutils \
automake which file cmake \
linux-headers-virtual \
${PYTHON_COMPILE_DEPS}
fi
# Install newest autoconf
build_autoconf $AUTOCONF_ROOT $AUTOCONF_HASH
autoconf --version
# Compile the latest Python releases.
# (In order to have a proper SSL module, Python is compiled
# against a recent openssl [see env vars above], which is linked
# statically. We delete openssl afterwards.)
build_openssl $OPENSSL_ROOT $OPENSSL_HASH
/build_scripts/install_cpython.sh
PY39_BIN=/opt/python/cp39-cp39/bin
# Our openssl doesn't know how to find the system CA trust store
# (https://github.com/pypa/manylinux/issues/53)
# And it's not clear how up-to-date that is anyway
# So let's just use the same one pip and everyone uses
$PY39_BIN/pip install certifi
ln -s $($PY39_BIN/python -c 'import certifi; print(certifi.where())') \
/opt/_internal/certs.pem
# If you modify this line you also have to modify the versions in the
# Dockerfiles:
export SSL_CERT_FILE=/opt/_internal/certs.pem
# Install newest curl
build_curl $CURL_ROOT $CURL_HASH
rm -rf /usr/local/include/curl /usr/local/lib/libcurl* /usr/local/lib/pkgconfig/libcurl.pc
hash -r
curl --version
curl-config --features
# Install patchelf (latest with unreleased bug fixes)
curl -sLOk https://nixos.org/releases/patchelf/patchelf-0.10/patchelf-0.10.tar.gz
# check_sha256sum patchelf-0.9njs2.tar.gz $PATCHELF_HASH
tar -xzf patchelf-0.10.tar.gz
(cd patchelf-0.10 && ./configure && make && make install)
rm -rf patchelf-0.10.tar.gz patchelf-0.10
# Install latest pypi release of auditwheel
$PY39_BIN/pip install auditwheel
ln -s $PY39_BIN/auditwheel /usr/local/bin/auditwheel
# Clean up development headers and other unnecessary stuff for
# final image
if [ "$(uname -m)" != "s390x" ] ; then
yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \
avahi freetype bitstream-vera-fonts \
${PYTHON_COMPILE_DEPS} || true > /dev/null 2>&1
yum -y install ${MANYLINUX1_DEPS}
yum -y clean all > /dev/null 2>&1
yum list installed
else
apt purge -y ${PYTHON_COMPILE_DEPS} || true > /dev/null 2>&1
fi
# we don't need libpython*.a, and they're many megabytes
find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f
# Strip what we can -- and ignore errors, because this just attempts to strip
# *everything*, including non-ELF files:
find /opt/_internal -type f -print0 \
| xargs -0 -n1 strip --strip-unneeded 2>/dev/null || true
# We do not need the Python test suites, or indeed the precompiled .pyc and
# .pyo files. Partially cribbed from:
# https://github.com/docker-library/python/blob/master/3.4/slim/Dockerfile
find /opt/_internal \
\( -type d -a -name test -o -name tests \) \
-o \( -type f -a -name '*.pyc' -o -name '*.pyo' \) \
-print0 | xargs -0 rm -f
for PYTHON in /opt/python/*/bin/python; do
# Smoke test to make sure that our Pythons work, and do indeed detect as
# being manylinux compatible:
$PYTHON $MY_DIR/manylinux1-check.py
# Make sure that SSL cert checking works
$PYTHON $MY_DIR/ssl-check.py
done
# Fix libc headers to remain compatible with C99 compilers.
find /usr/include/ -type f -exec sed -i 's/\bextern _*inline_*\b/extern __inline __attribute__ ((__gnu_inline__))/g' {} +
# Now we can delete our built SSL
rm -rf /usr/local/ssl

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

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

View File

@ -1,35 +0,0 @@
# cf. https://github.com/pypa/manylinux/issues/53
GOOD_SSL = "https://google.com"
BAD_SSL = "https://self-signed.badssl.com"
import sys
print("Testing SSL certificate checking for Python:", sys.version)
if sys.version_info[:2] < (2, 7) or sys.version_info[:2] < (3, 4):
print("This version never checks SSL certs; skipping tests")
sys.exit(0)
if sys.version_info[0] >= 3:
from urllib.request import urlopen
EXC = OSError
else:
from urllib import urlopen
EXC = IOError
print(f"Connecting to {GOOD_SSL} should work")
urlopen(GOOD_SSL)
print("...it did, yay.")
print(f"Connecting to {BAD_SSL} should fail")
try:
urlopen(BAD_SSL)
# If we get here then we failed:
print("...it DIDN'T!!!!!11!!1one!")
sys.exit(1)
except EXC:
print("...it did, yay.")

View File

@ -30,14 +30,9 @@ dill==0.3.7
#Pinned versions: 0.3.7
#test that import: dynamo/test_replay_record.py test_dataloader.py test_datapipe.py test_serialization.py
expecttest==0.2.1
expecttest==0.1.6
#Description: method for writing tests where test framework auto populates
# the expected output based on previous runs
#Pinned versions: 0.2.1
#test that import:
fbscribelogger==0.1.6
#Description: write to scribe from authenticated jobs on CI
#Pinned versions: 0.1.6
#test that import:
@ -90,10 +85,10 @@ librosa>=0.6.2 ; python_version < "3.11"
#Pinned versions:
#test that import:
mypy==1.11.2
mypy==1.9.0
# Pin MyPy version because new errors are likely to appear with each release
#Description: linter
#Pinned versions: 1.10.0
#Pinned versions: 1.9.0
#test that import: test_typing.py, test_type_hints.py
networkx==2.8.8
@ -109,7 +104,7 @@ networkx==2.8.8
#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
numba==0.49.0 ; python_version < "3.9"
numba==0.55.2 ; 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
@ -139,9 +134,9 @@ opt-einsum==3.3
#Pinned versions: 3.3
#test that import: test_linalg.py
optree==0.12.1
optree==0.11.0
#Description: A library for tree manipulation
#Pinned versions: 0.12.1
#Pinned versions: 0.11.0
#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
#test_pytree.py, test_ops.py, test_control_flow.py, test_modules.py,
#common_utils.py, test_eager_transforms.py, test_python_dispatch.py,
@ -223,7 +218,7 @@ pygments==2.15.0
#test that import:
scikit-image==0.19.3 ; python_version < "3.10"
scikit-image==0.22.0 ; python_version >= "3.10"
scikit-image==0.20.0 ; python_version >= "3.10"
#Description: image processing routines
#Pinned versions:
#test that import: test_nn.py
@ -274,10 +269,6 @@ lintrunner==0.12.5
#Pinned versions: 0.12.5
#test that import:
redis>=4.0.0
#Description: redis database
#test that import: anything that tests OSS caching/mocking (inductor/test_codecache.py, inductor/test_max_autotune.py)
rockset==1.0.3
#Description: queries Rockset
#Pinned versions: 1.0.3
@ -315,30 +306,9 @@ pywavelets==1.5.0 ; python_version >= "3.12"
#Pinned versions: 1.4.1
#test that import:
lxml==5.0.0
lxml==5.0.0.
#Description: This is a requirement of unittest-xml-reporting
# Python-3.9 binaries
PyGithub==2.3.0
sympy==1.12.1 ; python_version == "3.8"
sympy==1.13.1 ; python_version >= "3.9"
#Description: Required by coremltools, also pinned in .github/requirements/pip-requirements-macOS.txt
#Pinned versions:
#test that import:
onnx==1.16.1
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
onnxscript==0.1.0.dev20240817
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
parameterized==0.8.1
#Description: Parameterizes unittests, both the tests themselves and the entire testing class
#Pinned versions:
#test that import:

View File

@ -1 +1 @@
3.1.0
3.0.0

View File

@ -103,14 +103,6 @@ COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
ARG HALIDE
# Build and install halide
COPY ./common/install_halide.sh install_halide.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/halide.txt halide.txt
RUN if [ -n "${HALIDE}" ]; then bash ./install_halide.sh; fi
RUN rm install_halide.sh common_utils.sh halide.txt
# 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
@ -156,12 +148,6 @@ COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash install_cusparselt.sh
RUN rm install_cusparselt.sh
# Install CUDSS
ARG CUDA_VERSION
COPY ./common/install_cudss.sh install_cudss.sh
RUN bash install_cudss.sh
RUN rm install_cudss.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

View File

@ -68,8 +68,6 @@ 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
ADD ./common/install_miopen.sh install_miopen.sh
RUN bash ./install_miopen.sh ${ROCM_VERSION} && rm install_miopen.sh
ENV ROCM_PATH /opt/rocm
ENV PATH /opt/rocm/bin:$PATH
ENV PATH /opt/rocm/hcc/bin:$PATH
@ -102,10 +100,10 @@ ARG TRITON
# try to reach out to S3, which docker build runners don't have access
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
# Install AOTriton
COPY ./aotriton_version.txt aotriton_version.txt
@ -123,8 +121,5 @@ RUN bash ./install_cache.sh && rm install_cache.sh
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
# Install LLVM dev version (Defined in the pytorch/builder github repository)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
USER jenkins
CMD ["bash"]

View File

@ -30,7 +30,6 @@ RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ARG DOCS
ARG BUILD_ENVIRONMENT
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
ENV DOCS=$DOCS

View File

@ -50,7 +50,7 @@ RUN bash ./install_lcov.sh && rm install_lcov.sh
# Install cuda and cudnn
ARG CUDA_VERSION
COPY ./common/install_cuda.sh install_cuda.sh
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
@ -155,14 +155,6 @@ COPY ci_commit_pins/executorch.txt executorch.txt
RUN if [ -n "${EXECUTORCH}" ]; then bash ./install_executorch.sh; fi
RUN rm install_executorch.sh common_utils.sh executorch.txt
ARG HALIDE
# Build and install halide
COPY ./common/install_halide.sh install_halide.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/halide.txt halide.txt
RUN if [ -n "${HALIDE}" ]; then bash ./install_halide.sh; fi
RUN rm install_halide.sh common_utils.sh halide.txt
ARG ONNX
# Install ONNX dependencies
COPY ./common/install_onnx.sh ./common/common_utils.sh ./

View File

@ -1 +1,42 @@
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.

View File

@ -49,8 +49,13 @@ if [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
fi
# Enable LLVM dependency for TensorExpr testing
export USE_LLVM=/opt/llvm
export LLVM_DIR=/opt/llvm/lib/cmake/llvm
if [[ "$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 [[ "$BUILD_ENVIRONMENT" == *executorch* ]]; then
# To build test_edge_op_registration
@ -171,8 +176,7 @@ fi
if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
# shellcheck disable=SC1091
source /opt/intel/oneapi/compiler/latest/env/vars.sh
# XPU kineto feature dependencies are not fully ready, disable kineto build as temp WA
export USE_KINETO=0
export USE_XPU=1
fi
# sccache will fail for CUDA builds if all cores are used for compiling
@ -226,13 +230,9 @@ if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* ]]
export BUILD_STATIC_RUNTIME_BENCHMARK=ON
fi
if [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
export CMAKE_BUILD_TYPE=RelWithAssert
fi
# Do not change workspace permissions for ROCm CI jobs
# as it can leave workspace with bad permissions for cancelled jobs
if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *s390x* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
# Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
cleanup_workspace() {
@ -278,32 +278,18 @@ else
# set only when building other architectures
# or building non-XLA tests.
if [[ "$BUILD_ENVIRONMENT" != *rocm* &&
"$BUILD_ENVIRONMENT" != *s390x* &&
"$BUILD_ENVIRONMENT" != *xla* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
# Install numpy-2.0.2 for builds which are backward compatible with 1.X
python -mpip install --pre numpy==2.0.2
fi
WERROR=1 python setup.py clean
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
BUILD_LIBTORCH_WHL=1 BUILD_PYTHON_ONLY=0 python setup.py bdist_wheel
BUILD_LIBTORCH_WHL=0 BUILD_PYTHON_ONLY=1 python setup.py bdist_wheel --cmake
else
WERROR=1 python setup.py bdist_wheel
# Install numpy-2.0 release candidate for builds
# Which should be backward compatible with Numpy-1.X
python -mpip install --pre numpy==2.0.0rc1
fi
WERROR=1 python setup.py bdist_wheel
else
python setup.py clean
if [[ "$BUILD_ENVIRONMENT" == *xla* ]]; then
source .ci/pytorch/install_cache_xla.sh
fi
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
echo "USE_SPLIT_BUILD cannot be used with xla or rocm"
exit 1
else
python setup.py bdist_wheel
fi
python setup.py bdist_wheel
fi
pip_install_whl "$(echo dist/*.whl)"
@ -341,11 +327,10 @@ else
CUSTOM_OP_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-op-build"
CUSTOM_OP_TEST="$PWD/test/custom_operator"
python --version
SITE_PACKAGES="$(python -c 'import site; print(";".join([x for x in site.getsitepackages()] + [x + "/torch" for x in site.getsitepackages()]))')"
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="$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -355,10 +340,10 @@ else
JIT_HOOK_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/jit-hook-build"
JIT_HOOK_TEST="$PWD/test/jit_hooks"
python --version
SITE_PACKAGES="$(python -c 'import site; print(";".join([x for x in site.getsitepackages()] + [x + "/torch" for x in site.getsitepackages()]))')"
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="$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -370,7 +355,7 @@ else
python --version
mkdir -p "$CUSTOM_BACKEND_BUILD"
pushd "$CUSTOM_BACKEND_BUILD"
cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES" -DPython_EXECUTABLE="$(which python)" \
cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
-DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
make VERBOSE=1
popd
@ -403,6 +388,6 @@ fi
# snadampal: skipping it till sccache support added for aarch64
# https://github.com/pytorch/pytorch/issues/121559
if [[ "$BUILD_ENVIRONMENT" != *aarch64* && "$BUILD_ENVIRONMENT" != *s390x* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *aarch64* ]]; then
print_sccache_stats
fi

View File

@ -56,29 +56,9 @@ function assert_git_not_dirty() {
function pip_install_whl() {
# This is used to install PyTorch and other build artifacts wheel locally
# without using any network connection
# Convert the input arguments into an array
local args=("$@")
# Check if the first argument contains multiple paths separated by spaces
if [[ "${args[0]}" == *" "* ]]; then
# Split the string by spaces into an array
IFS=' ' read -r -a paths <<< "${args[0]}"
# Loop through each path and install individually
for path in "${paths[@]}"; do
echo "Installing $path"
python3 -mpip install --no-index --no-deps "$path"
done
else
# Loop through each argument and install individually
for path in "${args[@]}"; do
echo "Installing $path"
python3 -mpip install --no-index --no-deps "$path"
done
fi
python3 -mpip install --no-index --no-deps "$@"
}
function pip_install() {
# retry 3 times
# old versions of pip don't have the "--progress-bar" flag
@ -179,7 +159,7 @@ function install_torchvision() {
}
function install_tlparse() {
pip_install --user "tlparse==0.3.25"
pip_install --user "tlparse==0.3.7"
PATH="$(python -m site --user-base)/bin:$PATH"
}
@ -198,7 +178,7 @@ function install_torchrec_and_fbgemm() {
function clone_pytorch_xla() {
if [[ ! -d ./xla ]]; then
git clone --recursive --quiet https://github.com/pytorch/xla.git
git clone --recursive -b r2.4 https://github.com/pytorch/xla.git
pushd xla
# pin the xla hash so that we don't get broken by changes to xla
git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"
@ -208,6 +188,28 @@ function clone_pytorch_xla() {
fi
}
function checkout_install_torchdeploy() {
local commit
commit=$(get_pinned_commit multipy)
pushd ..
git clone --recurse-submodules https://github.com/pytorch/multipy.git
pushd multipy
git checkout "${commit}"
python multipy/runtime/example/generate_examples.py
BUILD_CUDA_TESTS=1 pip install -e .
popd
popd
}
function test_torch_deploy(){
pushd ..
pushd multipy
./multipy/runtime/build/test_deploy
./multipy/runtime/build/test_deploy_gpu
popd
popd
}
function checkout_install_torchbench() {
local commit
commit=$(get_pinned_commit torchbench)
@ -222,8 +224,6 @@ function checkout_install_torchbench() {
# to install and test other models
python install.py --continue_on_fail
fi
echo "Print all dependencies after TorchBench is installed"
python -mpip freeze
popd
}

View File

@ -1,4 +1,4 @@
from datetime import datetime, timedelta, timezone
from datetime import datetime, timedelta
from tempfile import mkdtemp
from cryptography import x509
@ -6,7 +6,6 @@ from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.x509.oid import NameOID
temp_dir = mkdtemp()
print(temp_dir)
@ -42,10 +41,10 @@ def create_cert(path, C, ST, L, O, key):
.issuer_name(issuer)
.public_key(key.public_key())
.serial_number(x509.random_serial_number())
.not_valid_before(datetime.now(timezone.utc))
.not_valid_before(datetime.utcnow())
.not_valid_after(
# Our certificate will be valid for 10 days
datetime.now(timezone.utc)
datetime.utcnow()
+ timedelta(days=10)
)
.add_extension(
@ -88,10 +87,10 @@ def sign_certificate_request(path, csr_cert, ca_cert, private_ca_key):
.issuer_name(ca_cert.subject)
.public_key(csr_cert.public_key())
.serial_number(x509.random_serial_number())
.not_valid_before(datetime.now(timezone.utc))
.not_valid_before(datetime.utcnow())
.not_valid_after(
# Our certificate will be valid for 10 days
datetime.now(timezone.utc)
datetime.utcnow()
+ timedelta(days=10)
# Sign our certificate with our private key
)

View File

@ -9,13 +9,15 @@ if [[ -n "$CONDA_ENV" ]]; then
export PATH="$CONDA_ENV/bin":$PATH
fi
# Test that OpenMP is enabled
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
# 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
popd
setup_test_python() {
# The CircleCI worker hostname doesn't resolve to an address.
@ -25,9 +27,8 @@ setup_test_python() {
echo "Ninja version: $(ninja --version)"
echo "Python version: $(which python) ($(python --version))"
# Set the limit on open file handles to 16384
# might help with intermittent compiler test failures
ulimit -n 16384
# Increase default limit on open file handles from 256 to 1024
ulimit -n 1024
}
test_python_all() {

View File

@ -18,9 +18,8 @@ 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_compute_comm_reordering
time python test/run_test.py --verbose -i distributed/test_cuda_p2p
time python test/run_test.py --verbose -i distributed/test_store
time python test/run_test.py --verbose -i distributed/test_symmetric_memory
time python test/run_test.py --verbose -i distributed/test_pg_wrapper
time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
# FSDP tests
@ -44,15 +43,16 @@ time python test/run_test.py --verbose -i distributed/_tensor/test_dtensor_compi
time python test/run_test.py --verbose -i distributed/test_device_mesh
# DTensor/TP tests
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_ddp_2d_parallel
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_fsdp_2d_parallel
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_examples
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_random_state
# FSDP2 tests
time python test/run_test.py --verbose -i distributed/_composable/fsdp/test_fully_shard_training -- -k test_2d_mlp_with_nd_mesh
# ND composability tests
time python test/run_test.py --verbose -i distributed/_composable/test_composability/test_2d_composability
time python test/run_test.py --verbose -i distributed/_composable/test_composability/test_pp_composability
# Pipelining composability tests
time python test/run_test.py --verbose -i distributed/pipelining/test_composability.py
# Other tests
time python test/run_test.py --verbose -i test_cuda_primary_ctx

View File

@ -3,7 +3,6 @@ import json
import math
import sys
parser = argparse.ArgumentParser()
parser.add_argument(
"--test-name", dest="test_name", action="store", required=True, help="test name"

View File

@ -3,7 +3,6 @@ import sys
import numpy
sample_data_list = sys.argv[1:]
sample_data_list = [float(v.strip()) for v in sample_data_list]

View File

@ -1,7 +1,6 @@
import json
import sys
data_file_path = sys.argv[1]
commit_hash = sys.argv[2]

View File

@ -1,6 +1,5 @@
import sys
log_file_path = sys.argv[1]
with open(log_file_path) as f:

View File

@ -6,9 +6,6 @@
set -ex
# Suppress ANSI color escape sequences
export TERM=vt100
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
@ -169,7 +166,7 @@ fi
if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
# Source Intel oneAPI envrioment script to enable xpu runtime related libraries
# refer to https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu/2-5.html
# refer to https://www.intel.com/content/www/us/en/docs/oneapi/programming-guide/2024-0/use-the-setvars-and-oneapi-vars-scripts-with-linux.html
# shellcheck disable=SC1091
source /opt/intel/oneapi/compiler/latest/env/vars.sh
# Check XPU status before testing
@ -252,7 +249,9 @@ fi
# This tests that the debug asserts are working correctly.
if [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
echo "We are in debug mode: $BUILD_ENVIRONMENT. Expect the python assertion to fail"
(cd test && ! get_exit_code python -c "import torch; torch._C._crash_if_debug_asserts_fail(424242)")
# TODO: Enable the check after we setup the build to run debug asserts without having
# to do a full (and slow) debug build
# (cd test && ! get_exit_code python -c "import torch; torch._C._crash_if_debug_asserts_fail(424242)")
elif [[ "$BUILD_ENVIRONMENT" != *-bazel-* ]]; then
# Noop when debug is disabled. Skip bazel jobs because torch isn't available there yet.
echo "We are not in debug mode: $BUILD_ENVIRONMENT. Expect the assertion to pass"
@ -265,6 +264,18 @@ elif [[ $TEST_CONFIG == 'nogpu_AVX512' ]]; then
export ATEN_CPU_CAPABILITY=avx2
fi
# temp workarounds for https://github.com/pytorch/pytorch/issues/126692, remove when fixed
if [[ "$BUILD_ENVIRONMENT" != *-bazel-* ]]; then
pushd test
CUDA_VERSION=$(python -c "import torch; print(torch.version.cuda)")
if [ "$CUDA_VERSION" == "12.4" ]; then
ISCUDA124="cu124"
else
ISCUDA124=""
fi
popd
fi
test_python_legacy_jit() {
time python test/run_test.py --include test_jit_legacy test_jit_fuser_legacy --verbose
assert_git_not_dirty
@ -278,9 +289,6 @@ test_python_shard() {
# Bare --include flag is not supported and quoting for lint ends up with flag not being interpreted correctly
# shellcheck disable=SC2086
# modify LD_LIBRARY_PATH to ensure it has the conda env.
# This set of tests has been shown to be buggy without it for the split-build
time python test/run_test.py --exclude-jit-executor --exclude-distributed-tests $INCLUDE_CLAUSE --shard "$1" "$NUM_TEST_SHARDS" --verbose $PYTHON_TEST_EXTRA_OPTION
assert_git_not_dirty
@ -319,14 +327,14 @@ test_inductor_distributed() {
python test/run_test.py -i inductor/test_aot_inductor.py -k test_replicate_on_devices --verbose
python test/run_test.py -i distributed/test_c10d_functional_native.py --verbose
python test/run_test.py -i distributed/_tensor/test_dtensor_compile.py --verbose
python test/run_test.py -i distributed/tensor/parallel/test_micro_pipeline_tp.py --verbose
python test/run_test.py -i distributed/tensor/parallel/test_fsdp_2d_parallel.py --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_comm.py --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_training.py -k test_train_parity_multi_group --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_training.py -k test_train_parity_with_activation_checkpointing --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_training.py -k test_train_parity_2d_mlp --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_training.py -k test_train_parity_hsdp --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_training.py -k test_train_parity_2d_transformer_checkpoint_resume --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_training.py -k test_gradient_accumulation --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_state_dict.py -k test_dp_state_dict_save_load --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_frozen.py --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_mixed_precision.py -k test_compute_dtype --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_mixed_precision.py -k test_reduce_dtype --verbose
@ -339,34 +347,18 @@ test_inductor_distributed() {
assert_git_not_dirty
}
test_inductor_shard() {
if [[ -z "$NUM_TEST_SHARDS" ]]; then
echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
exit 1
fi
test_inductor() {
python tools/dynamo/verify_dynamo.py
python test/run_test.py --inductor \
--include test_modules test_ops test_ops_gradients test_torch \
--shard "$1" "$NUM_TEST_SHARDS" \
--verbose
python test/run_test.py --inductor --include test_modules test_ops test_ops_gradients test_torch --verbose
# Do not add --inductor for the following inductor unit tests, otherwise we will fail because of nested dynamo state
python test/run_test.py \
--include inductor/test_torchinductor inductor/test_torchinductor_opinfo inductor/test_aot_inductor \
--shard "$1" "$NUM_TEST_SHARDS" \
--verbose
}
python test/run_test.py --include inductor/test_torchinductor inductor/test_torchinductor_opinfo inductor/test_aot_inductor --verbose
test_inductor_aoti() {
# docker build uses bdist_wheel which does not work with test_aot_inductor
# TODO: need a faster way to build
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
# We need to hipify before building again
python3 tools/amd_build/build_amd.py
if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
BUILD_AOT_INDUCTOR_TEST=1 python setup.py develop
CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="${TORCH_LIB_DIR}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference
fi
BUILD_AOT_INDUCTOR_TEST=1 python setup.py develop
CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="${TORCH_LIB_DIR}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference
}
test_inductor_cpp_wrapper_abi_compatible() {
@ -375,15 +367,16 @@ test_inductor_cpp_wrapper_abi_compatible() {
mkdir -p "$TEST_REPORTS_DIR"
echo "Testing Inductor cpp wrapper mode with TORCHINDUCTOR_ABI_COMPATIBLE=1"
# cpu stack allocation causes segfault and needs more investigation
PYTORCH_TESTING_DEVICE_ONLY_FOR="" python test/run_test.py --include inductor/test_cpu_cpp_wrapper
python test/run_test.py --include inductor/test_cuda_cpp_wrapper inductor/test_cpu_repro
python test/run_test.py --include inductor/test_cuda_cpp_wrapper
TORCHINDUCTOR_CPP_WRAPPER=1 python benchmarks/dynamo/timm_models.py --device cuda --accuracy --amp \
--training --inductor --disable-cudagraphs --only vit_base_patch16_224 \
--output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_training.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_training.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_timm_training.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${ISCUDA124}/inductor_timm_training.csv"
}
# "Global" flags for inductor benchmarking controlled by TEST_CONFIG
@ -394,22 +387,7 @@ test_inductor_cpp_wrapper_abi_compatible() {
# .github/workflows/inductor-perf-test-nightly.yml
DYNAMO_BENCHMARK_FLAGS=()
pr_time_benchmarks() {
pip_install --user "fbscribelogger"
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
PYTHONPATH=$(pwd)/benchmarks/dynamo/pr_time_benchmarks source benchmarks/dynamo/pr_time_benchmarks/benchmark_runner.sh "$TEST_REPORTS_DIR/pr_time_benchmarks_results.csv" "benchmarks/dynamo/pr_time_benchmarks/benchmarks"
echo "benchmark results on current PR: "
cat "$TEST_REPORTS_DIR/pr_time_benchmarks_results.csv"
}
if [[ "${TEST_CONFIG}" == *pr_time_benchmarks* ]]; then
pr_time_benchmarks
exit 0
elif [[ "${TEST_CONFIG}" == *dynamo_eager* ]]; then
if [[ "${TEST_CONFIG}" == *dynamo_eager* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--backend eager)
elif [[ "${TEST_CONFIG}" == *aot_eager* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--backend aot_eager)
@ -423,7 +401,7 @@ if [[ "${TEST_CONFIG}" == *dynamic* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--dynamic-shapes --dynamic-batch-only)
fi
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
if [[ "${TEST_CONFIG}" == *cpu_inductor* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--device cpu)
else
DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
@ -447,18 +425,6 @@ test_perf_for_dashboard() {
# TODO: All the accuracy tests can be skipped once the CI accuracy checking is stable enough
local targets=(accuracy performance)
local device=cuda
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
if [[ "${TEST_CONFIG}" == *cpu_x86* ]]; then
device=cpu_x86
elif [[ "${TEST_CONFIG}" == *cpu_aarch64* ]]; then
device=cpu_aarch64
fi
test_inductor_set_cpu_affinity
elif [[ "${TEST_CONFIG}" == *cuda_a10g* ]]; then
device=cuda_a10g
fi
for mode in "${modes[@]}"; do
if [[ "$mode" == "inference" ]]; then
dtype=bfloat16
@ -474,62 +440,56 @@ test_perf_for_dashboard() {
fi
if [[ "$DASHBOARD_TAG" == *default-true* ]]; then
$TASKSET python "benchmarks/dynamo/$suite.py" \
python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_no_cudagraphs_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_no_cudagraphs_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *cudagraphs-true* ]]; then
$TASKSET python "benchmarks/dynamo/$suite.py" \
python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" "$@" \
--output "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *dynamic-true* ]]; then
$TASKSET python "benchmarks/dynamo/$suite.py" \
python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" --dynamic-shapes \
--dynamic-batch-only "$@" \
--output "$TEST_REPORTS_DIR/${backend}_dynamic_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_dynamic_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *cppwrapper-true* ]] && [[ "$mode" == "inference" ]]; then
TORCHINDUCTOR_CPP_WRAPPER=1 $TASKSET python "benchmarks/dynamo/$suite.py" \
TORCHINDUCTOR_CPP_WRAPPER=1 python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_cpp_wrapper_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_cpp_wrapper_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *freezing_cudagraphs-true* ]] && [[ "$mode" == "inference" ]]; then
$TASKSET python "benchmarks/dynamo/$suite.py" \
python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" "$@" --freezing \
--output "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_freezing_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_freezing_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *freeze_autotune_cudagraphs-true* ]] && [[ "$mode" == "inference" ]]; then
TORCHINDUCTOR_MAX_AUTOTUNE=1 $TASKSET python "benchmarks/dynamo/$suite.py" \
TORCHINDUCTOR_MAX_AUTOTUNE=1 python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" "$@" --freezing \
--output "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_freezing_autotune_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_freezing_autotune_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *aotinductor-true* ]] && [[ "$mode" == "inference" ]]; then
if [[ "$target" == "accuracy" ]]; then
# Also collect Export pass rate and display as a separate row
$TASKSET python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --export --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_export_${suite}_${dtype}_${mode}_${device}_${target}.csv"
fi
TORCHINDUCTOR_ABI_COMPATIBLE=1 $TASKSET python "benchmarks/dynamo/$suite.py" \
TORCHINDUCTOR_ABI_COMPATIBLE=1 python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --export-aot-inductor --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_aot_inductor_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_aot_inductor_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *maxautotune-true* ]]; then
TORCHINDUCTOR_MAX_AUTOTUNE=1 $TASKSET python "benchmarks/dynamo/$suite.py" \
TORCHINDUCTOR_MAX_AUTOTUNE=1 python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" "$@" \
--output "$TEST_REPORTS_DIR/${backend}_max_autotune_${suite}_${dtype}_${mode}_${device}_${target}.csv"
--output "$TEST_REPORTS_DIR/${backend}_max_autotune_${suite}_${dtype}_${mode}_cuda_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *cudagraphs_low_precision-true* ]] && [[ "$mode" == "inference" ]]; then
# TODO: This has a new dtype called quant and the benchmarks script needs to be updated to support this.
# The tentative command is as follows. It doesn't work now, but it's ok because we only need mock data
# to fill the dashboard.
$TASKSET python "benchmarks/dynamo/$suite.py" \
python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --quant --backend "$backend" "$@" \
--output "$TEST_REPORTS_DIR/${backend}_cudagraphs_low_precision_${suite}_quant_${mode}_${device}_${target}.csv" || true
--output "$TEST_REPORTS_DIR/${backend}_cudagraphs_low_precision_${suite}_quant_${mode}_cuda_${target}.csv" || true
# Copy cudagraph results as mock data, easiest choice?
cp "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_${suite}_${dtype}_${mode}_${device}_${target}.csv" \
"$TEST_REPORTS_DIR/${backend}_cudagraphs_low_precision_${suite}_quant_${mode}_${device}_${target}.csv"
cp "$TEST_REPORTS_DIR/${backend}_with_cudagraphs_${suite}_${dtype}_${mode}_cuda_${target}.csv" \
"$TEST_REPORTS_DIR/${backend}_cudagraphs_low_precision_${suite}_quant_${mode}_cuda_${target}.csv"
fi
done
done
@ -566,19 +526,11 @@ test_single_dynamo_benchmark() {
test_perf_for_dashboard "$suite" \
"${DYNAMO_BENCHMARK_FLAGS[@]}" "$@" "${partition_flags[@]}"
else
if [[ "${TEST_CONFIG}" == *aot_inductor* && "${TEST_CONFIG}" != *cpu_aot_inductor* ]]; then
if [[ "${TEST_CONFIG}" == *aot_inductor* ]]; then
# Test AOTInductor with the ABI-compatible mode on CI
# This can be removed once the ABI-compatible mode becomes default.
# For CPU device, we perfer non ABI-compatible mode on CI when testing AOTInductor.
export TORCHINDUCTOR_ABI_COMPATIBLE=1
fi
if [[ "${TEST_CONFIG}" == *_avx2* ]]; then
TEST_CONFIG=${TEST_CONFIG//_avx2/}
fi
if [[ "${TEST_CONFIG}" == *_avx512* ]]; then
TEST_CONFIG=${TEST_CONFIG//_avx512/}
fi
python "benchmarks/dynamo/$suite.py" \
--ci --accuracy --timing --explain \
"${DYNAMO_BENCHMARK_FLAGS[@]}" \
@ -586,26 +538,18 @@ test_single_dynamo_benchmark() {
--output "$TEST_REPORTS_DIR/${name}_${suite}.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/${name}_$suite.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${TEST_CONFIG}_${name}.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${ISCUDA124}/${TEST_CONFIG}_${name}.csv"
python benchmarks/dynamo/check_graph_breaks.py \
--actual "$TEST_REPORTS_DIR/${name}_$suite.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${TEST_CONFIG}_${name}.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${ISCUDA124}/${TEST_CONFIG}_${name}.csv"
fi
}
test_inductor_micro_benchmark() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
test_inductor_set_cpu_affinity
fi
python benchmarks/gpt_fast/benchmark.py --output "${TEST_REPORTS_DIR}/gpt_fast_benchmark.csv"
}
test_inductor_halide() {
python test/run_test.py --include inductor/test_halide.py --verbose
assert_git_not_dirty
}
test_dynamo_benchmark() {
# Usage: test_dynamo_benchmark huggingface 0
TEST_REPORTS_DIR=$(pwd)/test/test-reports
@ -620,15 +564,11 @@ test_dynamo_benchmark() {
elif [[ "${TEST_CONFIG}" == *perf* ]]; then
test_single_dynamo_benchmark "dashboard" "$suite" "$shard_id" "$@"
else
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
local dt="float32"
if [[ "${TEST_CONFIG}" == *amp* ]]; then
dt="amp"
fi
if [[ "${TEST_CONFIG}" == *cpu_inductor* ]]; then
if [[ "${TEST_CONFIG}" == *freezing* ]]; then
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --"$dt" --freezing "$@"
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --float32 --freezing "$@"
else
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --"$dt" "$@"
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --float32 "$@"
fi
elif [[ "${TEST_CONFIG}" == *aot_inductor* ]]; then
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
@ -652,7 +592,7 @@ test_inductor_torchbench_smoketest_perf() {
--bfloat16 --inference --inductor --only moco --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${ISCUDA124}/inductor_torchbench_inference.csv"
python benchmarks/dynamo/torchbench.py --device cuda --performance --backend inductor --float16 --training \
--batch-size-file "$(realpath benchmarks/dynamo/torchbench_models_list.txt)" --only hf_Bert \
@ -667,7 +607,13 @@ test_inductor_torchbench_smoketest_perf() {
# https://github.com/pytorch/pytorch/actions/runs/7158691360/job/19491437314,
# and thus we lower its threshold to reduce flakiness. If this continues to be a problem,
# we switch to use some other model.
python benchmarks/dynamo/check_perf_csv.py -f "$TEST_REPORTS_DIR/inductor_inference_smoketest.csv" -t 4.9
# Use 4.7 for cuda 12.4, change back to 4.9 after fixing https://github.com/pytorch/pytorch/issues/126692
if [ "$CUDA_VERSION" == "12.4" ]; then
THRESHOLD=4.7
else
THRESHOLD=4.9
fi
python benchmarks/dynamo/check_perf_csv.py -f "$TEST_REPORTS_DIR/inductor_inference_smoketest.csv" -t $THRESHOLD
# Check memory compression ratio for a few models
for test in hf_Albert timm_vision_transformer; do
@ -686,81 +632,52 @@ test_inductor_torchbench_smoketest_perf() {
--only $test --output "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_huggingface_training.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${ISCUDA124}/inductor_huggingface_training.csv"
done
}
test_inductor_get_core_number() {
if [[ "${TEST_CONFIG}" == *aarch64* ]]; then
echo "$(($(lscpu | grep 'Cluster(s):' | awk '{print $2}') * $(lscpu | grep 'Core(s) per cluster:' | awk '{print $4}')))"
else
echo "$(($(lscpu | grep 'Socket(s):' | awk '{print $2}') * $(lscpu | grep 'Core(s) per socket:' | awk '{print $4}')))"
fi
}
test_inductor_set_cpu_affinity(){
#set jemalloc
JEMALLOC_LIB="$(find /usr/lib -name libjemalloc.so.2)"
export LD_PRELOAD="$JEMALLOC_LIB":"$LD_PRELOAD"
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"
if [[ "${TEST_CONFIG}" != *aarch64* ]]; then
# Use Intel OpenMP for x86
IOMP_LIB="$(dirname "$(which python)")/../lib/libiomp5.so"
export LD_PRELOAD="$IOMP_LIB":"$LD_PRELOAD"
export KMP_AFFINITY=granularity=fine,compact,1,0
export KMP_BLOCKTIME=1
fi
cores=$(test_inductor_get_core_number)
export OMP_NUM_THREADS=$cores
end_core=$((cores-1))
export TASKSET="taskset -c 0-$end_core"
}
test_inductor_torchbench_cpu_smoketest_perf(){
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
test_inductor_set_cpu_affinity
#set jemalloc
JEMALLOC_LIB="/usr/lib/x86_64-linux-gnu/libjemalloc.so.2"
IOMP_LIB="$(dirname "$(which python)")/../lib/libiomp5.so"
export LD_PRELOAD="$JEMALLOC_LIB":"$IOMP_LIB":"$LD_PRELOAD"
export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:-1"
export KMP_AFFINITY=granularity=fine,compact,1,0
export KMP_BLOCKTIME=1
CORES=$(lscpu | grep Core | awk '{print $4}')
export OMP_NUM_THREADS=$CORES
end_core=$(( CORES-1 ))
MODELS_SPEEDUP_TARGET=benchmarks/dynamo/expected_ci_speedup_inductor_torchbench_cpu.csv
grep -v '^ *#' < "$MODELS_SPEEDUP_TARGET" | while IFS=',' read -r -a model_cfg
do
local model_name=${model_cfg[0]}
local data_type=${model_cfg[2]}
local speedup_target=${model_cfg[5]}
local backend=${model_cfg[1]}
if [[ ${model_cfg[4]} == "cpp" ]]; then
local data_type=${model_cfg[1]}
local speedup_target=${model_cfg[4]}
if [[ ${model_cfg[3]} == "cpp" ]]; then
export TORCHINDUCTOR_CPP_WRAPPER=1
else
unset TORCHINDUCTOR_CPP_WRAPPER
fi
local output_name="$TEST_REPORTS_DIR/inductor_inference_${model_cfg[0]}_${model_cfg[1]}_${model_cfg[2]}_${model_cfg[3]}_cpu_smoketest.csv"
if [[ ${model_cfg[3]} == "dynamic" ]]; then
$TASKSET python benchmarks/dynamo/torchbench.py \
if [[ ${model_cfg[2]} == "dynamic" ]]; then
taskset -c 0-"$end_core" python benchmarks/dynamo/torchbench.py \
--inference --performance --"$data_type" -dcpu -n50 --only "$model_name" --dynamic-shapes \
--dynamic-batch-only --freezing --timeout 9000 --"$backend" --output "$output_name"
--dynamic-batch-only --freezing --timeout 9000 --backend=inductor --output "$output_name"
else
$TASKSET python benchmarks/dynamo/torchbench.py \
taskset -c 0-"$end_core" python benchmarks/dynamo/torchbench.py \
--inference --performance --"$data_type" -dcpu -n50 --only "$model_name" \
--freezing --timeout 9000 --"$backend" --output "$output_name"
--freezing --timeout 9000 --backend=inductor --output "$output_name"
fi
cat "$output_name"
# The threshold value needs to be actively maintained to make this check useful.
python benchmarks/dynamo/check_perf_csv.py -f "$output_name" -t "$speedup_target"
done
# Add a few ABI-compatible accuracy tests for CPU. These can be removed once we turn on ABI-compatible as default.
TORCHINDUCTOR_ABI_COMPATIBLE=1 python benchmarks/dynamo/timm_models.py --device cpu --accuracy \
--bfloat16 --inference --export-aot-inductor --disable-cudagraphs --only adv_inception_v3 \
--output "$TEST_REPORTS_DIR/aot_inductor_smoke_test.csv"
TORCHINDUCTOR_ABI_COMPATIBLE=1 python benchmarks/dynamo/timm_models.py --device cpu --accuracy \
--bfloat16 --inference --export-aot-inductor --disable-cudagraphs --only beit_base_patch16_224 \
--output "$TEST_REPORTS_DIR/aot_inductor_smoke_test.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/aot_inductor_smoke_test.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/aot_inductor_timm_inference.csv"
}
test_torchbench_gcp_smoketest(){
@ -1070,117 +987,15 @@ test_xla() {
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
# Set LD_LIBRARY_PATH for C++ tests
export LD_LIBRARY_PATH="/opt/conda/lib/:${LD_LIBRARY_PATH}"
CMAKE_PREFIX_PATH="${SITE_PACKAGES}/torch:${CMAKE_PREFIX_PATH}" XLA_SKIP_MP_OP_TESTS=1 run_torch_xla_tests "$(pwd)" "$(pwd)/xla"
CMAKE_PREFIX_PATH="${SITE_PACKAGES}/torch:${CMAKE_PREFIX_PATH}" XLA_SKIP_MP_OP_TESTS=1 XLA_SKIP_XLA_OP_TESTS=1 run_torch_xla_tests "$(pwd)" "$(pwd)/xla"
assert_git_not_dirty
}
function check_public_api_test_fails {
test_name=$1
invalid_item_name=$2
invalid_item_desc=$3
echo "Running public API test '${test_name}'..."
test_output=$(python test/test_public_bindings.py -k "${test_name}" 2>&1) && ret=$? || ret=$?
# Ensure test fails correctly.
if [ "$ret" -eq 0 ]; then
cat << EOF
Expected the public API test '${test_name}' to fail after introducing
${invalid_item_desc}, but it succeeded! Check test/test_public_bindings.py
for any changes that may have broken the test.
EOF
return 1
fi
# Ensure invalid item is in the test output.
echo "${test_output}" | grep -q "${invalid_item_name}" && ret=$? || ret=$?
if [ $ret -ne 0 ]; then
cat << EOF
Expected the public API test '${test_name}' to identify ${invalid_item_desc}, but
it didn't! It's possible the test may not have run. Check test/test_public_bindings.py
for any changes that may have broken the test.
EOF
return 1
fi
echo "Success! '${test_name}' identified ${invalid_item_desc} ${invalid_item_name}."
return 0
}
# Do NOT run this test before any other tests, like test_python_shard, etc.
# Because this function uninstalls the torch built from branch and installs
# the torch built on its base commit.
test_forward_backward_compatibility() {
set -x
# First, validate public API tests in the torch built from branch.
# Step 1. Make sure the public API test "test_correct_module_names" fails when a new file
# introduces an invalid public API function.
new_filename=$(mktemp XXXXXXXX.py -p "${TORCH_INSTALL_DIR}")
BAD_PUBLIC_FUNC=$(
cat << 'EOF'
def new_public_func():
pass
# valid public API functions have __module__ set correctly
new_public_func.__module__ = None
EOF
)
echo "${BAD_PUBLIC_FUNC}" >> "${new_filename}"
invalid_api="torch.$(basename -s '.py' "${new_filename}").new_public_func"
echo "Created an invalid public API function ${invalid_api}..."
check_public_api_test_fails \
"test_correct_module_names" \
"${invalid_api}" \
"an invalid public API function" && ret=$? || ret=$?
rm -v "${new_filename}"
if [ "$ret" -ne 0 ]; then
exit 1
fi
# Step 2. Make sure that the public API test "test_correct_module_names" fails when an existing
# file is modified to introduce an invalid public API function.
EXISTING_FILEPATH="${TORCH_INSTALL_DIR}/nn/parameter.py"
cp -v "${EXISTING_FILEPATH}" "${EXISTING_FILEPATH}.orig"
echo "${BAD_PUBLIC_FUNC}" >> "${EXISTING_FILEPATH}"
invalid_api="torch.nn.parameter.new_public_func"
echo "Appended an invalid public API function to existing file ${EXISTING_FILEPATH}..."
check_public_api_test_fails \
"test_correct_module_names" \
"${invalid_api}" \
"an invalid public API function" && ret=$? || ret=$?
mv -v "${EXISTING_FILEPATH}.orig" "${EXISTING_FILEPATH}"
if [ "$ret" -ne 0 ]; then
exit 1
fi
# Step 3. Make sure that the public API test "test_modules_can_be_imported" fails when a module
# cannot be imported.
new_module_dir=$(mktemp XXXXXXXX -d -p "${TORCH_INSTALL_DIR}")
echo "invalid syntax garbage" > "${new_module_dir}/__init__.py"
invalid_module_name="torch.$(basename "${new_module_dir}")"
check_public_api_test_fails \
"test_modules_can_be_imported" \
"${invalid_module_name}" \
"a non-importable module" && ret=$? || ret=$?
rm -rv "${new_module_dir}"
if [ "$ret" -ne 0 ]; then
exit 1
fi
# Next, build torch from the merge base.
REPO_DIR=$(pwd)
if [[ "${BASE_SHA}" == "${SHA1}" ]]; then
echo "On trunk, we should compare schemas with torch built from the parent commit"
@ -1354,21 +1169,15 @@ test_executorch() {
pushd /executorch
export PYTHON_EXECUTABLE=python
export EXECUTORCH_BUILD_PYBIND=ON
export CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
# NB: We need to rebuild ExecuTorch runner here because it depends on PyTorch
# from the PR
# NB: We need to build ExecuTorch runner here and not inside the Docker image
# because it depends on PyTorch
# shellcheck disable=SC1091
source .ci/scripts/setup-linux.sh cmake
echo "Run ExecuTorch unit tests"
pytest -v -n auto
# shellcheck disable=SC1091
LLVM_PROFDATA=llvm-profdata-12 LLVM_COV=llvm-cov-12 bash test/run_oss_cpp_tests.sh
source .ci/scripts/utils.sh
build_executorch_runner "cmake"
echo "Run ExecuTorch regression tests for some models"
# NB: This is a sample model, more can be added here
export PYTHON_EXECUTABLE=python
# TODO(huydhn): Add more coverage here using ExecuTorch's gather models script
# shellcheck disable=SC1091
source .ci/scripts/test.sh mv3 cmake xnnpack-quantization-delegation ''
@ -1382,16 +1191,14 @@ test_executorch() {
assert_git_not_dirty
}
test_linux_aarch64() {
test_linux_aarch64(){
python test/run_test.py --include test_modules test_mkldnn test_mkldnn_fusion test_openmp test_torch test_dynamic_shapes \
test_transformers test_multiprocessing test_numpy_interop \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
test_transformers test_multiprocessing test_numpy_interop --verbose
# Dynamo tests
python test/run_test.py --include dynamo/test_compile dynamo/test_backends dynamo/test_comptime dynamo/test_config \
dynamo/test_functions dynamo/test_fx_passes_pre_grad dynamo/test_interop dynamo/test_model_output dynamo/test_modules \
dynamo/test_optimizers dynamo/test_recompile_ux dynamo/test_recompiles \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
dynamo/test_optimizers dynamo/test_recompile_ux dynamo/test_recompiles --verbose
# Inductor tests
python test/run_test.py --include inductor/test_torchinductor inductor/test_benchmark_fusion inductor/test_codecache \
@ -1401,15 +1208,14 @@ test_linux_aarch64() {
inductor/test_max_autotune inductor/test_memory_planning inductor/test_metrics inductor/test_multi_kernel inductor/test_pad_mm \
inductor/test_pattern_matcher inductor/test_perf inductor/test_profiler inductor/test_select_algorithm inductor/test_smoke \
inductor/test_split_cat_fx_passes inductor/test_standalone_compile inductor/test_torchinductor \
inductor/test_torchinductor_codegen_dynamic_shapes inductor/test_torchinductor_dynamic_shapes inductor/test_memory \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
inductor/test_torchinductor_codegen_dynamic_shapes inductor/test_torchinductor_dynamic_shapes --verbose
}
if ! [[ "${BUILD_ENVIRONMENT}" == *libtorch* || "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
(cd test && python -c "import torch; print(torch.__config__.show())")
(cd test && python -c "import torch; print(torch.__config__.parallel_info())")
fi
if [[ "${BUILD_ENVIRONMENT}" == *aarch64* && "${TEST_CONFIG}" != *perf_cpu_aarch64* ]]; then
if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
test_linux_aarch64
elif [[ "${TEST_CONFIG}" == *backward* ]]; then
test_forward_backward_compatibility
@ -1431,10 +1237,11 @@ elif [[ "$TEST_CONFIG" == distributed ]]; then
if [[ "${SHARD_NUMBER}" == 1 ]]; then
test_rpc
fi
elif [[ "$TEST_CONFIG" == deploy ]]; then
checkout_install_torchdeploy
test_torch_deploy
elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
test_inductor_distributed
elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
test_inductor_halide
elif [[ "${TEST_CONFIG}" == *inductor-micro-benchmark* ]]; then
test_inductor_micro_benchmark
elif [[ "${TEST_CONFIG}" == *huggingface* ]]; then
@ -1446,14 +1253,13 @@ elif [[ "${TEST_CONFIG}" == *timm* ]]; then
id=$((SHARD_NUMBER-1))
test_dynamo_benchmark timm_models "$id"
elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
if [[ "${TEST_CONFIG}" == *cpu_inductor* ]]; then
install_torchaudio cpu
else
install_torchaudio cuda
fi
install_torchtext
install_torchvision
TORCH_CUDA_ARCH_LIST="8.0;8.6" pip_install git+https://github.com/pytorch/ao.git
id=$((SHARD_NUMBER-1))
# https://github.com/opencv/opencv-python/issues/885
pip_install opencv-python==4.8.0.74
@ -1461,9 +1267,9 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
checkout_install_torchbench hf_Bert hf_Albert nanogpt timm_vision_transformer
PYTHONPATH=$(pwd)/torchbench test_inductor_torchbench_smoketest_perf
elif [[ "${TEST_CONFIG}" == *inductor_torchbench_cpu_smoketest_perf* ]]; then
checkout_install_torchbench timm_vision_transformer phlippe_densenet basic_gnn_edgecnn \
checkout_install_torchbench timm_vision_transformer phlippe_densenet basic_gnn_gcn \
llama_v2_7b_16h resnet50 timm_efficientnet mobilenet_v3_large timm_resnest \
functorch_maml_omniglot yolov3 mobilenet_v2 resnext50_32x4d densenet121 mnasnet1_0
shufflenet_v2_x1_0 hf_GPT2
PYTHONPATH=$(pwd)/torchbench test_inductor_torchbench_cpu_smoketest_perf
elif [[ "${TEST_CONFIG}" == *torchbench_gcp_smoketest* ]]; then
checkout_install_torchbench
@ -1472,7 +1278,7 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
checkout_install_torchbench
# Do this after checkout_install_torchbench to ensure we clobber any
# nightlies that torchbench may pull in
if [[ "${TEST_CONFIG}" != *cpu* ]]; then
if [[ "${TEST_CONFIG}" != *cpu_inductor* ]]; then
install_torchrec_and_fbgemm
fi
PYTHONPATH=$(pwd)/torchbench test_dynamo_benchmark torchbench "$id"
@ -1480,20 +1286,17 @@ elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper_abi_compatible* ]]; then
install_torchvision
test_inductor_cpp_wrapper_abi_compatible
elif [[ "${TEST_CONFIG}" == *inductor* ]]; then
elif [[ "${TEST_CONFIG}" == *inductor* && "${SHARD_NUMBER}" == 1 ]]; then
install_torchvision
test_inductor_shard "${SHARD_NUMBER}"
if [[ "${SHARD_NUMBER}" == 1 ]]; then
if [[ "${BUILD_ENVIRONMENT}" != linux-jammy-py3.9-gcc11-build ]]; then
test_inductor_distributed
fi
fi
elif [[ "${TEST_CONFIG}" == *dynamo* ]]; then
test_inductor
test_inductor_distributed
elif [[ "${TEST_CONFIG}" == *dynamo* && "${SHARD_NUMBER}" == 1 && $NUM_TEST_SHARDS -gt 1 ]]; then
install_torchvision
test_dynamo_shard 1
test_aten
elif [[ "${TEST_CONFIG}" == *dynamo* && $SHARD_NUMBER -gt 1 && $NUM_TEST_SHARDS -gt 1 ]]; then
install_torchvision
test_dynamo_shard "${SHARD_NUMBER}"
if [[ "${SHARD_NUMBER}" == 1 ]]; then
test_aten
fi
elif [[ "${BUILD_ENVIRONMENT}" == *rocm* && -n "$TESTS_TO_INCLUDE" ]]; then
install_torchvision
test_python_shard "$SHARD_NUMBER"

View File

@ -24,12 +24,6 @@ call %INSTALLER_DIR%\install_sccache.bat
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
if "%USE_XPU%"=="1" (
:: Install xpu support packages
call %INSTALLER_DIR%\install_xpu.bat
if errorlevel 1 exit /b 1
)
:: Miniconda has been installed as part of the Windows AMI with all the dependencies.
:: We just need to activate it here
call %INSTALLER_DIR%\activate_miniconda3.bat
@ -49,16 +43,6 @@ if "%VC_VERSION%" == "" (
)
if errorlevel 1 goto fail
if not errorlevel 0 goto fail
if "%USE_XPU%"=="1" (
:: Activate xpu environment - VS env is required for xpu
call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
if errorlevel 1 exit /b 1
:: Reduce build time. Only have MTL self-hosted runner now
SET TORCH_XPU_ARCH_LIST=xe-lpg
SET USE_KINETO=0
)
@echo on
popd
@ -81,6 +65,13 @@ set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
set CUDNN_ROOT_DIR=%CUDA_PATH%
set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
set CUDNN_ROOT_DIR=%CUDA_PATH%
set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
:cuda_build_end

View File

@ -1,91 +0,0 @@
@echo on
REM Description: Install Intel Support Packages on Windows
REM BKM reference: https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpu/2-5.html
set XPU_INSTALL_MODE=%~1
if "%XPU_INSTALL_MODE%"=="" goto xpu_bundle_install_start
if "%XPU_INSTALL_MODE%"=="bundle" goto xpu_bundle_install_start
if "%XPU_INSTALL_MODE%"=="driver" goto xpu_driver_install_start
if "%XPU_INSTALL_MODE%"=="all" goto xpu_driver_install_start
:arg_error
echo Illegal XPU installation mode. The value can be "bundle"/"driver"/"all"
echo If keep the value as space, will use default "bundle" mode
exit /b 1
:xpu_driver_install_start
:: TODO Need more testing for driver installation
set XPU_DRIVER_LINK=https://downloadmirror.intel.com/830975/gfx_win_101.5972.exe
curl -o xpu_driver.exe --retry 3 --retry-all-errors -k %XPU_DRIVER_LINK%
echo "XPU Driver installing..."
start /wait "Intel XPU Driver Installer" "xpu_driver.exe"
if errorlevel 1 exit /b 1
del xpu_driver.exe
if "%XPU_INSTALL_MODE%"=="driver" goto xpu_install_end
:xpu_bundle_install_start
set XPU_BUNDLE_PARENT_DIR=C:\Program Files (x86)\Intel\oneAPI
set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-for-pytorch-gpu-dev_p_0.5.3.37_offline.exe
set XPU_PTI_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-pti-dev_p_0.9.0.37_offline.exe
set XPU_BUNDLE_VERSION=0.5.3+31
set XPU_PTI_VERSION=0.9.0+36
set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.intel-for-pytorch-gpu-dev.product
set XPU_PTI_PRODUCT_NAME=intel.oneapi.win.intel-pti-dev.product
set XPU_BUNDLE_INSTALLED=0
set XPU_PTI_INSTALLED=0
set XPU_BUNDLE_UNINSTALL=0
set XPU_PTI_UNINSTALL=0
:: Check if XPU bundle is target version or already installed
if exist "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" goto xpu_bundle_ver_check
goto xpu_bundle_install
:xpu_bundle_ver_check
"%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --list-products > xpu_bundle_installed_ver.log
for /f "tokens=1,2" %%a in (xpu_bundle_installed_ver.log) do (
if "%%a"=="%XPU_BUNDLE_PRODUCT_NAME%" (
echo %%a Installed Version: %%b
set XPU_BUNDLE_INSTALLED=1
if not "%XPU_BUNDLE_VERSION%"=="%%b" (
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %XPU_BUNDLE_PRODUCT_NAME% --product-ver %%b --log-dir uninstall_bundle
set XPU_BUNDLE_UNINSTALL=1
)
)
if "%%a"=="%XPU_PTI_PRODUCT_NAME%" (
echo %%a Installed Version: %%b
set XPU_PTI_INSTALLED=1
if not "%XPU_PTI_VERSION%"=="%%b" (
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %XPU_PTI_PRODUCT_NAME% --product-ver %%b --log-dir uninstall_bundle
set XPU_PTI_UNINSTALL=1
)
)
)
if errorlevel 1 exit /b 1
if exist xpu_bundle_installed_ver.log del xpu_bundle_installed_ver.log
if "%XPU_BUNDLE_INSTALLED%"=="0" goto xpu_bundle_install
if "%XPU_BUNDLE_UNINSTALL%"=="1" goto xpu_bundle_install
if "%XPU_PTI_INSTALLED%"=="0" goto xpu_pti_install
if "%XPU_PTI_UNINSTALL%"=="1" goto xpu_pti_install
goto xpu_install_end
:xpu_bundle_install
curl -o xpu_bundle.exe --retry 3 --retry-all-errors -k %XPU_BUNDLE_URL%
echo "XPU Bundle installing..."
start /wait "Intel Pytorch Bundle Installer" "xpu_bundle.exe" --action=install --eula=accept --silent --log-dir install_bundle
if errorlevel 1 exit /b 1
del xpu_bundle.exe
:xpu_pti_install
curl -o xpu_pti.exe --retry 3 --retry-all-errors -k %XPU_PTI_URL%
echo "XPU PTI installing..."
start /wait "Intel PTI Installer" "xpu_pti.exe" --action=install --eula=accept --silent --log-dir install_bundle
if errorlevel 1 exit /b 1
del xpu_pti.exe
:xpu_install_end

View File

@ -4,7 +4,6 @@ import os
import subprocess
import sys
COMMON_TESTS = [
(
"Checking that torch is available",

View File

@ -40,6 +40,7 @@ set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
set CUDNN_ROOT_DIR=%CUDA_PATH%
set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
set NUMBAPRO_CUDALIB=%CUDA_PATH%\bin
set NUMBAPRO_LIBDEVICE=%CUDA_PATH%\nvvm\libdevice

View File

@ -31,6 +31,6 @@ if ERRORLEVEL 1 exit /b 1
:: Run tests C++-side and load the exported script module.
cd build
set PATH=%TMP_DIR_WIN%\build\torch\lib;%PATH%
set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
test_custom_backend.exe model.pt
if ERRORLEVEL 1 exit /b 1

View File

@ -31,6 +31,6 @@ if ERRORLEVEL 1 exit /b 1
:: Run tests C++-side and load the exported script module.
cd build
set PATH=%TMP_DIR_WIN%\build\torch\lib;%PATH%
set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
test_custom_ops.exe model.pt
if ERRORLEVEL 1 exit /b 1

View File

@ -5,7 +5,7 @@ if errorlevel 1 exit /b 1
set CWD=%cd%
set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\bin
set PATH=%TMP_DIR_WIN%\build\torch\lib;%PATH%
set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
set TORCH_CPP_TEST_MNIST_PATH=%CWD%\test\cpp\api\mnist
python tools\download_mnist.py --quiet -d %TORCH_CPP_TEST_MNIST_PATH%

View File

@ -40,12 +40,6 @@ python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==
# Install Z3 optional dependency for Windows builds.
python -m pip install z3-solver==4.12.2.0
# Install tlparse for test\dynamo\test_structured_trace.py UTs.
python -m pip install tlparse==0.3.25
# Install parameterized
python -m pip install parameterized==0.8.1
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

View File

@ -5,7 +5,6 @@ import sys
import yaml
# Need to import modules that lie on an upward-relative path
sys.path.append(os.path.join(sys.path[0], ".."))

View File

@ -46,12 +46,14 @@ if [[ "\$python_nodot" = *310* ]]; then
PROTOBUF_PACKAGE="protobuf>=3.19.0"
fi
if [[ "\$python_nodot" = *39* ]]; then
if [[ "\$python_nodot" = *39* ]]; then
# There's an issue with conda channel priority where it'll randomly pick 1.19 over 1.20
# we set a lower boundary here just to be safe
NUMPY_PIN=">=1.20"
fi
# Move debug wheels out of the package dir so they don't get installed
mkdir -p /tmp/debug_final_pkgs
mv /final_pkgs/debug-*.zip /tmp/debug_final_pkgs || echo "no debug packages to move"
@ -81,7 +83,7 @@ if [[ "$PACKAGE_TYPE" == conda ]]; then
"numpy\${NUMPY_PIN}" \
mkl>=2018 \
ninja \
sympy>=1.12 \
sympy \
typing-extensions \
${PROTOBUF_PACKAGE}
if [[ "$DESIRED_CUDA" == 'cpu' ]]; then
@ -95,16 +97,8 @@ if [[ "$PACKAGE_TYPE" == conda ]]; then
)
elif [[ "$PACKAGE_TYPE" != libtorch ]]; then
if [[ "\$BUILD_ENVIRONMENT" != *s390x* ]]; then
if [[ "$USE_SPLIT_BUILD" == "true" ]]; then
pkg_no_python="$(ls -1 /final_pkgs/torch_no_python* | sort |tail -1)"
pkg_torch="$(ls -1 /final_pkgs/torch-* | sort |tail -1)"
# todo: after folder is populated use the pypi_pkg channel instead
pip install "\$pkg_no_python" "\$pkg_torch" --index-url "https://download.pytorch.org/whl/\${CHANNEL}/${DESIRED_CUDA}_pypi_pkg"
retry pip install -q numpy protobuf typing-extensions
else
pip install "\$pkg" --index-url "https://download.pytorch.org/whl/\${CHANNEL}/${DESIRED_CUDA}"
retry pip install -q numpy protobuf typing-extensions
fi
pip install "\$pkg" --index-url "https://download.pytorch.org/whl/\${CHANNEL}/${DESIRED_CUDA}"
retry pip install -q numpy protobuf typing-extensions
else
pip install "\$pkg"
retry pip install -q numpy protobuf typing-extensions
@ -119,14 +113,6 @@ fi
# Test the package
/builder/check_binary.sh
if [[ "\$GPU_ARCH_TYPE" != *s390x* && "\$GPU_ARCH_TYPE" != *xpu* && "\$GPU_ARCH_TYPE" != *rocm* && "$PACKAGE_TYPE" != libtorch ]]; then
# Exclude s390, xpu, rocm and libtorch builds from smoke testing
python /builder/test/smoke_test/smoke_test.py --package=torchonly --torch-compile-check disabled
fi
# Clean temp files
cd /builder && git clean -ffdx
# =================== The above code will be executed inside Docker container ===================
EOL
echo

View File

@ -33,9 +33,9 @@ if [[ -z "$DOCKER_IMAGE" ]]; then
if [[ "$PACKAGE_TYPE" == conda ]]; then
export DOCKER_IMAGE="pytorch/conda-cuda"
elif [[ "$DESIRED_CUDA" == cpu ]]; then
export DOCKER_IMAGE="pytorch/manylinux:cpu"
export DOCKER_IMAGE="pytorch/manylinux-cpu"
else
export DOCKER_IMAGE="pytorch/manylinux-builder:${DESIRED_CUDA:2}"
export DOCKER_IMAGE="pytorch/manylinux-cuda${DESIRED_CUDA:2}"
fi
fi
@ -77,7 +77,6 @@ TRITON_VERSION=$(cat $PYTORCH_ROOT/.ci/docker/triton_version.txt)
# Here PYTORCH_EXTRA_INSTALL_REQUIREMENTS is already set for the all the wheel builds hence append TRITON_CONSTRAINT
TRITON_CONSTRAINT="platform_system == 'Linux' and platform_machine == 'x86_64' and python_version < '3.13'"
if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" ]]; then
# Only linux Python < 3.13 are supported wheels for triton
TRITON_REQUIREMENT="triton==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"
if [[ -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*dev.* ]]; then
TRITON_SHORTHASH=$(cut -c1-10 $PYTORCH_ROOT/.ci/docker/ci_commit_pins/triton.txt)
@ -90,7 +89,7 @@ fi
if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*rocm.* && $(uname) == "Linux" ]]; then
TRITON_REQUIREMENT="pytorch-triton-rocm==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"
if [[ -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*dev.* ]]; then
TRITON_SHORTHASH=$(cut -c1-10 $PYTORCH_ROOT/.ci/docker/ci_commit_pins/triton.txt)
TRITON_SHORTHASH=$(cut -c1-10 $PYTORCH_ROOT/.ci/docker/ci_commit_pins/triton-rocm.txt)
TRITON_REQUIREMENT="pytorch-triton-rocm==${TRITON_VERSION}+${TRITON_SHORTHASH}; ${TRITON_CONSTRAINT}"
fi
if [[ -z "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" ]]; then
@ -100,18 +99,30 @@ if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_B
fi
fi
# Set triton via PYTORCH_EXTRA_INSTALL_REQUIREMENTS for triton xpu package
if [[ "$PACKAGE_TYPE" =~ .*wheel.* && -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*xpu.* && $(uname) == "Linux" ]]; then
TRITON_REQUIREMENT="pytorch-triton-xpu==${TRITON_VERSION}; ${TRITON_CONSTRAINT}"
if [[ -n "$PYTORCH_BUILD_VERSION" && "$PYTORCH_BUILD_VERSION" =~ .*dev.* ]]; then
TRITON_SHORTHASH=$(cut -c1-10 $PYTORCH_ROOT/.ci/docker/ci_commit_pins/triton-xpu.txt)
TRITON_REQUIREMENT="pytorch-triton-xpu==${TRITON_VERSION}+${TRITON_SHORTHASH}; ${TRITON_CONSTRAINT}"
fi
if [[ -z "${PYTORCH_EXTRA_INSTALL_REQUIREMENTS:-}" ]]; then
export PYTORCH_EXTRA_INSTALL_REQUIREMENTS="${TRITON_REQUIREMENT}"
else
export PYTORCH_EXTRA_INSTALL_REQUIREMENTS="${PYTORCH_EXTRA_INSTALL_REQUIREMENTS} | ${TRITON_REQUIREMENT}"
JAVA_HOME=
BUILD_JNI=OFF
if [[ "$PACKAGE_TYPE" == 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 path
POSSIBLE_JAVA_HOMES+=("$PWD/pytorch/.circleci/windows-jni/")
for JH in "${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" == "$PWD/pytorch/.circleci/windows-jni/" && "$OSTYPE" != "msys" ]] ; then
break
fi
echo "Found jni.h under $JH"
JAVA_HOME="$JH"
BUILD_JNI=ON
break
fi
done
if [ -z "$JAVA_HOME" ]; then
echo "Did not find jni.h"
fi
fi
cat >"$envfile" <<EOL
@ -124,7 +135,6 @@ export DESIRED_PYTHON="${DESIRED_PYTHON:-}"
export DESIRED_CUDA="$DESIRED_CUDA"
export LIBTORCH_VARIANT="${LIBTORCH_VARIANT:-}"
export BUILD_PYTHONLESS="${BUILD_PYTHONLESS:-}"
export USE_SPLIT_BUILD="${USE_SPLIT_BUILD:-}"
if [[ "${OSTYPE}" == "msys" ]]; then
export LIBTORCH_CONFIG="${LIBTORCH_CONFIG:-}"
if [[ "${LIBTORCH_CONFIG:-}" == 'debug' ]]; then
@ -148,6 +158,8 @@ export TORCH_CONDA_BUILD_FOLDER='pytorch-nightly'
export ANACONDA_USER='pytorch'
export USE_FBGEMM=1
export JAVA_HOME=$JAVA_HOME
export BUILD_JNI=$BUILD_JNI
export PIP_UPLOAD_FOLDER="$PIP_UPLOAD_FOLDER"
export DOCKER_IMAGE="$DOCKER_IMAGE"

View File

@ -10,11 +10,6 @@ export SCCACHE_BUCKET=ossci-compiler-cache
export SCCACHE_IGNORE_SERVER_IO_ERROR=1
export VC_YEAR=2019
if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
export VC_YEAR=2022
export USE_SCCACHE=0
fi
echo "Free space on filesystem before build:"
df -h

View File

@ -6,10 +6,6 @@ source "${BINARY_ENV_FILE:-/c/w/env}"
export CUDA_VERSION="${DESIRED_CUDA/cu/}"
export VC_YEAR=2019
if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
export VC_YEAR=2022
fi
pushd "$BUILDER_ROOT"
./windows/internal/smoke_test.bat

View File

@ -8,7 +8,6 @@ import time
import requests
AZURE_PIPELINE_BASE_URL = "https://aiinfra.visualstudio.com/PyTorch/"
AZURE_DEVOPS_PAT_BASE64 = os.environ.get("AZURE_DEVOPS_PAT_BASE64_SECRET", "")
PIPELINE_ID = "911"

View File

@ -62,6 +62,4 @@ readability-string-compare,
'
HeaderFilterRegex: '^(aten/|c10/|torch/).*$'
WarningsAsErrors: '*'
CheckOptions:
misc-header-include-cycle.IgnoredFilesList: 'format.h;ivalue.h;custom_class.h;Dict.h;List.h'
...

View File

@ -5,7 +5,7 @@ git submodule sync
git submodule update --init --recursive
# This takes some time
make setup-lint
make setup_lint
# Add CMAKE_PREFIX_PATH to bashrc
echo 'export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}' >> ~/.bashrc

View File

@ -2,12 +2,12 @@
# NOTE: **Mirror any changes** to this file the [tool.ruff] config in pyproject.toml
# before we can fully move to use ruff
enable-extensions = G
select = B,C,E,F,G,P,SIM1,SIM911,T4,W,B9,TOR0,TOR1,TOR2,TOR9
select = B,C,E,F,G,P,SIM1,T4,W,B9,TOR0,TOR1,TOR2,TOR9
max-line-length = 120
# C408 ignored because we like the dict keyword argument syntax
# E501 is not flexible enough, we're using B950 instead
ignore =
E203,E305,E402,E501,E704,E721,E741,F405,F841,F999,W503,W504,C408,E302,W291,E303,
E203,E305,E402,E501,E721,E741,F405,F841,F999,W503,W504,C408,E302,W291,E303,
# shebang has extra meaning in fbcode lints, so I think it's not worth trying
# to line this up with executable bit
EXE001,
@ -55,9 +55,6 @@ per-file-ignores =
torch/distributed/_functional_collectives.py: TOR901
torch/distributed/_spmd/data_parallel.py: TOR901
torch/distributed/_tensor/_collective_utils.py: TOR901
# This is a full package that happen to live within the test
# folder, so ok to skip
test/cpp_extensions/open_registration_extension/pytorch_openreg/_aten_impl.py: TOR901
optional-ascii-coding = True
exclude =
./.git,

View File

@ -40,7 +40,3 @@ e6ec0efaf87703c5f889cfc20b29be455885d58d
a53cda1ddc15336dc1ff0ce1eff2a49cdc5f882e
# 2024-01-02 clangformat: fused adam #116583
9dc68d1aa9e554d09344a10fff69f7b50b2d23a0
# 2024-06-28 enable UFMT in `torch/storage.py`
d80939e5e9337e8078f11489afefec59fd42f93b
# 2024-06-28 enable UFMT in `torch.utils.data`
7cf0b90e49689d45be91aa539fdf54cf2ea8a9a3

View File

@ -3,25 +3,33 @@ self-hosted-runner:
# GitHub hosted x86 Linux runners
- linux.20_04.4x
- linux.20_04.16x
# Repo-specific LF hosted ARC runners
- linux.large.arc
# Organization-wide AWS Linux Runners
- linux.large
- linux.2xlarge
- linux.4xlarge
- linux.9xlarge.ephemeral
- am2.linux.9xlarge.ephemeral
- linux.12xlarge
- linux.12xlarge.ephemeral
- linux.24xlarge
- linux.24xlarge.ephemeral
- linux.arm64.2xlarge
- linux.arm64.2xlarge.ephemeral
- linux.arm64.m7g.4xlarge
- linux.arm64.m7g.4xlarge.ephemeral
- linux.4xlarge.nvidia.gpu
- linux.8xlarge.nvidia.gpu
- linux.16xlarge.nvidia.gpu
- linux.g5.4xlarge.nvidia.gpu
# Pytorch/pytorch AWS Linux Runners on Linux Foundation account
- am2.linux.large
- am2.linux.2xlarge
- am2.linux.4xlarge
- am2.linux.12xlarge
- am2.linux.24xlarge
- am2.linux.arm64.2xlarge
- am2.linux.arm64.2xlarge.ephemeral
- am2.linux.4xlarge.nvidia.gpu
- am2.linux.8xlarge.nvidia.gpu
- am2.linux.16xlarge.nvidia.gpu
- am2.linux.g5.4xlarge.nvidia.gpu
# Organization-wide AWS Linux Runners on Linux Foundation account
- lf.linux.large
- lf.linux.2xlarge
- lf.linux.4xlarge
@ -35,8 +43,6 @@ self-hosted-runner:
# Repo-specific IBM hosted S390x runner
- linux.s390x
# Organization wide AWS Windows runners
- windows.g4dn.xlarge
- windows.g4dn.xlarge.nonephemeral
- windows.4xlarge
- windows.4xlarge.nonephemeral
- windows.8xlarge.nvidia.gpu
@ -55,5 +61,3 @@ self-hosted-runner:
- macos-latest-xlarge
- macos-13-xlarge
- macos-14-xlarge
# Organization-wide Intel hosted XPU runners
- linux.idc.xpu

View File

@ -14,14 +14,12 @@ runs:
- name: Cleans up diskspace
shell: bash
run: |
set -ex
diskspace_cutoff=${{ inputs.diskspace-cutoff }}
docker_root_dir=$(docker info -f '{{.DockerRootDir}}')
diskspace=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
diskspace=$(df -H / --output=pcent | sed -n 2p | sed 's/%//' | sed 's/ //')
msg="Please file an issue on pytorch/pytorch reporting the faulty runner. Include a link to the runner logs so the runner can be identified"
if [[ "$diskspace" -ge "$diskspace_cutoff" ]] ; then
docker system prune -af
diskspace_new=$(df -H --output=pcent ${docker_root_dir} | sed -n 2p | sed 's/%//' | sed 's/ //')
diskspace_new=$(df -H / --output=pcent | sed -n 2p | sed 's/%//' | sed 's/ //')
if [[ "$diskspace_new" -gt "$diskspace_cutoff" ]] ; then
echo "Error: Available diskspace is less than $diskspace_cutoff percent. Not enough diskspace."
echo "$msg"

View File

@ -41,9 +41,6 @@ outputs:
ci-verbose-test-logs:
description: True if ci-verbose-test-logs label was on PR or [ci-verbose-test-logs] in PR body.
value: ${{ steps.filter.outputs.ci-verbose-test-logs }}
ci-test-showlocals:
description: True if ci-test-showlocals label was on PR or [ci-test-showlocals] in PR body.
value: ${{ steps.filter.outputs.ci-test-showlocals }}
ci-no-test-timeout:
description: True if ci-no-test-timeout label was on PR or [ci-no-test-timeout] in PR body.
value: ${{ steps.filter.outputs.ci-no-test-timeout }}
@ -57,7 +54,7 @@ outputs:
runs:
using: composite
steps:
- uses: nick-fields/retry@v3.0.0
- uses: nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
name: Setup dependencies
env:
GITHUB_TOKEN: ${{ inputs.github-token }}

207
.github/actions/linux-build/action.yml vendored Normal file
View File

@ -0,0 +1,207 @@
name: linux-build
inputs:
build-environment:
required: true
description: Top-level label for what's being built/tested.
docker-image-name:
required: true
description: Name of the base docker image to build with.
build-generates-artifacts:
required: false
default: "true"
description: If set, upload generated build artifacts.
build-with-debug:
required: false
default: "false"
description: If set, build in debug mode.
sync-tag:
required: false
default: ""
description: |
If this is set, our linter will use this to make sure that every other
job with the same `sync-tag` is identical.
cuda-arch-list:
required: false
default: "5.2"
description: Runner label to select worker type
runner:
required: false
default: "linux.2xlarge"
description: |
List of CUDA architectures CI build should target.
test-matrix:
required: false
type: string
description: |
An option JSON description of what test configs to run later on. This
is moved here from the Linux test workflow so that we can apply filter
logic using test-config labels earlier and skip unnecessary builds
s3-bucket:
description: S3 bucket to download artifact
required: false
default: "gha-artifacts"
aws-role-to-assume:
description: role to assume for downloading artifacts
required: false
default: ""
GITHUB_TOKEN:
description: GitHub token
required: true
HUGGING_FACE_HUB_TOKEN:
description: Hugging Face Hub token
required: false
default: ""
outputs:
docker-image:
value: ${{ steps.calculate-docker-image.outputs.docker-image }}
description: The docker image containing the built PyTorch.
test-matrix:
value: ${{ steps.filter.outputs.test-matrix }}
description: An optional JSON description of what test configs to run later on.
runs:
using: composite
steps:
- name: Setup Linux
uses: ./.github/actions/setup-linux
- name: configure aws credentials
uses: aws-actions/configure-aws-credentials@v3
if: ${{ inputs.aws-role-to-assume != '' }}
with:
role-to-assume: ${{ inputs.aws-role-to-assume }}
role-session-name: gha-linux-build
role-duration-seconds: 10800
aws-region: us-east-1
- name: Calculate docker image
id: calculate-docker-image
uses: pytorch/test-infra/.github/actions/calculate-docker-image@main
with:
docker-image-name: ${{ inputs.docker-image-name }}
- name: Use following to pull public copy of the image
id: print-ghcr-mirror
env:
ECR_DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
shell: bash
run: |
tag=${ECR_DOCKER_IMAGE##*/}
echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
- name: Pull docker image
uses: pytorch/test-infra/.github/actions/pull-docker-image@main
with:
docker-image: ${{ steps.calculate-docker-image.outputs.docker-image }}
- name: Parse ref
id: parse-ref
shell: bash
run: .github/scripts/parse_ref.py
- name: Get workflow job id
id: get-job-id
uses: ./.github/actions/get-workflow-job-id
if: always()
with:
github-token: ${{ inputs.GITHUB_TOKEN }}
# Apply the filter logic to the build step too if the test-config label is already there
- name: Select all requested test configurations (if the test matrix is available)
id: filter
uses: ./.github/actions/filter-test-configs
with:
github-token: ${{ inputs.GITHUB_TOKEN }}
test-matrix: ${{ inputs.test-matrix }}
job-name: ${{ steps.get-job-id.outputs.job-name }}
- name: Download pytest cache
uses: ./.github/actions/pytest-cache-download
continue-on-error: true
with:
cache_dir: .pytest_cache
job_identifier: ${{ github.workflow }}_${{ inputs.build-environment }}
s3_bucket: ${{ inputs.s3-bucket }}
- name: Build
if: steps.filter.outputs.is-test-matrix-empty == 'False' || inputs.test-matrix == ''
id: build
env:
BUILD_ENVIRONMENT: ${{ inputs.build-environment }}
BRANCH: ${{ steps.parse-ref.outputs.branch }}
# TODO duplicated
AWS_DEFAULT_REGION: us-east-1
PR_NUMBER: ${{ github.event.pull_request.number }}
SHA1: ${{ github.event.pull_request.head.sha || github.sha }}
SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
SCCACHE_S3_KEY_PREFIX: ${{ github.workflow }}
XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
PR_LABELS: ${{ toJson(github.event.pull_request.labels.*.name) }}
TORCH_CUDA_ARCH_LIST: ${{ inputs.cuda-arch-list }}
DOCKER_IMAGE: ${{ steps.calculate-docker-image.outputs.docker-image }}
XLA_CUDA: ${{ contains(inputs.build-environment, 'xla') && '0' || '' }}
DEBUG: ${{ inputs.build-with-debug == 'true' && '1' || '0' }}
OUR_GITHUB_JOB_ID: ${{ steps.get-job-id.outputs.job-id }}
HUGGING_FACE_HUB_TOKEN: ${{ inputs.HUGGING_FACE_HUB_TOKEN }}
shell: bash
run: |
# detached container should get cleaned up by teardown_ec2_linux
container_name=$(docker run \
-e BUILD_ENVIRONMENT \
-e MAX_JOBS="$(nproc --ignore=2)" \
-e AWS_DEFAULT_REGION \
-e PR_NUMBER \
-e SHA1 \
-e BRANCH \
-e SCCACHE_BUCKET \
-e SCCACHE_S3_KEY_PREFIX \
-e XLA_CUDA \
-e XLA_CLANG_CACHE_S3_BUCKET_NAME \
-e SKIP_SCCACHE_INITIALIZATION=1 \
-e TORCH_CUDA_ARCH_LIST \
-e PR_LABELS \
-e OUR_GITHUB_JOB_ID \
-e HUGGING_FACE_HUB_TOKEN \
--env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
--security-opt seccomp=unconfined \
--cap-add=SYS_PTRACE \
--tty \
--detach \
--user jenkins \
-v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
-w /var/lib/jenkins/workspace \
"${DOCKER_IMAGE}"
)
docker exec -t "${container_name}" sh -c '.ci/pytorch/build.sh'
- name: Archive artifacts into zip
if: inputs.build-generates-artifacts == 'true' && steps.build.outcome != 'skipped'
shell: bash
run: |
zip -1 -r artifacts.zip dist/ build/custom_test_artifacts build/lib build/bin .additional_ci_files
- name: Store PyTorch Build Artifacts on S3
uses: seemethere/upload-artifact-s3@v5
if: inputs.build-generates-artifacts == 'true' && steps.build.outcome != 'skipped'
with:
name: ${{ inputs.build-environment }}
retention-days: 14
if-no-files-found: error
path: artifacts.zip
s3-bucket: ${{ inputs.s3-bucket }}
- name: Upload sccache stats
if: steps.build.outcome != 'skipped'
uses: seemethere/upload-artifact-s3@v5
with:
s3-prefix: |
${{ github.repository }}/${{ github.run_id }}/${{ github.run_attempt }}/artifact
retention-days: 365
if-no-files-found: warn
path: sccache-stats-*.json
s3-bucket: ${{ inputs.s3-bucket }}
- name: Teardown Linux
uses: pytorch/test-infra/.github/actions/teardown-linux@main
if: always()

View File

@ -167,7 +167,6 @@ runs:
REENABLED_ISSUES: ${{ steps.keep-going.outputs.reenabled-issues }}
CONTINUE_THROUGH_ERROR: ${{ steps.keep-going.outputs.keep-going }}
VERBOSE_TEST_LOGS: ${{ steps.keep-going.outputs.ci-verbose-test-logs }}
TEST_SHOWLOCALS: ${{ steps.keep-going.outputs.ci-test-showlocals }}
NO_TEST_TIMEOUT: ${{ steps.keep-going.outputs.ci-no-test-timeout }}
NO_TD: ${{ steps.keep-going.outputs.ci-no-td }}
TD_DISTRIBUTED: ${{ steps.keep-going.outputs.ci-td-distributed }}

View File

@ -17,7 +17,7 @@ inputs:
runs:
using: composite
steps:
- uses: nick-fields/retry@v3.0.0
- uses: nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
name: Setup dependencies
with:
shell: bash

View File

@ -24,7 +24,7 @@ inputs:
runs:
using: composite
steps:
- uses: nick-fields/retry@v3.0.0
- uses: nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
name: Setup dependencies
with:
shell: bash

View File

@ -44,7 +44,7 @@ runs:
fi
- name: Log in to ECR
uses: nick-fields/retry@v3.0.0
uses: nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
env:
AWS_RETRY_MODE: standard
AWS_MAX_ATTEMPTS: "5"
@ -59,13 +59,6 @@ runs:
aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
--password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
# For LF Runners we need to make sure we also login to Meta's ECR docker registry too.
META_AWS_ACCOUNT_ID=308535385114
if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then
aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \
--password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com"
fi
- name: Preserve github env variables for use in docker
shell: bash
run: |

View File

@ -31,7 +31,7 @@ runs:
# retry this step several time similar to how checkout-pytorch GHA does
- name: Cleanup workspace
if: always()
uses: nick-fields/retry@v3.0.0
uses: nick-fields/retry@v2.8.2
env:
EXTRA_DELETE_DIR: ${{ inputs.extra-delete-dir }}
with:

View File

@ -26,7 +26,6 @@ runs:
-e PYTORCH_FINAL_PACKAGE_DIR \
-e PYTORCH_ROOT \
-e SKIP_ALL_TESTS \
-e USE_SPLIT_BUILD \
--tty \
--detach \
-v "${GITHUB_WORKSPACE}/pytorch:/pytorch" \
@ -36,8 +35,7 @@ runs:
"${DOCKER_IMAGE}"
)
echo "CONTAINER_NAME=${container_name}" >> "$GITHUB_ENV"
if [[ "${GPU_ARCH_TYPE}" != "rocm" && "${BUILD_ENVIRONMENT}" != "linux-aarch64-binary-manywheel" && "${BUILD_ENVIRONMENT}" != "linux-s390x-binary-manywheel" && "${GPU_ARCH_TYPE}" != "xpu" ]]; then
if [[ "${GPU_ARCH_TYPE}" != "rocm" && "${BUILD_ENVIRONMENT}" != "linux-aarch64-binary-manywheel" && "${BUILD_ENVIRONMENT}" != "linux-s390x-binary-manywheel" ]]; then
# Propagate download.pytorch.org IP to container. This is only needed on Linux non aarch64 runner
grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" bash -c "/bin/cat >> /etc/hosts"
fi
@ -48,9 +46,10 @@ runs:
docker exec -t "${container_name}" bash -c "source ${BINARY_ENV_FILE} && bash -x /run.sh"
- name: Cleanup docker
if: always() && (env.BUILD_ENVIRONMENT == 'linux-s390x-binary-manywheel' || env.GPU_ARCH_TYPE == 'xpu')
if: always() && env.BUILD_ENVIRONMENT == 'linux-s390x-binary-manywheel'
shell: bash
run: |
# on s390x or xpu stop the container for clean worker stop
# on s390x stop the container for clean worker stop
# ignore expansion of "docker ps -q" since it could be empty
# shellcheck disable=SC2046
docker stop "${{ env.CONTAINER_NAME }}" || true
docker stop $(docker ps -q) || true

View File

@ -1 +1 @@
ba696ea3dfec4cbe693bf06a84c75dc196077f5b
b829e936f7cc61b48149f5f957a451a38bf2a178

View File

@ -1 +1 @@
2eb4a60ed14a38260b85b0c765161f0ce45be6d1
r2.4

View File

@ -1,50 +1,13 @@
# Use this to auto apply labels based on other labels. Applies to both PRs and
# issues. Currently only supports any and all
- any:
- "module: opcheck"
then:
- "module: custom-operators"
- any:
- "module: custom-operators"
- "module: functionalization"
- "module: custom operators"
- "module: aotdispatch"
- "module: higher order operators"
- "module: fakeTensor"
- "module: ProxyTensor"
- "module: library"
- "module: reinplacing"
then:
- "module: pt2-dispatcher"
- any:
- "module: vmap"
then:
- "module: functorch"
- any:
- "module: reinplacing"
then:
- "module: inductor"
- any:
- "module: pt2 optimizer"
then:
- "module: dynamo"
- any:
- "module: flex attention"
then:
- "module: higher order operators"
- any:
- "module: aotinductor"
then:
- "oncall: export"
- any:
- "module: dynamo"
- "module: pt2-dispatcher"
- "module: inductor"
- "module: aotinductor"
- "module: cudagraphs"
- "oncall: export"
- "module: startup-tracing-compile"
- "module: compiled autograd"
- "module: flex attention"
- "module: dynamic shapes"
then:
- "oncall: pt2"

1
.github/labeler.yml vendored
View File

@ -29,6 +29,7 @@
- torch/fx/experimental/recording.py
- torch/fx/experimental/sym_node.py
- torch/fx/experimental/validator.py
- torch/fx/experimental/_sym_dispatch_mode.py
- torch/fx/experimental/proxy_tensor.py
- test/distributed/_tensor/test_dtensor_compile.py
- test/distributed/tensor/parallel/test_fsdp_2d_parallel.py

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