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Author SHA1 Message Date
1eba9b3aa3 change the test wheel to release wheel when release wheel available (#145884)
change the test wheel to release wheel when release wheel available (#145252)

change the test wheel to release wheel when release wheel available

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145252
Approved by: https://github.com/seemethere, https://github.com/atalman

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
(cherry picked from commit 9003d81144fcda2d96814cf9126dbe2b9deb7de7)

Co-authored-by: Zheng, Zhaoqiong <zhaoqiong.zheng@intel.com>
2025-01-28 16:09:34 -08:00
2236df1770 [CUDA] Change slim-wheel libraries load order (#145662)
[CUDA] Change slim-wheel libraries load order (#145638)

There is no libnvjitlink in  CUDA-11.x , so attempts to load it first will abort the execution and prevent the script from preloading nvrtc

Fixes issues reported in https://github.com/pytorch/pytorch/pull/145614#issuecomment-2613107072

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145638
Approved by: https://github.com/atalman, https://github.com/kit1980, https://github.com/malfet

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
(cherry picked from commit 2a70de7e9257e3f8c2874a10e3612c8939b79867)

Co-authored-by: Wei Wang <weiwan@nvidia.com>
2025-01-24 14:54:25 -08:00
3207040966 [CD] Fix slim-wheel cuda_nvrtc import problem (#145614)
[CD] Fix slim-wheel cuda_nvrtc import problem (#145582)

Similar fix as: https://github.com/pytorch/pytorch/pull/144816

Fixes: https://github.com/pytorch/pytorch/issues/145580

Found during testing of https://github.com/pytorch/pytorch/issues/138340

Please note both nvrtc and nvjitlink exist for cuda 11.8, 12.4 and 12.6 hence we can safely remove if statement. Preloading can apply to all supporting cuda versions.

CUDA 11.8 path:
```
(.venv) root@b4ffe5c8ac8c:/pytorch/.ci/pytorch/smoke_test# ls /.venv/lib/python3.12/site-packages/torch/lib/../../nvidia/cuda_nvrtc/lib
__init__.py  __pycache__  libnvrtc-builtins.so.11.8  libnvrtc-builtins.so.12.4  libnvrtc.so.11.2  libnvrtc.so.12
(.venv) root@b4ffe5c8ac8c:/pytorch/.ci/pytorch/smoke_test# ls /.venv/lib/python3.12/site-packages/torch/lib/../../nvidia/nvjitlink/lib
__init__.py  __pycache__  libnvJitLink.so.12
```

Test with rc 2.6 and CUDA 11.8:
```
python cudnn_test.py
2.6.0+cu118
---------------------------------------------SDPA-Flash---------------------------------------------
ALL GOOD
---------------------------------------------SDPA-CuDNN---------------------------------------------
ALL GOOD
```

Thank you @nWEIdia for discovering this issue

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145582
Approved by: https://github.com/nWEIdia, https://github.com/eqy, https://github.com/kit1980, https://github.com/malfet

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
(cherry picked from commit 9752c7c1c819ce9027806c20492adc235dddecd6)

Co-authored-by: atalman <atalman@fb.com>
2025-01-24 08:40:13 -08:00
ca3c3a63b8 [Release-Only] Remove ptx from Linux CUDA 12.6 binary builds (#145616)
Cuda 12.6 remove +ptx
2025-01-24 08:39:52 -08:00
7be6b5db47 Fix IdentationError of code example (#145525)
Fix IdentationError of code example  (#145251)

I found there is IndentationError when try to copy paste the example of inference with torch.compile
fix the format in this pr

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145251
Approved by: https://github.com/mikaylagawarecki

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
(cherry picked from commit fef92c9447c6786b095fdbada6cfe7280c510e59)

Co-authored-by: Zheng, Zhaoqiong <zhaoqiong.zheng@intel.com>
2025-01-24 09:16:57 -05:00
dcb8ad070f update get start xpu (#145286)
update get start xpu (#143183)

- Support new Intel client GPU on Windows [Intel® Arc™ B-Series graphics](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/desktop/b-series/overview.html) and [Intel® Core™ Ultra Series 2 with Intel® Arc™ Graphics](https://www.intel.com/content/www/us/en/products/details/processors/core-ultra.html)
- Support vision/audio prebuilt wheels on Windows
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143183
Approved by: https://github.com/EikanWang, https://github.com/leslie-fang-intel, https://github.com/atalman, https://github.com/malfet

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
(cherry picked from commit 465a1cfe2e8a49cb72df3bb33e78bf1572e13e51)

Co-authored-by: ZhaoqiongZ <106125927+ZhaoqiongZ@users.noreply.github.com>
2025-01-24 09:15:54 -05:00
8d4b8a920a Prevent legacy_load when weights_only=True (correctly) (#145111)
Prevent legacy_load when weights_only=True (correctly) (#145020)

Only prevent `legacy_load` (.tar format removed in https://github.com/pytorch/pytorch/pull/713), not the whole of `_legacy_load` (.tar format + _use_new_zipfile_serialization=False)

Differential Revision: [D68301405](https://our.internmc.facebook.com/intern/diff/D68301405)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145020
Approved by: https://github.com/kit1980, https://github.com/albanD

(cherry picked from commit 0eda02a94c754e2256ff1701bcc03c40ece2bbef)

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
2025-01-17 15:02:28 -08:00
9c34a2076b Revert "Prevent _legacy_load with weights_only=True (#144993)"
This reverts commit cd15d7b29fea0886d1ae655da9bec767caa8c672.
2025-01-17 14:30:47 -08:00
cd15d7b29f Prevent _legacy_load with weights_only=True (#144993)
Prevent _legacy_load with weights_only=True (#144914)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144914
Approved by: https://github.com/malfet, https://github.com/albanD

(cherry picked from commit 7c3aa1da1c97812af54d41f3f0eff2ef922c0f32)

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
2025-01-16 13:54:48 -08:00
a2639bc255 [Release/2.6] Enable python-3.13t aarch64 builds (#144878)
* [BE] [CD] Remove pygit2 dep for aarch64_wheel build (#144716)

As it's incompatible with 3.13t and only used to fetch the branch name, which could be done by running
```
git rev-parse --abbrev-ref HEAD
```

Also, remove yet another reference to long gone `master` branch.

Test plan:
  Download `manywheel-py3_11-cpu-aarch64.zip` produced by this PR, install it inside docker container and check it's version
```
# pip install torch-2.7.0.dev20250113+cpu-cp311-cp311-manylinux_2_28_aarch64.whl
...
Installing collected packages: mpmath, typing-extensions, sympy, networkx, MarkupSafe, fsspec, filelock, jinja2, torch
Successfully installed MarkupSafe-3.0.2 filelock-3.16.1 fsspec-2024.12.0 jinja2-3.1.5 mpmath-1.3.0 networkx-3.4.2 sympy-1.13.1 torch-2.7.0.dev20250113+cpu typing-extensions-4.12.2
root@434f2540345e:/# python
Python 3.11.9 (main, Aug  1 2024, 23:33:10) [GCC 12.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.__version__
'2.7.0.dev20250113+cpu'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144716
Approved by: https://github.com/atalman
ghstack dependencies: #144696, #144697

(cherry picked from commit 58302c4eaa6e48fd503f6d4e18e5945954ed02be)

* [CD] Enable python3.13t builds for aarch64 (#144698)

But make sure that right numpy version is picked (2.0.2 does not support 3.13)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144698
Approved by: https://github.com/atalman
ghstack dependencies: #144696, #144697, #144716

(cherry picked from commit 6053242890e91a78eb31f50d2d5cd3c2858feac1)

* Regenerate workflow
2025-01-15 12:03:39 -08:00
1d2c22157e [CD] Fix slim-wheel nvjit-link import problem (#144816)
[CD] Fix slim-wheel nvjit-link import problem (#141063)

When other toolkit (say CUDA-12.3)  is installed and `LD_LIBRARY_PATH` points to there, import torch will fail with
```
ImportError: /usr/local/lib/python3.10/dist-packages/torch/lib/../../nvidia/cusparse/lib/libcusparse.so.12: undefined symbol: __nvJitLinkComplete_12_4, version libnvJitLink.so.12
```
It could not be worked around by tweaking rpath, as it also depends on the library load order, which are not guaranteed by any linker. Instead solve this by preloading `nvjitlink` right after global deps are loaded, by running something along the lines of the following
```python
        if version.cuda in ["12.4", "12.6"]:
            with open("/proc/self/maps") as f:
                _maps = f.read()
            # libtorch_global_deps.so always depends in cudart, check if its installed via wheel
            if "nvidia/cuda_runtime/lib/libcudart.so" in _maps:
                # If all abovementioned conditions are met, preload nvjitlink
                _preload_cuda_deps("nvjitlink", "libnvJitLink.so.*[0-9]")
```

Fixes https://github.com/pytorch/pytorch/issues/140797

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141063
Approved by: https://github.com/kit1980

Co-authored-by: Sergii Dymchenko <sdym@meta.com>
(cherry picked from commit f2975717f3c268ca4164f92268fc4f4a8f080eb7)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-01-15 08:49:50 -08:00
232eb253fa [BE] Parametrize test_min_max (#144814)
[BE] Parametrize `test_min_max` (#144249)

It's better to have one unit test per dtype rather a combined one
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144249
Approved by: https://github.com/Skylion007

(cherry picked from commit 11a0663eebdf9e8ac1bb12f128f073333c5c5093)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-01-14 17:24:40 -08:00
e19c13d89d [cherry-pick] [dtensor] improve doc of the DTensor class (#144099) (#144740)
[dtensor] improve doc of the DTensor class (#144099)

as titled: explicitly list all public members to make sure the public
API stays consistent, also use groupwise as the member order to make doc
look better

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144099
Approved by: https://github.com/awgu

(cherry picked from commit 48a05ee7735709406b782474e66f0c6231e2ad2e)
2025-01-14 16:34:26 -05:00
4658a06320 Use random64 in Fischer-Yates algorithm for large N (#143682) (#144735)
Fixes bug in randperm https://nbsanity.com/static/a4774194938414dedcec7d6e99727d31/Shuffling_20in_20torch_20vs_20numpy-public.html

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143682
Approved by: https://github.com/eqy, https://github.com/albanD, https://github.com/malfet

Co-authored-by: Natalia Gimelshein <ngimel@meta.com>
2025-01-13 23:25:13 -08:00
a61b5b1d6a [MPS] Fix bitwise shifts for uint8 (#144732)
[MPS] Fix bitwise shifts for uint8 (#144251)

Previosly all bitwise operations were aliased to the same type, but this is wrong for shift ops

Rather than building an overly complex logic, let's just instantiate using shared `scalarToMetalTypeString` helper function

Fixes https://github.com/pytorch/pytorch/issues/144190
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144251
Approved by: https://github.com/Skylion007
ghstack dependencies: #144249, #144250

(cherry picked from commit e56768f030b0802143d1a9adf7830ba3187a3049)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-01-13 17:49:55 -08:00
574210ee5b [CI] Add Triton 3.13t build (#144578)
[CI] Add Triton 3.13t build (#143212)

By just extending the matrix and invoking script with appropriate cpython runtime
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143212
Approved by: https://github.com/clee2000, https://github.com/atalman, https://github.com/seemethere

(cherry picked from commit 515abb774435d831bdea23b650920cddbc3c06cd)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2025-01-13 17:48:24 -08:00
e2067a6f50 Revert "Use random64 in Fischer-Yates algorithm for large N (#143682)… (#144730)
Revert "Use random64 in Fischer-Yates algorithm for large N (#143682) (#143875)"

This reverts commit b1a10ecad96f04db9baff453ae42ef4dd45b62f4.
2025-01-13 17:40:38 -08:00
6e30474706 [MPS] Fix conv backward for channels last (cont) (#144570)
[MPS] Fix conv backward for channels last (cont) (#143196)

This is a continuation of https://github.com/pytorch/pytorch/issues/140902 but extends the same logic to input.

Looks like existing channels-last logic just produced incorrect results on pre MacOS-15 versions and fails on MacOS-15, so removing it feels like a right idea

Fixes https://github.com/pytorch/pytorch/issues/142344
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143196
Approved by: https://github.com/manuelcandales

(cherry picked from commit 8a0401832952cfc59464429c2bca62d7db41854a)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2025-01-10 11:07:30 -08:00
eb30434c97 Extend bmm tiling to work up to 2^32 elem in any single output dim (#144558)
Extend bmm tiling to work up to 2^32 elem in any single output dim (#143095)

The previous tiling implementation worked for up to 2^32 total elements per single batch entry. This extends the functionality to support the dimensions encountered in ComfyUI (output shape: 1,72250,72250).

Fixes #141909
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143095
Approved by: https://github.com/kulinseth

(cherry picked from commit afa313e669e53142618c9116c9337c7b7a54a9e9)

Co-authored-by: Joona Havukainen <jhavukainen@apple.com>
2025-01-10 11:06:04 -08:00
47f4e56498 [ONNX] Update images and APIs to onnx_dynamo.rst (#144428)
[ONNX] Update images and APIs to onnx_dynamo.rst (#144358)

Update the result image of exporting, and delete the functions/class that belongs to `torch.onnx.dynamo_export`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144358
Approved by: https://github.com/justinchuby, https://github.com/malfet

(cherry picked from commit a742859fc277d5867fc9b0234142c46e68e6925a)

Co-authored-by: titaiwangms <titaiwang@microsoft.com>
2025-01-10 10:48:39 -08:00
983ea0eee5 [ONNX] Avoid overwriting overlapped decomposed functions (#144418)
[ONNX] Avoid overwriting overlapped decomposed functions (#142831)

Fixes #141770

The decomposed function in `torch.export.default_decompositions().items()` is overwritten by `torch._decomp.decomposition_table`. As from `torch.onnx.export()` perspective, we should rather respect the table of decompositions in `torch.export.default_decompositions().items()` and avoid overwriting it with `torch._decomp.decomposition_table.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142831
Approved by: https://github.com/justinchuby

(cherry picked from commit 0ddb33ba2299542f9558d949aa482a9ca30ceb30)

Co-authored-by: titaiwangms <titaiwang@microsoft.com>
2025-01-10 10:44:23 -08:00
518294705e [ONNX] Handle list values as 0d inputs (#144417)
[ONNX] Handle list values as 0d inputs (#144343)

Handle list values as 0d inputs instead of 1d, as the `SymInt`s are expected to be 0d tensors in ONNX.

This PR reshapes int64 values into 1D tensors in a list, assuming they are 0D tensors initially.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144343
Approved by: https://github.com/gramalingam, https://github.com/titaiwangms

(cherry picked from commit 7c9cf287c232cfb62da98ed6e0e10aac77847aae)

Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
2025-01-10 10:43:39 -08:00
a99cc48bcd ROCm SDPA: Ensure attn_mask has the same dtype with q (#144398)
ROCm SDPA: Ensure attn_mask has the same dtype with q (#143242)

This is required by current AOTriton's backend.

Fixes NaN when calling SDPA ME backend with `q.dtype() != attn_mask.dtype()` when training llama2 using transformers+deepspeed+pytorch

Corresponding CUDA check seems to be here:
708ce3c008/aten/src/ATen/native/transformers/cuda/attention.cu (L1331-L1336)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143242
Approved by: https://github.com/jeffdaily

(cherry picked from commit 3068ce0337d1ab6eddb09e3febcad079eb990a86)

Co-authored-by: Xinya Zhang <Xinya.Zhang@amd.com>
2025-01-10 10:42:59 -08:00
4d9de27d56 Amazon Linux 2023: Preload cusparseLt.so (#144493)
Amazon Linux 2023: Preload cusparseLt.so (#144477)

Fixes https://github.com/pytorch/pytorch/issues/144433

Test with some debug statements added:

```
>>> import torch
trying to load libcublas.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cublas/lib/libcublas.so.12']
trying to load libcublas.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cublas/lib/libcublas.so.12
trying to load libcudnn.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cudnn/lib/libcudnn.so.9']
trying to load libcudnn.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cudnn/lib/libcudnn.so.9
trying to load libnvrtc.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so.12']
trying to load libnvrtc.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cuda_nvrtc/lib/libnvrtc.so.12
trying to load libcudart.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12']
trying to load libcudart.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cuda_runtime/lib/libcudart.so.12
trying to load libcupti.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cuda_cupti/lib/libcupti.so.12']
trying to load libcupti.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cuda_cupti/lib/libcupti.so.12
trying to load libcufft.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cufft/lib/libcufft.so.11']
trying to load libcufft.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cufft/lib/libcufft.so.11
trying to load libcurand.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/curand/lib/libcurand.so.10']
trying to load libcurand.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/curand/lib/libcurand.so.10
trying to load libnvJitLink.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/nvjitlink/lib/libnvJitLink.so.12']
trying to load libnvJitLink.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/nvjitlink/lib/libnvJitLink.so.12
trying to load libcusparse.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cusparse/lib/libcusparse.so.12']
trying to load libcusparse.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cusparse/lib/libcusparse.so.12
trying to load libcusparseLt.so.*[0-9] from []
trying to load libcusparseLt.so.*[0-9] from /usr/local/lib/python3.9/site-packages/cusparselt/lib/libcusparseLt.so.0
trying to load libcusolver.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/cusolver/lib/libcusolver.so.11']
trying to load libcusolver.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/cusolver/lib/libcusolver.so.11
trying to load libnccl.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/nccl/lib/libnccl.so.2']
trying to load libnccl.so.*[0-9] from /usr/local/lib/python3.9/site-packages/nvidia/nccl/lib/libnccl.so.2
trying to load libnvToolsExt.so.*[0-9] from ['/usr/local/lib/python3.9/site-packages/nvidia/nvtx/lib/libnvToolsExt.so.1']
trying to load libnvToolsExt.so.*[0-9] from /usr/local/lib/python3.9/site-
packages/nvidia/nvtx/lib/libnvToolsExt.so.1
/usr/local/lib64/python3.9/site-packages/torch/_subclasses/functional_tensor.py:275: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:81.)
  cpu = _conversion_method_template(device=torch.device("cpu"))
>>> exit()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144477
Approved by: https://github.com/Skylion007, https://github.com/nWEIdia

(cherry picked from commit 2b241a8206843f43f0568b7b65473ebb593c4740)

Co-authored-by: atalman <atalman@fb.com>
2025-01-10 09:13:52 -05:00
d155d8ad6a [3.13t] use sysconfig to check for Python nogil builds (#144393)
[3.13t] use sysconfig to check for Python nogil builds (#144361)

`sys._is_gil_enabled()` wasn't working in certain cases, according to @atalman

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144361
Approved by: https://github.com/atalman

(cherry picked from commit f7000350905be5073892e0b23df681c0281be0f0)

Co-authored-by: William Wen <williamwen@meta.com>
2025-01-10 09:10:38 -05:00
e1858b614e Fix PythonMod printing (#144335)
* Fix precedence of bitwise and/or printing (#143197)

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143197
Approved by: https://github.com/albanD, https://github.com/williamwen42

(cherry picked from commit 8f404467707ea43860af0f71d8c0867afe047732)

* Fix PythonMod printing (#144078)

Fixes #144075
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144078
Approved by: https://github.com/anijain2305

(cherry picked from commit 301b9c8a90002fa621d93b108e54460066226629)

---------

Co-authored-by: Edward Z. Yang <ezyang@meta.com>
2025-01-10 09:08:47 -05:00
be126bccee [inductor][cpu] Fix bmm b_index for dynamic expressions in inductor autotuner (#144248)
[inductor][cpu] Fix bmm b_index for dynamic expressions in inductor autotuner (#143141)

Fixes #143102

Addresses 2 problems relating to dynamic batch size in BMM autotuner:
1. With dynamic batch size, when the input is a sympy Mult expression, such as `s0*8` which occurs in many dynamo benchmark models. We address this by using `size_hints` to solve for any expressions. This is safe since this section of the code is only called to generate inputs for benchmarking.
2. Some epilogue nodes may use the dynamic batch size as part of the codegen, for example when an input to the epilogue node is transposed and has dynamic batch size in the stride of other dimensions. When these epilogue nodes exist, if the sizevar is not already present in the `kernel.args`, it will create a new sizevar with a name. It is possible that subsequent calls to `def_kernel` could overwrite this variable name, so to avoid this we pass all the sizevars as `extra_sizevars` to the calls to `def_kernel` for the GEMM functions, so no variable renaming happens later in the BMM definition.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143141
Approved by: https://github.com/jansel, https://github.com/leslie-fang-intel, https://github.com/jgong5

(cherry picked from commit 51a37a42e0e50df6b199732f2680afa5ed14c94f)

Co-authored-by: Mitchell, Frost <frost.mitchell@intel.com>
2025-01-10 09:05:28 -05:00
8c03454867 Set maximum supported version of Python as 3.13 (#144409)
Set maximum supported version of Python as 3.13 (#144396)

Same as https://github.com/pytorch/pytorch/pull/119743 Required for Release 2.6.0
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144396
Approved by: https://github.com/Skylion007, https://github.com/albanD, https://github.com/malfet

(cherry picked from commit e14c36d3f498e4ec513459209eb95dc392ba9876)

Co-authored-by: atalman <atalman@fb.com>
2025-01-10 09:02:00 -05:00
7092dc521b Link to transformer tutorial in transformer docs (#144482)
Link to transformer tutorial in transformer docs (#144425)

<img width="1045" alt="Screenshot 2025-01-08 at 4 50 20 PM" src="https://github.com/user-attachments/assets/05adfecb-8a23-4c48-9a2c-50c5b3f886b0" />

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144425
Approved by: https://github.com/albanD

(cherry picked from commit b8f383107eebd9495a0f132d58a970e178e15930)

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
2025-01-09 14:04:45 -08:00
f35ab0e353 [CD] Aarch64 builds should not override OVERRIDE_PACKAGE_VERSION envvar (#144347)
[CD] Aarch64 builds should not override `OVERRIDE_PACKAGE_VERSION` envvar (#144285)

Currently our nightly aarch64 binaries have correct suffixes +cpu or +cu126. But release binaries are missing these suffixes. Hence to correct this, make sure are nightly and release binaries are consistent, I propose this change.

I see that override is already set correctly in release workflow:
https://github.com/pytorch/pytorch/actions/runs/12383179841/job/34565381200

For CPU:
```
OVERRIDE_PACKAGE_VERSION="2.6.0+cpu"
```

For CUDA:
```
OVERRIDE_PACKAGE_VERSION="2.6.0+cu126"
```

The removed code will set : OVERRIDE_PACKAGE_VERSION="2.6.0" for both cuda and cpu builds for release binaries.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/144285
Approved by: https://github.com/malfet, https://github.com/tinglvv

(cherry picked from commit 8d35333498e9433a379611746c177285fa51c8c5)

Co-authored-by: atalman <atalman@fb.com>
2025-01-07 17:48:28 -08:00
3a3de27475 Fix int8 mm V.ops.mul dispatching (#144336)
Fix int8 mm V.ops.mul dispatching (#143127)

This is sort of subtle - because we were doing `V.ops.mul` at binding time, we dont redispatch later when we invoke the epilogue. and then later running into assertion checking in pr above.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143127
Approved by: https://github.com/drisspg
ghstack dependencies: #143048

(cherry picked from commit 7968732f5b84ac6509d800a54bfb23fb791d3b88)

Co-authored-by: eellison <elias.ellison@gmail.com>
2025-01-07 17:45:58 -08:00
7d3292c0d3 Fix batch-specific attention mod for NJT + Flex (#144330)
Fix batch-specific attention mod for NJT + Flex (#143866)

Fixes #143788
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143866
Approved by: https://github.com/Skylion007, https://github.com/cpuhrsch

(cherry picked from commit 228b228449872d4aa515f2f2ebbd25bb0b8d85bf)

Co-authored-by: Joel Schlosser <jbschlosser@meta.com>
2025-01-07 17:43:10 -08:00
478a99c59b Update torch-xpu-ops commit pin (#144209)
Update torch-xpu-ops commit pin (#143984)

Update the torch-xpu-ops commit to [28cfac20ec662abdb0ac98faf122450013e8f520](28cfac20ec), includes:

- Disable batch_norm vectorization path to fix accuracy issues.
- Fix the LSRM/RNN implementation error.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143984
Approved by: https://github.com/EikanWang, https://github.com/ruidazeng, https://github.com/desertfire, https://github.com/jansel

(cherry picked from commit 1e881ceecfe80532206ca4e0acb64391fab8b935)

Co-authored-by: Yutao Xu <yutao.xu@intel.com>
2025-01-07 16:50:13 -08:00
4e4182dbd0 [ROCm] Add miopen_batch_norm to meta_registrations to fix AOTI issue (#144028)
[ROCm] Add miopen_batch_norm to meta_registrations to fix AOTI issue (#143569)

Currently the upstream example for AOTI usage breaks on ROCm (https://pytorch.org/tutorials/recipes/torch_export_aoti_python.html)

```
File "/root/upstream/torch/_dynamo/exc.py", line 317, in unimplemented
    raise Unsupported(msg, case_name=case_name)
torch._dynamo.exc.Unsupported: unsupported operator: aten.miopen_batch_norm.default (see https://docs.google.com/document/d/1GgvOe7C8_NVOMLOCwDaYV1mXXyHMXY7ExoewHqooxrs/edit#heading=h.64r4npvq0w0 for how to fix)

from user code:
   File "/root/vision/torchvision/models/resnet.py", line 285, in forward
    return self._forward_impl(x)
  File "/root/vision/torchvision/models/resnet.py", line 269, in _forward_impl
    x = self.bn1(x)
```

This PR adds a meta_registration for miopen_batch_norm to resolve this issue

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143569
Approved by: https://github.com/jeffdaily

(cherry picked from commit 27b0d41f0ab45bc281b9e9bb594df3277783017d)

Co-authored-by: Jack Taylor <jack.taylor@amd.com>
2025-01-06 16:09:55 -08:00
929efb4531 [Release/2.6][MPS] Fix crash on CPU scalars (#144096)
* [MPS] Fix fmin/fmax for scalar argument (#143934)

CPU scalar promotion to GPU is allowed for CUDA and shoudl be allowed for MPS as well (at the very least it should not crash)

Fixes https://github.com/pytorch/pytorch/issues/143933 https://github.com/pytorch/pytorch/issues/142203
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143934
Approved by: https://github.com/Skylion007

(cherry picked from commit 3054aae493a5347cf8187b5ce611b9a38aace202)

* [MPS] Handle implicit cpu-scalar-to-gpu transfer (#144055)

Followup after https://github.com/pytorch/pytorch/pull/143934, this check is no longer necessary and fixes a subset of inductor tests

Before `pytest test/inductor/test_torchinductor.py -k _mps` reports 463
failed, 291 passed, 32 skipped after 456 failed, 298 passed, 32 skipped
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144055
Approved by: https://github.com/Skylion007

(cherry picked from commit a93e75d1e2635ae1bf9a6c24cbe8fb2a6d65bfd9)
2025-01-06 12:16:55 -08:00
f01a678e02 [ROCm] Guard triton backend call around cuda.is_available (#144027)
[ROCm] Guard triton backend call around cuda.is_available (#143570)

To resolve: https://github.com/pytorch/test-infra/issues/6082

Calling into Triton's get_backend_options will initialise CUDA and break CPU-only environments that may have hip installed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143570
Approved by: https://github.com/atalman, https://github.com/jeffdaily

(cherry picked from commit 66172578f918974cca995b0d6f740903a35b1fa5)

Co-authored-by: Jack Taylor <108682042+jataylo@users.noreply.github.com>
2025-01-06 12:10:28 -08:00
23e390c711 Respect ROCR_VISIBLE_DEVICES on AMD GPU device discovery (#144026)
Respect ROCR_VISIBLE_DEVICES on AMD GPU device discovery (#142292)

Reland of #140320 after failing test on trunk. Fixes potential environment clobbering in test, makes ROCr+HIP devices (if specified together) more robust to index errors.

Fixes #140318

Pull Request resolved: https://github.com/pytorch/pytorch/pull/142292
Approved by: https://github.com/jataylo, https://github.com/huydhn, https://github.com/jeffdaily

Co-authored-by: Jack Taylor <108682042+jataylo@users.noreply.github.com>
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
(cherry picked from commit c0d710634fcce172490c3ace0de977829b38bc06)

Co-authored-by: Tal Ben-Nun <tbennun@users.noreply.github.com>
2025-01-06 12:07:00 -08:00
41811ae689 [CD] Remove redundant triton dependency for xpu wheels (#143983)
[CD] Remove redundant triton dependency for xpu wheels (#143839)

Due to XPU CD wheels enabled pypi dependencies by https://github.com/pytorch/pytorch/pull/141135, so the PYTORCH_EXTRA_INSTALL_REQUIREMENTS has value for XPU CD wheel build.
Works for https://github.com/pytorch/pytorch/issues/139722 and https://github.com/pytorch/pytorch/issues/114850
Fixes #143838

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143839
Approved by: https://github.com/huydhn

(cherry picked from commit 438698b20b585fc13f4439f8a9a93d63079eba37)

Co-authored-by: chuanqiw <chuanqi.wang@intel.com>
2025-01-02 10:44:24 -08:00
d9eeddd49f Remove assert from partitioner.py (#143608)
Remove assert from partitioner.py (#143376)

Remove erroneous assert assuming a dependent (user) node to be in the partition. This partially reverts #136616 by removing the assert.

Tested locally with a failing ExecuTorch Arm test using
```
$ python -m examples.arm.aot_arm_compiler --model_name mv2 --target ethos-u55-128 --delegate --quantize
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143376
Approved by: https://github.com/tarun292

(cherry picked from commit 6829897682b2b46a592a92d84417cb4124c26e88)

Co-authored-by: Digant Desai <digantdesai@meta.com>
2024-12-26 14:16:22 -08:00
5eb54f6ebf torch/accelerator: fix device type comparison (#143541) (#143781)
This was failing without the fix:
```
python -c 'import torch; d=torch.device("xpu:0"); torch.accelerator.current_stream(d)'
```
with:
```
ValueError: xpu doesn't match the current accelerator xpu.
```

CC: @guangyey, @EikanWang

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143541
Approved by: https://github.com/guangyey, https://github.com/albanD

(cherry picked from commit 7314cf44ae719dfbc9159496030ce84d152686e4)

Co-authored-by: Dmitry Rogozhkin <dmitry.v.rogozhkin@intel.com>
2024-12-26 14:15:47 -08:00
b1a10ecad9 Use random64 in Fischer-Yates algorithm for large N (#143682) (#143875)
Fixes bug in randperm https://nbsanity.com/static/a4774194938414dedcec7d6e99727d31/Shuffling_20in_20torch_20vs_20numpy-public.html

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143682
Approved by: https://github.com/eqy, https://github.com/albanD
2024-12-26 11:53:04 -08:00
31b520a599 Revert "Exclude py 31.3t triton package from PyTorch 3.13t wheel" (#143767)
Revert "Exclude py 31.3t triton package from PyTorch 3.13t wheel (#143244)"

This reverts commit c92f6871e6d879f129103fa18cb7c2477d43d013.
2024-12-24 12:09:13 -05:00
f61bf202b3 [Inductor] Constrain the shape of other tensor for Conv/Linear + broa… (#143617)
[Inductor] Constrain the shape of other tensor for Conv/Linear + broadcast add fusion. (#141759)

Fix https://github.com/pytorch/pytorch/issues/141671.

Summary:
The performance regression of these two timm_models is caused by Conv/Linear + broadcast add fusion run into oneDNN ref path. This PR constrains the shape of other tensor for Conv/Linear + broadcast add fusion to fix this issue.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141759
Approved by: https://github.com/jgong5, https://github.com/leslie-fang-intel, https://github.com/jansel
2024-12-23 12:19:41 -08:00
4b9b7def3d [Inductor] Fix _can_be_inplace function (#143279) (#143452)
Summary:
Modify _can_be_inplace function: return False if `_other.data` is an instance of `ir.BaseView`.

Fix https://github.com/pytorch/pytorch/issues/143280.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143279
Approved by: https://github.com/leslie-fang-intel, https://github.com/jansel, https://github.com/jgong5
2024-12-23 12:15:35 -08:00
9b688182f7 [dynamo, 3.13t] raise error if torch.compile is attempted in 3.13t (nogil) (#143594)
[dynamo, 3.13t] raise error if torch.compile is attempted in 3.13t (nogil) (#143404)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143404
Approved by: https://github.com/colesbury, https://github.com/atalman

(cherry picked from commit e1e83015d24f49cf2ffb0c67a3524cc9ac62463a)
2024-12-19 11:41:10 -08:00
22775e0e8c [Reland 2.6][BE][accelerator] formalize API name {current,set}_device_{idx => index} (#143186)
[BE][accelerator] formalize API name `{current,set}_device_{idx => index}` (#140542)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/140542
Approved by: https://github.com/guangyey, https://github.com/albanD
2024-12-18 11:52:49 -08:00
c953e748eb [MPS] Use metal shaders for all view ops (#143520)
[MPS] Use metal shaders for all view ops (#143375)

Before this PR Metal  shaders were used to scatter/gather 1-5 dimensional tensors.
This PR introduces generalized ones that could be used for any dimensionality and as results  gets rid of 700+ lines complex and untested code that might not even work as expected.
Generalized gather shader looks as follows
```metal
kernel void gather_kernel_n(uint linear_index           [[thread_position_in_grid]],
                            constant void * src_        [[buffer(0)]],
                            device void * dst_          [[buffer(1)]],
                            constant uint32_t * size    [[buffer(2)]],
                            constant uint32_t * stride  [[buffer(3)]],
                            constant uint32_t & numel   [[buffer(4)]],
                            constant int32_t & ndim     [[buffer(5)]]) {{
    if (linear_index >= numel) return;

    constant {0} * src = (constant {0} *)src_;
    device {1} * dst = (device {1} *)dst_;

    uint64_t src_offs = 0;
    auto src_idx = linear_index;
    for(int dim = ndim - 1; dim >= 0; --dim) {{
      src_offs += stride[dim] * (src_idx % size[dim]);
      src_idx /= size[dim];
    }}

    dst[linear_index] = cast<{1}>(src[src_offs]);
}}
```

Which, according to the following benchmark
```python
from timeit import default_timer

import torch
import torch.utils.cpp_extension
from torch.utils.benchmark import Measurement, Timer

t = Timer(
    stmt=f"y.copy_(x);torch.mps.synchronize()",
    setup=f"x=torch.rand(4, 5, 16, 64, 33, 24, dtype=torch.float32, device='mps')[:,:,:,:24,:24,];y=torch.empty(x.shape, device=x.device, dtype=x.dtype)",
    language="python", timer=default_timer
)
print(t.blocked_autorange())
```
Is almost twice as fast as previous implementation (i.e. on Mac Book M2 Pro it returns 2.9ms for MPS version vs 1.5ms for shader one

On MacOS Sequoia [`gatherWithUpdatesTensor: indicesTensor:...`](https://developer.apple.com/documentation/metalperformanceshadersgraph/mpsgraph/gather(withupdatestensor:indicestensor:axis:batchdimensions:name:)?language=objc) crashes if invoked with complex data type, as one can see by running the code below
```swift
import Metal
import MetalPerformanceShadersGraph

func gatherComplexMPS(device: MTLDevice,
                inp_buf: MTLBuffer, idx_buf: MTLBuffer,
                out_buf: MTLBuffer,
                inp_elem: Int, upd_elem: Int) {
  let graph = MPSGraph()
  let inputPlaceholder = graph.placeholder(shape: [inp_elem as NSNumber], dataType: .complexFloat32, name: nil)
  let indicesPlaceholder = graph.placeholder(shape: [upd_elem as NSNumber], dataType: .int64, name: nil)
  let outNode = graph.gather(withUpdatesTensor: inputPlaceholder, indicesTensor: indicesPlaceholder, axis: 0, batchDimensions: 0, name: nil)
  let mpsInputBuffer = MPSGraphTensorData(inp_buf, shape: [inp_elem as NSNumber], dataType: .complexFloat32)
  let mpsIndicesBuffer = MPSGraphTensorData(idx_buf, shape: [upd_elem as NSNumber], dataType: .int64)
  let mpsOutputBuffer = MPSGraphTensorData(out_buf, shape: [inp_elem as NSNumber], dataType: .complexFloat32)
  guard let queue = device.makeCommandQueue() else { fatalError("Can't make queue") }
  graph.run(with: queue, feeds: [inputPlaceholder: mpsInputBuffer,
                               indicesPlaceholder: mpsIndicesBuffer ],
            targetOperations: nil, resultsDictionary: [outNode: mpsOutputBuffer])
}

func makeBufferWithValues<T>(device: MTLDevice, values: [T]) -> MTLBuffer {
  guard let buf = device.makeBuffer(length: values.count * MemoryLayout<T>.size, options: [.storageModeShared]) else { fatalError("Can't alloc") }
  let buf_data = buf.contents().assumingMemoryBound(to: T.self)
  for i in 0..<values.count {
    buf_data[i] = values[i]
  }
  return buf
}

guard let device = MTLCopyAllDevices().first else { fatalError("Not Metal device found") }
print("Using device \(device.name)")

let inp_buf = makeBufferWithValues(device: device, values: [1.0, 2.0 , 3.0, 4.0, 5.0, 6.0, 7.0, 8.0])
let idx_buf = makeBufferWithValues(device: device, values: [0, 1, 2, 3])
guard let out_buf = device.makeBuffer(length:8 * MemoryLayout<Float>.size, options: [.storageModeShared]) else { fatalError("Can't alloc") }

gatherComplexMPS(device: device, inp_buf: inp_buf, idx_buf: idx_buf, out_buf: out_buf, inp_elem: 4, upd_elem: 4)
```

Fixes https://github.com/pytorch/pytorch/issues/143140
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143375
Approved by: https://github.com/albanD

(cherry picked from commit 24a18d76c8619c0c4760c94aebef6ae7867fe1e6)

Co-authored-by: Nikita Shulga <nikita.shulga@gmail.com>
2024-12-18 11:46:11 -08:00
6628b70f02 Prevent torch.jit.load path in torch.load when weights_only=True (#143506)
Prevent torch.jit.load path in torch.load when weights_only=True (#143326)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143326
Approved by: https://github.com/albanD

(cherry picked from commit ac8342f8817570494faa85b54d8857d307959a68)

Co-authored-by: Mikayla Gawarecki <mikaylagawarecki@gmail.com>
2024-12-18 11:39:41 -08:00
0cdf8b1d09 Enable CPP/CUDAExtension with py_limited_api for python agnosticism (#143448)
Enable CPP/CUDAExtension with py_limited_api for python agnosticism (#138088)

Getting tested with ao, but now there is a real test i added.

## What does this PR do?

We want to allow custom PyTorch extensions to be able to build one wheel for multiple Python versions, in other words, achieve python agnosticism. It turns out that there is such a way that setuptools/Python provides already! Namely, if the user promises to use only the Python limited API in their extension, they can pass in `py_limited_api` to their Extension class and to the bdist_wheel command (with a min python version) in order to build 1 wheel that will suffice across multiple Python versions.

Sounds lovely! Why don't people do that already with PyTorch? Well 2 things. This workflow is hardly documented (even searching for python agnostic specifically does not reveal many answers) so I'd expect that people simply don't know about it. But even if they did, _PyTorch_ custom Extensions would still not work because we always link torch_python, which does not abide by py_limited_api rules.

So this is where this PR comes in! We respect when the user specifies py_limited_api and skip linking torch_python under that condition, allowing users to enroll in the provided functionality I just described.

## How do I know this PR works?

I manually tested my silly little ultra_norm locally (with `import python_agnostic`) and wrote a test case for the extension showing that
- torch_python doesn't show up in the ldd tree
- no Py- symbols show up
It may be a little confusing that our test case is actually python-free (more clean than python-agnostic) but it is sufficient (and not necessary) towards showing that this change works.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/138088
Approved by: https://github.com/ezyang, https://github.com/albanD

(cherry picked from commit be27dbf2b806e5d9c8d63ac6f6f96712299f98c3)

Co-authored-by: Jane Xu <janeyx@meta.com>
2024-12-17 15:28:26 -08:00
46f5510d20 Fix search icon (#143120)
Fix search icon (#142808)

Removing:

.pytorch-left-menu-search input[type=text] {
    background-image: none;
}
so that the search icon correctly appears in the sphinx searchbox

Also, fixing scrolling

Pull Request resolved: https://github.com/pytorch/pytorch/pull/142808
Approved by: https://github.com/albanD

(cherry picked from commit 0f78be5573016e65c0b493b788f40b10a6e18060)

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
2024-12-17 15:11:48 -08:00
f9e99fc62f Create build_directory if it does not exist when generating ninja build file (#143345)
Create build_directory if it does not exist when generating ninja build file (#143328)

Fixes: https://github.com/pytorch/vision/issues/8816
I am observing this failure on Windows, Python 3.13 vision builds:
```
Emitting ninja build file C:\actions-runner\_work\vision\vision\pytorch\vision\build\temp.win-amd64-cpython-313\Release\build.ninja...
error: [Errno 2] No such file or directory: 'C:\\actions-runner\\_work\\vision\\vision\\pytorch\\vision\\build\\temp.win-amd64-cpython-313\\Release\\build.ninja'
ERROR conda.cli.main_run:execute(49): `conda run packaging/windows/internal/vc_env_helper.bat python setup.py bdist_wheel` failed. (See above for error)
```

Adding the code above fixes it, confirmed by running `` python setup.py bdist_wheel`` :
```
building 'torchvision._C' extension
Emitting ninja build file C:\actions-runner\_work\vision\vision\pytorch\vision\build\temp.win-amd64-cpython-313\Release\build.ninja...
Creating build directory C:\actions-runner\_work\vision\vision\pytorch\vision\build\temp.win-amd64-cpython-313\Release
Compiling objects...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)
[1/26] cl /showIncludes /nologo /O2 /W3 /GL /DNDEBUG /MD /MD /wd4819 /wd4251 /wd4244 /wd4267 /wd4275 /wd4018 /wd4190 /wd4624 /wd4067 /wd4068 /EHsc -Dtorchvision_EXPORTS -IC:\actions-runner\_work\vision\vision\pytorch\vision\torchvision\csrc -IC:\actions-runner\_work\_temp\conda_environment_12361066769\Lib\site-packages\torch\include -IC:\actions-runner\_work\_temp\conda_environment_12361066769\Lib\site-packages\torch\include\torch\csrc\api\include -IC:\actions-runner\_work\_temp\conda_environment_12361066769\Lib\site-packages\torch\include\TH -IC:\actions-runner\_work\_temp\conda_environment_12361066769\Lib\site-packages\torch\include\THC -IC:\actions-runner\_work\_temp\conda_environment_12361066769\include -IC:\actions-runner\_work\_temp\conda_environment_12361066769\Include "-IC:\Pr
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143328
Approved by: https://github.com/kit1980, https://github.com/albanD

(cherry picked from commit dd2cd4279e8d46cce4a35dd1a52017a127809640)

Co-authored-by: atalman <atalman@fb.com>
2024-12-17 18:06:37 -05:00
1d3ffeb7ea [CD] Fix XPU linux CD whl test failure (#143292)
[CD] Fix XPU linux CD whl test failure (#143268)

Follow https://github.com/pytorch/pytorch/pull/142482, refer the original fix PR https://github.com/pytorch/pytorch/pull/130742 and new issue in https://github.com/pytorch/pytorch/actions/runs/12323126436/job/34403681230
Works for https://github.com/pytorch/pytorch/issues/114850

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143268
Approved by: https://github.com/atalman

(cherry picked from commit a8cc19bb51931991253315b31c64ca2db2505cd6)

Co-authored-by: chuanqiw <chuanqi.wang@intel.com>
2024-12-16 17:27:49 -05:00
c92f6871e6 Exclude py 31.3t triton package from PyTorch 3.13t wheel (#143244)
Exclude py 31.3t triton package from PyTorch 3.13t wheel (#143218)

Follow up after https://github.com/pytorch/pytorch/pull/143162
Include triton only for 3.13 packages not 3.13t
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143218
Approved by: https://github.com/kit1980

(cherry picked from commit 3bfdf6f0633e6feb067e032009256c740a2a2665)

Co-authored-by: atalman <atalman@fb.com>
2024-12-16 10:20:30 -05:00
2b84debd97 [CD] Test torch.compile on 3.13 (#143243)
[CD] Test torch.compile on 3.13 (#143207)

Follow up after https://github.com/pytorch/pytorch/pull/143162
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143207
Approved by: https://github.com/atalman, https://github.com/ZainRizvi

(cherry picked from commit 625b4edb975da25818eeae27cdbf9ba916973961)

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
2024-12-16 10:18:31 -05:00
5fbc4aa90a Linux Wheels: Remove triton dependency python < 3.13 constraint (#143199)
Linux Wheels: Remove triton dependency python < 3.13 constraint (#143162)

We do build pytorch-triton package for python 3.13 : https://github.com/pytorch/pytorch/actions/runs/12304476674/job/34344764271
Hence constraint is no longer needed.
This stack enabled torch.compile for Python 3.13 : https://github.com/pytorch/pytorch/pull/141264
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143162
Approved by: https://github.com/kit1980

(cherry picked from commit 04bb82f09760b8336ae6761b9aa51c2c525d12bb)

Co-authored-by: Andrey Talman <atalman@fb.com>
2024-12-13 09:44:24 -08:00
5363f7d9fd [CD] Use Anaconda cmake for Mac builds (#143133)
[CD] Use Anaconda cmake for Mac builds (#143054)

To find Anaconda-env-installed OpenMP
(As OpenMP from PyPI is looking for it in a different places)

For posterity: our build script names are very confusing:
 - [`.ci/wheel/build_wheel.sh`](6cb6e8d790/.ci/wheel/build_wheel.sh) is only used for MacOS wheel/libtorch builds
 - [`.ci/manywheel/build.sh`](6cb6e8d790/.ci/manywheel/build.sh) are used for Linux wheel/libtorch builds
 - [`.ci/pytorch/windows/build_pytorch.bat`](6cb6e8d790/.ci/pytorch/windows/build_pytorch.bat) is used for Windows wheel builds

Fixes https://github.com/pytorch/pytorch/issues/142873
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143054
Approved by: https://github.com/Jack-Khuu, https://github.com/atalman

(cherry picked from commit 4d8357e912ec5a9d60f10b44bb699950e4472488)

Co-authored-by: Nikita Shulga <nshulga@meta.com>
2024-12-12 12:41:42 -08:00
f3c0886c05 Remove Checkout pytorch/builder for Linux Binary Builds (#143125) (#143131)
Follow Up after: https://github.com/pytorch/pytorch/pull/142282

Remove Checkout pytorch/builder for Linux Binary Builds
I believe we where not using builder already. Hence remove this checkout.
We should be using scripts from this folder:
```
/pytorch/.ci/${{ inputs.PACKAGE_TYPE }}/build.sh
```

TODO: Will followup with removing BUILDER_ROOT everywhere from PyTorch repo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143125
Approved by: https://github.com/kit1980

Co-authored-by: atalman <atalman@fb.com>
2024-12-12 12:04:25 -08:00
aad1c160a7 Cherry-pick reverts dfe5669 and 1b3f8b7 (#143092)
* Revert "[RELAND] Add device-agnostic runtime Device/Stream C++ API (#138677)"

This reverts commit 734bb01460d59e661e9114e7aa17e04821e4b57a.

Reverted https://github.com/pytorch/pytorch/pull/138677 on behalf of https://github.com/huydhn due to Sorry for reverting your change but the new test is still very flaky on MacOS even when it does not segfault anymore ([comment](https://github.com/pytorch/pytorch/pull/133572#issuecomment-2537256522))

* Revert "[RELAND] Add UTs for accelerator device-agnostic runtime APIs (#133572)"

This reverts commit 209119424922b135fef39aba1f25da3b67f5879a.

Reverted https://github.com/pytorch/pytorch/pull/133572 on behalf of https://github.com/huydhn due to Sorry for reverting your change but the new test is still very flaky on MacOS even when it does not segfault anymore ([comment](https://github.com/pytorch/pytorch/pull/133572#issuecomment-2537256522))

---------

Co-authored-by: PyTorch MergeBot <pytorchmergebot@users.noreply.github.com>
2024-12-11 21:35:44 -08:00
af92bad804 [RELEASE-ONLY CHANGES] Branch Cut for Release 2.6 (#143085)
* Run apply-release-changes.sh

* Use test in docker-release.yml

* Fix spaces in lint.yaml
2024-12-11 17:47:33 -08:00
c69eae32ba Use validate-docker-images workflow from test-infra (#143083)
Use validate-docker-images workflow from test-infra (#143081)

After PR: https://github.com/pytorch/test-infra/pull/6029 use validate-docker-images.yml from test-infra.
Related to: https://github.com/pytorch/builder/issues/2054

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143081
Approved by: https://github.com/huydhn

(cherry picked from commit bd7d81db9e8b54cd7042fcb724b9b445e6641cf9)

Co-authored-by: atalman <atalman@fb.com>
2024-12-11 16:31:29 -08:00
9457 changed files with 298588 additions and 631994 deletions

View File

@ -2,7 +2,7 @@ build --cxxopt=--std=c++17
build --copt=-I.
# Bazel does not support including its cc_library targets as system
# headers. We work around this for generated code
# (e.g. torch/headeronly/macros/cmake_macros.h) by making the generated directory a
# (e.g. c10/macros/cmake_macros.h) by making the generated directory a
# system include path.
build --copt=-isystem --copt bazel-out/k8-fastbuild/bin
build --copt=-isystem --copt bazel-out/darwin-fastbuild/bin

View File

@ -3,10 +3,6 @@ set -eux -o pipefail
GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
if [[ "$GPU_ARCH_VERSION" == *"12.9"* ]]; then
export TORCH_CUDA_ARCH_LIST="8.0;9.0;10.0;12.0"
fi
SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
source $SCRIPTPATH/aarch64_ci_setup.sh
@ -18,14 +14,13 @@ cd /
# on the mounted pytorch repo
git config --global --add safe.directory /pytorch
pip install -r /pytorch/requirements.txt
pip install auditwheel==6.2.0
pip install auditwheel
if [ "$DESIRED_CUDA" = "cpu" ]; then
echo "BASE_CUDA_VERSION is not set. Building cpu wheel."
#USE_PRIORITIZED_TEXT_FOR_LD for enable linker script optimization https://github.com/pytorch/pytorch/pull/121975/files
USE_PRIORITIZED_TEXT_FOR_LD=1 python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn
else
echo "BASE_CUDA_VERSION is set to: $DESIRED_CUDA"
export USE_SYSTEM_NCCL=1
#USE_PRIORITIZED_TEXT_FOR_LD for enable linker script optimization https://github.com/pytorch/pytorch/pull/121975/files
USE_PRIORITIZED_TEXT_FOR_LD=1 python /pytorch/.ci/aarch64_linux/aarch64_wheel_ci_build.py --enable-mkldnn --enable-cuda
fi

View File

@ -4,9 +4,10 @@
import os
import shutil
from subprocess import check_call, check_output
from typing import List
def list_dir(path: str) -> list[str]:
def list_dir(path: str) -> List[str]:
"""'
Helper for getting paths for Python
"""
@ -31,47 +32,33 @@ def build_ArmComputeLibrary() -> None:
"build=native",
]
acl_install_dir = "/acl"
acl_checkout_dir = os.getenv("ACL_SOURCE_DIR", "ComputeLibrary")
if os.path.isdir(acl_install_dir):
shutil.rmtree(acl_install_dir)
if not os.path.isdir(acl_checkout_dir) or not len(os.listdir(acl_checkout_dir)):
check_call(
[
"git",
"clone",
"https://github.com/ARM-software/ComputeLibrary.git",
"-b",
"v25.02",
"--depth",
"1",
"--shallow-submodules",
]
)
acl_checkout_dir = "ComputeLibrary"
os.makedirs(acl_install_dir)
check_call(
[
"git",
"clone",
"https://github.com/ARM-software/ComputeLibrary.git",
"-b",
"v24.09",
"--depth",
"1",
"--shallow-submodules",
]
)
check_call(
["scons", "Werror=1", f"-j{os.cpu_count()}"] + acl_build_flags,
["scons", "Werror=1", "-j8", f"build_dir=/{acl_install_dir}/build"]
+ acl_build_flags,
cwd=acl_checkout_dir,
)
for d in ["arm_compute", "include", "utils", "support", "src", "build"]:
for d in ["arm_compute", "include", "utils", "support", "src"]:
shutil.copytree(f"{acl_checkout_dir}/{d}", f"{acl_install_dir}/{d}")
def replace_tag(filename) -> None:
with open(filename) as f:
lines = f.readlines()
for i, line in enumerate(lines):
if line.startswith("Tag:"):
lines[i] = line.replace("-linux_", "-manylinux_2_28_")
print(f"Updated tag from {line} to {lines[i]}")
break
with open(filename, "w") as f:
f.writelines(lines)
def package_cuda_wheel(wheel_path, desired_cuda) -> None:
def update_wheel(wheel_path) -> None:
"""
Package the cuda wheel libraries
Update the cuda wheel libraries
"""
folder = os.path.dirname(wheel_path)
wheelname = os.path.basename(wheel_path)
@ -79,7 +66,6 @@ def package_cuda_wheel(wheel_path, desired_cuda) -> None:
os.system(f"unzip {wheel_path} -d {folder}/tmp")
libs_to_copy = [
"/usr/local/cuda/extras/CUPTI/lib64/libcupti.so.12",
"/usr/local/cuda/extras/CUPTI/lib64/libnvperf_host.so",
"/usr/local/cuda/lib64/libcudnn.so.9",
"/usr/local/cuda/lib64/libcublas.so.12",
"/usr/local/cuda/lib64/libcublasLt.so.12",
@ -89,10 +75,10 @@ def package_cuda_wheel(wheel_path, desired_cuda) -> None:
"/usr/local/cuda/lib64/libcusparseLt.so.0",
"/usr/local/cuda/lib64/libcusolver.so.11",
"/usr/local/cuda/lib64/libcurand.so.10",
"/usr/local/cuda/lib64/libnccl.so.2",
"/usr/local/cuda/lib64/libnvToolsExt.so.1",
"/usr/local/cuda/lib64/libnvJitLink.so.12",
"/usr/local/cuda/lib64/libnvrtc.so.12",
"/usr/local/cuda/lib64/libnvshmem_host.so.3",
"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.6",
"/usr/local/cuda/lib64/libcudnn_adv.so.9",
"/usr/local/cuda/lib64/libcudnn_cnn.so.9",
"/usr/local/cuda/lib64/libcudnn_graph.so.9",
@ -104,19 +90,18 @@ def package_cuda_wheel(wheel_path, desired_cuda) -> None:
"/usr/lib64/libgfortran.so.5",
"/acl/build/libarm_compute.so",
"/acl/build/libarm_compute_graph.so",
"/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_lapack_core.so.0",
"/usr/local/lib/libnvpl_blas_core.so.0",
]
if "129" in desired_cuda:
if enable_cuda:
libs_to_copy += [
"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.9",
"/usr/local/cuda/lib64/libcufile.so.0",
"/usr/local/cuda/lib64/libcufile_rdma.so.1",
"/usr/local/lib/libnvpl_lapack_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_blas_lp64_gomp.so.0",
"/usr/local/lib/libnvpl_lapack_core.so.0",
"/usr/local/lib/libnvpl_blas_core.so.0",
]
else:
libs_to_copy += [
"/opt/OpenBLAS/lib/libopenblas.so.0",
]
# Copy libraries to unzipped_folder/a/lib
for lib_path in libs_to_copy:
lib_name = os.path.basename(lib_path)
@ -125,13 +110,6 @@ def package_cuda_wheel(wheel_path, desired_cuda) -> None:
f"cd {folder}/tmp/torch/lib/; "
f"patchelf --set-rpath '$ORIGIN' --force-rpath {folder}/tmp/torch/lib/{lib_name}"
)
# Make sure the wheel is tagged with manylinux_2_28
for f in os.scandir(f"{folder}/tmp/"):
if f.is_dir() and f.name.endswith(".dist-info"):
replace_tag(f"{f.path}/WHEEL")
break
os.mkdir(f"{folder}/cuda_wheel")
os.system(f"cd {folder}/tmp/; zip -r {folder}/cuda_wheel/{wheelname} *")
shutil.move(
@ -148,9 +126,6 @@ def complete_wheel(folder: str) -> str:
"""
wheel_name = list_dir(f"/{folder}/dist")[0]
# Please note for cuda we don't run auditwheel since we use custom script to package
# the cuda dependencies to the wheel file using update_wheel() method.
# However we need to make sure filename reflects the correct Manylinux platform.
if "pytorch" in folder and not enable_cuda:
print("Repairing Wheel with AuditWheel")
check_call(["auditwheel", "repair", f"dist/{wheel_name}"], cwd=folder)
@ -162,14 +137,7 @@ def complete_wheel(folder: str) -> str:
f"/{folder}/dist/{repaired_wheel_name}",
)
else:
repaired_wheel_name = wheel_name.replace(
"linux_aarch64", "manylinux_2_28_aarch64"
)
print(f"Renaming {wheel_name} wheel to {repaired_wheel_name}")
os.rename(
f"/{folder}/dist/{wheel_name}",
f"/{folder}/dist/{repaired_wheel_name}",
)
repaired_wheel_name = wheel_name
print(f"Copying {repaired_wheel_name} to artifacts")
shutil.copy2(
@ -206,13 +174,10 @@ if __name__ == "__main__":
).decode()
print("Building PyTorch wheel")
build_vars = "CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000 "
# MAX_JOB=5 is not required for CPU backend (see commit 465d98b)
if enable_cuda:
build_vars += "MAX_JOBS=5 "
build_vars = "MAX_JOBS=5 CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000 "
os.system("cd /pytorch; python setup.py clean")
override_package_version = os.getenv("OVERRIDE_PACKAGE_VERSION")
desired_cuda = os.getenv("DESIRED_CUDA")
if override_package_version is not None:
version = override_package_version
build_vars += (
@ -228,11 +193,12 @@ if __name__ == "__main__":
check_output(["cat", "version.txt"], cwd="/pytorch").decode().strip()[:-2]
)
if enable_cuda:
desired_cuda = os.getenv("DESIRED_CUDA")
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date}+{desired_cuda} PYTORCH_BUILD_NUMBER=1 "
else:
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date} PYTORCH_BUILD_NUMBER=1 "
elif branch.startswith(("v1.", "v2.")):
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1 : branch.find('-')]} PYTORCH_BUILD_NUMBER=1 "
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1:branch.find('-')]} PYTORCH_BUILD_NUMBER=1 "
if enable_mkldnn:
build_ArmComputeLibrary()
@ -256,6 +222,6 @@ if __name__ == "__main__":
print("Updating Cuda Dependency")
filename = os.listdir("/pytorch/dist/")
wheel_path = f"/pytorch/dist/{filename[0]}"
package_cuda_wheel(wheel_path, desired_cuda)
update_wheel(wheel_path)
pytorch_wheel_name = complete_wheel("/pytorch/")
print(f"Build Complete. Created {pytorch_wheel_name}..")

View File

@ -12,22 +12,22 @@ import os
import subprocess
import sys
import time
from typing import Optional, Union
from typing import Dict, List, Optional, Tuple, Union
import boto3
# AMI images for us-east-1, change the following based on your ~/.aws/config
os_amis = {
"ubuntu18_04": "ami-078eece1d8119409f", # login_name: ubuntu
"ubuntu20_04": "ami-052eac90edaa9d08f", # login_name: ubuntu
"ubuntu22_04": "ami-0c6c29c5125214c77", # login_name: ubuntu
"redhat8": "ami-0698b90665a2ddcf1", # login_name: ec2-user
}
ubuntu20_04_ami = os_amis["ubuntu20_04"]
ubuntu18_04_ami = os_amis["ubuntu18_04"]
def compute_keyfile_path(key_name: Optional[str] = None) -> tuple[str, str]:
def compute_keyfile_path(key_name: Optional[str] = None) -> Tuple[str, str]:
if key_name is None:
key_name = os.getenv("AWS_KEY_NAME")
if key_name is None:
@ -57,7 +57,7 @@ def ec2_instances_by_id(instance_id):
def start_instance(
key_name, ami=ubuntu20_04_ami, instance_type="t4g.2xlarge", ebs_size: int = 50
key_name, ami=ubuntu18_04_ami, instance_type="t4g.2xlarge", ebs_size: int = 50
):
inst = ec2.create_instances(
ImageId=ami,
@ -96,7 +96,7 @@ class RemoteHost:
self.keyfile_path = keyfile_path
self.login_name = login_name
def _gen_ssh_prefix(self) -> list[str]:
def _gen_ssh_prefix(self) -> List[str]:
return [
"ssh",
"-o",
@ -108,13 +108,13 @@ class RemoteHost:
]
@staticmethod
def _split_cmd(args: Union[str, list[str]]) -> list[str]:
def _split_cmd(args: Union[str, List[str]]) -> List[str]:
return args.split() if isinstance(args, str) else args
def run_ssh_cmd(self, args: Union[str, list[str]]) -> None:
def run_ssh_cmd(self, args: Union[str, List[str]]) -> None:
subprocess.check_call(self._gen_ssh_prefix() + self._split_cmd(args))
def check_ssh_output(self, args: Union[str, list[str]]) -> str:
def check_ssh_output(self, args: Union[str, List[str]]) -> str:
return subprocess.check_output(
self._gen_ssh_prefix() + self._split_cmd(args)
).decode("utf-8")
@ -157,7 +157,7 @@ class RemoteHost:
def using_docker(self) -> bool:
return self.container_id is not None
def run_cmd(self, args: Union[str, list[str]]) -> None:
def run_cmd(self, args: Union[str, List[str]]) -> None:
if not self.using_docker():
return self.run_ssh_cmd(args)
assert self.container_id is not None
@ -178,7 +178,7 @@ class RemoteHost:
if rc != 0:
raise subprocess.CalledProcessError(rc, docker_cmd)
def check_output(self, args: Union[str, list[str]]) -> str:
def check_output(self, args: Union[str, List[str]]) -> str:
if not self.using_docker():
return self.check_ssh_output(args)
assert self.container_id is not None
@ -230,7 +230,7 @@ class RemoteHost:
)
self.download_file(remote_file, local_file)
def list_dir(self, path: str) -> list[str]:
def list_dir(self, path: str) -> List[str]:
return self.check_output(["ls", "-1", path]).split("\n")
@ -327,7 +327,7 @@ def build_ArmComputeLibrary(host: RemoteHost, git_clone_flags: str = "") -> None
]
)
host.run_cmd(
f"git clone https://github.com/ARM-software/ComputeLibrary.git -b v25.02 {git_clone_flags}"
f"git clone https://github.com/ARM-software/ComputeLibrary.git -b v24.09 {git_clone_flags}"
)
host.run_cmd(f"cd ComputeLibrary && scons Werror=1 -j8 {acl_build_flags}")
@ -358,7 +358,7 @@ def checkout_repo(
branch: str = "main",
url: str,
git_clone_flags: str,
mapping: dict[str, tuple[str, str]],
mapping: Dict[str, Tuple[str, str]],
) -> Optional[str]:
for prefix in mapping:
if not branch.startswith(prefix):
@ -438,7 +438,9 @@ def build_torchvision(
)
build_vars += f"BUILD_VERSION={version}.dev{build_date}"
elif build_version is not None:
build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
build_vars += (
f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-')[0]}"
)
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
@ -493,7 +495,9 @@ def build_torchdata(
)
build_vars += f"BUILD_VERSION={version}.dev{build_date}"
elif build_version is not None:
build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
build_vars += (
f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-')[0]}"
)
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
@ -549,7 +553,9 @@ def build_torchtext(
)
build_vars += f"BUILD_VERSION={version}.dev{build_date}"
elif build_version is not None:
build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
build_vars += (
f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-')[0]}"
)
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
@ -607,15 +613,15 @@ def build_torchaudio(
)
build_vars += f"BUILD_VERSION={version}.dev{build_date}"
elif build_version is not None:
build_vars += f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-', maxsplit=1)[0]}"
build_vars += (
f"BUILD_VERSION={build_version} PYTORCH_VERSION={branch[1:].split('-')[0]}"
)
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
host.run_cmd(
f"cd audio && export FFMPEG_ROOT=$(pwd)/third_party/ffmpeg && export USE_FFMPEG=1 \
host.run_cmd(f"cd audio && export FFMPEG_ROOT=$(pwd)/third_party/ffmpeg && export USE_FFMPEG=1 \
&& ./packaging/ffmpeg/build.sh \
&& {build_vars} python3 setup.py bdist_wheel"
)
&& {build_vars} python3 setup.py bdist_wheel")
wheel_name = host.list_dir("audio/dist")[0]
embed_libgomp(host, use_conda, os.path.join("audio", "dist", wheel_name))
@ -649,6 +655,18 @@ def configure_system(
"sudo apt-get install -y python3-dev python3-yaml python3-setuptools python3-wheel python3-pip"
)
host.run_cmd("pip3 install dataclasses typing-extensions")
# Install and switch to gcc-8 on Ubuntu-18.04
if not host.using_docker() and host.ami == ubuntu18_04_ami and compiler == "gcc-8":
host.run_cmd("sudo apt-get install -y g++-8 gfortran-8")
host.run_cmd(
"sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 100"
)
host.run_cmd(
"sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 100"
)
host.run_cmd(
"sudo update-alternatives --install /usr/bin/gfortran gfortran /usr/bin/gfortran-8 100"
)
if not use_conda:
print("Installing Cython + numpy from PyPy")
host.run_cmd("sudo pip3 install Cython")
@ -661,7 +679,7 @@ def build_domains(
branch: str = "main",
use_conda: bool = True,
git_clone_flags: str = "",
) -> tuple[str, str, str, str]:
) -> Tuple[str, str, str, str]:
vision_wheel_name = build_torchvision(
host, branch=branch, use_conda=use_conda, git_clone_flags=git_clone_flags
)
@ -688,7 +706,7 @@ def start_build(
pytorch_build_number: Optional[str] = None,
shallow_clone: bool = True,
enable_mkldnn: bool = False,
) -> tuple[str, str, str, str, str]:
) -> Tuple[str, str, str, str, str]:
git_clone_flags = " --depth 1 --shallow-submodules" if shallow_clone else ""
if host.using_docker() and not use_conda:
print("Auto-selecting conda option for docker images")
@ -739,7 +757,7 @@ def start_build(
version = host.check_output("cat pytorch/version.txt").strip()[:-2]
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={version}.dev{build_date} PYTORCH_BUILD_NUMBER=1"
if branch.startswith(("v1.", "v2.")):
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1 : branch.find('-')]} PYTORCH_BUILD_NUMBER=1"
build_vars += f"BUILD_TEST=0 PYTORCH_BUILD_VERSION={branch[1:branch.find('-')]} PYTORCH_BUILD_NUMBER=1"
if host.using_docker():
build_vars += " CMAKE_SHARED_LINKER_FLAGS=-Wl,-z,max-page-size=0x10000"
if enable_mkldnn:
@ -912,9 +930,9 @@ def parse_arguments():
parser.add_argument("--debug", action="store_true")
parser.add_argument("--build-only", action="store_true")
parser.add_argument("--test-only", type=str)
group = parser.add_mutually_exclusive_group()
group.add_argument("--os", type=str, choices=list(os_amis.keys()))
group.add_argument("--ami", type=str)
parser.add_argument(
"--os", type=str, choices=list(os_amis.keys()), default="ubuntu20_04"
)
parser.add_argument(
"--python-version",
type=str,
@ -944,13 +962,7 @@ def parse_arguments():
if __name__ == "__main__":
args = parse_arguments()
ami = (
args.ami
if args.ami is not None
else os_amis[args.os]
if args.os is not None
else ubuntu20_04_ami
)
ami = os_amis[args.os]
keyfile_path, key_name = compute_keyfile_path(args.key_name)
if args.list_instances:
@ -1004,7 +1016,7 @@ if __name__ == "__main__":
install_condaforge_python(host, args.python_version)
sys.exit(0)
python_version = args.python_version if args.python_version is not None else "3.9"
python_version = args.python_version if args.python_version is not None else "3.8"
if args.use_torch_from_pypi:
configure_system(host, compiler=args.compiler, python_version=python_version)

View File

@ -10,3 +10,5 @@ example: `py2-cuda9.0-cudnn7-ubuntu16.04`. The Docker images that are
built on Jenkins and are used in triggered builds already have this
environment variable set in their manifest. Also see
`./docker/jenkins/*/Dockerfile` and search for `BUILD_ENVIRONMENT`.
Our Jenkins installation is located at https://ci.pytorch.org/jenkins/.

View File

@ -5,7 +5,7 @@ source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
pip install click mock tabulate networkx==2.0
pip -q install "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
pip -q install --user "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
fi
# Skip tests in environments where they are not built/applicable
@ -13,6 +13,10 @@ if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
echo 'Skipping tests'
exit 0
fi
if [[ "${BUILD_ENVIRONMENT}" == *-rocm* ]]; then
# temporary to locate some kernel issues on the CI nodes
export HSAKMT_DEBUG_LEVEL=4
fi
# These additional packages are needed for circleci ROCm builds.
if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
# Need networkx 2.0 because bellmand_ford was moved in 2.1 . Scikit-image by
@ -147,8 +151,8 @@ export DNNL_MAX_CPU_ISA=AVX2
if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
# TODO(sdym@meta.com) remove this when the linked issue resolved.
# py is temporary until https://github.com/Teemu/pytest-sugar/issues/241 is fixed
pip install py==1.11.0
pip install pytest-sugar
pip install --user py==1.11.0
pip install --user pytest-sugar
# NB: Warnings are disabled because they make it harder to see what
# the actual erroring test is
"$PYTHON" \

View File

@ -34,106 +34,5 @@ See `build.sh` for valid build environments (it's the giant switch).
./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
# Set flags (see build.sh) and build image
sudo bash -c 'TRITON=1 ./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
sudo bash -c 'PROTOBUF=1 ./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
```
## [Guidance] Adding a New Base Docker Image
### Background
The base Docker images in directory `.ci/docker/` are built by the `docker-builds.yml` workflow. Those images are used throughout the PyTorch CI/CD pipeline. You should only create or modify a base Docker image if you need specific environment changes or dependencies before building PyTorch on CI.
1. **Automatic Rebuilding**:
- The Docker image building process is triggered automatically when changes are made to files in the `.ci/docker/*` directory
- This ensures all images stay up-to-date with the latest dependencies and configurations
2. **Image Reuse in PyTorch Build Workflows** (example: linux-build):
- The images generated by `docker-builds.yml` are reused in `_linux-build.yml` through the `calculate-docker-image` step
- The `_linux-build.yml` workflow:
- Pulls the Docker image determined by the `calculate-docker-image` step
- Runs a Docker container with that image
- Executes `.ci/pytorch/build.sh` inside the container to build PyTorch
3. **Usage in Test Workflows** (example: linux-test):
- The same Docker images are also used in `_linux-test.yml` for running tests
- The `_linux-test.yml` workflow follows a similar pattern:
- It uses the `calculate-docker-image` step to determine which Docker image to use
- It pulls the Docker image and runs a container with that image
- It installs the wheels from the artifacts generated by PyTorch build jobs
- It executes test scripts (like `.ci/pytorch/test.sh` or `.ci/pytorch/multigpu-test.sh`) inside the container
### Understanding File Purposes
#### `.ci/docker/build.sh` vs `.ci/pytorch/build.sh`
- **`.ci/docker/build.sh`**:
- Used for building base Docker images
- Executed by the `docker-builds.yml` workflow to pre-build Docker images for CI
- Contains configurations for different Docker build environments
- **`.ci/pytorch/build.sh`**:
- Used for building PyTorch inside a Docker container
- Called by workflows like `_linux-build.yml` after the Docker container is started
- Builds PyTorch wheels and other artifacts
#### `.ci/docker/ci_commit_pins/` vs `.github/ci_commit_pins`
- **`.ci/docker/ci_commit_pins/`**:
- Used for pinning dependency versions during base Docker image building
- Ensures consistent environments for building PyTorch
- Changes here trigger base Docker image rebuilds
- **`.github/ci_commit_pins`**:
- Used for pinning dependency versions during PyTorch building and tests
- Ensures consistent dependencies for PyTorch across different builds
- Used by build scripts running inside Docker containers
### Step-by-Step Guide for Adding a New Base Docker Image
#### 1. Add Pinned Commits (If Applicable)
We use pinned commits for build stability. The `nightly.yml` workflow checks and updates pinned commits for certain repository dependencies daily.
If your new Docker image needs a library installed from a specific pinned commit or built from source:
1. Add the repository you want to track in `nightly.yml` and `merge-rules.yml`
2. Add the initial pinned commit in `.ci/docker/ci_commit_pins/`. The text filename should match the one defined in step 1
#### 2. Configure the Base Docker Image
1. **Add new Base Docker image configuration** (if applicable):
Add the configuration in `.ci/docker/build.sh`. For example:
```bash
pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-new1)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
NEW_ARG_1=yes
;;
```
2. **Add build arguments to Docker build command**:
If you're introducing a new argument to the Docker build, make sure to add it in the Docker build step in `.ci/docker/build.sh`:
```bash
docker build \
....
--build-arg "NEW_ARG_1=${NEW_ARG_1}"
```
3. **Update Dockerfile logic**:
Update the Dockerfile to use the new argument. For example, in `ubuntu/Dockerfile`:
```dockerfile
ARG NEW_ARG_1
# Set up environment for NEW_ARG_1
RUN if [ -n "${NEW_ARG_1}" ]; then bash ./do_something.sh; fi
```
4. **Add the Docker configuration** in `.github/workflows/docker-builds.yml`:
The `docker-builds.yml` workflow pre-builds the Docker images whenever changes occur in the `.ci/docker/` directory. This includes the
pinned commit updates.

View File

@ -1,7 +1,6 @@
ARG CUDA_VERSION=12.6
ARG CUDA_VERSION=12.4
ARG BASE_TARGET=cuda${CUDA_VERSION}
ARG ROCM_IMAGE=rocm/dev-almalinux-8:6.3-complete
FROM amd64/almalinux:8.10-20250519 as base
FROM amd64/almalinux:8 as base
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
@ -9,10 +8,12 @@ ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=11
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
RUN yum -y update
RUN yum -y install epel-release
# install glibc-langpack-en make sure en_US.UTF-8 locale is available
RUN yum -y install glibc-langpack-en
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel openssl-devel yum-utils autoconf automake make gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
# Just add everything as a safe.directory for git since these will be used in multiple places with git
RUN git config --global --add safe.directory '*'
@ -40,50 +41,41 @@ RUN bash ./install_conda.sh && rm install_conda.sh
# Install CUDA
FROM base as cuda
ARG CUDA_VERSION=12.6
ARG CUDA_VERSION=12.4
RUN rm -rf /usr/local/cuda-*
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
# Preserve CUDA_VERSION for the builds
ENV CUDA_VERSION=${CUDA_VERSION}
# Make things in our path by default
ENV PATH=/usr/local/cuda-${CUDA_VERSION}/bin:$PATH
FROM cuda as 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
FROM cuda as cuda12.6
RUN bash ./install_cuda.sh 12.6
ENV DESIRED_CUDA=12.6
FROM cuda as cuda12.8
RUN bash ./install_cuda.sh 12.8
ENV DESIRED_CUDA=12.8
FROM cuda as cuda12.9
RUN bash ./install_cuda.sh 12.9
ENV DESIRED_CUDA=12.9
FROM cuda as cuda13.0
RUN bash ./install_cuda.sh 13.0
ENV DESIRED_CUDA=13.0
FROM ${ROCM_IMAGE} as rocm
ENV PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
ENV MKLROOT /opt/intel
# Install MNIST test data
FROM base as mnist
ADD ./common/install_mnist.sh install_mnist.sh
RUN bash ./install_mnist.sh
FROM base as all_cuda
COPY --from=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
COPY --from=cuda12.6 /usr/local/cuda-12.6 /usr/local/cuda-12.6
COPY --from=cuda12.8 /usr/local/cuda-12.8 /usr/local/cuda-12.8
COPY --from=cuda12.9 /usr/local/cuda-12.9 /usr/local/cuda-12.9
COPY --from=cuda13.0 /usr/local/cuda-13.0 /usr/local/cuda-13.0
# Final step
FROM ${BASE_TARGET} as final

View File

@ -1,70 +1,82 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -exou pipefail
set -eou pipefail
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGENAME:ARCHTAG"
echo "Usage: $0 IMAGE"
exit 1
fi
# Go from imagename:tag to tag
DOCKER_TAG_PREFIX=$(echo "${image}" | awk -F':' '{print $2}')
DOCKER_IMAGE_NAME="pytorch/${image}"
CUDA_VERSION=""
ROCM_VERSION=""
EXTRA_BUILD_ARGS=""
if [[ "${DOCKER_TAG_PREFIX}" == cuda* ]]; then
# extract cuda version from image name and tag. e.g. manylinux2_28-builder:cuda12.8 returns 12.8
CUDA_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'cuda' '{print $2}')
EXTRA_BUILD_ARGS="--build-arg CUDA_VERSION=${CUDA_VERSION}"
elif [[ "${DOCKER_TAG_PREFIX}" == rocm* ]]; then
# extract rocm version from image name and tag. e.g. manylinux2_28-builder:rocm6.2.4 returns 6.2.4
ROCM_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'rocm' '{print $2}')
EXTRA_BUILD_ARGS="--build-arg ROCM_IMAGE=rocm/dev-almalinux-8:${ROCM_VERSION}-complete"
fi
case ${DOCKER_TAG_PREFIX} in
cpu)
BASE_TARGET=base
;;
cuda*)
BASE_TARGET=cuda${CUDA_VERSION}
;;
rocm*)
BASE_TARGET=rocm
;;
*)
echo "ERROR: Unknown docker tag ${DOCKER_TAG_PREFIX}"
exit 1
;;
esac
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
export DOCKER_BUILDKIT=1
TOPDIR=$(git rev-parse --show-toplevel)
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
docker build \
--target final \
--progress plain \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
--build-arg "DEVTOOLSET_VERSION=11" \
${EXTRA_BUILD_ARGS} \
-t ${tmp_tag} \
$@ \
-f "${TOPDIR}/.ci/docker/almalinux/Dockerfile" \
${TOPDIR}/.ci/docker/
CUDA_VERSION=${CUDA_VERSION:-12.1}
if [ -n "${CUDA_VERSION}" ]; then
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=11" \
-t ${DOCKER_IMAGE_NAME} \
$@ \
-f "${TOPDIR}/.ci/docker/almalinux/Dockerfile" \
${TOPDIR}/.ci/docker/
)
if [[ "${DOCKER_TAG}" =~ ^cuda* ]]; then
# Test that we're using the right CUDA compiler
docker run --rm "${tmp_tag}" nvcc --version | grep "cuda_${CUDA_VERSION}"
(
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

@ -0,0 +1,5 @@
0.8b
manylinux_2_28
rocm6.2
6f8cbcac8a92775291bb1ba8f514d4beb350baf4
e938def5d32869fe2e00aec0300f354c9f157867bebdf2e104d732b94cb238d8

View File

@ -1,8 +1,4 @@
#!/bin/bash
# The purpose of this script is to:
# 1. Extract the set of parameters to be used for a docker build based on the provided image name.
# 2. Run docker build with the parameters found in step 1.
# 3. Run the built image and print out the expected and actual versions of packages installed.
set -ex
@ -50,23 +46,30 @@ if [[ "$image" == *xla* ]]; then
exit 0
fi
if [[ "$image" == *-jammy* ]]; then
if [[ "$image" == *-focal* ]]; then
UBUNTU_VERSION=20.04
elif [[ "$image" == *-jammy* ]]; then
UBUNTU_VERSION=22.04
elif [[ "$image" == *-noble* ]]; then
UBUNTU_VERSION=24.04
elif [[ "$image" == *ubuntu* ]]; then
extract_version_from_image_name ubuntu UBUNTU_VERSION
elif [[ "$image" == *centos* ]]; then
extract_version_from_image_name centos CENTOS_VERSION
fi
if [ -n "${UBUNTU_VERSION}" ]; then
OS="ubuntu"
elif [ -n "${CENTOS_VERSION}" ]; then
OS="centos"
else
echo "Unable to derive operating system base..."
exit 1
fi
DOCKERFILE="${OS}/Dockerfile"
if [[ "$image" == *rocm* ]]; then
# When using ubuntu - 22.04, start from Ubuntu docker image, instead of nvidia/cuda docker image.
if [[ "$image" == *cuda* && "$UBUNTU_VERSION" != "22.04" ]]; then
DOCKERFILE="${OS}-cuda/Dockerfile"
elif [[ "$image" == *rocm* ]]; then
DOCKERFILE="${OS}-rocm/Dockerfile"
elif [[ "$image" == *xpu* ]]; then
DOCKERFILE="${OS}-xpu/Dockerfile"
@ -76,181 +79,312 @@ elif [[ "$image" == *cuda*linter* ]]; then
elif [[ "$image" == *linter* ]]; then
# Use a separate Dockerfile for linter to keep a small image size
DOCKERFILE="linter/Dockerfile"
elif [[ "$image" == *riscv* ]]; then
# Use RISC-V specific Dockerfile
DOCKERFILE="ubuntu-cross-riscv/Dockerfile"
fi
# CMake 3.18 is needed to support CUDA17 language variant
CMAKE_VERSION=3.18.5
_UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
_UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
if [[ "$image" == *rocm* ]]; then
_UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
_UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
fi
tag=$(echo $image | awk -F':' '{print $2}')
# It's annoying to rename jobs every time you want to rewrite a
# configuration, so we hardcode everything here rather than do it
# from scratch
case "$tag" in
pytorch-linux-jammy-cuda12.4-cudnn9-py3-gcc11)
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc11)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9-inductor-benchmarks)
CUDA_VERSION=12.8.1
case "$image" in
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9)
CUDA_VERSION=12.1.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc9-inductor-benchmarks)
CUDA_VERSION=12.8.1
pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9-inductor-benchmarks)
CUDA_VERSION=12.1.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-cuda12.1-cudnn9-py3.12-gcc9-inductor-benchmarks)
CUDA_VERSION=12.1.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.13-gcc9-inductor-benchmarks)
CUDA_VERSION=12.8.1
pytorch-linux-focal-cuda12.4-cudnn9-py3.12-gcc9-inductor-benchmarks)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3.13-gcc9-inductor-benchmarks)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.13
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.12-gcc11-vllm)
CUDA_VERSION=12.8.1
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
TRITON=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9)
CUDA_VERSION=12.8.1
pytorch-linux-focal-cuda11.8-cudnn9-py3-gcc9)
CUDA_VERSION=11.8.0
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3-clang12-onnx)
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=12
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9)
CUDA_VERSION=12.1.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-py3-clang10-onnx)
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
ONNX=yes
;;
pytorch-linux-jammy-py3.9-clang12)
pytorch-linux-focal-py3.9-clang10)
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=12
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-rocm-n-py3 | pytorch-linux-jammy-rocm-n-py3-benchmarks | pytorch-linux-noble-rocm-n-py3)
if [[ $tag =~ "jammy" ]]; then
ANACONDA_PYTHON_VERSION=3.10
else
ANACONDA_PYTHON_VERSION=3.12
fi
GCC_VERSION=11
pytorch-linux-focal-py3.11-clang10)
ANACONDA_PYTHON_VERSION=3.11
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.4
NINJA_VERSION=1.9.0
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
CONDA_CMAKE=yes
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
if [[ $tag =~ "benchmarks" ]]; then
INDUCTOR_BENCHMARKS=yes
fi
;;
pytorch-linux-noble-rocm-alpha-py3)
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
pytorch-linux-focal-py3.9-gcc9)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=7.0
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.1
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.2.4
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-xpu-2024.0-py3)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
XPU_VERSION=0.5
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
PYTORCH_ROCM_ARCH="gfx90a;gfx942;gfx950"
;;
pytorch-linux-jammy-xpu-2025.0-py3)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
XPU_VERSION=2025.0
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-xpu-2025.1-py3)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
VISION=yes
XPU_VERSION=2025.1
NINJA_VERSION=1.9.0
TRITON=yes
;;
pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
pytorch-linux-jammy-py3.9-gcc11-inductor-benchmarks)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
CONDA_CMAKE=yes
TRITON=yes
DOCS=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-clang12)
pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-clang12)
ANACONDA_PYTHON_VERSION=3.9
CUDA_VERSION=12.8.1
CUDA_VERSION=11.8
CUDNN_VERSION=9
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
TRITON=yes
;;
pytorch-linux-jammy-py3-clang12-asan)
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3-clang15-asan)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=15
CONDA_CMAKE=yes
VISION=yes
;;
pytorch-linux-jammy-py3-clang18-asan)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=18
CONDA_CMAKE=yes
VISION=yes
;;
pytorch-linux-jammy-py3.9-gcc11)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
CONDA_CMAKE=yes
TRITON=yes
DOCS=yes
UNINSTALL_DILL=yes
@ -258,37 +392,44 @@ case "$tag" in
pytorch-linux-jammy-py3-clang12-executorch)
ANACONDA_PYTHON_VERSION=3.10
CLANG_VERSION=12
CONDA_CMAKE=yes
EXECUTORCH=yes
;;
pytorch-linux-jammy-py3.12-halide)
CUDA_VERSION=12.6
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
CONDA_CMAKE=yes
HALIDE=yes
TRITON=yes
;;
pytorch-linux-jammy-py3.12-triton-cpu)
CUDA_VERSION=12.6
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
CONDA_CMAKE=yes
TRITON_CPU=yes
;;
pytorch-linux-jammy-linter)
pytorch-linux-focal-linter)
# TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
# We will need to update mypy version eventually, but that's for another day. The task
# would be to upgrade mypy to 1.0.0 with Python 3.11
PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.9
CONDA_CMAKE=yes
;;
pytorch-linux-jammy-cuda12.8-cudnn9-py3.9-linter)
PYTHON_VERSION=3.9
CUDA_VERSION=12.8.1
pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-linter)
ANACONDA_PYTHON_VERSION=3.9
CUDA_VERSION=11.8
CONDA_CMAKE=yes
;;
pytorch-linux-jammy-aarch64-py3.10-gcc11)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
ACL=yes
PROTOBUF=yes
DB=yes
VISION=yes
OPENBLAS=yes
CONDA_CMAKE=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
@ -297,18 +438,19 @@ case "$tag" in
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
ACL=yes
PROTOBUF=yes
DB=yes
VISION=yes
OPENBLAS=yes
CONDA_CMAKE=yes
# snadampal: skipping llvm src build install because the current version
# from pytorch/llvm:9.0.1 is x86 specific
SKIP_LLVM_SRC_BUILD_INSTALL=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-noble-riscv64-py3.12-gcc14)
GCC_VERSION=14
;;
*)
# Catch-all for builds that are not hardcoded.
PROTOBUF=yes
DB=yes
VISION=yes
echo "image '$image' did not match an existing build configuration"
if [[ "$image" == *py* ]]; then
@ -316,6 +458,7 @@ case "$tag" in
fi
if [[ "$image" == *cuda* ]]; then
extract_version_from_image_name cuda CUDA_VERSION
extract_version_from_image_name cudnn CUDNN_VERSION
fi
if [[ "$image" == *rocm* ]]; then
extract_version_from_image_name rocm ROCM_VERSION
@ -323,7 +466,8 @@ case "$tag" in
TRITON=yes
# To ensure that any ROCm config will build using conda cmake
# and thus have LAPACK/MKL enabled
fi
CONDA_CMAKE=yes
fi
if [[ "$image" == *centos7* ]]; then
NINJA_VERSION=1.10.2
fi
@ -339,41 +483,53 @@ case "$tag" in
if [[ "$image" == *glibc* ]]; then
extract_version_from_image_name glibc GLIBC_VERSION
fi
if [[ "$image" == *cmake* ]]; then
extract_version_from_image_name cmake CMAKE_VERSION
fi
;;
esac
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
no_cache_flag=""
progress_flag=""
# Do not use cache and progress=plain when in CI
if [[ -n "${CI:-}" ]]; then
no_cache_flag="--no-cache"
progress_flag="--progress=plain"
#when using cudnn version 8 install it separately from cuda
if [[ "$image" == *cuda* && ${OS} == "ubuntu" ]]; then
IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
if [[ ${CUDNN_VERSION} == 9 ]]; then
IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
fi
fi
# Build image
docker build \
${no_cache_flag} \
${progress_flag} \
--no-cache \
--progress=plain \
--build-arg "BUILD_ENVIRONMENT=${image}" \
--build-arg "PROTOBUF=${PROTOBUF:-}" \
--build-arg "LLVMDEV=${LLVMDEV:-}" \
--build-arg "DB=${DB:-}" \
--build-arg "VISION=${VISION:-}" \
--build-arg "UBUNTU_VERSION=${UBUNTU_VERSION}" \
--build-arg "CENTOS_VERSION=${CENTOS_VERSION}" \
--build-arg "DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}" \
--build-arg "GLIBC_VERSION=${GLIBC_VERSION}" \
--build-arg "CLANG_VERSION=${CLANG_VERSION}" \
--build-arg "ANACONDA_PYTHON_VERSION=${ANACONDA_PYTHON_VERSION}" \
--build-arg "PYTHON_VERSION=${PYTHON_VERSION}" \
--build-arg "GCC_VERSION=${GCC_VERSION}" \
--build-arg "CUDA_VERSION=${CUDA_VERSION}" \
--build-arg "CUDNN_VERSION=${CUDNN_VERSION}" \
--build-arg "TENSORRT_VERSION=${TENSORRT_VERSION}" \
--build-arg "GRADLE_VERSION=${GRADLE_VERSION}" \
--build-arg "VULKAN_SDK_VERSION=${VULKAN_SDK_VERSION}" \
--build-arg "SWIFTSHADER=${SWIFTSHADER}" \
--build-arg "CMAKE_VERSION=${CMAKE_VERSION:-}" \
--build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
--build-arg "KATEX=${KATEX:-}" \
--build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
--build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx90a;gfx942}" \
--build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx90a}" \
--build-arg "IMAGE_NAME=${IMAGE_NAME}" \
--build-arg "UCX_COMMIT=${UCX_COMMIT}" \
--build-arg "UCC_COMMIT=${UCC_COMMIT}" \
--build-arg "CONDA_CMAKE=${CONDA_CMAKE}" \
--build-arg "TRITON=${TRITON}" \
--build-arg "TRITON_CPU=${TRITON_CPU}" \
--build-arg "ONNX=${ONNX}" \
@ -382,9 +538,7 @@ docker build \
--build-arg "EXECUTORCH=${EXECUTORCH}" \
--build-arg "HALIDE=${HALIDE}" \
--build-arg "XPU_VERSION=${XPU_VERSION}" \
--build-arg "UNINSTALL_DILL=${UNINSTALL_DILL}" \
--build-arg "ACL=${ACL:-}" \
--build-arg "OPENBLAS=${OPENBLAS:-}" \
--build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
--build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
-f $(dirname ${DOCKERFILE})/Dockerfile \
@ -401,7 +555,7 @@ docker build \
UBUNTU_VERSION=$(echo ${UBUNTU_VERSION} | sed 's/-rc$//')
function drun() {
docker run --rm "$tmp_tag" "$@"
docker run --rm "$tmp_tag" $*
}
if [[ "$OS" == "ubuntu" ]]; then
@ -427,14 +581,7 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
fi
if [ -n "$GCC_VERSION" ]; then
if [[ "$image" == *riscv* ]]; then
# Check RISC-V cross-compilation toolchain version
if !(drun riscv64-linux-gnu-gcc-${GCC_VERSION} --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
echo "RISC-V GCC_VERSION=$GCC_VERSION, but:"
drun riscv64-linux-gnu-gcc-${GCC_VERSION} --version
exit 1
fi
elif !(drun gcc --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
if !(drun gcc --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
echo "GCC_VERSION=$GCC_VERSION, but:"
drun gcc --version
exit 1
@ -456,23 +603,3 @@ if [ -n "$KATEX" ]; then
exit 1
fi
fi
HAS_TRITON=$(drun python -c "import triton" > /dev/null 2>&1 && echo "yes" || echo "no")
if [[ -n "$TRITON" || -n "$TRITON_CPU" ]]; then
if [ "$HAS_TRITON" = "no" ]; then
echo "expecting triton to be installed, but it is not"
exit 1
fi
elif [ "$HAS_TRITON" = "yes" ]; then
echo "expecting triton to not be installed, but it is"
exit 1
fi
# Sanity check cmake version. Executorch reinstalls cmake and I'm not sure if
# they support 4.0.0 yet, so exclude them from this check.
CMAKE_VERSION=$(drun cmake --version)
if [[ "$EXECUTORCH" != *yes* && "$CMAKE_VERSION" != *4.* ]]; then
echo "CMake version is not 4.0.0:"
drun cmake --version
exit 1
fi

View File

@ -17,8 +17,9 @@ RUN bash ./install_base.sh && rm install_base.sh
# Update CentOS git version
RUN yum -y remove git
RUN yum -y remove git-*
RUN yum -y install https://packages.endpointdev.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm && \
sed -i 's/packages.endpoint/packages.endpointdev/' /etc/yum.repos.d/endpoint.repo
RUN yum -y install https://packages.endpoint.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm || \
(yum -y install https://packages.endpointdev.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm && \
sed -i "s/packages.endpoint/packages.endpointdev/" /etc/yum.repos.d/endpoint.repo)
RUN yum install -y git
# Install devtoolset
@ -39,7 +40,7 @@ RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG BUILD_ENVIRONMENT
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
@ -47,6 +48,20 @@ COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
@ -60,7 +75,7 @@ COPY ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
RUN bash ./install_rocm_magma.sh
RUN rm install_rocm_magma.sh
COPY ./common/install_amdsmi.sh install_amdsmi.sh
RUN bash ./install_amdsmi.sh
@ -74,6 +89,12 @@ ENV MAGMA_HOME /opt/rocm/magma
ENV LANG en_US.utf8
ENV LC_ALL en_US.utf8
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
@ -92,6 +113,13 @@ COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
# Install AOTriton (Early fail)
COPY ./aotriton_version.txt aotriton_version.txt
COPY ./common/common_utils.sh common_utils.sh
COPY ./common/install_aotriton.sh install_aotriton.sh
RUN ["/bin/bash", "-c", "./install_aotriton.sh /opt/rocm && rm -rf install_aotriton.sh aotriton_version.txt common_utils.sh"]
ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
# Install ccache/sccache (do this last, so we get priority in PATH)
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH

View File

@ -1 +1 @@
56392aa978594cc155fa8af48cd949f5b5f1823a
6f638937d64e3396793956d75ee3e14802022745

View File

@ -1,2 +0,0 @@
transformers==4.54.0
soxr==0.5.0

View File

@ -0,0 +1 @@
243e186efbf7fb93328dd6b34927a4e8c8f24395

View File

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

View File

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

View File

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

View File

@ -1 +1 @@
5d535d7a2d4b435b1b5c1177fd8f04a12b942b9a
ac3470188b914c5d7a5058a7e28b9eb685a62427

View File

@ -1 +0,0 @@
e03a63be43e33596f7f0a43b0f530353785e4a59

View File

@ -1 +1 @@
0958dc9b2bb815e428f721f9da599dab0dc1c5d7
e98b6fcb8df5b44eb0d0addb6767c573d37ba024

View File

@ -1 +1 @@
f7888497a1eb9e98d4c07537f0d0bcfe180d1363
35c6c7c6284582b3f41c71c150e11b517acf074a

View File

@ -23,10 +23,6 @@ conda_install() {
as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION" $*
}
conda_install_through_forge() {
as_jenkins conda install -c conda-forge -q -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION" $*
}
conda_run() {
as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION --no-capture-output $*
}

View File

@ -1,7 +1,7 @@
set -euo pipefail
readonly version=v25.02
readonly src_host=https://github.com/ARM-software
readonly version=v24.04
readonly src_host=https://review.mlplatform.org/ml
readonly src_repo=ComputeLibrary
# Clone ACL

View File

@ -0,0 +1,23 @@
#!/bin/bash
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
TARBALL='aotriton.tar.gz'
# 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"
cd "${AOTRITON_INSTALL_PREFIX}"
# Must use -L to follow redirects
curl -L --retry 3 -o "${TARBALL}" "${AOTRITON_URL}"
ACTUAL_SHA256=$(sha256sum "${TARBALL}" | cut -d " " -f 1)
if [ "${SHA256}" != "${ACTUAL_SHA256}" ]; then
echo -n "Error: The SHA256 of downloaded tarball is ${ACTUAL_SHA256},"
echo " which does not match the expected value ${SHA256}."
exit
fi
tar xf "${TARBALL}" && rm -rf "${TARBALL}"

View File

@ -15,9 +15,6 @@ install_ubuntu() {
elif [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
cmake3="cmake=3.22*"
maybe_libiomp_dev=""
elif [[ "$UBUNTU_VERSION" == "24.04"* ]]; then
cmake3="cmake=3.28*"
maybe_libiomp_dev=""
else
cmake3="cmake=3.5*"
maybe_libiomp_dev="libiomp-dev"
@ -33,6 +30,14 @@ install_ubuntu() {
maybe_libomp_dev=""
fi
# HACK: UCC testing relies on libnccl library from NVIDIA repo, and version 2.16 crashes
# See https://github.com/pytorch/pytorch/pull/105260#issuecomment-1673399729
if [[ "$UBUNTU_VERSION" == "20.04"* && "$CUDA_VERSION" == "11.8"* ]]; then
maybe_libnccl_dev="libnccl2=2.15.5-1+cuda11.8 libnccl-dev=2.15.5-1+cuda11.8 --allow-downgrades --allow-change-held-packages"
else
maybe_libnccl_dev=""
fi
# Install common dependencies
apt-get update
# TODO: Some of these may not be necessary
@ -61,6 +66,7 @@ install_ubuntu() {
libasound2-dev \
libsndfile-dev \
${maybe_libomp_dev} \
${maybe_libnccl_dev} \
software-properties-common \
wget \
sudo \
@ -89,6 +95,9 @@ install_centos() {
ccache_deps="asciidoc docbook-dtds docbook-style-xsl libxslt"
numpy_deps="gcc-gfortran"
# Note: protobuf-c-{compiler,devel} on CentOS are too old to be used
# for Caffe2. That said, we still install them to make sure the build
# system opts to build/use protoc and libprotobuf from third-party.
yum install -y \
$ccache_deps \
$numpy_deps \

View File

@ -9,7 +9,7 @@ install_ubuntu() {
# Instead use lib and headers from OpenSSL1.1 installed in `install_openssl.sh``
apt-get install -y cargo
echo "Checking out sccache repo"
git clone https://github.com/mozilla/sccache -b v0.10.0
git clone https://github.com/mozilla/sccache -b v0.8.2
cd sccache
echo "Building sccache"
cargo build --release
@ -36,7 +36,11 @@ sed -e 's|PATH="\(.*\)"|PATH="/opt/cache/bin:\1"|g' -i /etc/environment
export PATH="/opt/cache/bin:$PATH"
# Setup compiler cache
install_ubuntu
if [ -n "$ROCM_VERSION" ]; then
curl --retry 3 http://repo.radeon.com/misc/.sccache_amd/sccache -o /opt/cache/bin/sccache
else
install_ubuntu
fi
chmod a+x /opt/cache/bin/sccache
function write_sccache_stub() {

View File

@ -4,10 +4,16 @@ set -ex
if [ -n "$CLANG_VERSION" ]; then
if [[ $UBUNTU_VERSION == 22.04 ]]; then
if [[ $CLANG_VERSION == 9 && $UBUNTU_VERSION == 18.04 ]]; then
sudo apt-get update
# gpg-agent is not available by default on 18.04
sudo apt-get install -y --no-install-recommends gpg-agent
wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
apt-add-repository "deb http://apt.llvm.org/bionic/ llvm-toolchain-bionic-${CLANG_VERSION} main"
elif [[ $UBUNTU_VERSION == 22.04 ]]; then
# work around ubuntu apt-get conflicts
sudo apt-get -y -f install
wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
if [[ $CLANG_VERSION == 18 ]]; then
apt-add-repository "deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-18 main"
fi
@ -35,7 +41,7 @@ if [ -n "$CLANG_VERSION" ]; then
# clang's packaging is a little messed up (the runtime libs aren't
# added into the linker path), so give it a little help
clang_lib=("/usr/lib/llvm-$CLANG_VERSION/lib/clang/"*"/lib/linux")
echo "$clang_lib" >/etc/ld.so.conf.d/clang.conf
echo "$clang_lib" > /etc/ld.so.conf.d/clang.conf
ldconfig
# Cleanup package manager

View File

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

View File

@ -4,8 +4,12 @@ set -ex
# Optionally install conda
if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download" # @lint-ignore
CONDA_FILE="Miniforge3-Linux-$(uname -m).sh"
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 "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
@ -17,6 +21,7 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
exit 1
;;
esac
mkdir -p /opt/conda
chown jenkins:jenkins /opt/conda
@ -57,33 +62,32 @@ if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
# libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
# which is provided in libstdcxx 12 and up.
conda_install libstdcxx-ng=12.3.0 --update-deps -c conda-forge
# Miniforge installer doesn't install sqlite by default
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
conda_install sqlite
fi
conda_install libstdcxx-ng=12.3.0 -c conda-forge
# Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
if [[ $(uname -m) != "aarch64" ]]; then
pip_install mkl==2024.2.0
pip_install mkl-static==2024.2.0
pip_install mkl-include==2024.2.0
if [[ $(uname -m) == "aarch64" ]]; then
conda_install "openblas==0.3.28=*openmp*"
else
conda_install "mkl=2021.4.0 mkl-include=2021.4.0"
fi
# Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
# and libpython-static for torch deploy
conda_install llvmdev=8.0.0 "libpython-static=${ANACONDA_PYTHON_VERSION}"
# Use conda cmake in some cases. Conda cmake will be newer than our supported
# min version (3.5 for xenial and 3.10 for bionic), so we only do it in those
# following builds that we know should use conda. Specifically, Ubuntu bionic
# and focal cannot find conda mkl with stock cmake, so we need a cmake from conda
if [ -n "${CONDA_CMAKE}" ]; then
conda_install cmake
fi
# Magma package names are concatenation of CUDA major and minor ignoring revision
# I.e. magma-cuda102 package corresponds to CUDA_VERSION=10.2 and CUDA_VERSION=10.2.89
# Magma is installed from a tarball in the ossci-linux bucket into the conda env
if [ -n "$CUDA_VERSION" ]; then
conda_run ${SCRIPT_FOLDER}/install_magma_conda.sh $(cut -f1-2 -d'.' <<< ${CUDA_VERSION})
fi
if [[ "$UBUNTU_VERSION" == "24.04"* ]] ; then
conda_install_through_forge libstdcxx-ng=14
${SCRIPT_FOLDER}/install_magma_conda.sh $(cut -f1-2 -d'.' <<< ${CUDA_VERSION}) ${ANACONDA_PYTHON_VERSION}
fi
# Install some other packages, including those needed for Python test reporting

View File

@ -3,10 +3,11 @@
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:-"3.9.0 3.10.1 3.11.0 3.12.0 3.13.0 3.13.0t 3.14.0 3.14.0t"}
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 "$1" ]; then
@ -23,8 +24,9 @@ function do_cpython_build {
tar -xzf Python-$py_ver.tgz
local additional_flags=""
if [[ "$py_ver" == *"t" ]]; then
if [ "$py_ver" == "3.13.0t" ]; then
additional_flags=" --disable-gil"
mv cpython-3.13/ cpython-3.13t/
fi
pushd $py_folder
@ -66,29 +68,32 @@ function do_cpython_build {
ln -s pip3 ${prefix}/bin/pip
fi
# install setuptools since python 3.12 is required to use distutils
# packaging is needed to create symlink since wheel no longer provides needed information
${prefix}/bin/pip install packaging==25.0 wheel==0.45.1 setuptools==80.9.0
local abi_tag=$(${prefix}/bin/python -c "from packaging.tags import interpreter_name, interpreter_version; import sysconfig ; from sysconfig import get_config_var; print('{0}{1}-{0}{1}{2}'.format(interpreter_name(), interpreter_version(), 't' if sysconfig.get_config_var('Py_GIL_DISABLED') else ''))")
ln -sf ${prefix} /opt/python/${abi_tag}
${prefix}/bin/pip install wheel==0.34.2 setuptools==68.2.2
local abi_tag=$(${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 $py_ver
local py_suffix=$py_ver
local py_folder=$py_ver
check_var $PYTHON_DOWNLOAD_URL
local py_ver_folder=$py_ver
# Special handling for nogil
if [[ "${py_ver}" == *"t" ]]; then
py_suffix=${py_ver::-1}
py_folder=$py_suffix
if [ "$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
# Only b3 is available now
if [ "$py_suffix" == "3.14.0" ]; then
py_suffix="3.14.0b3"
fi
wget -q $PYTHON_DOWNLOAD_URL/$py_folder/Python-$py_suffix.tgz -O Python-$py_ver.tgz
do_cpython_build $py_ver Python-$py_suffix
rm -f Python-$py_ver.tgz
}

View File

@ -2,182 +2,328 @@
set -ex
arch_path=''
targetarch=${TARGETARCH:-$(uname -m)}
if [ ${targetarch} = 'amd64' ] || [ "${targetarch}" = 'x86_64' ]; then
arch_path='x86_64'
else
arch_path='sbsa'
fi
NCCL_VERSION=v2.21.5-1
CUDNN_VERSION=9.5.1.17
NVSHMEM_VERSION=3.3.20
function install_cuda {
version=$1
runfile=$2
major_minor=${version%.*}
rm -rf /usr/local/cuda-${major_minor} /usr/local/cuda
if [[ ${arch_path} == 'sbsa' ]]; then
runfile="${runfile}_sbsa"
fi
runfile="${runfile}.run"
wget -q https://developer.download.nvidia.com/compute/cuda/${version}/local_installers/${runfile} -O ${runfile}
chmod +x ${runfile}
./${runfile} --toolkit --silent
rm -f ${runfile}
rm -f /usr/local/cuda && ln -s /usr/local/cuda-${major_minor} /usr/local/cuda
function install_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_cudnn {
cuda_major_version=$1
cudnn_version=$2
mkdir tmp_cudnn && cd tmp_cudnn
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
filepath="cudnn-linux-${arch_path}-${cudnn_version}_cuda${cuda_major_version}-archive"
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-${arch_path}/${filepath}.tar.xz
tar xf ${filepath}.tar.xz
cp -a ${filepath}/include/* /usr/local/cuda/include/
cp -a ${filepath}/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
function install_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_nvshmem {
cuda_major_version=$1 # e.g. "12"
nvshmem_version=$2 # e.g. "3.3.9"
function install_cusparselt_062 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.6.2.3-archive.tar.xz
tar xf libcusparse_lt-linux-x86_64-0.6.2.3-archive.tar.xz
cp -a libcusparse_lt-linux-x86_64-0.6.2.3-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-x86_64-0.6.2.3-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
case "${arch_path}" in
sbsa)
dl_arch="aarch64"
;;
x86_64)
dl_arch="x64"
;;
*)
dl_arch="${arch}"
;;
esac
function install_cusparselt_063 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.6.3.2-archive.tar.xz
tar xf libcusparse_lt-linux-x86_64-0.6.3.2-archive.tar.xz
cp -a libcusparse_lt-linux-x86_64-0.6.3.2-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-x86_64-0.6.3.2-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
tmpdir="tmp_nvshmem"
mkdir -p "${tmpdir}" && cd "${tmpdir}"
function install_118 {
CUDNN_VERSION=9.1.0.70
echo "Installing CUDA 11.8 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.4.0"
rm -rf /usr/local/cuda-11.8 /usr/local/cuda
# install CUDA 11.8.0 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run
chmod +x cuda_11.8.0_520.61.05_linux.run
./cuda_11.8.0_520.61.05_linux.run --toolkit --silent
rm -f cuda_11.8.0_520.61.05_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-11.8 /usr/local/cuda
# nvSHMEM license: https://docs.nvidia.com/nvshmem/api/sla.html
# This pattern is a lie as it is not consistent across versions, for 3.3.9 it was cuda_ver-arch-nvshhem-ver
filename="libnvshmem-linux-${arch_path}-${nvshmem_version}_cuda${cuda_major_version}-archive"
suffix=".tar.xz"
url="https://developer.download.nvidia.com/compute/redist/nvshmem/${nvshmem_version}/builds/cuda${cuda_major_version}/txz/agnostic/${dl_arch}/${filename}${suffix}"
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${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
# download, unpack, install
wget -q "${url}"
tar xf "${filename}${suffix}"
cp -a "${filename}/include/"* /usr/local/cuda/include/
cp -a "${filename}/lib/"* /usr/local/cuda/lib64/
# 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 $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
# cleanup
cd ..
rm -rf "${tmpdir}"
install_cusparselt_040
echo "nvSHMEM ${nvshmem_version} for CUDA ${cuda_major_version} (${arch_path}) installed."
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 {
CUDNN_VERSION=9.1.0.70
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.6.2"
install_cuda 12.4.1 cuda_12.4.1_550.54.15_linux
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.2"
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
install_cudnn 12 $CUDNN_VERSION
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${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
CUDA_VERSION=12.4 bash install_nccl.sh
# 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
CUDA_VERSION=12.4 bash install_cusparselt.sh
install_cusparselt_062
ldconfig
}
function install_126 {
CUDNN_VERSION=9.10.2.21
echo "Installing CUDA 12.6.3 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
install_cuda 12.6.3 cuda_12.6.3_560.35.05_linux
echo "Installing CUDA 12.6.3 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.3"
rm -rf /usr/local/cuda-12.6 /usr/local/cuda
# install CUDA 12.6.3 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux.run
chmod +x cuda_12.6.3_560.35.05_linux.run
./cuda_12.6.3_560.35.05_linux.run --toolkit --silent
rm -f cuda_12.6.3_560.35.05_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.6 /usr/local/cuda
install_cudnn 12 $CUDNN_VERSION
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${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
install_nvshmem 12 $NVSHMEM_VERSION
# 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
CUDA_VERSION=12.6 bash install_nccl.sh
CUDA_VERSION=12.6 bash install_cusparselt.sh
install_cusparselt_063
ldconfig
}
function install_129 {
CUDNN_VERSION=9.10.2.21
echo "Installing CUDA 12.9.1 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
# install CUDA 12.9.1 in the same container
install_cuda 12.9.1 cuda_12.9.1_575.57.08_linux
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"
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
install_cudnn 12 $CUDNN_VERSION
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"
install_nvshmem 12 $NVSHMEM_VERSION
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
CUDA_VERSION=12.9 bash install_nccl.sh
# 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/{}"
CUDA_VERSION=12.9 bash install_cusparselt.sh
# 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
ldconfig
#####################################################################################
# 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 install_128 {
CUDNN_VERSION=9.8.0.87
echo "Installing CUDA 12.8.1 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
# install CUDA 12.8.1 in the same container
install_cuda 12.8.1 cuda_12.8.1_570.124.06_linux
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"
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
install_cudnn 12 $CUDNN_VERSION
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"
install_nvshmem 12 $NVSHMEM_VERSION
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
CUDA_VERSION=12.8 bash install_nccl.sh
# 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/{}"
CUDA_VERSION=12.8 bash install_cusparselt.sh
# 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
ldconfig
#####################################################################################
# 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 install_130 {
CUDNN_VERSION=9.12.0.46
NVSHMEM_VERSION=3.3.20
echo "Installing CUDA 13.0 and cuDNN ${CUDNN_VERSION} and NVSHMEM and NCCL and cuSparseLt-0.7.1"
# install CUDA 13.0 in the same container
install_cuda 13.0.0 cuda_13.0.0_580.65.06_linux
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"
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
install_cudnn 13 $CUDNN_VERSION
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"
install_nvshmem 13 $NVSHMEM_VERSION
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
if [[ -n "$OVERRIDE_GENCODE_CUDNN" ]]; then
export GENCODE_CUDNN=$OVERRIDE_GENCODE_CUDNN
fi
CUDA_VERSION=13.0 bash install_nccl.sh
# 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/{}"
CUDA_VERSION=13.0 bash install_cusparselt.sh
# 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
ldconfig
#####################################################################################
# CUDA 12.4 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 $CUDA_BASE/nsight-systems-2023.4.4/
}
function prune_126 {
echo "Pruning CUDA 12.6"
#####################################################################################
# CUDA 12.6 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.6/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.6/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$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 12.6 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-12.6/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.3.2 $CUDA_BASE/nsight-systems-2024.5.1/
}
# idiomatic parameter and option handling in sh
while test $# -gt 0
do
case "$1" in
12.4) install_124;
11.8) install_118; prune_118
;;
12.6|12.6.*) install_126;
12.1) install_121; prune_121
;;
12.8|12.8.*) install_128;
12.4) install_124; prune_124
;;
12.9|12.9.*) install_129;
;;
13.0|13.0.*) install_130;
12.6) install_126; prune_126
;;
*) echo "bad argument $1"; exit 1
;;

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@ -0,0 +1,175 @@
#!/bin/bash
# Script used only in CD pipeline
set -ex
NCCL_VERSION=v2.21.5-1
CUDNN_VERSION=9.5.1.17
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-sbsa/libcusparse_lt-linux-sbsa-0.6.2.3-archive.tar.xz
tar xf libcusparse_lt-linux-sbsa-0.6.2.3-archive.tar.xz
cp -a libcusparse_lt-linux-sbsa-0.6.2.3-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-sbsa-0.6.2.3-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_cusparselt_063 {
# 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-0.6.3.2-archive.tar.xz
tar xf libcusparse_lt-linux-sbsa-0.6.3.2-archive.tar.xz
cp -a libcusparse_lt-linux-sbsa-0.6.3.2-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-sbsa-0.6.3.2-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_124 {
CUDNN_VERSION=9.1.0.70
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.2"
rm -rf /usr/local/cuda-12.4 /usr/local/cuda
# install CUDA 12.4.1 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.4.1/local_installers/cuda_12.4.1_550.54.15_linux_sbsa.run
chmod +x cuda_12.4.1_550.54.15_linux_sbsa.run
./cuda_12.4.1_550.54.15_linux_sbsa.run --toolkit --silent
rm -f cuda_12.4.1_550.54.15_linux_sbsa.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.4 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive.tar.xz
tar xf cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive.tar.xz
cp -a cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-sbsa-${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 ${NCCL_VERSION} --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_062
ldconfig
}
function prune_124 {
echo "Pruning CUDA 12.4"
#####################################################################################
# CUDA 12.4 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.4/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.4/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$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 12.4 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/
}
function install_126 {
echo "Installing CUDA 12.6.3 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.6.3"
rm -rf /usr/local/cuda-12.6 /usr/local/cuda
# install CUDA 12.6.3 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.6.3/local_installers/cuda_12.6.3_560.35.05_linux_sbsa.run
chmod +x cuda_12.6.3_560.35.05_linux_sbsa.run
./cuda_12.6.3_560.35.05_linux_sbsa.run --toolkit --silent
rm -f cuda_12.6.3_560.35.05_linux_sbsa.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.6 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-sbsa/cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive.tar.xz
tar xf cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive.tar.xz
cp -a cudnn-linux-sbsa-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-sbsa-${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 ${NCCL_VERSION} --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_063
ldconfig
}
function prune_126 {
echo "Pruning CUDA 12.6"
#####################################################################################
# CUDA 12.6 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.6/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.6/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$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 12.6 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-12.6/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.3.2 $CUDA_BASE/nsight-systems-2024.5.1/
}
# idiomatic parameter and option handling in sh
while test $# -gt 0
do
case "$1" in
12.4) install_124; prune_124
;;
12.6) install_126; prune_126
;;
*) echo "bad argument $1"; exit 1
;;
esac
shift
done

View File

@ -0,0 +1,24 @@
#!/bin/bash
if [[ -n "${CUDNN_VERSION}" ]]; then
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn
pushd tmp_cudnn
if [[ ${CUDA_VERSION:0:4} == "12.6" ]]; then
CUDNN_NAME="cudnn-linux-x86_64-9.5.1.17_cuda12-archive"
elif [[ ${CUDA_VERSION:0:2} == "12" ]]; then
CUDNN_NAME="cudnn-linux-x86_64-9.1.0.70_cuda12-archive"
elif [[ ${CUDA_VERSION:0:2} == "11" ]]; then
CUDNN_NAME="cudnn-linux-x86_64-9.1.0.70_cuda11-archive"
else
print "Unsupported CUDA version ${CUDA_VERSION}"
exit 1
fi
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/${CUDNN_NAME}.tar.xz
tar xf ${CUDNN_NAME}.tar.xz
cp -a ${CUDNN_NAME}/include/* /usr/local/cuda/include/
cp -a ${CUDNN_NAME}/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cudnn
ldconfig
fi

View File

@ -5,23 +5,7 @@ set -ex
# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && cd tmp_cusparselt
if [[ ${CUDA_VERSION:0:4} =~ "13" ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.8.0.4_cuda13-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} =~ ^12\.[5-9]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.7.1.0-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} == "12.4" ]]; then
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[2-6]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
@ -29,8 +13,17 @@ elif [[ ${CUDA_VERSION:0:4} == "12.4" ]]; then
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.6.2.3-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
else
echo "Not sure which libcusparselt version to install for this ${CUDA_VERSION}"
elif [[ ${CUDA_VERSION:0:4} == "12.1" ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.5.2.1-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.4.0.7-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz
fi
tar xf ${CUSPARSELT_NAME}.tar.xz

38
.ci/docker/common/install_db.sh Executable file
View File

@ -0,0 +1,38 @@
#!/bin/bash
set -ex
install_ubuntu() {
apt-get update
# Cleanup
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
}
install_centos() {
# Need EPEL for many packages we depend on.
# See http://fedoraproject.org/wiki/EPEL
yum --enablerepo=extras install -y epel-release
# Cleanup
yum clean all
rm -rf /var/cache/yum
rm -rf /var/lib/yum/yumdb
rm -rf /var/lib/yum/history
}
# Install base packages depending on the base OS
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
ubuntu)
install_ubuntu
;;
centos)
install_centos
;;
*)
echo "Unable to determine OS..."
exit 1
;;
esac

View File

@ -13,7 +13,7 @@ clone_executorch() {
# and fetch the target commit
pushd executorch
git checkout "${EXECUTORCH_PINNED_COMMIT}"
git submodule update --init --recursive
git submodule update --init
popd
chown -R jenkins executorch
@ -37,12 +37,7 @@ install_conda_dependencies() {
install_pip_dependencies() {
pushd executorch
as_jenkins bash install_executorch.sh
# A workaround, ExecuTorch has moved to numpy 2.0 which is not compatible with the current
# numba and scipy version used in PyTorch CI
conda_run pip uninstall -y numba scipy
as_jenkins bash install_requirements.sh --pybind xnnpack
popd
}
@ -50,9 +45,10 @@ setup_executorch() {
pushd executorch
export PYTHON_EXECUTABLE=python
export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
export EXECUTORCH_BUILD_PYBIND=ON
export CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
as_jenkins .ci/scripts/setup-linux.sh --build-tool cmake || true
as_jenkins .ci/scripts/setup-linux.sh cmake || true
popd
}

View File

@ -17,7 +17,7 @@ if [ -n "${UBUNTU_VERSION}" ];then
libopenblas-dev libeigen3-dev libatlas-base-dev libzstd-dev
fi
pip_install numpy scipy imageio cmake ninja
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 \
@ -35,9 +35,7 @@ git clone https://github.com/halide/Halide.git
pushd Halide
git checkout ${COMMIT} && git submodule update --init --recursive
pip_install -r requirements.txt
# NOTE: pybind has a requirement for cmake > 3.5 so set the minimum cmake version here with a flag
# Context: https://github.com/pytorch/pytorch/issues/150420
cmake -G Ninja -DCMAKE_POLICY_VERSION_MINIMUM=3.5 -DCMAKE_BUILD_TYPE=Release -S . -B build
cmake -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}

View File

@ -5,42 +5,28 @@ set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
function install_huggingface() {
pip_install -r huggingface-requirements.txt
local version
commit=$(get_pinned_commit huggingface)
pip_install "git+https://github.com/huggingface/transformers@${commit}"
}
function install_timm() {
local commit
commit=$(get_pinned_commit timm)
# TODO (huydhn): There is no torchvision release on 3.13 when I write this, so
# I'm using nightly here instead. We just need to package to be able to install
# TIMM. Removing this once vision has a release on 3.13
if [[ "${ANACONDA_PYTHON_VERSION}" == "3.13" ]]; then
pip_install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cu124
fi
pip_install "git+https://github.com/huggingface/pytorch-image-models@${commit}"
}
function install_torchbench() {
local commit
commit=$(get_pinned_commit torchbench)
git clone https://github.com/pytorch/benchmark torchbench
pushd torchbench
git checkout "$commit"
python install.py --continue_on_fail
echo "Print all dependencies after TorchBench is installed"
python -mpip freeze
popd
chown -R jenkins torchbench
chown -R jenkins /opt/conda
# Clean up
conda_run pip uninstall -y cmake torch torchvision triton
}
# Pango is needed for weasyprint which is needed for doctr
conda_install pango
# Stable packages are ok here, just to satisfy TorchBench check
pip_install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
install_torchbench
install_huggingface
install_timm
# Clean up
conda_run pip uninstall -y torch torchvision torchaudio triton torchao

View File

@ -2,6 +2,8 @@
set -ex
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
if [ -n "${UBUNTU_VERSION}" ]; then
apt update
apt-get install -y clang doxygen git graphviz nodejs npm libtinfo5
@ -13,8 +15,8 @@ chown -R jenkins pytorch
pushd pytorch
# Install all linter dependencies
pip install -r requirements.txt
lintrunner init
pip_install -r requirements.txt
conda_run lintrunner init
# Cache .lintbin directory as part of the Docker image
cp -r .lintbin /tmp

View File

@ -1,23 +1,26 @@
#!/usr/bin/env bash
# Script that installs magma from tarball inside conda environment.
# It replaces anaconda magma-cuda package which is no longer published.
# Execute it inside active conda environment.
# See issue: https://github.com/pytorch/pytorch/issues/138506
# Script that replaces the magma install from a conda package
set -eou pipefail
cuda_version_nodot=${1/./}
anaconda_dir=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
function do_install() {
cuda_version_nodot=${1/./}
anaconda_python_version=$2
MAGMA_VERSION="2.6.1"
magma_archive="magma-cuda${cuda_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://ossci-linux.s3.us-east-1.amazonaws.com/${magma_archive}
tar -xvf "${magma_archive}"
mv include/* "${anaconda_dir}/include/"
mv lib/* "${anaconda_dir}/lib"
popd
)
MAGMA_VERSION="2.6.1"
magma_archive="magma-cuda${cuda_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
anaconda_dir="/opt/conda/envs/py_${anaconda_python_version}"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://ossci-linux.s3.us-east-1.amazonaws.com/${magma_archive}
tar -xvf "${magma_archive}"
mv include/* "${anaconda_dir}/include/"
mv lib/* "${anaconda_dir}/lib"
popd
)
}
do_install $1 $2

View File

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

View File

@ -4,15 +4,10 @@ set -ex
[ -n "$NINJA_VERSION" ]
arch=$(uname -m)
if [ "$arch" == "aarch64" ]; then
url="https://github.com/ninja-build/ninja/releases/download/v${NINJA_VERSION}/ninja-linux-aarch64.zip"
else
url="https://github.com/ninja-build/ninja/releases/download/v${NINJA_VERSION}/ninja-linux.zip"
fi
url="https://github.com/ninja-build/ninja/releases/download/v${NINJA_VERSION}/ninja-linux.zip"
pushd /tmp
wget --no-verbose --output-document=ninja-linux.zip "$url"
unzip ninja-linux.zip -d /usr/local/bin
rm -f ninja-linux.zip
popd
popd

View File

@ -8,6 +8,16 @@ retry () {
"$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
}
# A bunch of custom pip dependencies for ONNX
pip_install \
beartype==0.15.0 \
filelock==3.9.0 \
flatbuffers==2.0 \
mock==5.0.1 \
ninja==1.10.2 \
networkx==2.5 \
numpy==1.24.2
# ONNXRuntime should be installed before installing
# onnx-weekly. Otherwise, onnx-weekly could be
# overwritten by onnx.
@ -19,13 +29,17 @@ pip_install \
transformers==4.36.2
pip_install coloredlogs packaging
pip_install onnxruntime==1.22.1
pip_install onnxscript==0.3.1
pip_install onnxruntime==1.18.1
pip_install onnx==1.16.2
pip_install onnxscript==0.1.0.dev20241124 --no-deps
# required by onnxscript
pip_install ml_dtypes
# Cache the transformers model to be used later by ONNX tests. We need to run the transformers
# package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/
IMPORT_SCRIPT_FILENAME="/tmp/onnx_import_script.py"
as_jenkins echo 'import transformers; transformers.GPTJForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gptj");' > "${IMPORT_SCRIPT_FILENAME}"
as_jenkins echo 'import transformers; transformers.AutoModel.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3");' > "${IMPORT_SCRIPT_FILENAME}"
# Need a PyTorch version for transformers to work
pip_install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu

View File

@ -4,9 +4,9 @@
set -ex
cd /
git clone https://github.com/OpenMathLib/OpenBLAS.git -b "${OPENBLAS_VERSION:-v0.3.30}" --depth 1 --shallow-submodules
git clone https://github.com/OpenMathLib/OpenBLAS.git -b v0.3.28 --depth 1 --shallow-submodules
OPENBLAS_CHECKOUT_DIR="OpenBLAS"
OPENBLAS_BUILD_FLAGS="
NUM_THREADS=128
USE_OPENMP=1
@ -14,8 +14,9 @@ NO_SHARED=0
DYNAMIC_ARCH=1
TARGET=ARMV8
CFLAGS=-O3
BUILD_BFLOAT16=1
"
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

@ -0,0 +1,19 @@
#!/bin/bash
set -ex
pb_dir="/usr/temp_pb_install_dir"
mkdir -p $pb_dir
# On the nvidia/cuda:9-cudnn7-devel-centos7 image we need this symlink or
# else it will fail with
# g++: error: ./../lib64/crti.o: No such file or directory
ln -s /usr/lib64 "$pb_dir/lib64"
curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz" --retry 3
tar -xvz --no-same-owner -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
NPROC=$[$(nproc) - 2]
pushd "$pb_dir" && ./configure && make -j${NPROC} && make -j${NPROC} check && sudo make -j${NRPOC} install && sudo ldconfig
popd
rm -rf $pb_dir

View File

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

View File

@ -8,11 +8,13 @@ ver() {
install_ubuntu() {
apt-get update
# gpg-agent is not available by default
apt-get install -y --no-install-recommends gpg-agent
if [[ $(ver $UBUNTU_VERSION) -ge $(ver 22.04) ]]; then
echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' \
| sudo tee /etc/apt/preferences.d/rocm-pin-600
if [[ $UBUNTU_VERSION == 18.04 ]]; then
# gpg-agent is not available by default on 18.04
apt-get install -y --no-install-recommends gpg-agent
fi
if [[ $UBUNTU_VERSION == 20.04 ]]; then
# gpg-agent is not available by default on 20.04
apt-get install -y --no-install-recommends gpg-agent
fi
apt-get install -y kmod
apt-get install -y wget
@ -21,34 +23,13 @@ install_ubuntu() {
apt-get install -y libc++1
apt-get install -y libc++abi1
# Make sure rocm packages from repo.radeon.com have highest priority
cat << EOF > /etc/apt/preferences.d/rocm-pin-600
Package: *
Pin: release o=repo.radeon.com
Pin-Priority: 600
EOF
# we want the patch version of 6.4 instead
if [[ $(ver $ROCM_VERSION) -eq $(ver 6.4) ]]; then
ROCM_VERSION="${ROCM_VERSION}.2"
fi
# Default url values
rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu"
# Special case for ROCM_VERSION == 7.0
if [[ $(ver "$ROCM_VERSION") -eq $(ver 7.0) ]]; then
rocm_baseurl="https://repo.radeon.com/rocm/apt/7.0_alpha2"
amdgpu_baseurl="https://repo.radeon.com/amdgpu/30.10_alpha2/ubuntu"
fi
# Add amdgpu repository
UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
echo "deb [arch=amd64] https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
# Add rocm repository
wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
echo "deb [arch=amd64] ${rocm_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/rocm.list
apt-get update --allow-insecure-repositories
@ -81,38 +62,6 @@ EOF
sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
done
# ROCm 6.3 had a regression where initializing static code objects had significant overhead
# CI no longer builds for ROCm 6.3, but
# ROCm 6.4 did not yet fix the regression, also HIP branch names are different
if [[ $(ver $ROCM_VERSION) -ge $(ver 6.4) ]] && [[ $(ver $ROCM_VERSION) -lt $(ver 7.0) ]]; then
if [[ $(ver $ROCM_VERSION) -eq $(ver 6.4.2) ]]; then
HIP_TAG=rocm-6.4.2
CLR_HASH=74d78ba3ac4bac235d02bcb48511c30b5cfdd457 # branch release/rocm-rel-6.4.2-statco-hotfix
elif [[ $(ver $ROCM_VERSION) -eq $(ver 6.4.1) ]]; then
HIP_TAG=rocm-6.4.1
CLR_HASH=efe6c35790b9206923bfeed1209902feff37f386 # branch release/rocm-rel-6.4.1-statco-hotfix
elif [[ $(ver $ROCM_VERSION) -eq $(ver 6.4) ]]; then
HIP_TAG=rocm-6.4.0
CLR_HASH=600f5b0d2baed94d5121e2174a9de0851b040b0c # branch release/rocm-rel-6.4-statco-hotfix
fi
# clr build needs CppHeaderParser but can only find it using conda's python
python -m pip install CppHeaderParser
git clone https://github.com/ROCm/HIP -b $HIP_TAG
HIP_COMMON_DIR=$(readlink -f HIP)
git clone https://github.com/jeffdaily/clr
pushd clr
git checkout $CLR_HASH
popd
mkdir -p clr/build
pushd clr/build
# Need to point CMake to the correct python installation to find CppHeaderParser
cmake .. -DPython3_EXECUTABLE=/opt/conda/envs/py_${ANACONDA_PYTHON_VERSION}/bin/python3 -DCLR_BUILD_HIP=ON -DHIP_COMMON_DIR=$HIP_COMMON_DIR
make -j
cp hipamd/lib/libamdhip64.so.6.4.* /opt/rocm/lib/libamdhip64.so.6.4.*
popd
rm -rf HIP clr
fi
# Cleanup
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

View File

@ -115,7 +115,7 @@ index a5007ffc..13fa07fc 100644
if (!fp) {
- fprintf(stderr, "%s: %s\n", AMDGPU_ASIC_ID_TABLE,
- strerror(errno));
+ //fprintf(stderr, "amdgpu.ids: No such file or directory\n");
+ fprintf(stderr, "amdgpu.ids: No such file or directory\n");
return;
}

View File

@ -1,37 +1,50 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
#!/bin/bash
# Script used in CI and CD pipeline
set -eou pipefail
set -ex
function do_install() {
rocm_version=$1
if [[ ${rocm_version} =~ ^[0-9]+\.[0-9]+\.[0-9]+$ ]]; then
# chop off any patch version
rocm_version="${rocm_version%.*}"
fi
# Magma build scripts need `python`
ln -sf /usr/bin/python3 /usr/bin/python
rocm_version_nodot=${rocm_version//./}
ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
case "$ID" in
almalinux)
yum install -y gcc-gfortran
;;
*)
echo "No preinstalls to build magma..."
;;
esac
# Version 2.7.2 + ROCm related updates
MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
MKLROOT=${MKLROOT:-/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION}
rocm_dir="/opt/rocm"
(
set -x
tmp_dir=$(mktemp -d)
pushd ${tmp_dir}
curl -OLs https://ossci-linux.s3.us-east-1.amazonaws.com/${magma_archive}
if tar -xvf "${magma_archive}"
then
mkdir -p "${rocm_dir}/magma"
mv include "${rocm_dir}/magma/include"
mv lib "${rocm_dir}/magma/lib"
else
echo "${magma_archive} not found, skipping magma install"
fi
popd
)
}
# "install" hipMAGMA into /opt/rocm/magma by copying after build
git clone https://bitbucket.org/icl/magma.git
pushd magma
do_install $1
# Version 2.7.2 + ROCm related updates
git checkout a1625ff4d9bc362906bd01f805dbbe12612953f6
cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
echo 'LIBDIR += -L$(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 'DEVCCFLAGS += --gpu-max-threads-per-block=256' >> make.inc
export PATH="${PATH}:/opt/rocm/bin"
if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then
amdgpu_targets=`echo $PYTORCH_ROCM_ARCH | sed 's/;/ /g'`
else
amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
fi
for arch in $amdgpu_targets; do
echo "DEVCCFLAGS += --offload-arch=$arch" >> make.inc
done
# hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
sed -i 's/^FOPENMP/#FOPENMP/g' make.inc
make -f make.gen.hipMAGMA -j $(nproc)
LANG=C.UTF-8 make lib/libmagma.so -j $(nproc) MKLROOT="${MKLROOT}"
make testing/testing_dgemm -j $(nproc) MKLROOT="${MKLROOT}"
popd
mv magma /opt/rocm

View File

@ -0,0 +1,24 @@
#!/bin/bash
set -ex
[ -n "${SWIFTSHADER}" ]
retry () {
$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
}
_https_amazon_aws=https://ossci-android.s3.amazonaws.com
# SwiftShader
_swiftshader_dir=/var/lib/jenkins/swiftshader
_swiftshader_file_targz=swiftshader-abe07b943-prebuilt.tar.gz
mkdir -p $_swiftshader_dir
_tmp_swiftshader_targz="/tmp/${_swiftshader_file_targz}"
curl --silent --show-error --location --fail --retry 3 \
--output "${_tmp_swiftshader_targz}" "$_https_amazon_aws/${_swiftshader_file_targz}"
tar -C "${_swiftshader_dir}" -xzf "${_tmp_swiftshader_targz}"
export VK_ICD_FILENAMES="${_swiftshader_dir}/build/Linux/vk_swiftshader_icd.json"

View File

@ -2,16 +2,14 @@
set -ex
mkdir -p /opt/triton
if [ -z "${TRITON}" ] && [ -z "${TRITON_CPU}" ]; then
echo "TRITON and TRITON_CPU are not set. Exiting..."
exit 0
fi
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
get_pip_version() {
conda_run pip list | grep -w $* | head -n 1 | awk '{print $2}'
get_conda_version() {
as_jenkins conda list -n py_$ANACONDA_PYTHON_VERSION | grep -w $* | head -n 1 | awk '{print $2}'
}
conda_reinstall() {
as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y --force-reinstall $*
}
if [ -n "${XPU_VERSION}" ]; then
@ -33,9 +31,11 @@ if [ -n "${UBUNTU_VERSION}" ];then
apt-get install -y gpg-agent
fi
# Keep the current cmake and numpy version here, so we can reinstall them later
CMAKE_VERSION=$(get_pip_version cmake)
NUMPY_VERSION=$(get_pip_version numpy)
if [ -n "${CONDA_CMAKE}" ]; then
# Keep the current cmake and numpy version here, so we can reinstall them later
CMAKE_VERSION=$(get_conda_version cmake)
NUMPY_VERSION=$(get_conda_version numpy)
fi
if [ -z "${MAX_JOBS}" ]; then
export MAX_JOBS=$(nproc)
@ -51,13 +51,7 @@ as_jenkins git clone --recursive ${TRITON_REPO} triton
cd triton
as_jenkins git checkout ${TRITON_PINNED_COMMIT}
as_jenkins git submodule update --init --recursive
# Old versions of python have setup.py in ./python; newer versions have it in ./
if [ ! -f setup.py ]; then
cd python
fi
pip_install pybind11==2.13.6
cd python
# TODO: remove patch setup.py once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
as_jenkins sed -i -e 's/https:\/\/tritonlang.blob.core.windows.net\/llvm-builds/https:\/\/oaitriton.blob.core.windows.net\/public\/llvm-builds/g' setup.py
@ -66,42 +60,28 @@ if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}"
# Triton needs at least gcc-9 to build
apt-get install -y g++-9
CXX=g++-9 conda_run python setup.py bdist_wheel
CXX=g++-9 pip_install -e .
elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
# Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
add-apt-repository -y ppa:ubuntu-toolchain-r/test
apt-get install -y g++-9
CXX=g++-9 conda_run python setup.py bdist_wheel
CXX=g++-9 pip_install -e .
else
conda_run python setup.py bdist_wheel
pip_install -e .
fi
# Copy the wheel to /opt for multi stage docker builds
cp dist/*.whl /opt/triton
# Install the wheel for docker builds that don't use multi stage
pip_install dist/*.whl
# TODO: This is to make sure that the same cmake and numpy version from install conda
# script is used. Without this step, the newer cmake version (3.25.2) downloaded by
# triton build step via pip will fail to detect conda MKL. Once that issue is fixed,
# this can be removed.
#
# The correct numpy version also needs to be set here because conda claims that it
# causes inconsistent environment. Without this, conda will attempt to install the
# latest numpy version, which fails ASAN tests with the following import error: Numba
# needs NumPy 1.20 or less.
# Note that we install numpy with pip as conda might not have the version we want
if [ -n "${CMAKE_VERSION}" ]; then
pip_install "cmake==${CMAKE_VERSION}"
fi
if [ -n "${NUMPY_VERSION}" ]; then
pip_install "numpy==${NUMPY_VERSION}"
fi
# IMPORTANT: helion needs to be installed without dependencies.
# It depends on torch and triton. We don't want to install
# triton and torch from production on Docker CI images
if [[ "$ANACONDA_PYTHON_VERSION" != 3.9* ]]; then
pip_install helion --no-deps
if [ -n "${CONDA_CMAKE}" ]; then
# TODO: This is to make sure that the same cmake and numpy version from install conda
# script is used. Without this step, the newer cmake version (3.25.2) downloaded by
# triton build step via pip will fail to detect conda MKL. Once that issue is fixed,
# this can be removed.
#
# The correct numpy version also needs to be set here because conda claims that it
# causes inconsistent environment. Without this, conda will attempt to install the
# latest numpy version, which fails ASAN tests with the following import error: Numba
# needs NumPy 1.20 or less.
conda_reinstall cmake="${CMAKE_VERSION}"
# Note that we install numpy with pip as conda might not have the version we want
pip_install --force-reinstall numpy=="${NUMPY_VERSION}"
fi

View File

@ -8,12 +8,6 @@ else
with_cuda=no
fi
if [[ -d "/opt/rocm" ]]; then
with_rocm=/opt/rocm
else
with_rocm=no
fi
function install_ucx() {
set -ex
git clone --recursive https://github.com/openucx/ucx.git
@ -25,7 +19,6 @@ function install_ucx() {
./configure --prefix=$UCX_HOME \
--enable-mt \
--with-cuda=$with_cuda \
--with-rocm=$with_rocm \
--enable-profiling \
--enable-stats
time make -j
@ -43,29 +36,12 @@ function install_ucc() {
git submodule update --init --recursive
./autogen.sh
# We only run distributed tests on Tesla M60 and A10G
NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
if [[ -n "$ROCM_VERSION" ]]; then
if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then
amdgpu_targets=`echo $PYTORCH_ROCM_ARCH | sed 's/;/ /g'`
else
amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
fi
for arch in $amdgpu_targets; do
HIP_OFFLOAD="$HIP_OFFLOAD --offload-arch=$arch"
done
else
HIP_OFFLOAD="all-arch-no-native"
fi
./configure --prefix=$UCC_HOME \
--with-ucx=$UCX_HOME \
--with-cuda=$with_cuda \
--with-nvcc-gencode="${NVCC_GENCODE}" \
--with-rocm=$with_rocm \
--with-rocm-arch="${HIP_OFFLOAD}"
--with-nvcc-gencode="${NVCC_GENCODE}"
time make -j
sudo make install

View File

@ -0,0 +1,24 @@
#!/bin/bash
set -ex
[ -n "${VULKAN_SDK_VERSION}" ]
retry () {
$* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
}
_vulkansdk_dir=/var/lib/jenkins/vulkansdk
_tmp_vulkansdk_targz=/tmp/vulkansdk.tar.gz
curl \
--silent \
--show-error \
--location \
--fail \
--retry 3 \
--output "${_tmp_vulkansdk_targz}" "https://ossci-android.s3.amazonaws.com/vulkansdk-linux-x86_64-${VULKAN_SDK_VERSION}.tar.gz"
mkdir -p "${_vulkansdk_dir}"
tar -C "${_vulkansdk_dir}" -xzf "${_tmp_vulkansdk_targz}" --strip-components 1
rm -rf "${_tmp_vulkansdk_targz}"

View File

@ -26,7 +26,7 @@ function install_ubuntu() {
wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
| gpg --dearmor > /usr/share/keyrings/oneapi-archive-keyring.gpg.gpg
echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg.gpg] \
https://apt.repos.intel.com/oneapi all main" \
https://apt.repos.intel.com/${XPU_REPO_NAME} all main" \
| tee /etc/apt/sources.list.d/oneAPI.list
# Update the packages list and repository index
@ -34,27 +34,18 @@ function install_ubuntu() {
# The xpu-smi packages
apt-get install -y flex bison xpu-smi
if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
# Compute and Media Runtimes
apt-get install -y \
intel-opencl-icd intel-level-zero-gpu level-zero \
intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
# Development Packages
apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
else # rolling driver
apt-get install -y \
intel-opencl-icd libze-intel-gpu1 libze1 \
intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc
apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev
# Compute and Media Runtimes
apt-get install -y \
intel-opencl-icd intel-level-zero-gpu level-zero \
intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
if [[ "${XPU_DRIVER_TYPE,,}" == "rolling" ]]; then
apt-get install -y intel-ocloc
fi
# Development Packages
apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
# Install Intel Support Packages
apt-get install -y ${XPU_PACKAGES}
@ -65,10 +56,14 @@ function install_ubuntu() {
function install_rhel() {
. /etc/os-release
if [[ ! " 8.8 8.10 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
echo "RHEL version ${VERSION_ID} not supported"
exit
if [[ "${ID}" == "rhel" ]]; then
if [[ ! " 8.8 8.9 9.0 9.2 9.3 " =~ " ${VERSION_ID} " ]]; then
echo "RHEL version ${VERSION_ID} not supported"
exit
fi
elif [[ "${ID}" == "almalinux" ]]; then
# Workaround for almalinux8 which used by quay.io/pypa/manylinux_2_28_x86_64
VERSION_ID="8.8"
fi
dnf install -y 'dnf-command(config-manager)'
@ -79,7 +74,7 @@ function install_rhel() {
tee > /etc/yum.repos.d/oneAPI.repo << EOF
[oneAPI]
name=Intel for Pytorch GPU dev repository
baseurl=https://yum.repos.intel.com/oneapi
baseurl=https://yum.repos.intel.com/${XPU_REPO_NAME}
enabled=1
gpgcheck=1
repo_gpgcheck=1
@ -123,7 +118,7 @@ function install_sles() {
https://repositories.intel.com/gpu/sles/${VERSION_SP}${XPU_DRIVER_VERSION}/unified/intel-gpu-${VERSION_SP}.repo
rpm --import https://repositories.intel.com/gpu/intel-graphics.key
# To add the online network network package repository for the Intel Support Packages
zypper addrepo https://yum.repos.intel.com/oneapi oneAPI
zypper addrepo https://yum.repos.intel.com/${XPU_REPO_NAME} oneAPI
rpm --import https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
# The xpu-smi packages
@ -139,17 +134,17 @@ function install_sles() {
}
# Default use GPU driver rolling releases
XPU_DRIVER_VERSION=""
if [[ "${XPU_DRIVER_TYPE,,}" == "lts" ]]; then
# Use GPU driver LTS releases
XPU_DRIVER_VERSION="/lts/2350"
# Default use GPU driver LTS releases
XPU_DRIVER_VERSION="/lts/2350"
if [[ "${XPU_DRIVER_TYPE,,}" == "rolling" ]]; then
# Use GPU driver rolling releases
XPU_DRIVER_VERSION=""
fi
# Default use Intel® oneAPI Deep Learning Essentials 2025.0
if [[ "$XPU_VERSION" == "2025.1" ]]; then
XPU_PACKAGES="intel-deep-learning-essentials-2025.1"
else
XPU_REPO_NAME="intel-for-pytorch-gpu-dev"
XPU_PACKAGES="intel-for-pytorch-gpu-dev-0.5 intel-pti-dev-0.9"
if [[ "$XPU_VERSION" == "2025.0" ]]; then
XPU_REPO_NAME="oneapi"
XPU_PACKAGES="intel-deep-learning-essentials-2025.0"
fi

View File

@ -49,28 +49,29 @@ RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM cpu as cuda
ADD ./common/install_cuda.sh install_cuda.sh
ADD ./common/install_magma.sh install_magma.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
ENV CUDA_HOME /usr/local/cuda
FROM cuda as 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 cuda as cuda12.6
RUN bash ./install_cuda.sh 12.6
RUN bash ./install_magma.sh 12.6
RUN ln -sf /usr/local/cuda-12.6 /usr/local/cuda
FROM cuda as cuda12.8
RUN bash ./install_cuda.sh 12.8
RUN bash ./install_magma.sh 12.8
RUN ln -sf /usr/local/cuda-12.8 /usr/local/cuda
FROM cuda as cuda12.9
RUN bash ./install_cuda.sh 12.9
RUN bash ./install_magma.sh 12.9
RUN ln -sf /usr/local/cuda-12.9 /usr/local/cuda
FROM cpu as rocm
ARG ROCM_VERSION
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ENV MKLROOT /opt/intel
@ -85,11 +86,18 @@ ADD ./common/install_rocm_magma.sh install_rocm_magma.sh
# gfortran and python needed for building magma from source for ROCm
RUN apt-get update -y && \
apt-get install gfortran -y && \
apt-get install python3 python-is-python3 -y && \
apt-get install python -y && \
apt-get clean
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION} && rm install_rocm_magma.sh
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

View File

@ -1,67 +1,83 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -eoux pipefail
set -eou pipefail
image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGENAME:ARCHTAG"
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=${DOCKER:-docker}
# Go from imagename:tag to tag
DOCKER_TAG_PREFIX=$(echo "${image}" | awk -F':' '{print $2}')
GPU_ARCH_VERSION=""
if [[ "${DOCKER_TAG_PREFIX}" == cuda* ]]; then
# extract cuda version from image name. e.g. manylinux2_28-builder:cuda12.8 returns 12.8
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'cuda' '{print $2}')
elif [[ "${DOCKER_TAG_PREFIX}" == rocm* ]]; then
# extract rocm version from image name. e.g. manylinux2_28-builder:rocm6.2.4 returns 6.2.4
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'rocm' '{print $2}')
fi
case ${DOCKER_TAG_PREFIX} in
case ${GPU_ARCH_TYPE} in
cpu)
BASE_TARGET=cpu
DOCKER_TAG=cpu
GPU_IMAGE=ubuntu:20.04
DOCKER_GPU_BUILD_ARG=""
;;
cuda*)
cuda)
BASE_TARGET=cuda${GPU_ARCH_VERSION}
DOCKER_TAG=cuda${GPU_ARCH_VERSION}
GPU_IMAGE=ubuntu:20.04
DOCKER_GPU_BUILD_ARG=""
;;
rocm*)
# we want the patch version of 6.4 instead
if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
fi
rocm)
BASE_TARGET=rocm
GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
DOCKER_TAG=rocm${GPU_ARCH_VERSION}
GPU_IMAGE=rocm/dev-ubuntu-20.04:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx1030;gfx1100;gfx1101;gfx942"
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
;;
*)
echo "ERROR: Unrecognized DOCKER_TAG_PREFIX: ${DOCKER_TAG_PREFIX}"
echo "ERROR: Unrecognized GPU_ARCH_TYPE: ${GPU_ARCH_TYPE}"
exit 1
;;
esac
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
DOCKER_BUILDKIT=1 ${DOCKER} build \
--target final \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--build-arg "BASE_TARGET=${BASE_TARGET}" \
-t "${tmp_tag}" \
$@ \
-f "${TOPDIR}/.ci/docker/libtorch/Dockerfile" \
"${TOPDIR}/.ci/docker/"
(
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

@ -18,31 +18,28 @@ COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG PYTHON_VERSION
ARG PIP_CMAKE
# Put venv into the env vars so users don't need to activate it
ENV PATH /var/lib/jenkins/ci_env/bin:$PATH
ENV VIRTUAL_ENV /var/lib/jenkins/ci_env
COPY requirements-ci.txt /opt/requirements-ci.txt
COPY ./common/install_python.sh install_python.sh
RUN bash ./install_python.sh && rm install_python.sh /opt/requirements-ci.txt
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ./common/install_magma_conda.sh install_magma_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh install_magma_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# Install cuda and cudnn
ARG CUDA_VERSION
COPY ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu* install_cusparselt.sh
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
# Note that Docker build forbids copying file outside the build context
COPY ./common/install_linter.sh install_linter.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_linter.sh
RUN rm install_linter.sh
RUN chown -R jenkins:jenkins /var/lib/jenkins/ci_env
RUN rm install_linter.sh common_utils.sh
USER jenkins
CMD ["bash"]

View File

@ -15,19 +15,20 @@ COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG PYTHON_VERSION
ENV PATH /var/lib/jenkins/ci_env/bin:$PATH
ENV VIRTUAL_ENV /var/lib/jenkins/ci_env
COPY requirements-ci.txt /opt/requirements-ci.txt
COPY ./common/install_python.sh install_python.sh
RUN bash ./install_python.sh && rm install_python.sh /opt/requirements-ci.txt
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# Note that Docker build forbids copying file outside the build context
COPY ./common/install_linter.sh install_linter.sh
COPY ./common/common_utils.sh common_utils.sh
RUN bash ./install_linter.sh
RUN rm install_linter.sh
RUN chown -R jenkins:jenkins /var/lib/jenkins/ci_env
RUN rm install_linter.sh common_utils.sh
USER jenkins
CMD ["bash"]

View File

@ -0,0 +1,207 @@
# 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
# Install Anaconda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
ENV PATH /opt/conda/bin:$PATH
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

View File

@ -0,0 +1,153 @@
# 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

View File

@ -7,8 +7,8 @@ ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
ARG DEVTOOLSET_VERSION=13
RUN yum install -y sudo wget curl perl util-linux xz bzip2 git patch which perl zlib-devel yum-utils gcc-toolset-${DEVTOOLSET_VERSION}-gcc gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran gcc-toolset-${DEVTOOLSET_VERSION}-gdb
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
@ -26,20 +26,17 @@ ADD ./common/install_openssl.sh install_openssl.sh
RUN bash ./install_openssl.sh && rm install_openssl.sh
# remove unnecessary python versions
# 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=12.6
ARG BASE_CUDA_VERSION=11.8
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh install_nccl.sh ci_commit_pins/nccl-cu* install_cusparselt.sh
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh
FROM base as intel
# MKL
@ -47,7 +44,7 @@ ADD ./common/install_mkl.sh install_mkl.sh
RUN bash ./install_mkl.sh && rm install_mkl.sh
FROM base as magma
ARG BASE_CUDA_VERSION=12.6
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
@ -64,7 +61,7 @@ ADD ./common/install_libpng.sh install_libpng.sh
RUN bash ./install_libpng.sh && rm install_libpng.sh
FROM ${GPU_IMAGE} as common
ARG DEVTOOLSET_VERSION=13
ARG DEVTOOLSET_VERSION=11
ENV LC_ALL en_US.UTF-8
ENV LANG en_US.UTF-8
ENV LANGUAGE en_US.UTF-8
@ -87,12 +84,13 @@ RUN yum install -y \
wget \
which \
xz \
glibc-langpack-en \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran \
gcc-toolset-${DEVTOOLSET_VERSION}-gdb
gcc-toolset-${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
@ -103,7 +101,6 @@ ENV SSL_CERT_FILE=/opt/_internal/certs.pem
# Install LLVM version
COPY --from=openssl /opt/openssl /opt/openssl
COPY --from=base /opt/python /opt/python
COPY --from=base /usr/local/lib/ /usr/local/lib/
COPY --from=base /opt/_internal /opt/_internal
COPY --from=base /usr/local/bin/auditwheel /usr/local/bin/auditwheel
COPY --from=intel /opt/intel /opt/intel
@ -117,8 +114,8 @@ COPY --from=libpng /usr/local/lib/pkgconfig /usr/local/
COPY --from=jni /usr/local/include/jni.h /usr/local/include/jni.h
FROM common as cpu_final
ARG BASE_CUDA_VERSION=12.6
ARG DEVTOOLSET_VERSION=13
ARG BASE_CUDA_VERSION=11.8
ARG DEVTOOLSET_VERSION=11
# Install Anaconda
ADD ./common/install_conda_docker.sh install_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh
@ -157,14 +154,11 @@ ENV ROCM_PATH /opt/rocm
# and avoid 3.21.0 cmake+ninja issues with ninja inserting "-Wl,--no-as-needed" in LINK_FLAGS for static linker
RUN python3 -m pip install --upgrade pip && \
python3 -mpip install cmake==3.28.4
# replace the libdrm in /opt/amdgpu with custom amdgpu.ids lookup path
ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
# ROCm 6.4 rocm-smi depends on system drm.h header
RUN yum install -y libdrm-devel
ENV MKLROOT /opt/intel
ADD ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION} && rm install_rocm_magma.sh
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
@ -175,6 +169,6 @@ ENV XPU_DRIVER_TYPE ROLLING
RUN python3 -m pip install --upgrade pip && \
python3 -mpip install cmake==3.28.4
ADD ./common/install_xpu.sh install_xpu.sh
ENV XPU_VERSION 2025.1
ENV XPU_VERSION 2025.0
RUN bash ./install_xpu.sh && rm install_xpu.sh
RUN pushd /opt/_internal && tar -xJf static-libs-for-embedding-only.tar.xz && popd

View File

@ -1,8 +1,9 @@
FROM quay.io/pypa/manylinux_2_28_aarch64 as base
ARG GCCTOOLSET_VERSION=13
# Graviton needs GCC 10 or above for the build. GCC12 is the default version in almalinux-8.
ARG GCCTOOLSET_VERSION=11
# Language variables
# Language variabes
ENV LC_ALL=en_US.UTF-8
ENV LANG=en_US.UTF-8
ENV LANGUAGE=en_US.UTF-8
@ -35,16 +36,7 @@ RUN yum install -y \
yasm \
zstd \
sudo \
gcc-toolset-${GCCTOOLSET_VERSION}-gcc \
gcc-toolset-${GCCTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${GCCTOOLSET_VERSION}-gcc-gfortran \
gcc-toolset-${GCCTOOLSET_VERSION}-gdb
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
gcc-toolset-${GCCTOOLSET_VERSION}-toolchain
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/bin:$PATH
@ -58,13 +50,12 @@ RUN git config --global --add safe.directory "*"
FROM base as openblas
# Install openblas
ARG OPENBLAS_VERSION
ADD ./common/install_openblas.sh install_openblas.sh
RUN bash ./install_openblas.sh && rm install_openblas.sh
FROM base as final
# remove unnecessary python versions
# 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

View File

@ -0,0 +1,94 @@
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_10.5.0-4ubuntu2_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

View File

@ -1,7 +1,7 @@
FROM quay.io/pypa/manylinux_2_28_aarch64 as base
# Cuda ARM build needs gcc 11
ARG DEVTOOLSET_VERSION=13
ARG DEVTOOLSET_VERSION=11
# Language variables
ENV LC_ALL=en_US.UTF-8
@ -34,10 +34,7 @@ RUN yum install -y \
zstd \
libgomp \
sudo \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-c++ \
gcc-toolset-${DEVTOOLSET_VERSION}-gcc-gfortran \
gcc-toolset-${DEVTOOLSET_VERSION}-gdb
gcc-toolset-${DEVTOOLSET_VERSION}-toolchain
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
@ -60,7 +57,7 @@ RUN bash ./install_openssl.sh && rm install_openssl.sh
ENV SSL_CERT_FILE=/opt/_internal/certs.pem
FROM openssl as final
# remove unnecessary python versions
# 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
@ -69,11 +66,8 @@ RUN rm -rf /opt/python/cp34-cp34m /opt/_internal/cpython-3.4.6
FROM base as cuda
ARG BASE_CUDA_VERSION
# Install CUDA
ADD ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./common/install_cusparselt.sh install_cusparselt.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh install_nccl.sh ci_commit_pins/nccl-cu* install_cusparselt.sh
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

View File

@ -5,9 +5,7 @@ ENV LC_ALL=C.UTF-8
ENV LANG=C.UTF-8
ENV LANGUAGE=C.UTF-8
# there is a bugfix in gcc >= 14 for precompiled headers and s390x vectorization interaction.
# with earlier gcc versions test/inductor/test_cpu_cpp_wrapper.py will fail.
ARG DEVTOOLSET_VERSION=14
ARG DEVTOOLSET_VERSION=13
# Installed needed OS packages. This is to support all
# the binary builds (torch, vision, audio, text, data)
RUN yum -y install epel-release
@ -44,7 +42,6 @@ RUN yum install -y \
llvm-devel \
libzstd-devel \
python3.12-devel \
python3.12-test \
python3.12-setuptools \
python3.12-pip \
python3-virtualenv \
@ -60,8 +57,7 @@ RUN yum install -y \
libxslt-devel \
libxml2-devel \
openssl-devel \
valgrind \
ninja-build
valgrind
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
@ -105,37 +101,24 @@ CMD ["/bin/bash"]
# install test dependencies:
# - grpcio requires system openssl, bundled crypto fails to build
# - ml_dtypes 0.4.0 requires some fixes provided in later commits to build
RUN dnf install -y \
hdf5-devel \
python3-h5py \
git
protobuf-devel \
protobuf-c-devel \
protobuf-lite-devel \
wget \
patch
RUN env GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=True pip3 install grpcio
# cmake-3.28.0 from pip for onnxruntime
RUN python3 -mpip install cmake==3.28.0
# build onnxruntime 1.21.0 from sources.
# it is not possible to build it from sources using pip,
# so just build it from upstream repository.
# h5py is dependency of onnxruntime_training.
# h5py==3.11.0 builds with hdf5-devel 1.10.5 from repository.
# h5py 3.11.0 doesn't build with numpy >= 2.3.0.
# install newest flatbuffers version first:
# for some reason old version is getting pulled in otherwise.
# packaging package is required for onnxruntime wheel build.
RUN pip3 install flatbuffers && \
pip3 install cython 'pkgconfig>=1.5.5' 'setuptools>=77' 'numpy<2.3.0' && \
pip3 install --no-build-isolation h5py==3.11.0 && \
pip3 install packaging && \
git clone https://github.com/microsoft/onnxruntime && \
cd onnxruntime && git checkout v1.21.0 && \
RUN env GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=True pip3 install grpcio==1.65.4
RUN cd ~ && \
git clone https://github.com/jax-ml/ml_dtypes && \
cd ml_dtypes && \
git checkout v0.4.0 && \
git submodule update --init --recursive && \
wget https://github.com/microsoft/onnxruntime/commit/f57db79743c4d1a3553aa05cf95bcd10966030e6.patch && \
patch -p1 < f57db79743c4d1a3553aa05cf95bcd10966030e6.patch && \
./build.sh --config Release --parallel 0 --enable_pybind \
--build_wheel --enable_training --enable_training_apis \
--enable_training_ops --skip_tests --allow_running_as_root \
--compile_no_warning_as_error && \
pip3 install ./build/Linux/Release/dist/onnxruntime_training-*.whl && \
cd .. && /bin/rm -rf ./onnxruntime
wget https://github.com/jax-ml/ml_dtypes/commit/b969f76914d6b30676721bc92bf0f6021a0d1321.patch && \
wget https://github.com/jax-ml/ml_dtypes/commit/d4e6d035ecda073eab8bcf60f4eef572ee7087e6.patch && \
patch -p1 < b969f76914d6b30676721bc92bf0f6021a0d1321.patch && \
patch -p1 < d4e6d035ecda073eab8bcf60f4eef572ee7087e6.patch && \
python3 setup.py bdist_wheel && \
pip3 install dist/*.whl && \
rm -rf ml_dtypes

View File

@ -1,7 +1,7 @@
#!/usr/bin/env bash
# Script used only in CD pipeline
set -exou pipefail
set -eou pipefail
TOPDIR=$(git rev-parse --show-toplevel)
@ -9,115 +9,151 @@ image="$1"
shift
if [ -z "${image}" ]; then
echo "Usage: $0 IMAGE:ARCHTAG"
echo "Usage: $0 IMAGE"
exit 1
fi
# Go from imagename:tag to tag
DOCKER_TAG_PREFIX=$(echo "${image}" | awk -F':' '{print $2}')
DOCKER_IMAGE="pytorch/${image}"
GPU_ARCH_VERSION=""
if [[ "${DOCKER_TAG_PREFIX}" == cuda* ]]; then
# extract cuda version from image name. e.g. manylinux2_28-builder:cuda12.8 returns 12.8
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'cuda' '{print $2}')
elif [[ "${DOCKER_TAG_PREFIX}" == rocm* ]]; then
# extract rocm version from image name. e.g. manylinux2_28-builder:rocm6.2.4 returns 6.2.4
GPU_ARCH_VERSION=$(echo "${DOCKER_TAG_PREFIX}" | awk -F'rocm' '{print $2}')
fi
DOCKER_REGISTRY="${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:-}
OPENBLAS_VERSION=${OPENBLAS_VERSION:-}
WITH_PUSH=${WITH_PUSH:-}
case ${image} in
manylinux2_28-builder:cpu)
case ${GPU_ARCH_TYPE} in
cpu)
TARGET=cpu_final
DOCKER_TAG=cpu
GPU_IMAGE=centos:7
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
;;
cpu-manylinux_2_28)
TARGET=cpu_final
DOCKER_TAG=cpu
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13"
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
manylinux2_28_aarch64-builder:cpu-aarch64)
cpu-aarch64)
TARGET=final
GPU_IMAGE=arm64v8/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=13 --build-arg NINJA_VERSION=1.12.1"
MANY_LINUX_VERSION="2_28_aarch64"
OPENBLAS_VERSION="v0.3.30"
DOCKER_TAG=cpu-aarch64
GPU_IMAGE=arm64v8/centos:7
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=10"
MANY_LINUX_VERSION="aarch64"
;;
manylinuxcxx11-abi-builder:cpu-cxx11-abi)
cpu-aarch64-2_28)
TARGET=final
DOCKER_TAG=cpu-aarch64
GPU_IMAGE=arm64v8/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28_aarch64"
;;
cpu-cxx11-abi)
TARGET=final
DOCKER_TAG=cpu-cxx11-abi
GPU_IMAGE=""
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=9"
MANY_LINUX_VERSION="cxx11-abi"
;;
manylinuxs390x-builder:cpu-s390x)
cpu-s390x)
TARGET=final
DOCKER_TAG=cpu-s390x
GPU_IMAGE=s390x/almalinux:8
DOCKER_GPU_BUILD_ARG=""
MANY_LINUX_VERSION="s390x"
;;
manylinux2_28-builder:cuda11*)
cuda)
TARGET=cuda_final
DOCKER_TAG=cuda${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=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28"
;;
manylinux2_28-builder:cuda12*)
cuda-aarch64)
TARGET=cuda_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
MANY_LINUX_VERSION="2_28"
;;
manylinuxaarch64-builder:cuda*)
TARGET=cuda_final
GPU_IMAGE=amd64/almalinux:8
DOCKER_GPU_BUILD_ARG="--build-arg BASE_CUDA_VERSION=${GPU_ARCH_VERSION} --build-arg DEVTOOLSET_VERSION=13"
DOCKER_TAG=cuda${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"
;;
manylinux2_28-builder:rocm*)
# we want the patch version of 6.4 instead
if [[ $(ver $GPU_ARCH_VERSION) -eq $(ver 6.4) ]]; then
GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
fi
rocm|rocm-manylinux_2_28)
TARGET=rocm_final
MANY_LINUX_VERSION="2_28"
DEVTOOLSET_VERSION="11"
GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
DOCKER_TAG=rocm${GPU_ARCH_VERSION}
GPU_IMAGE=rocm/dev-centos-7:${GPU_ARCH_VERSION}-complete
DEVTOOLSET_VERSION="9"
if [ ${GPU_ARCH_TYPE} == "rocm-manylinux_2_28" ]; then
MANY_LINUX_VERSION="2_28"
DEVTOOLSET_VERSION="11"
GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
fi
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101"
DOCKER_GPU_BUILD_ARG="--build-arg ROCM_VERSION=${GPU_ARCH_VERSION} --build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}"
;;
manylinux2_28-builder:xpu)
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 image name: ${image}"
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
# Only activate this if in CI
if [ "$(uname -m)" != "s390x" ] && [ -v CI ]; then
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
(
set -x
if [ "$(uname -m)" != "s390x" ]; then
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
sudo systemctl daemon-reload
sudo systemctl restart docker
fi
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 "${DOCKER_IMAGE_SHA_TAG}"
fi
)
fi
tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
DOCKER_BUILDKIT=1 docker build \
${DOCKER_GPU_BUILD_ARG} \
--build-arg "GPU_IMAGE=${GPU_IMAGE}" \
--build-arg "OPENBLAS_VERSION=${OPENBLAS_VERSION}" \
--target "${TARGET}" \
-t "${tmp_tag}" \
$@ \
-f "${TOPDIR}/.ci/docker/manywheel/Dockerfile${DOCKERFILE_SUFFIX}" \
"${TOPDIR}/.ci/docker/"

View File

@ -97,7 +97,7 @@ find /opt/_internal -type f -print0 \
| xargs -0 -n1 strip --strip-unneeded 2>/dev/null || true
# We do not need the Python test suites, or indeed the precompiled .pyc and
# .pyo files. Partially cribbed from:
# https://github.com/docker-library/python/blob/master/3.4/slim/Dockerfile # @lint-ignore
# https://github.com/docker-library/python/blob/master/3.4/slim/Dockerfile
find /opt/_internal \
\( -type d -a -name test -o -name tests \) \
-o \( -type f -a -name '*.pyc' -o -name '*.pyo' \) \

View File

@ -2,8 +2,8 @@
# Helper utilities for build
# Script used only in CD pipeline
OPENSSL_DOWNLOAD_URL=https://www.openssl.org/source/old/1.1.1/ # @lint-ignore
CURL_DOWNLOAD_URL=https://curl.se/download
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

View File

@ -16,7 +16,6 @@ click
#test that import:
coremltools==5.0b5 ; python_version < "3.12"
coremltools==8.3 ; python_version == "3.12"
#Description: Apple framework for ML integration
#Pinned versions: 5.0b5
#test that import:
@ -31,10 +30,10 @@ 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==0.3.0
expecttest==0.2.1
#Description: method for writing tests where test framework auto populates
# the expected output based on previous runs
#Pinned versions: 0.3.0
#Pinned versions: 0.2.1
#test that import:
fbscribelogger==0.1.7
@ -42,15 +41,15 @@ fbscribelogger==0.1.7
#Pinned versions: 0.1.6
#test that import:
flatbuffers==24.12.23
flatbuffers==2.0
#Description: cross platform serialization library
#Pinned versions: 24.12.23
#Pinned versions: 2.0
#test that import:
hypothesis==5.35.1
# Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
#Description: advanced library for generating parametrized tests
#Pinned versions: 5.35.1
#Pinned versions: 3.44.6, 4.53.2
#test that import: test_xnnpack_integration.py, test_pruning_op.py, test_nn.py
junitparser==2.1.1
@ -63,12 +62,10 @@ lark==0.12.0
#Pinned versions: 0.12.0
#test that import:
librosa>=0.6.2 ; python_version < "3.11" and platform_machine != "s390x"
librosa==0.10.2 ; python_version == "3.12" and platform_machine != "s390x"
librosa>=0.6.2 ; python_version < "3.11"
#Description: A python package for music and audio analysis
#Pinned versions: >=0.6.2
#test that import: test_spectral_ops.py
#librosa depends on numba; disable it for s390x while numba is disabled too
#mkl #this breaks linux-bionic-rocm4.5-py3.7
#Description: Intel oneAPI Math Kernel Library
@ -93,10 +90,10 @@ librosa==0.10.2 ; python_version == "3.12" and platform_machine != "s390x"
#Pinned versions:
#test that import:
mypy==1.16.0
mypy==1.13.0
# Pin MyPy version because new errors are likely to appear with each release
#Description: linter
#Pinned versions: 1.16.0
#Pinned versions: 1.10.0
#test that import: test_typing.py, test_type_hints.py
networkx==2.8.8
@ -105,21 +102,19 @@ networkx==2.8.8
#Pinned versions: 2.8.8
#test that import: functorch
ninja==1.11.1.3
#Description: build system. Used in some tests. Used in build to generate build
#time tracing information
#Pinned versions: 1.11.1.3
#ninja
#Description: build system. Note that it install from
#here breaks things so it is commented out
#Pinned versions: 1.10.0.post1
#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
numba==0.49.0 ; python_version < "3.9" and platform_machine != "s390x"
numba==0.55.2 ; python_version == "3.9" and platform_machine != "s390x"
numba==0.55.2 ; python_version == "3.10" and platform_machine != "s390x"
numba==0.60.0 ; python_version == "3.12" and platform_machine != "s390x"
numba==0.49.0 ; python_version < "3.9"
numba==0.55.2 ; python_version == "3.9"
numba==0.55.2 ; python_version == "3.10"
#Description: Just-In-Time Compiler for Numerical Functions
#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
#test that import: test_numba_integration.py
#For numba issue see https://github.com/pytorch/pytorch/issues/51511
#Need release > 0.61.2 for s390x due to https://github.com/numba/numba/pull/10073
#numpy
#Description: Provides N-dimensional arrays and linear algebra
@ -168,10 +163,10 @@ pillow==11.0.0
#Pinned versions: 10.3.0
#test that import:
protobuf==5.29.4
#Description: Google's data interchange format
#Pinned versions: 5.29.4
#test that import: test_tensorboard.py, test/onnx/*
protobuf==3.20.2
#Description: Googles data interchange format
#Pinned versions: 3.20.1
#test that import: test_tensorboard.py
psutil
#Description: information on running processes and system utilization
@ -223,9 +218,9 @@ pygments==2.15.0
#Pinned versions: 2.12.0
#test that import: the doctests
#pyyaml
#PyYAML
#Description: data serialization format
#Pinned versions: 6.0.2
#Pinned versions:
#test that import:
#requests
@ -235,7 +230,7 @@ pygments==2.15.0
#rich
#Description: rich text and beautiful formatting in the terminal
#Pinned versions: 14.1.0
#Pinned versions: 10.9.0
#test that import:
scikit-image==0.19.3 ; python_version < "3.10"
@ -285,9 +280,9 @@ unittest-xml-reporting<=3.2.0,>=2.0.0
#test that import:
#lintrunner is supported on aarch64-linux only from 0.12.4 version
lintrunner==0.12.7
lintrunner==0.12.5
#Description: all about linters!
#Pinned versions: 0.12.7
#Pinned versions: 0.12.5
#test that import:
redis>=4.0.0
@ -299,7 +294,7 @@ ghstack==0.8.0
#Pinned versions: 0.8.0
#test that import:
jinja2==3.1.6
jinja2==3.1.4
#Description: jinja2 template engine
#Pinned versions: 3.1.4
#test that import:
@ -309,7 +304,7 @@ pytest-cpp==2.3.0
#Pinned versions: 2.3.0
#test that import:
z3-solver==4.15.1.0 ; platform_machine != "s390x"
z3-solver==4.12.2.0
#Description: The Z3 Theorem Prover Project
#Pinned versions:
#test that import:
@ -334,17 +329,17 @@ lxml==5.3.0
PyGithub==2.3.0
sympy==1.13.3
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==1.18.0
#Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
onnx==1.17.0
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
onnxscript==0.3.1
onnxscript==0.1.0.dev20240817
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
@ -358,41 +353,21 @@ parameterized==0.8.1
#Pinned versions: 1.24.0
#test that import: test_sac_estimator.py
pwlf==2.2.1
pwlf==2.2.1 ; python_version >= "3.8"
#Description: required for testing torch/distributed/_tools/sac_estimator.py
#Pinned versions: 2.2.1
#test that import: test_sac_estimator.py
# To build PyTorch itself
pyyaml
pyzstd
setuptools>=70.1.0
six
# To build PyTorch itself
astunparse
PyYAML
setuptools
ninja==1.11.1 ; platform_machine == "aarch64"
scons==4.5.2 ; platform_machine == "aarch64"
pulp==2.9.0
pulp==2.9.0 ; python_version >= "3.8"
#Description: required for testing ilp formulaiton under torch/distributed/_tools
#Pinned versions: 2.9.0
#test that import: test_sac_ilp.py
dataclasses_json==0.6.7
#Description: required for data pipeline and scripts under tools/stats
#Pinned versions: 0.6.7
#test that import:
cmake==4.0.0
#Description: required for building
tlparse==0.3.30
#Description: required for log parsing
cuda-bindings>=12.0,<13.0 ; platform_machine != "s390x"
#Description: required for testing CUDAGraph::raw_cuda_graph(). See https://nvidia.github.io/cuda-python/cuda-bindings/latest/support.html for how this version was chosen. Note "Any fix in the latest bindings would be backported to the prior major version" means that only the newest version of cuda-bindings will get fixes. Depending on the latest version of 12.x is okay because all 12.y versions will be supported via "CUDA minor version compatibility". Pytorch builds against 13.z versions of cuda toolkit work with 12.x versions of cuda-bindings as well because newer drivers work with old toolkits.
#test that import: test_cuda.py
setuptools-git-versioning==2.1.0
scikit-build==0.18.1
pyre-extensions==0.0.32
tabulate==0.9.0
#Description: These package are needed to build FBGEMM and torchrec on PyTorch CI

View File

@ -1,28 +1,18 @@
sphinx==5.3.0
#Description: This is used to generate PyTorch docs
#Pinned versions: 5.3.0
-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@722b7e6f9ca512fcc526ad07d62b3d28c50bb6cd#egg=pytorch_sphinx_theme2
-e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
# TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
# but it doesn't seem to work and hangs around idly. The initial thought that it is probably
# something related to Docker setup. We can investigate this later.
# but it doesn't seem to work and hangs around idly. The initial thought is probably
# something related to Docker setup. We can investigate this later
sphinxcontrib.katex==0.8.6
#Description: This is used to generate PyTorch docs
#Pinned versions: 0.8.6
sphinxext-opengraph==0.9.1
matplotlib==3.5.3
#Description: This is used to generate PyTorch docs
#Pinned versions: 0.9.1
sphinx_sitemap==2.6.0
#Description: This is used to generate sitemap for PyTorch docs
#Pinned versions: 2.6.0
matplotlib==3.5.3 ; python_version < "3.13"
matplotlib==3.6.3 ; python_version >= "3.13"
#Description: This is used to generate PyTorch docs
#Pinned versions: 3.6.3 if python > 3.12. Otherwise 3.5.3.
#Pinned versions: 3.5.3
tensorboard==2.13.0 ; python_version < "3.13"
tensorboard==2.18.0 ; python_version >= "3.13"
@ -50,12 +40,11 @@ IPython==8.12.0
#Pinned versions: 8.12.0
myst-nb==0.17.2
#Description: This is used to generate PyTorch functorch and torch.compile docs.
#Pinned versions: 0.17.2
#Description: This is used to generate PyTorch functorch docs
#Pinned versions: 0.13.2
# The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
python-etcd==0.4.5
sphinx-copybutton==0.5.0
sphinx-design==0.4.0
sphinxcontrib-mermaid==1.0.0
sphinx-panels==0.4.1
myst-parser==0.18.1

View File

@ -1 +1 @@
3.4.0
3.2.0

View File

@ -1 +0,0 @@
3.4.0

View File

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

View File

@ -0,0 +1,175 @@
ARG UBUNTU_VERSION
ARG CUDA_VERSION
ARG IMAGE_NAME
FROM ${IMAGE_NAME}
ARG UBUNTU_VERSION
ARG CUDA_VERSION
ENV DEBIAN_FRONTEND noninteractive
# Install common dependencies (so that this step can be cached separately)
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
ARG CONDA_CMAKE
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
COPY ./common/install_conda.sh install_conda.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ./common/install_magma_conda.sh install_magma_conda.sh
RUN bash ./install_conda.sh && rm install_conda.sh install_magma_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
# Install gcc
ARG GCC_VERSION
COPY ./common/install_gcc.sh install_gcc.sh
RUN bash ./install_gcc.sh && rm install_gcc.sh
# Install clang
ARG CLANG_VERSION
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
ENV INSTALLED_VISION ${VISION}
# (optional) Install UCC
ARG UCX_COMMIT
ARG UCC_COMMIT
ENV UCX_COMMIT $UCX_COMMIT
ENV UCC_COMMIT $UCC_COMMIT
ENV UCX_HOME /usr
ENV UCC_HOME /usr
ADD ./common/install_ucc.sh install_ucc.sh
RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
COPY ./common/install_openssl.sh install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
RUN bash ./install_openssl.sh
ENV OPENSSL_DIR /opt/openssl
ARG INDUCTOR_BENCHMARKS
ARG ANACONDA_PYTHON_VERSION
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface.txt huggingface.txt
COPY ci_commit_pins/timm.txt timm.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
ARG TRITON
# Install triton, this needs to be done before sccache because the latter will
# try to reach out to S3, which docker build runners don't have access
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
ARG HALIDE
# Build and install halide
COPY ./common/install_halide.sh install_halide.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/halide.txt halide.txt
RUN if [ -n "${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:$PATH
# See https://github.com/pytorch/pytorch/issues/82174
# TODO(sdym@fb.com):
# check if this is needed after full off Xenial migration
ENV CARGO_NET_GIT_FETCH_WITH_CLI true
RUN bash ./install_cache.sh && rm install_cache.sh
ENV CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
# Add jni.h for java host build
COPY ./common/install_jni.sh install_jni.sh
COPY ./java/jni.h jni.h
RUN bash ./install_jni.sh && rm install_jni.sh
# Install Open MPI for CUDA
COPY ./common/install_openmpi.sh install_openmpi.sh
RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
RUN rm install_openmpi.sh
# Include BUILD_ENVIRONMENT environment variable in image
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
# AWS specific CUDA build guidance
ENV TORCH_CUDA_ARCH_LIST Maxwell
ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
ENV CUDA_PATH /usr/local/cuda
# Install LLVM dev version (Defined in the pytorch/builder github repository)
COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
# Install CUDNN
ARG CUDNN_VERSION
ARG CUDA_VERSION
COPY ./common/install_cudnn.sh install_cudnn.sh
RUN if [ -n "${CUDNN_VERSION}" ]; then bash install_cudnn.sh; fi
RUN rm install_cudnn.sh
# Install CUSPARSELT
ARG CUDA_VERSION
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
RUN if [ -h /usr/local/cuda-12.1/cuda-12.1 ]; then rm /usr/local/cuda-12.1/cuda-12.1; fi
RUN if [ -h /usr/local/cuda-12.4/cuda-12.4 ]; then rm /usr/local/cuda-12.4/cuda-12.4; fi
USER jenkins
CMD ["bash"]

View File

@ -14,18 +14,19 @@ ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install clang
ARG LLVMDEV
ARG CLANG_VERSION
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG BUILD_ENVIRONMENT
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
COPY requirements-ci.txt /opt/conda/requirements-ci.txt
@ -38,10 +39,19 @@ ARG GCC_VERSION
COPY ./common/install_gcc.sh install_gcc.sh
RUN bash ./install_gcc.sh && rm install_gcc.sh
# Install clang
ARG CLANG_VERSION
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
@ -56,7 +66,7 @@ COPY ./common/install_rocm.sh install_rocm.sh
RUN bash ./install_rocm.sh
RUN rm install_rocm.sh
COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
RUN bash ./install_rocm_magma.sh ${ROCM_VERSION}
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
@ -75,32 +85,11 @@ COPY ./common/install_amdsmi.sh install_amdsmi.sh
RUN bash ./install_amdsmi.sh
RUN rm install_amdsmi.sh
# (optional) Install UCC
ARG UCX_COMMIT
ARG UCC_COMMIT
ENV UCX_COMMIT $UCX_COMMIT
ENV UCC_COMMIT $UCC_COMMIT
ENV UCX_HOME /usr
ENV UCC_HOME /usr
ADD ./common/install_ucc.sh install_ucc.sh
RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
COPY ./common/install_openssl.sh install_openssl.sh
ENV OPENSSL_ROOT_DIR /opt/openssl
RUN bash ./install_openssl.sh
ENV OPENSSL_DIR /opt/openssl
ARG INDUCTOR_BENCHMARKS
ARG ANACONDA_PYTHON_VERSION
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/timm.txt timm.txt
COPY ci_commit_pins/torchbench.txt torchbench.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
@ -118,17 +107,18 @@ COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
# Install AOTriton
COPY ./aotriton_version.txt aotriton_version.txt
COPY ./common/common_utils.sh common_utils.sh
COPY ./common/install_aotriton.sh install_aotriton.sh
RUN ["/bin/bash", "-c", "./install_aotriton.sh /opt/rocm && rm -rf install_aotriton.sh aotriton_version.txt common_utils.sh"]
ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
# Install ccache/sccache (do this last, so we get priority in PATH)
COPY ./common/install_cache.sh install_cache.sh
ENV PATH /opt/cache/bin:$PATH
RUN bash ./install_cache.sh && rm install_cache.sh
# Install Open MPI for ROCm
COPY ./common/install_openmpi.sh install_openmpi.sh
RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
RUN rm install_openmpi.sh
# Include BUILD_ENVIRONMENT environment variable in image
ARG BUILD_ENVIRONMENT
ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}

View File

@ -28,6 +28,7 @@ RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ARG DOCS
ARG BUILD_ENVIRONMENT
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
@ -56,10 +57,10 @@ RUN rm install_openssl.sh
ARG INDUCTOR_BENCHMARKS
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/huggingface.txt huggingface.txt
COPY ci_commit_pins/timm.txt timm.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
# Install XPU Dependencies
ARG XPU_VERSION
@ -72,10 +73,17 @@ ARG TRITON
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton-xpu.txt triton-xpu.txt
COPY triton_xpu_version.txt triton_version.txt
COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton-xpu.txt triton_version.txt
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
@ -83,6 +91,12 @@ RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
ENV INSTALLED_VISION ${VISION}
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh

View File

@ -1,6 +1,6 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION} as base
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
@ -28,6 +28,7 @@ RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ARG DOCS
ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
@ -51,17 +52,9 @@ RUN bash ./install_lcov.sh && rm install_lcov.sh
# Install cuda and cudnn
ARG CUDA_VERSION
COPY ./common/install_cuda.sh install_cuda.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu* install_cusparselt.sh
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
# No effect if cuda not installed
ENV USE_SYSTEM_NCCL=1
ENV NCCL_INCLUDE_DIR="/usr/local/cuda/include/"
ENV NCCL_LIB_DIR="/usr/local/cuda/lib64/"
# (optional) Install UCC
ARG UCX_COMMIT
@ -74,6 +67,20 @@ ADD ./common/install_ucc.sh install_ucc.sh
RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
RUN rm install_ucc.sh
# (optional) Install protobuf for ONNX
ARG PROTOBUF
COPY ./common/install_protobuf.sh install_protobuf.sh
RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
RUN rm install_protobuf.sh
ENV INSTALLED_PROTOBUF ${PROTOBUF}
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
RUN rm install_db.sh
ENV INSTALLED_DB ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
@ -81,6 +88,24 @@ RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
ENV INSTALLED_VISION ${VISION}
# (optional) Install Vulkan SDK
ARG VULKAN_SDK_VERSION
COPY ./common/install_vulkan_sdk.sh install_vulkan_sdk.sh
RUN if [ -n "${VULKAN_SDK_VERSION}" ]; then bash ./install_vulkan_sdk.sh; fi
RUN rm install_vulkan_sdk.sh
# (optional) Install swiftshader
ARG SWIFTSHADER
COPY ./common/install_swiftshader.sh install_swiftshader.sh
RUN if [ -n "${SWIFTSHADER}" ]; then bash ./install_swiftshader.sh; fi
RUN rm install_swiftshader.sh
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
@ -96,28 +121,26 @@ RUN rm install_openssl.sh
ARG INDUCTOR_BENCHMARKS
COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/huggingface-requirements.txt huggingface-requirements.txt
COPY ci_commit_pins/huggingface.txt huggingface.txt
COPY ci_commit_pins/timm.txt timm.txt
COPY ci_commit_pins/torchbench.txt torchbench.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface-requirements.txt torchbench.txt
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
ARG TRITON
ARG TRITON_CPU
# Create a separate stage for building Triton and Triton-CPU. install_triton
# will check for the presence of env vars
FROM base as triton-builder
# Install triton, this needs to be done before sccache because the latter will
# try to reach out to S3, which docker build runners don't have access
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton.txt triton.txt
COPY ci_commit_pins/triton-cpu.txt triton-cpu.txt
RUN bash ./install_triton.sh
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton.txt
FROM base as final
COPY --from=triton-builder /opt/triton /opt/triton
RUN if [ -n "${TRITON}" ] || [ -n "${TRITON_CPU}" ]; then pip install /opt/triton/*.whl; chown -R jenkins:jenkins /opt/conda; fi
RUN rm -rf /opt/triton
ARG TRITON_CPU
COPY ./common/install_triton.sh install_triton.sh
COPY ./common/common_utils.sh common_utils.sh
COPY ci_commit_pins/triton-cpu.txt triton-cpu.txt
RUN if [ -n "${TRITON_CPU}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton-cpu.txt
ARG EXECUTORCH
# Build and install executorch
@ -148,12 +171,6 @@ RUN if [ -n "${ACL}" ]; then bash ./install_acl.sh; fi
RUN rm install_acl.sh
ENV INSTALLED_ACL ${ACL}
ARG OPENBLAS
COPY ./common/install_openblas.sh install_openblas.sh
RUN if [ -n "${OPENBLAS}" ]; then bash ./install_openblas.sh; fi
RUN rm install_openblas.sh
ENV INSTALLED_OPENBLAS ${OPENBLAS}
# Install ccache/sccache (do this last, so we get priority in PATH)
ARG SKIP_SCCACHE_INSTALL
COPY ./common/install_cache.sh install_cache.sh

View File

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

View File

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

View File

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

View File

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

View File

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

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

View File

@ -1,14 +0,0 @@
"""
Logger Utility helpers for CLI tasks.
"""
import logging
import sys
def setup_logging(level: int = logging.INFO):
logging.basicConfig(
level=level,
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
stream=sys.stdout,
)

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@ -1,62 +0,0 @@
"""Path utility helpers for CLI tasks."""
import logging
import shutil
from pathlib import Path
from typing import Union
logger = logging.getLogger(__name__)
def get_path(path: Union[str, Path], resolve: bool = False) -> Path:
"""Convert to Path object, optionally resolving to absolute path."""
if not path:
raise ValueError("Path cannot be None or empty")
result = Path(path)
return result.resolve() if resolve else result
def ensure_dir_exists(path: Union[str, Path]) -> Path:
"""Create directory if it doesn't exist."""
path_obj = get_path(path)
path_obj.mkdir(parents=True, exist_ok=True)
return path_obj
def remove_dir(path: Union[str, Path, None]) -> None:
"""Remove directory if it exists."""
if not path:
return
path_obj = get_path(path)
if path_obj.exists():
shutil.rmtree(path_obj)
def force_create_dir(path: Union[str, Path]) -> Path:
"""Remove directory if exists, then create fresh empty directory."""
remove_dir(path)
return ensure_dir_exists(path)
def copy(src: Union[str, Path], dst: Union[str, Path]) -> None:
"""Copy file or directory from src to dst."""
src_path = get_path(src, resolve=True)
dst_path = get_path(dst, resolve=True)
if not src_path.exists():
raise FileNotFoundError(f"Source does not exist: {src_path}")
dst_path.parent.mkdir(parents=True, exist_ok=True)
if src_path.is_file():
shutil.copy2(src_path, dst_path)
elif src_path.is_dir():
shutil.copytree(src_path, dst_path, dirs_exist_ok=True)
else:
raise ValueError(f"Unsupported path type: {src_path}")
def is_path_exist(path: Union[str, Path, None]) -> bool:
"""Check if path exists."""
return bool(path and get_path(path).exists())

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@ -1,79 +0,0 @@
"""
General Utility helpers for CLI tasks.
"""
import logging
import os
import shlex
import subprocess
import sys
from typing import Optional
logger = logging.getLogger(__name__)
def run_command(
cmd: str,
use_shell: bool = False,
log_cmd: bool = True,
cwd: Optional[str] = None,
env: Optional[dict] = None,
check: bool = True,
) -> int:
"""Run a command with optional shell execution."""
if use_shell:
args = cmd
log_prefix = "[shell]"
executable = "/bin/bash"
else:
args = shlex.split(cmd)
log_prefix = "[cmd]"
executable = None
if log_cmd:
display_cmd = cmd if use_shell else " ".join(args)
logger.info("%s %s", log_prefix, display_cmd)
run_env = {**os.environ, **(env or {})}
proc = subprocess.run(
args,
shell=use_shell,
executable=executable,
stdout=sys.stdout,
stderr=sys.stderr,
cwd=cwd,
env=run_env,
check=False,
)
if check and proc.returncode != 0:
logger.error(
"%s Command failed (exit %s): %s", log_prefix, proc.returncode, cmd
)
raise subprocess.CalledProcessError(
proc.returncode, args if not use_shell else cmd
)
return proc.returncode
def str2bool(value: Optional[str]) -> bool:
"""Convert environment variables to boolean values."""
if not value:
return False
if not isinstance(value, str):
raise ValueError(
f"Expected a string value for boolean conversion, got {type(value)}"
)
value = value.strip().lower()
true_value_set = {"1", "true", "t", "yes", "y", "on", "enable", "enabled", "found"}
false_value_set = {"0", "false", "f", "no", "n", "off", "disable"}
if value in true_value_set:
return True
if value in false_value_set:
return False
raise ValueError(f"Invalid string value for boolean conversion: {value}")

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@ -1,263 +0,0 @@
import logging
import os
import textwrap
from dataclasses import dataclass
from pathlib import Path
from typing import Optional
from cli.lib.common.cli_helper import BaseRunner
from cli.lib.common.docker_helper import local_image_exists
from cli.lib.common.envs_helper import (
env_bool_field,
env_path_field,
env_str_field,
with_params_help,
)
from cli.lib.common.git_helper import clone_external_repo
from cli.lib.common.path_helper import (
copy,
ensure_dir_exists,
force_create_dir,
get_path,
is_path_exist,
)
from cli.lib.common.utils import run_command
logger = logging.getLogger(__name__)
# Default path for docker build artifacts
_DEFAULT_RESULT_PATH = "./shared"
# Temp folder in vllm work place to cp torch whls in vllm work directory for docker build
_VLLM_TEMP_FOLDER = "tmp"
@dataclass
class VllmBuildParameters:
"""
Parameters defining the vllm external input configurations.
Combine with VllmDockerBuildArgs to define the vllm build environment
"""
# USE_TORCH_WHEEL: when true, use local Torch wheels; requires TORCH_WHEELS_PATH.
# Otherwise docker build pull torch nightly during build
# TORCH_WHEELS_PATH: directory containing local torch wheels when use_torch_whl is True
use_torch_whl: bool = env_bool_field("USE_TORCH_WHEEL", True)
torch_whls_path: Path = env_path_field("TORCH_WHEELS_PATH", "./dist")
# USE_LOCAL_BASE_IMAGE: when true, use an existing local Docker base image; requires BASE_IMAGE
# Otherwise, pull dockerfile's default image remotely
# BASE_IMAGE: name:tag (only needed when use_local_base_image is True)
use_local_base_image: bool = env_bool_field("USE_LOCAL_BASE_IMAGE", True)
base_image: str = env_str_field("BASE_IMAGE")
# USE_LOCAL_DOCKERFILE: when true("1"), use a local Dockerfile; requires DOCKERFILE_PATH.
# otherwise, use vllm's default dockerfile.torch_nightly for build
# DOCKERFILE_PATH: path to Dockerfile used when use_local_dockerfile is True"
use_local_dockerfile: bool = env_bool_field("USE_LOCAL_DOCKERFILE", True)
dockerfile_path: Path = env_path_field(
"DOCKERFILE_PATH", ".github/ci_configs/vllm/Dockerfile.tmp_vllm"
)
# OUTPUT_DIR: where docker buildx (local exporter) will write artifacts
output_dir: Path = env_path_field("OUTPUT_DIR", "external/vllm")
# --- Build args ----------------------------------------------------------
target_stage: str = env_str_field("TARGET_STAGE", "export-wheels")
tag_name: str = env_str_field("TAG", "vllm-wheels")
cuda_version: str = env_str_field("CUDA_VERSION", "12.8.1")
python_version: str = env_str_field("PYTHON_VERSION", "3.12")
max_jobs: str = env_str_field("MAX_JOBS", "64")
sccache_bucket: str = env_str_field("SCCACHE_BUCKET")
sccache_region: str = env_str_field("SCCACHE_REGION")
torch_cuda_arch_list: str = env_str_field("TORCH_CUDA_ARCH_LIST", "8.9")
def __post_init__(self):
checks = [
(
self.use_torch_whl, # flag
True, # trigger_value
"torch_whls_path", # resource
is_path_exist, # check_func
"TORCH_WHEELS_PATH is not provided, but USE_TORCH_WHEEL is set to 1",
),
(
self.use_local_base_image,
True,
"base_image",
local_image_exists,
f"BASE_IMAGE {self.base_image} does not found, but USE_LOCAL_BASE_IMAGE is set to 1",
),
(
self.use_local_dockerfile,
True,
"dockerfile_path",
is_path_exist,
" DOCKERFILE_PATH path does not found, but USE_LOCAL_DOCKERFILE is set to 1",
),
]
for flag, trigger_value, attr_name, check_func, error_msg in checks:
value = getattr(self, attr_name)
if flag == trigger_value:
if not value or not check_func(value):
raise ValueError(error_msg)
else:
logger.info("flag %s is not set", flag)
if not self.output_dir:
raise ValueError("missing required output_dir")
@with_params_help(VllmBuildParameters)
class VllmBuildRunner(BaseRunner):
"""
Build vLLM using docker buildx.
Environment variable options:
"USE_TORCH_WHEEL": "1: use local wheels; 0: pull nightly from pypi",
"TORCH_WHEELS_PATH": "Path to local wheels (when USE_TORCH_WHEEL=1)",
"USE_LOCAL_BASE_IMAGE": "1: use local base image; 0: default image",
"BASE_IMAGE": "name:tag to indicate base image the dockerfile depends on (when USE_LOCAL_BASE_IMAGE=1)",
"USE_LOCAL_DOCKERFILE": "1: use local Dockerfile; 0: vllm repo default dockerfile.torch_nightly",
"DOCKERFILE_PATH": "Path to Dockerfile (when USE_LOCAL_DOCKERFILE=1)",
"OUTPUT_DIR": "e.g. './shared'",
"TORCH_CUDA_ARCH_LIST": "e.g. '8.0' or '8.0;9.0'",
"CUDA_VERSION": "e.g. '12.8.1'",
"PYTHON_VERSION": "e.g. '3.12'",
"MAX_JOBS": "e.g. '64'",
"SCCACHE_BUCKET": "e.g. 'my-bucket'",
"SCCACHE_REGION": "e.g. 'us-west-2'",
"""
def __init__(self, args=None):
self.work_directory = "vllm"
def run(self):
"""
main function to run vllm build
1. prepare vllm build environment
2. prepare the docker build command args
3. run docker build
"""
inputs = VllmBuildParameters()
clone_vllm()
self.cp_dockerfile_if_exist(inputs)
# cp torch wheels from root direct to vllm workspace if exist
self.cp_torch_whls_if_exist(inputs)
ensure_dir_exists(inputs.output_dir)
cmd = self._generate_docker_build_cmd(inputs)
logger.info("Running docker build: \n %s", cmd)
run_command(cmd, cwd="vllm", env=os.environ.copy())
def cp_torch_whls_if_exist(self, inputs: VllmBuildParameters) -> str:
if not inputs.use_torch_whl:
return ""
tmp_dir = f"./{self.work_directory}/{_VLLM_TEMP_FOLDER}"
tmp_path = Path(tmp_dir)
force_create_dir(tmp_path)
copy(inputs.torch_whls_path, tmp_dir)
return tmp_dir
def cp_dockerfile_if_exist(self, inputs: VllmBuildParameters):
if not inputs.use_local_dockerfile:
logger.info("using vllm default dockerfile.torch_nightly for build")
return
dockerfile_path = get_path(inputs.dockerfile_path, resolve=True)
vllm_torch_dockerfile = Path(
f"./{self.work_directory}/docker/Dockerfile.nightly_torch"
)
copy(dockerfile_path, vllm_torch_dockerfile)
def get_result_path(self, path):
"""
Get the absolute path of the result path
"""
if not path:
path = _DEFAULT_RESULT_PATH
abs_path = get_path(path, resolve=True)
return abs_path
def _get_torch_wheel_path_arg(self, torch_whl_dir: Optional[Path]) -> str:
if not torch_whl_dir:
return ""
return f"--build-arg TORCH_WHEELS_PATH={_VLLM_TEMP_FOLDER}"
def _get_base_image_args(self, inputs: VllmBuildParameters) -> tuple[str, str, str]:
"""
Returns:
- base_image_arg: docker buildx arg string for base image
- final_base_image_arg: docker buildx arg string for vllm-base stage
- pull_flag: --pull=true or --pull=false depending on whether the image exists locally
"""
if not inputs.use_local_base_image:
return "", "", ""
base_image = inputs.base_image
# set both base image and final base image to the same local image
base_image_arg = f"--build-arg BUILD_BASE_IMAGE={base_image}"
final_base_image_arg = f"--build-arg FINAL_BASE_IMAGE={base_image}"
if local_image_exists(base_image):
pull_flag = "--pull=false"
return base_image_arg, final_base_image_arg, pull_flag
logger.info(
"[INFO] Local image not found:%s will try to pull from remote", {base_image}
)
return base_image_arg, final_base_image_arg, ""
def _generate_docker_build_cmd(
self,
inputs: VllmBuildParameters,
) -> str:
base_image_arg, final_base_image_arg, pull_flag = self._get_base_image_args(
inputs
)
torch_arg = self._get_torch_wheel_path_arg(inputs.torch_whls_path)
return textwrap.dedent(
f"""
docker buildx build \
--output type=local,dest={inputs.output_dir} \
-f docker/Dockerfile.nightly_torch \
{pull_flag} \
{torch_arg} \
{base_image_arg} \
{final_base_image_arg} \
--build-arg max_jobs={inputs.max_jobs} \
--build-arg CUDA_VERSION={inputs.cuda_version} \
--build-arg PYTHON_VERSION={inputs.python_version} \
--build-arg USE_SCCACHE={int(bool(inputs.sccache_bucket and inputs.sccache_region))} \
--build-arg SCCACHE_BUCKET_NAME={inputs.sccache_bucket} \
--build-arg SCCACHE_REGION_NAME={inputs.sccache_region} \
--build-arg torch_cuda_arch_list='{inputs.torch_cuda_arch_list}' \
--target {inputs.target_stage} \
-t {inputs.tag_name} \
--progress=plain .
"""
).strip()
def clone_vllm():
clone_external_repo(
target="vllm",
repo="https://github.com/vllm-project/vllm.git",
dst="vllm",
update_submodules=True,
)

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@ -1,38 +0,0 @@
# main.py
import argparse
import logging
from cli.build_cli.register_build import register_build_commands
from cli.lib.common.logger import setup_logging
logger = logging.getLogger(__name__)
def main():
# Define top-level parser
parser = argparse.ArgumentParser(description="Lumos CLI")
subparsers = parser.add_subparsers(dest="command", required=True)
parser.add_argument(
"--log-level", default="INFO", help="Log level (DEBUG, INFO, WARNING, ERROR)"
)
# registers second-level subcommands
register_build_commands(subparsers)
# parse args after all options are registered
args = parser.parse_args()
# setup global logging
setup_logging(getattr(logging, args.log_level.upper(), logging.INFO))
logger.debug("Parsed args: %s", args)
if hasattr(args, "func"):
args.func(args)
else:
parser.print_help()
if __name__ == "__main__":
main()

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@ -1,22 +0,0 @@
[project]
name = "lumen-ci"
version = "0.1.0"
dependencies = [
"pyyaml==6.0.2",
"GitPython==3.1.45",
"docker==7.1.0",
"pytest==7.3.2",
]
[tool.setuptools]
packages = ["cli"]
[tool.setuptools.package-dir]
cli = "cli"
[tool.ruff.lint]
# Enable preview mode for linting
preview = true
# Now you can select your preview rules, like RUF048
extend-select = ["RUF048"]

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@ -1,47 +0,0 @@
# tests/test_cli.py
import io
import sys
import unittest
from contextlib import redirect_stderr, redirect_stdout
from unittest.mock import patch
from cli.run import main
class TestArgparseCLI(unittest.TestCase):
@patch("cli.build_cli.register_build.VllmBuildRunner.run", return_value=None)
@patch("cli.build_cli.register_build.VllmBuildRunner.__init__", return_value=None)
def test_cli_run_build_external(self, mock_init, mock_run):
from cli.run import main # import after patches if needed
test_args = ["cli.run", "build", "external", "vllm"]
with patch.object(sys, "argv", test_args):
# argparse may call sys.exit on error; capture to avoid test aborts
try:
main()
except SystemExit:
pass
mock_init.assert_called_once() # got constructed
mock_run.assert_called_once_with() # run() called
def test_build_help(self):
test_args = ["cli.run", "build", "--help"]
with patch.object(sys, "argv", test_args):
stdout = io.StringIO()
stderr = io.StringIO()
# --help always raises SystemExit(0)
with self.assertRaises(SystemExit) as cm:
with redirect_stdout(stdout), redirect_stderr(stderr):
main()
self.assertEqual(cm.exception.code, 0)
output = stdout.getvalue()
self.assertIn("usage", output)
self.assertIn("external", output)
if __name__ == "__main__":
unittest.main()

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@ -1,115 +0,0 @@
import argparse
import io
import unittest
from contextlib import redirect_stderr
from unittest.mock import patch
from cli.lib.common.cli_helper import BaseRunner, register_targets, RichHelp, TargetSpec
# ---- Dummy runners for unittests----
class FooRunner(BaseRunner):
"""Foo description from docstring."""
def run(self) -> None: # replaced by mock
pass
class BarRunner(BaseRunner):
def run(self) -> None: # replaced by mock
pass
def add_foo_args(p: argparse.ArgumentParser) -> None:
p.add_argument("--x", type=int, required=True, help="x value")
def common_args(p: argparse.ArgumentParser) -> None:
p.add_argument("--verbose", action="store_true", help="verbose flag")
def build_parser(specs: dict[str, TargetSpec]) -> argparse.ArgumentParser:
parser = argparse.ArgumentParser(prog="app", formatter_class=RichHelp)
register_targets(
parser=parser,
target_specs=specs,
common_args=common_args,
)
return parser
def get_subparser(
parser: argparse.ArgumentParser, name: str
) -> argparse.ArgumentParser:
subparsers_action = next(
a
for a in parser._subparsers._group_actions # type: ignore[attr-defined]
if isinstance(a, argparse._SubParsersAction)
)
return subparsers_action.choices[name]
class TestRegisterTargets(unittest.TestCase):
def test_metavar_lists_targets(self):
specs: dict[str, TargetSpec] = {
"foo": {"runner": FooRunner, "add_arguments": add_foo_args},
"bar": {"runner": BarRunner},
}
parser = build_parser(specs)
subparsers_action = next(
a
for a in parser._subparsers._group_actions # type: ignore[attr-defined]
if isinstance(a, argparse._SubParsersAction)
)
self.assertEqual(subparsers_action.metavar, "{foo,bar}")
def test_add_arguments_and_common_args_present(self):
specs: dict[str, TargetSpec] = {
"foo": {"runner": FooRunner, "add_arguments": add_foo_args},
}
parser = build_parser(specs)
foo = get_subparser(parser, "foo")
help_text = foo.format_help()
self.assertIn("--x", help_text)
self.assertIn("--verbose", help_text)
def test_runner_constructed_with_ns_and_run_called(self):
specs: dict[str, TargetSpec] = {
"foo": {"runner": FooRunner, "add_arguments": add_foo_args},
}
parser = build_parser(specs)
with (
patch.object(FooRunner, "__init__", return_value=None) as mock_init,
patch.object(FooRunner, "run", return_value=None) as mock_run,
):
ns = parser.parse_args(["foo", "--x", "3", "--verbose"])
ns.func(ns) # set by register_targets
# __init__ received the Namespace
self.assertEqual(mock_init.call_count, 1)
(called_ns,), _ = mock_init.call_args
self.assertIsInstance(called_ns, argparse.Namespace)
# run() called with no args
mock_run.assert_called_once_with()
def test_runner_docstring_used_as_description_when_missing(self):
specs: dict[str, TargetSpec] = {
"foo": {"runner": FooRunner, "add_arguments": add_foo_args},
}
parser = build_parser(specs)
foo = get_subparser(parser, "foo")
help_text = foo.format_help()
self.assertIn("Foo description from docstring.", help_text)
def test_missing_target_raises_systemexit_with_usage(self):
specs: dict[str, TargetSpec] = {"foo": {"runner": FooRunner}}
parser = build_parser(specs)
buf = io.StringIO()
with self.assertRaises(SystemExit), redirect_stderr(buf):
parser.parse_args([])
err = buf.getvalue()
self.assertIn("usage:", err)
if __name__ == "__main__":
unittest.main()

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@ -1,75 +0,0 @@
import unittest
from unittest import mock
from unittest.mock import MagicMock
import docker.errors as derr
from cli.lib.common.docker_helper import _get_client, local_image_exists
class TestDockerImageHelpers(unittest.TestCase):
def setUp(self):
# Reset the singleton in the target module
patcher = mock.patch("cli.lib.common.docker_helper._docker_client", None)
self.addCleanup(patcher.stop)
patcher.start()
def test_local_image_exists_true(self):
# Mock a docker client whose images.get returns an object (no exception)
mock_client = MagicMock()
mock_client.images.get.return_value = object()
ok = local_image_exists("repo:tag", client=mock_client)
self.assertTrue(ok)
def test_local_image_exists_not_found_false(self):
mock_client = MagicMock()
# Raise docker.errors.NotFound
mock_client.images.get.side_effect = derr.NotFound("nope")
ok = local_image_exists("missing:latest", client=mock_client)
self.assertFalse(ok)
def test_local_image_exists_api_error_false(self):
mock_client = MagicMock()
mock_client.images.get.side_effect = derr.APIError("boom", None)
ok = local_image_exists("broken:tag", client=mock_client)
self.assertFalse(ok)
def test_local_image_exists_uses_lazy_singleton(self):
# Patch docker.from_env used by _get_client()
with mock.patch(
"cli.lib.common.docker_helper.docker.from_env"
) as mock_from_env:
mock_docker_client = MagicMock()
mock_from_env.return_value = mock_docker_client
# First call should create and cache the client
c1 = _get_client()
self.assertIs(c1, mock_docker_client)
mock_from_env.assert_called_once()
# Second call should reuse cached client (no extra from_env calls)
c2 = _get_client()
self.assertIs(c2, mock_docker_client)
mock_from_env.assert_called_once() # still once
def test_local_image_exists_without_client_param_calls_get_client_once(self):
# Ensure _get_client is called and cached; local_image_exists should reuse it
with mock.patch("cli.lib.common.docker_helper._get_client") as mock_get_client:
mock_client = MagicMock()
mock_get_client.return_value = mock_client
# 1st call
local_image_exists("repo:tag")
# 2nd call
local_image_exists("repo:tag2")
# local_image_exists should call _get_client each time,
# but your _get_client itself caches docker.from_env.
self.assertEqual(mock_get_client.call_count, 2)
self.assertEqual(mock_client.images.get.call_count, 2)
mock_client.images.get.assert_any_call("repo:tag")
mock_client.images.get.assert_any_call("repo:tag2")
if __name__ == "__main__":
unittest.main()

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@ -1,149 +0,0 @@
import os
import unittest
from dataclasses import dataclass
from pathlib import Path
from unittest.mock import patch
import cli.lib.common.envs_helper as m
class TestEnvHelpers(unittest.TestCase):
def setUp(self):
# Keep a copy of the original environment to restore later
self._env_backup = dict(os.environ)
def tearDown(self):
# Restore environment to original state
os.environ.clear()
os.environ.update(self._env_backup)
# -------- get_env --------
def test_get_env_unset_returns_default(self):
with patch.dict(os.environ, {}, clear=True):
self.assertEqual(m.get_env("FOO", "default"), "default")
def test_get_env_empty_returns_default(self):
with patch.dict(os.environ, {"FOO": ""}, clear=True):
self.assertEqual(m.get_env("FOO", "default"), "default")
def test_get_env_set_returns_value(self):
with patch.dict(os.environ, {"FOO": "bar"}, clear=True):
self.assertEqual(m.get_env("FOO", "default"), "bar")
def test_get_env_not_exist_returns_default(self):
with patch.dict(os.environ, {"FOO": "bar"}, clear=True):
self.assertEqual(m.get_env("TEST_NOT_EXIST", "default"), "default")
def test_get_env_not_exist_without_default(self):
with patch.dict(os.environ, {"FOO": "bar"}, clear=True):
self.assertEqual(m.get_env("TEST_NOT_EXIST"), "")
# -------- env_bool --------
def test_env_bool_uses_default_when_unset(self):
with patch.dict(os.environ, {}, clear=True):
self.assertTrue(m.env_bool("FLAG", default=True))
self.assertFalse(m.env_bool("FLAG", default=False))
def test_env_bool_uses_str2bool_when_set(self):
# Patch str2bool used by env_bool so we don't depend on its exact behavior
def fake_str2bool(s: str) -> bool:
return s.lower() in {"1", "true", "yes", "on", "y"}
with (
patch.dict(os.environ, {"FLAG": "yEs"}, clear=True),
patch.object(m, "str2bool", fake_str2bool),
):
self.assertTrue(m.env_bool("FLAG", default=False))
# -------- env_path_optional / env_path --------
def test_env_path_optional_unset_returns_none_by_default(self):
with patch.dict(os.environ, {}, clear=True):
self.assertIsNone(m.env_path_optional("P"))
def test_env_path_optional_unset_returns_none_when_env_var_is_empty(self):
with patch.dict(os.environ, {"P": ""}, clear=True):
self.assertIsNone(m.env_path_optional("P"))
def test_env_path_optional_unset_returns_default_str(self):
# default as string; resolve=True by default -> absolute path
default_str = "x/y"
with patch.dict(os.environ, {}, clear=True):
p = m.env_path_optional("P", default=default_str)
self.assertIsInstance(p, Path)
self.assertIsNotNone(p)
if p:
self.assertTrue(p.is_absolute())
self.assertEqual(p.parts[-2:], ("x", "y"))
def test_env_path_optional_unset_returns_default_path_no_resolve(self):
d = Path("z")
with patch.dict(os.environ, {}, clear=True):
p = m.env_path_optional("P", default=d, resolve=False)
self.assertEqual(p, d)
def test_env_path_optional_respects_resolve_true(self):
with patch.dict(os.environ, {"P": "a/b"}, clear=True):
p = m.env_path_optional("P", resolve=True)
self.assertIsInstance(p, Path)
if p:
self.assertTrue(p.is_absolute())
def test_env_path_optional_respects_resolve_false(self):
with patch.dict(os.environ, {"P": "rel/dir"}, clear=True):
p = m.env_path_optional("P", resolve=False)
self.assertEqual(p, Path("rel/dir"))
if p:
self.assertFalse(p.is_absolute())
def test_env_path_raises_when_missing_and_default_none(self):
with patch.dict(os.environ, {}, clear=True):
with self.assertRaises(ValueError):
m.env_path("P", None, resolve=True)
def test_env_path_returns_path_when_present(self):
tmp = Path("./b").resolve()
with patch.dict(os.environ, {"P": str(tmp)}, clear=True):
p = m.env_path("P", None, resolve=True)
self.assertEqual(p, tmp)
# -------- dataclass field helpers --------
def test_dataclass_fields_read_env_at_instantiation(self):
@dataclass
class Cfg:
flag: bool = m.env_bool_field("FLAG", default=False)
out: Path = m.env_path_field("OUT", default="ab", resolve=True)
name: str = m.env_str_field("NAME", default="anon")
# First instantiation
with patch.dict(
os.environ, {"FLAG": "true", "OUT": "outdir", "NAME": "alice"}, clear=True
):
cfg1 = Cfg()
self.assertTrue(cfg1.flag)
self.assertIsInstance(cfg1.out, Path)
self.assertTrue(cfg1.out.is_absolute())
self.assertEqual(cfg1.name, "alice")
cfg1.name = "bob" # change instance value
self.assertEqual(cfg1.name, "bob") # change is reflected
# Change env; new instance should reflect new values
with patch.dict(os.environ, {"FLAG": "false", "NAME": ""}, clear=True):
cfg2 = Cfg()
self.assertFalse(cfg2.flag) # str2bool("false") -> False
self.assertTrue("ab" in str(cfg2.out))
self.assertIsInstance(cfg2.out, Path)
self.assertTrue(cfg2.out.is_absolute())
self.assertEqual(cfg2.name, "anon") # empty -> fallback to default
def test_dataclass_path_field_with_default_value(self):
@dataclass
class C2:
out: Path = m.env_path_field("OUT", default="some/dir", resolve=False)
with patch.dict(os.environ, {}, clear=True):
c = C2()
self.assertEqual(c.out, Path("some/dir"))
if __name__ == "__main__":
unittest.main()

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@ -1,122 +0,0 @@
# test_path_utils.py
# Run: pytest -q
import os
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from cli.lib.common.path_helper import (
copy,
ensure_dir_exists,
force_create_dir,
get_path,
is_path_exist,
remove_dir,
)
class TestPathHelper(unittest.TestCase):
def setUp(self):
self.tmpdir = TemporaryDirectory()
self.tmp_path = Path(self.tmpdir.name)
def tearDown(self):
self.tmpdir.cleanup()
# -------- get_path --------
def test_get_path_returns_path_for_str(self):
# Use relative path to avoid absolute-ness
rel_str = "sub/f.txt"
os.chdir(self.tmp_path)
p = get_path(rel_str, resolve=False)
self.assertIsInstance(p, Path)
self.assertFalse(p.is_absolute())
self.assertEqual(str(p), rel_str)
def test_get_path_resolves(self):
rel_str = "sub/f.txt"
p = get_path(str(self.tmp_path / rel_str), resolve=True)
self.assertTrue(p.is_absolute())
self.assertTrue(str(p).endswith(rel_str))
def test_get_path_with_path_input(self):
p_in = self.tmp_path / "sub/f.txt"
p_out = get_path(p_in, resolve=False)
self.assertTrue(str(p_out) == str(p_in))
def test_get_path_with_none_raises(self):
with self.assertRaises(ValueError):
get_path(None) # type: ignore[arg-type]
def test_get_path_invalid_type_raises(self):
with self.assertRaises(TypeError):
get_path(123) # type: ignore[arg-type]
# -------- ensure_dir_exists / force_create_dir / remove_dir --------
def test_ensure_dir_exists_creates_and_is_idempotent(self):
d = self.tmp_path / "made"
ensure_dir_exists(d)
self.assertTrue(d.exists() and d.is_dir())
ensure_dir_exists(d)
def test_force_create_dir_clears_existing(self):
d = self.tmp_path / "fresh"
(d / "inner").mkdir(parents=True)
(d / "inner" / "f.txt").write_text("x")
force_create_dir(d)
self.assertTrue(d.exists())
self.assertEqual(list(d.iterdir()), [])
def test_remove_dir_none_is_noop(self):
remove_dir(None) # type: ignore[arg-type]
def test_remove_dir_nonexistent_is_noop(self):
ghost = self.tmp_path / "ghost"
remove_dir(ghost)
def test_remove_dir_accepts_str(self):
d = self.tmp_path / "to_rm"
d.mkdir()
remove_dir(str(d))
self.assertFalse(d.exists())
# -------- copy --------
def test_copy_file_to_file(self):
src = self.tmp_path / "src.txt"
dst = self.tmp_path / "out" / "dst.txt"
src.write_text("hello")
copy(src, dst)
self.assertEqual(dst.read_text(), "hello")
def test_copy_dir_to_new_dir(self):
src = self.tmp_path / "srcdir"
(src / "a").mkdir(parents=True)
(src / "a" / "f.txt").write_text("content")
dst = self.tmp_path / "destdir"
copy(src, dst)
self.assertEqual((dst / "a" / "f.txt").read_text(), "content")
def test_copy_dir_into_existing_dir_overwrite_true_merges(self):
src = self.tmp_path / "srcdir"
dst = self.tmp_path / "destdir"
(src / "x").mkdir(parents=True)
(src / "x" / "new.txt").write_text("new")
dst.mkdir()
(dst / "existing.txt").write_text("old")
copy(src, dst)
self.assertEqual((dst / "existing.txt").read_text(), "old")
self.assertEqual((dst / "x" / "new.txt").read_text(), "new")
def test_is_str_path_exist(self):
p = self.tmp_path / "x.txt"
p.write_text("1")
self.assertTrue(is_path_exist(str(p)))
self.assertTrue(is_path_exist(p))
self.assertFalse(is_path_exist(str(self.tmp_path / "missing")))
self.assertFalse(is_path_exist(self.tmp_path / "missing"))
self.assertFalse(is_path_exist(""))
if __name__ == "__main__":
unittest.main()

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@ -1,181 +0,0 @@
import os
import tempfile
import unittest
from pathlib import Path
from unittest.mock import MagicMock, patch
import cli.lib.core.vllm as vllm
class TestVllmBuildParameters(unittest.TestCase):
@patch("cli.lib.core.vllm.local_image_exists", return_value=True)
@patch("cli.lib.core.vllm.is_path_exist", return_value=True)
@patch(
"cli.lib.common.envs_helper.env_path_optional",
side_effect=lambda name, default=None, resolve=True: {
"DOCKERFILE_PATH": Path("/abs/vllm/Dockerfile"),
"TORCH_WHEELS_PATH": Path("/abs/dist"),
"OUTPUT_DIR": Path("/abs/shared"),
}.get(name, Path(default) if default is not None else None),
)
@patch.dict(
os.environ,
{
"USE_TORCH_WHEEL": "1",
"USE_LOCAL_BASE_IMAGE": "1",
"USE_LOCAL_DOCKERFILE": "1",
"BASE_IMAGE": "my/image:tag",
"DOCKERFILE_PATH": "vllm/Dockerfile",
"TORCH_WHEELS_PATH": "dist",
"OUTPUT_DIR": "shared",
},
clear=True,
)
def test_params_success_normalizes_and_validates(
self, mock_env_path, mock_is_path, mock_local_img
):
params = vllm.VllmBuildParameters()
self.assertEqual(params.torch_whls_path, Path("/abs/dist"))
self.assertEqual(params.dockerfile_path, Path("/abs/vllm/Dockerfile"))
self.assertEqual(params.output_dir, Path("/abs/shared"))
self.assertEqual(params.base_image, "my/image:tag")
@patch("cli.lib.core.vllm.is_path_exist", return_value=False)
@patch.dict(
os.environ, {"USE_TORCH_WHEEL": "1", "TORCH_WHEELS_PATH": "dist"}, clear=True
)
def test_params_missing_torch_whls_raises(self, _is_path):
with tempfile.TemporaryDirectory() as td:
os.chdir(td)
with self.assertRaises(ValueError) as cm:
vllm.VllmBuildParameters(
use_local_base_image=False,
use_local_dockerfile=False,
)
err = cm.exception
self.assertIn("TORCH_WHEELS_PATH", str(err))
@patch("cli.lib.core.vllm.local_image_exists", return_value=False)
@patch.dict(
os.environ, {"USE_LOCAL_BASE_IMAGE": "1", "BASE_IMAGE": "img:tag"}, clear=True
)
def test_params_missing_local_base_image_raises(self, _local_img):
with tempfile.TemporaryDirectory() as td:
os.chdir(td)
with self.assertRaises(ValueError) as cm:
vllm.VllmBuildParameters(
use_torch_whl=False,
use_local_dockerfile=False,
)
err = cm.exception
self.assertIn("BASE_IMAGE", str(err))
@patch("cli.lib.core.vllm.is_path_exist", return_value=False)
@patch.dict(
os.environ,
{"USE_LOCAL_DOCKERFILE": "1", "DOCKERFILE_PATH": "Dockerfile"},
clear=True,
)
def test_params_missing_dockerfile_raises(self, _is_path):
with tempfile.TemporaryDirectory() as td:
os.chdir(td)
with self.assertRaises(ValueError) as cm:
vllm.VllmBuildParameters(
use_torch_whl=False,
use_local_base_image=False,
)
err = cm.exception
self.assertIn("DOCKERFILE_PATH", str(err))
@patch("cli.lib.core.vllm.is_path_exist", return_value=False)
@patch.dict(
os.environ,
{"OUTPUT_DIR": ""},
clear=True,
)
def test_params_missing_output_dir(self, _is_path):
with self.assertRaises(FileNotFoundError):
vllm.VllmBuildParameters()
class TestBuildCmdAndRun(unittest.TestCase):
@patch("cli.lib.core.vllm.local_image_exists", return_value=True)
def test_generate_docker_build_cmd_includes_bits(self, _exists):
runner = vllm.VllmBuildRunner()
# Craft inputs that simulate a prepared build
inputs = MagicMock()
inputs.output_dir = Path("/abs/out")
inputs.use_local_base_image = True
inputs.base_image = "img:tag"
inputs.torch_whls_path = Path("./vllm/tmp")
inputs.max_jobs = 64
inputs.cuda_version = "12.8.1"
inputs.python_version = "3.12"
inputs.sccache_bucket = "my-bucket"
inputs.sccache_region = "us-west-2"
inputs.torch_cuda_arch_list = "8.0;9.0"
inputs.target_stage = "export-wheels"
inputs.tag_name = "vllm-wheels"
cmd = runner._generate_docker_build_cmd(inputs)
squashed = " ".join(cmd.split()) # normalize whitespace for matching
self.assertIn("--output type=local,dest=/abs/out", squashed)
self.assertIn("-f docker/Dockerfile.nightly_torch", squashed)
self.assertIn("--pull=false", squashed)
self.assertIn("--build-arg TORCH_WHEELS_PATH=tmp", squashed)
self.assertIn("--build-arg BUILD_BASE_IMAGE=img:tag", squashed)
self.assertIn("--build-arg FINAL_BASE_IMAGE=img:tag", squashed)
self.assertIn("--build-arg max_jobs=64", squashed)
self.assertIn("--build-arg CUDA_VERSION=12.8.1", squashed)
self.assertIn("--build-arg PYTHON_VERSION=3.12", squashed)
self.assertIn("--build-arg USE_SCCACHE=1", squashed)
self.assertIn("--build-arg SCCACHE_BUCKET_NAME=my-bucket", squashed)
self.assertIn("--build-arg SCCACHE_REGION_NAME=us-west-2", squashed)
self.assertIn("--build-arg torch_cuda_arch_list='8.0;9.0'", squashed)
self.assertIn("--target export-wheels", squashed)
self.assertIn("-t vllm-wheels", squashed)
@patch("cli.lib.core.vllm.run_command")
@patch("cli.lib.core.vllm.ensure_dir_exists")
@patch("cli.lib.core.vllm.clone_vllm")
@patch.object(
vllm.VllmBuildRunner,
"_generate_docker_build_cmd",
return_value="docker buildx ...",
)
@patch.dict(
os.environ,
{
# Make __post_init__ validations pass cheaply
"USE_TORCH_WHEEL": "0",
"USE_LOCAL_BASE_IMAGE": "0",
"USE_LOCAL_DOCKERFILE": "0",
"OUTPUT_DIR": "shared",
},
clear=True,
)
def test_run_calls_clone_prepare_and_build(
self, mock_gen, mock_clone, mock_ensure, mock_run
):
# Stub parameters instance so we avoid FS/Docker accesses in run()
params = MagicMock()
params.output_dir = Path("shared")
params.use_local_dockerfile = False
params.use_torch_whl = False
with patch("cli.lib.core.vllm.VllmBuildParameters", return_value=params):
runner = vllm.VllmBuildRunner()
runner.run()
mock_clone.assert_called_once()
mock_ensure.assert_called_once_with(Path("shared"))
mock_gen.assert_called_once_with(params)
mock_run.assert_called_once()
# ensure we run in vllm workdir
_, kwargs = mock_run.call_args
assert kwargs.get("cwd") == "vllm"
if __name__ == "__main__":
unittest.main()

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@ -1,2 +0,0 @@
output/
magma-rocm*/

View File

@ -1,35 +0,0 @@
SHELL=/usr/bin/env bash
DOCKER_CMD ?= docker
DESIRED_ROCM ?= 6.4
DESIRED_ROCM_SHORT = $(subst .,,$(DESIRED_ROCM))
PACKAGE_NAME = magma-rocm
# inherit this from underlying docker image, do not pass this env var to docker
#PYTORCH_ROCM_ARCH ?= gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201
DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
-v $(shell git rev-parse --show-toplevel)/.ci:/builder \
-w /builder \
-e PACKAGE_NAME=${PACKAGE_NAME}${DESIRED_ROCM_SHORT} \
-e DESIRED_ROCM=${DESIRED_ROCM} \
"pytorch/almalinux-builder:rocm${DESIRED_ROCM}" \
magma-rocm/build_magma.sh
.PHONY: all
all: magma-rocm64
all: magma-rocm63
.PHONY:
clean:
$(RM) -r magma-*
$(RM) -r output
.PHONY: magma-rocm64
magma-rocm64: DESIRED_ROCM := 6.4
magma-rocm64:
$(DOCKER_RUN)
.PHONY: magma-rocm63
magma-rocm63: DESIRED_ROCM := 6.3
magma-rocm63:
$(DOCKER_RUN)

View File

@ -1,48 +0,0 @@
# Magma ROCm
This folder contains the scripts and configurations to build libmagma.so, linked for various versions of ROCm.
## Building
Look in the `Makefile` for available targets to build. To build any target, for example `magma-rocm63`, run
```
# Using `docker`
make magma-rocm63
# Using `podman`
DOCKER_CMD=podman make magma-rocm63
```
This spawns a `pytorch/manylinux-rocm<version>` docker image, which has the required `devtoolset` and ROCm versions installed.
Within the docker image, it runs `build_magma.sh` with the correct environment variables set, which package the necessary files
into a tarball, with the following structure:
```
.
├── include # header files
├── lib # libmagma.so
├── info
│ ├── licenses # license file
│ └── recipe # build script
```
More specifically, `build_magma.sh` copies over the relevant files from the `package_files` directory depending on the ROCm version.
Outputted binaries should be in the `output` folder.
## Pushing
Packages can be uploaded to an S3 bucket using:
```
aws s3 cp output/*/magma-cuda*.bz2 <bucket-with-path>
```
If you do not have upload permissions, please ping @seemethere or @soumith to gain access
## New versions
New ROCm versions can be added by creating a new make target with the next desired version. For ROCm version N.n, the target should be named `magma-rocmNn`.
Make sure to edit the appropriate environment variables (e.g., DESIRED_ROCM) in the `Makefile` accordingly. Remember also to check `build_magma.sh` to ensure the logic for copying over the files remains correct.

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@ -1,42 +0,0 @@
#!/usr/bin/env bash
set -eou pipefail
# Environment variables
# The script expects DESIRED_CUDA and PACKAGE_NAME to be set
ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)"
# Version 2.7.2 + ROCm related updates
MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
# Folders for the build
PACKAGE_FILES=${ROOT_DIR}/magma-rocm/package_files # metadata
PACKAGE_DIR=${ROOT_DIR}/magma-rocm/${PACKAGE_NAME} # build workspace
PACKAGE_OUTPUT=${ROOT_DIR}/magma-rocm/output # where tarballs are stored
PACKAGE_BUILD=${PACKAGE_DIR} # where the content of the tarball is prepared
PACKAGE_RECIPE=${PACKAGE_BUILD}/info/recipe
PACKAGE_LICENSE=${PACKAGE_BUILD}/info/licenses
mkdir -p ${PACKAGE_DIR} ${PACKAGE_OUTPUT}/linux-64 ${PACKAGE_BUILD} ${PACKAGE_RECIPE} ${PACKAGE_LICENSE}
# Fetch magma sources and verify checksum
pushd ${PACKAGE_DIR}
git clone https://bitbucket.org/icl/magma.git
pushd magma
git checkout ${MAGMA_VERSION}
popd
popd
# build
pushd ${PACKAGE_DIR}/magma
# The build.sh script expects to be executed from the sources root folder
INSTALL_DIR=${PACKAGE_BUILD} ${PACKAGE_FILES}/build.sh
popd
# Package recipe, license and tarball
# Folder and package name are backward compatible for the build workflow
cp ${PACKAGE_FILES}/build.sh ${PACKAGE_RECIPE}/build.sh
cp ${PACKAGE_DIR}/magma/COPYRIGHT ${PACKAGE_LICENSE}/COPYRIGHT
pushd ${PACKAGE_BUILD}
tar cjf ${PACKAGE_OUTPUT}/linux-64/${PACKAGE_NAME}-${MAGMA_VERSION}-1.tar.bz2 include lib info
echo Built in ${PACKAGE_OUTPUT}/linux-64/${PACKAGE_NAME}-${MAGMA_VERSION}-1.tar.bz2
popd

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