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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
4627 changed files with 120003 additions and 243805 deletions

View File

@ -3,12 +3,6 @@ set -eux -o pipefail
GPU_ARCH_VERSION=${GPU_ARCH_VERSION:-}
if [[ "$GPU_ARCH_VERSION" == *"12.6"* ]]; then
export TORCH_CUDA_ARCH_LIST="9.0"
elif [[ "$GPU_ARCH_VERSION" == *"12.8"* ]]; then
export TORCH_CUDA_ARCH_LIST="9.0;10.0;12.0"
fi
SCRIPTPATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
source $SCRIPTPATH/aarch64_ci_setup.sh
@ -20,7 +14,7 @@ cd /
# on the mounted pytorch repo
git config --global --add safe.directory /pytorch
pip install -r /pytorch/requirements.txt
pip install auditwheel==6.2.0
pip install auditwheel
if [ "$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

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
"""
@ -39,7 +40,7 @@ def build_ArmComputeLibrary() -> None:
"clone",
"https://github.com/ARM-software/ComputeLibrary.git",
"-b",
"v25.02",
"v24.09",
"--depth",
"1",
"--shallow-submodules",
@ -55,7 +56,7 @@ def build_ArmComputeLibrary() -> None:
shutil.copytree(f"{acl_checkout_dir}/{d}", f"{acl_install_dir}/{d}")
def update_wheel(wheel_path, desired_cuda) -> None:
def update_wheel(wheel_path) -> None:
"""
Update the cuda wheel libraries
"""
@ -77,6 +78,7 @@ def update_wheel(wheel_path, desired_cuda) -> None:
"/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/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",
@ -96,18 +98,6 @@ def update_wheel(wheel_path, desired_cuda) -> None:
"/usr/local/lib/libnvpl_lapack_core.so.0",
"/usr/local/lib/libnvpl_blas_core.so.0",
]
if "126" in desired_cuda:
libs_to_copy += [
"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.6",
"/usr/local/cuda/lib64/libcufile.so.0",
"/usr/local/cuda/lib64/libcufile_rdma.so.1",
]
elif "128" in desired_cuda:
libs_to_copy += [
"/usr/local/cuda/lib64/libnvrtc-builtins.so.12.8",
"/usr/local/cuda/lib64/libcufile.so.0",
"/usr/local/cuda/lib64/libcufile_rdma.so.1",
]
else:
libs_to_copy += [
"/opt/OpenBLAS/lib/libopenblas.so.0",
@ -188,7 +178,6 @@ if __name__ == "__main__":
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 += (
@ -204,11 +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()
@ -232,6 +222,6 @@ if __name__ == "__main__":
print("Updating Cuda Dependency")
filename = os.listdir("/pytorch/dist/")
wheel_path = f"/pytorch/dist/{filename[0]}"
update_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):
@ -619,11 +619,9 @@ def build_torchaudio(
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))
@ -657,6 +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")
@ -669,7 +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
)
@ -696,7 +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")
@ -747,7 +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:
@ -920,9 +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,
@ -952,13 +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:
@ -1012,7 +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

@ -44,8 +44,6 @@ FROM base as cuda
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/
ENV CUDA_HOME=/usr/local/cuda-${CUDA_VERSION}
# Preserve CUDA_VERSION for the builds
ENV CUDA_VERSION=${CUDA_VERSION}

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
@ -90,21 +86,32 @@ CMAKE_VERSION=3.18.5
_UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
_UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
if [[ "$image" == *rocm* ]]; then
_UCX_COMMIT=cc312eaa4655c0cc5c2bcd796db938f90563bcf6
_UCC_COMMIT=0c0fc21559835044ab107199e334f7157d6a0d3d
fi
# It's annoying to rename jobs every time you want to rewrite a
# configuration, so we hardcode everything here rather than do it
# from scratch
case "$image" in
pytorch-linux-focal-cuda12.6-cudnn9-py3-gcc11)
CUDA_VERSION=12.6.3
pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
CUDA_VERSION=12.4.1
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
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=${_UCX_COMMIT}
@ -118,6 +125,37 @@ case "$image" in
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-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=${_UCX_COMMIT}
@ -132,6 +170,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
@ -146,61 +185,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.13
GCC_VERSION=9
PROTOBUF=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-cuda12.6-cudnn9-py3-gcc9)
CUDA_VERSION=12.6.3
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-cuda12.6-cudnn9-py3-gcc9-inductor-benchmarks)
CUDA_VERSION=12.6.3
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-cuda12.6-cudnn9-py3.12-gcc9-inductor-benchmarks)
CUDA_VERSION=12.6.3
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=9
PROTOBUF=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
CONDA_CMAKE=yes
TRITON=yes
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-cuda12.6-cudnn9-py3.13-gcc9-inductor-benchmarks)
CUDA_VERSION=12.6.3
CUDNN_VERSION=9
ANACONDA_PYTHON_VERSION=3.13
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
@ -215,6 +200,49 @@ case "$image" in
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-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=${_UCX_COMMIT}
@ -226,6 +254,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
ONNX=yes
@ -234,7 +263,10 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
CONDA_CMAKE=yes
TRITON=yes
;;
@ -242,7 +274,10 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.11
CLANG_VERSION=10
PROTOBUF=yes
DB=yes
VISION=yes
VULKAN_SDK_VERSION=1.2.162.1
SWIFTSHADER=yes
CONDA_CMAKE=yes
TRITON=yes
;;
@ -250,42 +285,38 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-1-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.1
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=9
PROTOBUF=yes
DB=yes
VISION=yes
ROCM_VERSION=6.2.4
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-focal-rocm-n-py3)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
PROTOBUF=yes
VISION=yes
ROCM_VERSION=6.3
NINJA_VERSION=1.9.0
CONDA_CMAKE=yes
TRITON=yes
KATEX=yes
UCX_COMMIT=${_UCX_COMMIT}
UCC_COMMIT=${_UCC_COMMIT}
INDUCTOR_BENCHMARKS=yes
;;
pytorch-linux-jammy-xpu-2024.0-py3)
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
XPU_VERSION=0.5
NINJA_VERSION=1.9.0
@ -296,6 +327,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
XPU_VERSION=2025.0
NINJA_VERSION=1.9.0
@ -306,6 +338,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
CONDA_CMAKE=yes
@ -319,6 +352,7 @@ case "$image" in
CUDNN_VERSION=9
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
TRITON=yes
;;
@ -326,6 +360,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
CLANG_VERSION=12
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
TRITON=yes
@ -346,6 +381,7 @@ case "$image" in
ANACONDA_PYTHON_VERSION=3.9
GCC_VERSION=11
PROTOBUF=yes
DB=yes
VISION=yes
KATEX=yes
CONDA_CMAKE=yes
@ -360,7 +396,7 @@ case "$image" in
EXECUTORCH=yes
;;
pytorch-linux-jammy-py3.12-halide)
CUDA_VERSION=12.6
CUDA_VERSION=12.4
ANACONDA_PYTHON_VERSION=3.12
GCC_VERSION=11
CONDA_CMAKE=yes
@ -368,7 +404,7 @@ case "$image" in
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
@ -378,19 +414,20 @@ case "$image" in
# TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
# We will need to update mypy version eventually, but that's for another day. The task
# would be to upgrade mypy to 1.0.0 with Python 3.11
PYTHON_VERSION=3.9
PIP_CMAKE=yes
ANACONDA_PYTHON_VERSION=3.9
CONDA_CMAKE=yes
;;
pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-linter)
PYTHON_VERSION=3.9
ANACONDA_PYTHON_VERSION=3.9
CUDA_VERSION=11.8
PIP_CMAKE=yes
CONDA_CMAKE=yes
;;
pytorch-linux-jammy-aarch64-py3.10-gcc11)
ANACONDA_PYTHON_VERSION=3.10
GCC_VERSION=11
ACL=yes
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
# snadampal: skipping llvm src build install because the current version
@ -402,6 +439,7 @@ case "$image" in
GCC_VERSION=11
ACL=yes
PROTOBUF=yes
DB=yes
VISION=yes
CONDA_CMAKE=yes
# snadampal: skipping llvm src build install because the current version
@ -412,6 +450,7 @@ case "$image" in
*)
# Catch-all for builds that are not hardcoded.
PROTOBUF=yes
DB=yes
VISION=yes
echo "image '$image' did not match an existing build configuration"
if [[ "$image" == *py* ]]; then
@ -460,21 +499,14 @@ if [[ "$image" == *cuda* && ${OS} == "ubuntu" ]]; then
fi
fi
no_cache_flag=""
progress_flag=""
# Do not use cache and progress=plain when in CI
if [[ -n "${CI:-}" ]]; then
no_cache_flag="--no-cache"
progress_flag="--progress=plain"
fi
# Build image
docker build \
${no_cache_flag} \
${progress_flag} \
--no-cache \
--progress=plain \
--build-arg "BUILD_ENVIRONMENT=${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}" \
@ -482,22 +514,22 @@ docker build \
--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=${CONDA_CMAKE}" \
--build-arg "PIP_CMAKE=${PIP_CMAKE}" \
--build-arg "TRITON=${TRITON}" \
--build-arg "TRITON_CPU=${TRITON_CPU}" \
--build-arg "ONNX=${ONNX}" \
@ -523,7 +555,7 @@ docker build \
UBUNTU_VERSION=$(echo ${UBUNTU_VERSION} | sed 's/-rc$//')
function drun() {
docker run --rm "$tmp_tag" "$@"
docker run --rm "$tmp_tag" $*
}
if [[ "$OS" == "ubuntu" ]]; then
@ -571,14 +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

View File

@ -55,6 +55,13 @@ 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 ./
@ -68,7 +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 ${ROCM_VERSION}
RUN bash ./install_rocm_magma.sh
RUN rm install_rocm_magma.sh
COPY ./common/install_amdsmi.sh install_amdsmi.sh
RUN bash ./install_amdsmi.sh
@ -106,6 +113,13 @@ COPY triton_version.txt triton_version.txt
RUN if [ -n "${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 @@
7e487c24e1c20c3f4606c2d8aca2778873b00b4c
6f638937d64e3396793956d75ee3e14802022745

View File

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

View File

@ -1 +0,0 @@
v2.26.2-1

View File

@ -1 +1 @@
5d535d7a2d4b435b1b5c1177fd8f04a12b942b9a
ac3470188b914c5d7a5058a7e28b9eb685a62427

View File

@ -1 +1 @@
0bcc8265e677e5321606a3311bf71470f14456a8
e98b6fcb8df5b44eb0d0addb6767c573d37ba024

View File

@ -1 +1 @@
96316ce50fade7e209553aba4898cd9b82aab83b
35c6c7c6284582b3f41c71c150e11b517acf074a

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

@ -32,12 +32,8 @@ install_ubuntu() {
# 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
# TODO: Eliminate this hack, we should not relay on apt-get installation
# See https://github.com/pytorch/pytorch/issues/144768
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"
elif [[ "$UBUNTU_VERSION" == "20.04"* && "$CUDA_VERSION" == "12.4"* ]]; then
maybe_libnccl_dev="libnccl2=2.26.2-1+cuda12.4 libnccl-dev=2.26.2-1+cuda12.4 --allow-downgrades --allow-change-held-packages"
else
maybe_libnccl_dev=""
fi

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 v0.9.1
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

@ -62,11 +62,11 @@ 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
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
conda_install "openblas==0.3.29=*openmp*"
conda_install "openblas==0.3.28=*openmp*"
else
conda_install "mkl=2021.4.0 mkl-include=2021.4.0"
fi

View File

@ -7,7 +7,7 @@ PYTHON_DOWNLOAD_GITHUB_BRANCH=https://github.com/python/cpython/archive/refs/hea
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"}
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
@ -70,7 +70,7 @@ function do_cpython_build {
# install setuptools since python 3.12 is required to use distutils
${prefix}/bin/pip install wheel==0.34.2 setuptools==68.2.2
local abi_tag=$(${prefix}/bin/python -c "from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag; print('{0}{1}-{2}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag()))")
ln -sf ${prefix} /opt/python/${abi_tag}
ln -s ${prefix} /opt/python/${abi_tag}
}
function build_cpython {

View File

@ -2,6 +2,7 @@
set -ex
NCCL_VERSION=v2.21.5-1
CUDNN_VERSION=9.5.1.17
function install_cusparselt_040 {
@ -15,6 +16,17 @@ function install_cusparselt_040 {
rm -rf tmp_cusparselt
}
function install_cusparselt_052 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
wget -q https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/libcusparse_lt-linux-x86_64-0.5.2.1-archive.tar.xz
tar xf libcusparse_lt-linux-x86_64-0.5.2.1-archive.tar.xz
cp -a libcusparse_lt-linux-x86_64-0.5.2.1-archive/include/* /usr/local/cuda/include/
cp -a libcusparse_lt-linux-x86_64-0.5.2.1-archive/lib/* /usr/local/cuda/lib64/
popd
rm -rf tmp_cusparselt
}
function install_cusparselt_062 {
# cuSparseLt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && pushd tmp_cusparselt
@ -39,7 +51,7 @@ function install_cusparselt_063 {
function install_118 {
CUDNN_VERSION=9.1.0.70
echo "Installing CUDA 11.8 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.4.0"
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
@ -57,16 +69,56 @@ function install_118 {
cd ..
rm -rf tmp_cudnn
CUDA_VERSION=11.8 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
install_cusparselt_040
ldconfig
}
function install_121 {
echo "Installing CUDA 12.1 and cuDNN ${CUDNN_VERSION} and NCCL ${NCCL_VERSION} and cuSparseLt-0.5.2"
rm -rf /usr/local/cuda-12.1 /usr/local/cuda
# install CUDA 12.1.0 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda_12.1.1_530.30.02_linux.run
chmod +x cuda_12.1.1_530.30.02_linux.run
./cuda_12.1.1_530.30.02_linux.run --toolkit --silent
rm -f cuda_12.1.1_530.30.02_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.1 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz -O cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
tar xf cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive.tar.xz
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/include/* /usr/local/cuda/include/
cp -a cudnn-linux-x86_64-${CUDNN_VERSION}_cuda12-archive/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf tmp_cudnn
# NCCL license: https://docs.nvidia.com/deeplearning/nccl/#licenses
# Follow build: https://github.com/NVIDIA/nccl/tree/master?tab=readme-ov-file#build
git clone -b $NCCL_VERSION --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_052
ldconfig
}
function install_124 {
CUDNN_VERSION=9.1.0.70
echo "Installing CUDA 12.4.1 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.6.2"
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
@ -84,7 +136,14 @@ function install_124 {
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
install_cusparselt_062
@ -92,7 +151,7 @@ function install_124 {
}
function install_126 {
echo "Installing CUDA 12.6.3 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.6.3"
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
@ -110,7 +169,14 @@ function install_126 {
cd ..
rm -rf tmp_cudnn
CUDA_VERSION=12.6 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
install_cusparselt_063
@ -148,6 +214,37 @@ function prune_118 {
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2022.3.0 $CUDA_BASE/nsight-systems-2022.4.2/
}
function prune_121 {
echo "Pruning CUDA 12.1"
#####################################################################################
# CUDA 12.1 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.1/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.1/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$OVERRIDE_GENCODE" ]]; then
export GENCODE=$OVERRIDE_GENCODE
fi
# all CUDA libs except CuDNN and CuBLAS
ls $CUDA_LIB_DIR/ | grep "\.a" | grep -v "culibos" | grep -v "cudart" | grep -v "cudnn" | grep -v "cublas" | grep -v "metis" \
| xargs -I {} bash -c \
"echo {} && $NVPRUNE $GENCODE $CUDA_LIB_DIR/{} -o $CUDA_LIB_DIR/{}"
# prune CuDNN and CuBLAS
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublas_static.a -o $CUDA_LIB_DIR/libcublas_static.a
$NVPRUNE $GENCODE_CUDNN $CUDA_LIB_DIR/libcublasLt_static.a -o $CUDA_LIB_DIR/libcublasLt_static.a
#####################################################################################
# CUDA 12.1 prune visual tools
#####################################################################################
export CUDA_BASE="/usr/local/cuda-12.1/"
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2023.1.0 $CUDA_BASE/nsight-systems-2023.1.2/
}
function prune_124 {
echo "Pruning CUDA 12.4"
#####################################################################################
@ -216,45 +313,18 @@ function prune_126 {
rm -rf $CUDA_BASE/libnvvp $CUDA_BASE/nsightee_plugins $CUDA_BASE/nsight-compute-2024.3.2 $CUDA_BASE/nsight-systems-2024.5.1/
}
function install_128 {
CUDNN_VERSION=9.8.0.87
echo "Installing CUDA 12.8.0 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.6.3"
rm -rf /usr/local/cuda-12.8 /usr/local/cuda
# install CUDA 12.8.0 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux.run
chmod +x cuda_12.8.0_570.86.10_linux.run
./cuda_12.8.0_570.86.10_linux.run --toolkit --silent
rm -f cuda_12.8.0_570.86.10_linux.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.8 /usr/local/cuda
# cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
mkdir tmp_cudnn && cd tmp_cudnn
wget -q https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-${CUDNN_VERSION}_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=12.8 bash install_nccl.sh
install_cusparselt_063
ldconfig
}
# idiomatic parameter and option handling in sh
while test $# -gt 0
do
case "$1" in
11.8) install_118; prune_118
;;
12.1) install_121; prune_121
;;
12.4) install_124; prune_124
;;
12.6) install_126; prune_126
;;
12.8) install_128;
;;
*) echo "bad argument $1"; exit 1
;;
esac

View File

@ -3,7 +3,19 @@
set -ex
CUDNN_VERSION=9.8.0.87
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
@ -16,15 +28,16 @@ function install_cusparselt_063 {
rm -rf tmp_cusparselt
}
function install_128 {
echo "Installing CUDA 12.8.0 and cuDNN ${CUDNN_VERSION} and NCCL and cuSparseLt-0.6.3"
rm -rf /usr/local/cuda-12.8 /usr/local/cuda
# install CUDA 12.8.0 in the same container
wget -q https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_570.86.10_linux_sbsa.run
chmod +x cuda_12.8.0_570.86.10_linux_sbsa.run
./cuda_12.8.0_570.86.10_linux_sbsa.run --toolkit --silent
rm -f cuda_12.8.0_570.86.10_linux_sbsa.run
rm -f /usr/local/cuda && ln -s /usr/local/cuda-12.8 /usr/local/cuda
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
@ -35,18 +48,125 @@ function install_128 {
cd ..
rm -rf tmp_cudnn
CUDA_VERSION=12.8 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 ${NCCL_VERSION} --depth 1 https://github.com/NVIDIA/nccl.git
cd nccl && make -j src.build
cp -a build/include/* /usr/local/cuda/include/
cp -a build/lib/* /usr/local/cuda/lib64/
cd ..
rm -rf nccl
install_cusparselt_062
ldconfig
}
function prune_124 {
echo "Pruning CUDA 12.4"
#####################################################################################
# CUDA 12.4 prune static libs
#####################################################################################
export NVPRUNE="/usr/local/cuda-12.4/bin/nvprune"
export CUDA_LIB_DIR="/usr/local/cuda-12.4/lib64"
export GENCODE="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
export GENCODE_CUDNN="-gencode arch=compute_50,code=sm_50 -gencode arch=compute_60,code=sm_60 -gencode arch=compute_61,code=sm_61 -gencode arch=compute_70,code=sm_70 -gencode arch=compute_75,code=sm_75 -gencode arch=compute_80,code=sm_80 -gencode arch=compute_86,code=sm_86 -gencode arch=compute_90,code=sm_90"
if [[ -n "$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
12.8) install_128;
12.4) install_124; prune_124
;;
12.6) install_126; prune_126
;;
*) echo "bad argument $1"; exit 1
;;

View File

@ -4,9 +4,7 @@ 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} == "12.8" ]]; then
CUDNN_NAME="cudnn-linux-x86_64-9.8.0.87_cuda12-archive"
elif [[ ${CUDA_VERSION:0:4} == "12.6" ]]; then
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"

View File

@ -5,15 +5,7 @@ set -ex
# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
mkdir tmp_cusparselt && cd tmp_cusparselt
if [[ ${CUDA_VERSION:0:4} =~ ^12\.[5-8]$ ]]; then
arch_path='sbsa'
export TARGETARCH=${TARGETARCH:-$(uname -m)}
if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
arch_path='x86_64'
fi
CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.6.3.2-archive"
curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${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
@ -21,11 +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
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
else
echo "Not sure which libcusparselt version to install for this ${CUDA_VERSION}"
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

@ -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

@ -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

@ -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

@ -1,26 +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)
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

@ -31,15 +31,15 @@ pip_install \
pip_install coloredlogs packaging
pip_install onnxruntime==1.18.1
pip_install onnx==1.17.0
pip_install onnxscript==0.2.2 --no-deps
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

@ -4,7 +4,7 @@
set -ex
cd /
git clone https://github.com/OpenMathLib/OpenBLAS.git -b v0.3.29 --depth 1 --shallow-submodules
git clone https://github.com/OpenMathLib/OpenBLAS.git -b v0.3.28 --depth 1 --shallow-submodules
OPENBLAS_BUILD_FLAGS="

View File

@ -1,18 +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
if [ -n "${PIP_CMAKE}" ]; then
python -mpip install cmake==3.31.6
fi

View File

@ -8,6 +8,10 @@ ver() {
install_ubuntu() {
apt-get update
if [[ $UBUNTU_VERSION == 18.04 ]]; then
# gpg-agent is not available by default on 18.04
apt-get install -y --no-install-recommends gpg-agent
fi
if [[ $UBUNTU_VERSION == 20.04 ]]; then
# gpg-agent is not available by default on 20.04
apt-get install -y --no-install-recommends gpg-agent
@ -58,22 +62,6 @@ install_ubuntu() {
sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
done
# ROCm 6.3 had a regression where initializing static code objects had significant overhead
if [[ $(ver $ROCM_VERSION) -eq $(ver 6.3) ]]; then
# clr build needs CppHeaderParser but can only find it using conda's python
/opt/conda/bin/python -m pip install CppHeaderParser
git clone https://github.com/ROCm/HIP -b rocm-6.3.x
HIP_COMMON_DIR=$(readlink -f HIP)
git clone https://github.com/jeffdaily/clr -b release/rocm-rel-6.3-statco-hotfix
mkdir -p clr/build
pushd clr/build
cmake .. -DCLR_BUILD_HIP=ON -DHIP_COMMON_DIR=$HIP_COMMON_DIR
make -j
cp hipamd/lib/libamdhip64.so.6.3.* /opt/rocm/lib/libamdhip64.so.6.3.*
popd
rm -rf HIP clr
fi
# Cleanup
apt-get autoclean && apt-get clean
rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

View File

@ -25,9 +25,7 @@ python3 -m pip install meson ninja
###########################
### clone repo
###########################
# TEMPORARY FIX: https://gitlab.freedesktop.org/mesa/drm.git is down until 2025/03/22
# GIT_SSL_NO_VERIFY=true git clone https://gitlab.freedesktop.org/mesa/drm.git
GIT_SSL_NO_VERIFY=true git clone git://anongit.freedesktop.org/mesa/drm
GIT_SSL_NO_VERIFY=true git clone https://gitlab.freedesktop.org/mesa/drm.git
pushd drm
###########################
@ -117,7 +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

@ -1,28 +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=$1
rocm_version_nodot=${1//./}
# Magma build scripts need `python`
ln -sf /usr/bin/python3 /usr/bin/python
# Version 2.7.2 + ROCm related updates
MAGMA_VERSION=a1625ff4d9bc362906bd01f805dbbe12612953f6
magma_archive="magma-rocm${rocm_version_nodot}-${MAGMA_VERSION}-1.tar.bz2"
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
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}
tar -xvf "${magma_archive}"
mkdir -p "${rocm_dir}/magma"
mv include "${rocm_dir}/magma/include"
mv lib "${rocm_dir}/magma/lib"
popd
)
}
MKLROOT=${MKLROOT:-/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION}
do_install $1
# "install" hipMAGMA into /opt/rocm/magma by copying after build
git clone https://bitbucket.org/icl/magma.git
pushd magma
# Version 2.7.2 + ROCm related updates
git checkout a1625ff4d9bc362906bd01f805dbbe12612953f6
cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
echo 'LIBDIR += -L$(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

@ -2,12 +2,6 @@
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_conda_version() {
@ -58,7 +52,6 @@ cd triton
as_jenkins git checkout ${TRITON_PINNED_COMMIT}
as_jenkins git submodule update --init --recursive
cd python
pip_install pybind11==2.13.6
# TODO: remove patch setup.py once we have a proper fix for https://github.com/triton-lang/triton/issues/4527
as_jenkins sed -i -e 's/https:\/\/tritonlang.blob.core.windows.net\/llvm-builds/https:\/\/oaitriton.blob.core.windows.net\/public\/llvm-builds/g' setup.py
@ -67,22 +60,17 @@ if [ -n "${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
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

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

@ -49,8 +49,6 @@ 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/
ENV CUDA_HOME /usr/local/cuda
FROM cuda as cuda11.8
@ -58,6 +56,11 @@ 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
@ -68,13 +71,7 @@ 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 cpu as rocm
ARG ROCM_VERSION
ARG PYTORCH_ROCM_ARCH
ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
ENV MKLROOT /opt/intel
@ -93,7 +90,14 @@ RUN apt-get update -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

@ -39,8 +39,8 @@ case ${GPU_ARCH_TYPE} in
BASE_TARGET=rocm
DOCKER_TAG=rocm${GPU_ARCH_VERSION}
GPU_IMAGE=rocm/dev-ubuntu-20.04:${GPU_ARCH_VERSION}-complete
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH} --build-arg ROCM_VERSION=${GPU_ARCH_VERSION}"
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx1030;gfx1100;gfx1101;gfx942"
DOCKER_GPU_BUILD_ARG="--build-arg PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}"
;;
*)
echo "ERROR: Unrecognized GPU_ARCH_TYPE: ${GPU_ARCH_TYPE}"

View File

@ -18,30 +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/
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu*
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

@ -15,18 +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
ARG PIP_CMAKE
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 rm install_linter.sh common_utils.sh
USER jenkins
CMD ["bash"]

View File

@ -64,9 +64,7 @@ FROM base as cuda
ARG BASE_CUDA_VERSION=10.2
# 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/
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu*
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh
FROM base as intel
# MKL
@ -197,6 +195,13 @@ 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 ${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
# 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

@ -36,9 +36,7 @@ FROM base as cuda
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/
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh install_nccl.sh ci_commit_pins/nccl-cu*
RUN bash ./install_cuda.sh ${BASE_CUDA_VERSION} && rm install_cuda.sh
FROM base as intel
# MKL
@ -160,7 +158,7 @@ ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
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

View File

@ -38,12 +38,6 @@ RUN yum install -y \
sudo \
gcc-toolset-${GCCTOOLSET_VERSION}-toolchain
# (optional) Install non-default Ninja version
ARG NINJA_VERSION
COPY ./common/install_ninja.sh install_ninja.sh
RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
RUN rm install_ninja.sh
# Ensure the expected devtoolset is used
ENV PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/bin:$PATH
ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib64:/opt/rh/gcc-toolset-${GCCTOOLSET_VERSION}/root/usr/lib:$LD_LIBRARY_PATH

View File

@ -67,9 +67,7 @@ FROM base as cuda
ARG BASE_CUDA_VERSION
# Install CUDA
ADD ./common/install_cuda_aarch64.sh install_cuda_aarch64.sh
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
RUN bash ./install_cuda_aarch64.sh ${BASE_CUDA_VERSION} && rm install_cuda_aarch64.sh install_nccl.sh ci_commit_pins/nccl-cu*
RUN bash ./install_cuda_aarch64.sh ${BASE_CUDA_VERSION} && rm install_cuda_aarch64.sh
FROM base as magma
ARG BASE_CUDA_VERSION

View File

@ -42,7 +42,6 @@ RUN yum install -y \
llvm-devel \
libzstd-devel \
python3.12-devel \
python3.12-test \
python3.12-setuptools \
python3.12-pip \
python3-virtualenv \
@ -102,33 +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 \
protobuf-devel \
protobuf-c-devel \
protobuf-lite-devel \
hdf5-devel \
python3-h5py \
git
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.
# 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 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 && \
./build.sh --config Release --parallel 0 --enable_pybind --build_wheel --enable_training --enable_training_apis --enable_training_ops --skip_tests --allow_running_as_root && \
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

@ -48,7 +48,7 @@ case ${GPU_ARCH_TYPE} in
TARGET=final
DOCKER_TAG=cpu-aarch64
GPU_IMAGE=arm64v8/almalinux:8
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11 --build-arg NINJA_VERSION=1.12.1"
DOCKER_GPU_BUILD_ARG=" --build-arg DEVTOOLSET_VERSION=11"
MANY_LINUX_VERSION="2_28_aarch64"
;;
cpu-cxx11-abi)
@ -97,7 +97,7 @@ case ${GPU_ARCH_TYPE} in
DEVTOOLSET_VERSION="11"
GPU_IMAGE=rocm/dev-almalinux-8:${GPU_ARCH_VERSION}-complete
fi
PYTORCH_ROCM_ARCH="gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201"
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=${DEVTOOLSET_VERSION}"
;;
xpu)
@ -121,8 +121,7 @@ fi
(
set -x
# Only activate this if in CI
if [ "$(uname -m)" != "s390x" ] && [ -v CI ]; then
if [ "$(uname -m)" != "s390x" ]; then
# TODO: Remove LimitNOFILE=1048576 patch once https://github.com/pytorch/test-infra/issues/5712
# is resolved. This patch is required in order to fix timing out of Docker build on Amazon Linux 2023.
sudo sed -i s/LimitNOFILE=infinity/LimitNOFILE=1048576/ /usr/lib/systemd/system/docker.service
@ -140,7 +139,7 @@ fi
"${TOPDIR}/.ci/docker/"
)
GITHUB_REF=${GITHUB_REF:-"dev")}
GITHUB_REF=${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}

View File

@ -3,7 +3,7 @@
# Script used only in CD pipeline
OPENSSL_DOWNLOAD_URL=https://www.openssl.org/source/old/1.1.1/
CURL_DOWNLOAD_URL=https://curl.se/download
CURL_DOWNLOAD_URL=https://curl.askapache.com/download
AUTOCONF_DOWNLOAD_URL=https://ftp.gnu.org/gnu/autoconf

View File

@ -30,10 +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
@ -41,14 +41,11 @@ fbscribelogger==0.1.7
#Pinned versions: 0.1.6
#test that import:
flatbuffers==2.0 ; platform_machine != "s390x"
flatbuffers==2.0
#Description: cross platform serialization library
#Pinned versions: 2.0
#test that import:
flatbuffers ; platform_machine == "s390x"
#Description: cross platform serialization library; Newer version is required on s390x for new python version
hypothesis==5.35.1
# Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
#Description: advanced library for generating parametrized tests
@ -93,10 +90,10 @@ librosa>=0.6.2 ; python_version < "3.11"
#Pinned versions:
#test that import:
mypy==1.14.0
mypy==1.13.0
# Pin MyPy version because new errors are likely to appear with each release
#Description: linter
#Pinned versions: 1.14.0
#Pinned versions: 1.10.0
#test that import: test_typing.py, test_type_hints.py
networkx==2.8.8
@ -105,10 +102,10 @@ 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"
@ -283,9 +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
@ -297,7 +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:
@ -307,7 +304,7 @@ pytest-cpp==2.3.0
#Pinned versions: 2.3.0
#test that import:
z3-solver==4.12.6.0
z3-solver==4.12.2.0
#Description: The Z3 Theorem Prover Project
#Pinned versions:
#test that import:
@ -332,7 +329,7 @@ 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:
@ -342,7 +339,7 @@ onnx==1.17.0
#Pinned versions:
#test that import:
onnxscript==0.2.2
onnxscript==0.1.0.dev20240817
#Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
#Pinned versions:
#test that import:
@ -356,7 +353,7 @@ 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
@ -365,17 +362,12 @@ pwlf==2.2.1
# To build PyTorch itself
astunparse
PyYAML
pyzstd
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:

View File

@ -1 +1 @@
3.3.0
3.2.0

View File

@ -2,7 +2,7 @@ ARG UBUNTU_VERSION
ARG CUDA_VERSION
ARG IMAGE_NAME
FROM ${IMAGE_NAME} as base
FROM ${IMAGE_NAME}
ARG UBUNTU_VERSION
ARG CUDA_VERSION
@ -50,6 +50,13 @@ RUN if [ -n "${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 ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
@ -90,20 +97,14 @@ RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
RUN rm install_cmake.sh
ARG TRITON
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 triton_version.txt triton_version.txt
RUN bash ./install_triton.sh
FROM base as final
COPY --from=triton-builder /opt/triton /opt/triton
RUN if [ -n "${TRITON}" ]; then pip install /opt/triton/*.whl; chown -R jenkins:jenkins /opt/conda; fi
RUN rm -rf /opt/triton
RUN if [ -n "${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
@ -158,16 +159,6 @@ COPY ./common/install_cusparselt.sh install_cusparselt.sh
RUN bash install_cusparselt.sh
RUN rm install_cusparselt.sh
# Install NCCL
ARG CUDA_VERSION
COPY ./common/install_nccl.sh install_nccl.sh
COPY ./ci_commit_pins/nccl-cu* /ci_commit_pins/
RUN bash install_nccl.sh
RUN rm install_nccl.sh /ci_commit_pins/nccl-cu*
ENV USE_SYSTEM_NCCL=1
ENV NCCL_INCLUDE_DIR="/usr/local/cuda/include/"
ENV NCCL_LIB_DIR="/usr/local/cuda/lib64/"
# Install CUDSS
ARG CUDA_VERSION
COPY ./common/install_cudss.sh install_cudss.sh

View File

@ -14,20 +14,21 @@ ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
COPY ./common/install_base.sh install_base.sh
RUN bash ./install_base.sh && rm install_base.sh
# Install clang
ARG LLVMDEV
ARG CLANG_VERSION
COPY ./common/install_clang.sh install_clang.sh
RUN bash ./install_clang.sh && rm install_clang.sh
# Install user
COPY ./common/install_user.sh install_user.sh
RUN bash ./install_user.sh && rm install_user.sh
# Install katex
ARG KATEX
COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
# Install conda and other packages (e.g., numpy, pytest)
ARG ANACONDA_PYTHON_VERSION
ARG CONDA_CMAKE
ENV ANACONDA_PYTHON_VERSION=$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
@ -38,11 +39,6 @@ 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
@ -50,6 +46,13 @@ 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 ./
@ -63,7 +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
@ -82,32 +85,6 @@ 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.txt huggingface.txt
COPY ci_commit_pins/timm.txt timm.txt
RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
@ -130,17 +107,18 @@ COPY triton_version.txt triton_version.txt
RUN if [ -n "${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

@ -77,6 +77,13 @@ COPY triton_version.txt triton_version.txt
RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
RUN rm install_triton.sh common_utils.sh triton-xpu.txt triton_version.txt
# (optional) Install database packages like LMDB and LevelDB
ARG DB
COPY ./common/install_db.sh install_db.sh
RUN if [ -n "${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 ./

View File

@ -1,6 +1,6 @@
ARG UBUNTU_VERSION
FROM ubuntu:${UBUNTU_VERSION} as base
FROM ubuntu:${UBUNTU_VERSION}
ARG UBUNTU_VERSION
@ -52,16 +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/
RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh install_nccl.sh /ci_commit_pins/nccl-cu*
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
@ -81,6 +74,13 @@ 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 ${DB}
# (optional) Install vision packages like OpenCV
ARG VISION
COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
@ -88,6 +88,18 @@ RUN if [ -n "${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 "${SWIFTSHADER}" ]; then bash ./install_swiftshader.sh; fi
RUN rm install_swiftshader.sh
# (optional) Install non-default CMake version
ARG CMAKE_VERSION
COPY ./common/install_cmake.sh install_cmake.sh
@ -115,21 +127,20 @@ RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_d
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

View File

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

View File

@ -1,35 +0,0 @@
SHELL=/usr/bin/env bash
DOCKER_CMD ?= docker
DESIRED_ROCM ?= 6.3
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/manylinux2_28-builder:rocm${DESIRED_ROCM}-main" \
magma-rocm/build_magma.sh
.PHONY: all
all: magma-rocm63
all: magma-rocm624
.PHONY:
clean:
$(RM) -r magma-*
$(RM) -r output
.PHONY: magma-rocm63
magma-rocm63: DESIRED_ROCM := 6.3
magma-rocm63:
$(DOCKER_RUN)
.PHONY: magma-rocm624
magma-rocm624: DESIRED_ROCM := 6.2.4
magma-rocm624:
$(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.

View File

@ -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

View File

@ -1,38 +0,0 @@
# Magma build scripts need `python`
ln -sf /usr/bin/python3 /usr/bin/python
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
MKLROOT=${MKLROOT:-/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION}
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}"
cp -R lib ${INSTALL_DIR}
cp -R include ${INSTALL_DIR}

View File

@ -12,13 +12,13 @@ DOCKER_RUN = set -eou pipefail; ${DOCKER_CMD} run --rm -i \
-e PACKAGE_NAME=${PACKAGE_NAME}${DESIRED_CUDA_SHORT} \
-e DESIRED_CUDA=${DESIRED_CUDA} \
-e CUDA_ARCH_LIST="${CUDA_ARCH_LIST}" \
"pytorch/manylinux2_28-builder:cuda${DESIRED_CUDA}-main" \
"pytorch/manylinux-builder:cuda${DESIRED_CUDA}-main" \
magma/build_magma.sh
.PHONY: all
all: magma-cuda128
all: magma-cuda126
all: magma-cuda124
all: magma-cuda121
all: magma-cuda118
.PHONY:
@ -26,12 +26,6 @@ clean:
$(RM) -r magma-*
$(RM) -r output
.PHONY: magma-cuda128
magma-cuda128: DESIRED_CUDA := 12.8
magma-cuda128: CUDA_ARCH_LIST += -gencode arch=compute_100,code=sm_100 -gencode arch=compute_120,code=sm_120
magma-cuda128:
$(DOCKER_RUN)
.PHONY: magma-cuda126
magma-cuda126: DESIRED_CUDA := 12.6
magma-cuda126:
@ -42,6 +36,11 @@ magma-cuda124: DESIRED_CUDA := 12.4
magma-cuda124:
$(DOCKER_RUN)
.PHONY: magma-cuda121
magma-cuda121: DESIRED_CUDA := 12.1
magma-cuda121:
$(DOCKER_RUN)
.PHONY: magma-cuda118
magma-cuda118: DESIRED_CUDA := 11.8
magma-cuda118: CUDA_ARCH_LIST += -gencode arch=compute_37,code=sm_37

View File

@ -111,6 +111,12 @@ case ${DESIRED_PYTHON} in
;;
esac
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
export _GLIBCXX_USE_CXX11_ABI=1
else
export _GLIBCXX_USE_CXX11_ABI=0
fi
if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
echo "Calling build_amd.py at $(date)"
python tools/amd_build/build_amd.py
@ -203,6 +209,12 @@ if [[ -n "$BUILD_PYTHONLESS" ]]; then
mkdir -p /tmp/$LIBTORCH_HOUSE_DIR
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
LIBTORCH_ABI="cxx11-abi-"
else
LIBTORCH_ABI=
fi
zip -rq /tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION.zip libtorch
cp /tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-$PYTORCH_BUILD_VERSION.zip \
/tmp/$LIBTORCH_HOUSE_DIR/libtorch-$LIBTORCH_ABI$LIBTORCH_VARIANT-latest.zip

View File

@ -14,7 +14,6 @@ export USE_CUDA_STATIC_LINK=1
export INSTALL_TEST=0 # dont install test binaries into site-packages
export USE_CUPTI_SO=0
export USE_CUSPARSELT=${USE_CUSPARSELT:-1} # Enable if not disabled by libtorch build
export USE_CUFILE=${USE_CUFILE:-1}
# Keep an array of cmake variables to add to
if [[ -z "$CMAKE_ARGS" ]]; then
@ -44,6 +43,13 @@ if [[ -n "$DESIRED_CUDA" ]]; then
fi
fi
echo "Using CUDA $CUDA_VERSION as determined by DESIRED_CUDA"
# There really has to be a better way to do this - eli
# Possibly limiting builds to specific cuda versions be delimiting images would be a choice
if [[ "$OS_NAME" == *"Ubuntu"* ]]; then
echo "Switching to CUDA version ${DESIRED_CUDA}"
/builder/conda/switch_cuda_version.sh "${DESIRED_CUDA}"
fi
else
CUDA_VERSION=$(nvcc --version|grep release|cut -f5 -d" "|cut -f1 -d",")
echo "CUDA $CUDA_VERSION Detected"
@ -53,15 +59,23 @@ cuda_version_nodot=$(echo $CUDA_VERSION | tr -d '.')
TORCH_CUDA_ARCH_LIST="5.0;6.0;7.0;7.5;8.0;8.6"
case ${CUDA_VERSION} in
12.8)
TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6;9.0;10.0;12.0+PTX" #removing sm_50-sm_70 as these architectures are deprecated in CUDA 12.8 and will be removed in future releases
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
12.6)
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};9.0"
if [[ "$GPU_ARCH_TYPE" = "cuda-aarch64" ]]; then
TORCH_CUDA_ARCH_LIST="9.0"
else
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};9.0"
fi
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
12.4)
if [[ "$GPU_ARCH_TYPE" = "cuda-aarch64" ]]; then
TORCH_CUDA_ARCH_LIST="9.0"
else
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};9.0"
fi
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
12.1)
TORCH_CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST};9.0"
EXTRA_CAFFE2_CMAKE_FLAGS+=("-DATEN_NO_TEST=ON")
;;
@ -119,16 +133,7 @@ if [[ $USE_CUSPARSELT == "1" && $CUDA_VERSION == "11.8" ]]; then
)
fi
# Turn USE_CUFILE off for CUDA 11.8, 12.4 since nvidia-cufile-cu11 and 1.9.0.20 are
# not available in PYPI
if [[ $CUDA_VERSION == "11.8" || $CUDA_VERSION == "12.4" ]]; then
export USE_CUFILE=0
fi
# CUDA_VERSION 12.4, 12.6, 12.8
if [[ $CUDA_VERSION == 12* ]]; then
if [[ $CUDA_VERSION == "12.4" || $CUDA_VERSION == "12.6" ]]; then
export USE_STATIC_CUDNN=0
# Try parallelizing nvcc as well
export TORCH_NVCC_FLAGS="-Xfatbin -compress-all --threads 2"
@ -169,16 +174,6 @@ if [[ $CUDA_VERSION == 12* ]]; then
"libnvrtc.so.12"
"libnvrtc-builtins.so"
)
if [[ $USE_CUFILE == 1 ]]; then
DEPS_LIST+=(
"/usr/local/cuda/lib64/libcufile.so.0"
"/usr/local/cuda/lib64/libcufile_rdma.so.1"
)
DEPS_SONAME+=(
"libcufile.so.0"
"libcufile_rdma.so.1"
)
fi
else
echo "Using nvidia libs from pypi."
CUDA_RPATHS=(
@ -195,11 +190,6 @@ if [[ $CUDA_VERSION == 12* ]]; then
'$ORIGIN/../../nvidia/nccl/lib'
'$ORIGIN/../../nvidia/nvtx/lib'
)
if [[ $USE_CUFILE == 1 ]]; then
CUDA_RPATHS+=(
'$ORIGIN/../../nvidia/cufile/lib'
)
fi
CUDA_RPATHS=$(IFS=: ; echo "${CUDA_RPATHS[*]}")
export C_SO_RPATH=$CUDA_RPATHS':$ORIGIN:$ORIGIN/lib'
export LIB_SO_RPATH=$CUDA_RPATHS':$ORIGIN'
@ -285,7 +275,7 @@ else
exit 1
fi
# run_tests.sh requires DESIRED_CUDA to know what tests to exclude
# builder/test.sh requires DESIRED_CUDA to know what tests to exclude
export DESIRED_CUDA="$cuda_version_nodot"
# Switch `/usr/local/cuda` to the desired CUDA version

View File

@ -95,6 +95,12 @@ python setup.py clean
retry pip install -qr requirements.txt
retry pip install -q numpy==2.0.1
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
export _GLIBCXX_USE_CXX11_ABI=1
else
export _GLIBCXX_USE_CXX11_ABI=0
fi
if [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
echo "Calling build_amd.py at $(date)"
python tools/amd_build/build_amd.py
@ -163,6 +169,12 @@ fi
)
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
LIBTORCH_ABI="cxx11-abi-"
else
LIBTORCH_ABI=
fi
(
set -x

View File

@ -118,7 +118,7 @@ if [[ "$OS_NAME" == *"CentOS Linux"* || "$OS_NAME" == *"AlmaLinux"* ]]; then
fi
LIBDRM_PATH="/opt/amdgpu/lib64/libdrm.so.2"
LIBDRM_AMDGPU_PATH="/opt/amdgpu/lib64/libdrm_amdgpu.so.1"
if [[ $ROCM_INT -ge 60100 && $ROCM_INT -lt 60300 ]]; then
if [[ $ROCM_INT -ge 60100 ]]; then
# Below libs are direct dependencies of libhipsolver
LIBSUITESPARSE_CONFIG_PATH="/lib64/libsuitesparseconfig.so.4"
if [[ "$OS_NAME" == *"CentOS Linux"* ]]; then
@ -151,7 +151,7 @@ elif [[ "$OS_NAME" == *"Ubuntu"* ]]; then
fi
LIBDRM_PATH="/usr/lib/x86_64-linux-gnu/libdrm.so.2"
LIBDRM_AMDGPU_PATH="/usr/lib/x86_64-linux-gnu/libdrm_amdgpu.so.1"
if [[ $ROCM_INT -ge 60100 && $ROCM_INT -lt 60300 ]]; then
if [[ $ROCM_INT -ge 60100 ]]; then
# Below libs are direct dependencies of libhipsolver
LIBCHOLMOD_PATH="/lib/x86_64-linux-gnu/libcholmod.so.3"
# Below libs are direct dependencies of libcholmod
@ -186,6 +186,15 @@ do
OS_SO_FILES[${#OS_SO_FILES[@]}]=$file_name # Append lib to array
done
# FIXME: Temporary until https://github.com/pytorch/pytorch/pull/137443 lands
# Install AOTriton
if [ -e ${PYTORCH_ROOT}/.ci/docker/aotriton_version.txt ]; then
cp -a ${PYTORCH_ROOT}/.ci/docker/aotriton_version.txt aotriton_version.txt
bash ${PYTORCH_ROOT}/.ci/docker/common/install_aotriton.sh ${ROCM_HOME} && rm aotriton_version.txt
export AOTRITON_INSTALLED_PREFIX=${ROCM_HOME}/aotriton
ROCM_SO_FILES+=("libaotriton_v2.so")
fi
# rocBLAS library files
ROCBLAS_LIB_SRC=$ROCM_HOME/lib/rocblas/library
ROCBLAS_LIB_DST=lib/rocblas/library
@ -257,6 +266,20 @@ RCCL_SHARE_FILES=($(ls $RCCL_SHARE_SRC))
DEPS_AUX_SRCLIST+=(${RCCL_SHARE_FILES[@]/#/$RCCL_SHARE_SRC/})
DEPS_AUX_DSTLIST+=(${RCCL_SHARE_FILES[@]/#/$RCCL_SHARE_DST/})
# PyTorch 2.6+ (AOTriton 0.8b+)
# AKS = "AOTriton Kernel Storage", a file format to store GPU kernels compactly
if (( $(echo "${PYTORCH_VERSION} 2.6" | awk '{print ($1 >= $2)}') )); then
LIBAOTRITON_DIR=$(find "$ROCM_HOME/lib/" -name "libaotriton_v2.so" -printf '%h\n')
if [[ -z ${LIBAOTRITON_DIR} ]]; then
LIBAOTRITON_DIR=$(find "$ROCM_HOME/" -name "libaotriton_v2.so" -printf '%h\n')
fi
AKS_FILES=($(find "${LIBAOTRITON_DIR}/aotriton.images" -type f -name '*.aks?' -printf '%P\n'))
AKS_SRC="${LIBAOTRITON_DIR}/aotriton.images"
AKS_DST="lib/aotriton.images"
DEPS_AUX_SRCLIST+=(${AKS_FILES[@]/#/${AKS_SRC}/})
DEPS_AUX_DSTLIST+=(${AKS_FILES[@]/#/${AKS_DST}/})
fi
echo "PYTORCH_ROCM_ARCH: ${PYTORCH_ROCM_ARCH}"
SCRIPTPATH="$( cd "$(dirname "$0")" ; pwd -P )"

View File

@ -35,7 +35,7 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
fi
if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *clang* ]]; then
if [[ "$BUILD_ENVIRONMENT" != *cuda11.3* && "$BUILD_ENVIRONMENT" != *clang* ]]; then
# TODO: there is a linking issue when building with UCC using clang,
# disable it for now and to be fix later.
# TODO: disable UCC temporarily to enable CUDA 12.1 in CI
@ -173,7 +173,6 @@ if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
source /opt/intel/oneapi/compiler/latest/env/vars.sh
# XPU kineto feature dependencies are not fully ready, disable kineto build as temp WA
export USE_KINETO=0
export TORCH_XPU_ARCH_LIST=pvc
fi
# sccache will fail for CUDA builds if all cores are used for compiling
@ -192,7 +191,7 @@ fi
# We only build FlashAttention files for CUDA 8.0+, and they require large amounts of
# memory to build and will OOM
if [[ "$BUILD_ENVIRONMENT" == *cuda* ]] && [[ 1 -eq $(echo "${TORCH_CUDA_ARCH_LIST} >= 8.0" | bc) ]] && [ -z "$MAX_JOBS_OVERRIDE" ]; then
if [[ "$BUILD_ENVIRONMENT" == *cuda* ]] && [[ 1 -eq $(echo "${TORCH_CUDA_ARCH_LIST} >= 8.0" | bc) ]]; then
echo "WARNING: FlashAttention files require large amounts of memory to build and will OOM"
echo "Setting MAX_JOBS=(nproc-2)/3 to reduce memory usage"
export MAX_JOBS="$(( $(nproc --ignore=2) / 3 ))"
@ -229,7 +228,7 @@ if [[ "$BUILD_ENVIRONMENT" == *-debug* ]]; then
export CMAKE_BUILD_TYPE=RelWithAssert
fi
# Do not change workspace permissions for ROCm and s390x CI jobs
# Do not change workspace permissions for ROCm CI jobs
# as it can leave workspace with bad permissions for cancelled jobs
if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *s390x* && -d /var/lib/jenkins/workspace ]]; then
# Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
@ -248,7 +247,7 @@ if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *s390x* && -d /v
fi
if [[ "$BUILD_ENVIRONMENT" == *-bazel-* ]]; then
set -e -o pipefail
set -e
get_bazel
@ -277,8 +276,10 @@ else
# or building non-XLA tests.
if [[ "$BUILD_ENVIRONMENT" != *rocm* &&
"$BUILD_ENVIRONMENT" != *xla* ]]; then
# Install numpy-2.0.2 for builds which are backward compatible with 1.X
python -mpip install numpy==2.0.2
if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
# Install numpy-2.0.2 for builds which are backward compatible with 1.X
python -mpip install --pre numpy==2.0.2
fi
WERROR=1 python setup.py clean
@ -376,10 +377,8 @@ else
# This is an attempt to mitigate flaky libtorch build OOM error. By default, the build parallelization
# is set to be the number of CPU minus 2. So, let's try a more conservative value here. A 4xlarge has
# 16 CPUs
if [ -z "$MAX_JOBS_OVERRIDE" ]; then
MAX_JOBS=$(nproc --ignore=4)
export MAX_JOBS
fi
MAX_JOBS=$(nproc --ignore=4)
export MAX_JOBS
# NB: Install outside of source directory (at the same level as the root
# pytorch folder) so that it doesn't get cleaned away prior to docker push.

View File

@ -59,16 +59,78 @@ else
export install_root="$(dirname $(which python))/../lib/python${py_dot}/site-packages/torch/"
fi
###############################################################################
# Setup XPU ENV
###############################################################################
if [[ "$DESIRED_CUDA" == 'xpu' ]]; then
set +u
# Refer https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
source /opt/intel/oneapi/compiler/latest/env/vars.sh
source /opt/intel/oneapi/pti/latest/env/vars.sh
fi
###############################################################################
# Check GCC ABI
###############################################################################
# NOTE: As of https://github.com/pytorch/pytorch/issues/126551 we only produce
# wheels with cxx11-abi
# NOTE [ Building libtorch with old vs. new gcc ABI ]
#
# Packages built with one version of ABI could not be linked against by client
# C++ libraries that were compiled using the other version of ABI. Since both
# gcc ABIs are still common in the wild, we need to support both ABIs. Currently:
#
# - All the nightlies built on CentOS 7 + devtoolset7 use the old gcc ABI.
# - All the nightlies built on Ubuntu 16.04 + gcc 5.4 use the new gcc ABI.
echo "Checking that the gcc ABI is what we expect"
if [[ "$(uname)" != 'Darwin' ]]; then
# We also check that there are cxx11 symbols in libtorch
function is_expected() {
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* || "$DESIRED_CUDA" == *"rocm"* ]]; then
if [[ "$1" -gt 0 || "$1" == "ON " ]]; then
echo 1
fi
else
if [[ -z "$1" || "$1" == 0 || "$1" == "OFF" ]]; then
echo 1
fi
fi
}
# First we check that the env var in TorchConfig.cmake is correct
# We search for D_GLIBCXX_USE_CXX11_ABI=1 in torch/TorchConfig.cmake
torch_config="${install_root}/share/cmake/Torch/TorchConfig.cmake"
if [[ ! -f "$torch_config" ]]; then
echo "No TorchConfig.cmake found!"
ls -lah "$install_root/share/cmake/Torch"
exit 1
fi
echo "Checking the TorchConfig.cmake"
cat "$torch_config"
# The sed call below is
# don't print lines by default (only print the line we want)
# -n
# execute the following expression
# e
# replace lines that match with the first capture group and print
# s/.*D_GLIBCXX_USE_CXX11_ABI=\(.\)".*/\1/p
# any characters, D_GLIBCXX_USE_CXX11_ABI=, exactly one any character, a
# quote, any characters
# Note the exactly one single character after the '='. In the case that the
# variable is not set the '=' will be followed by a '"' immediately and the
# line will fail the match and nothing will be printed; this is what we
# want. Otherwise it will capture the 0 or 1 after the '='.
# /.*D_GLIBCXX_USE_CXX11_ABI=\(.\)".*/
# replace the matched line with the capture group and print
# /\1/p
actual_gcc_abi="$(sed -ne 's/.*D_GLIBCXX_USE_CXX11_ABI=\(.\)".*/\1/p' < "$torch_config")"
if [[ "$(is_expected "$actual_gcc_abi")" != 1 ]]; then
echo "gcc ABI $actual_gcc_abi not as expected."
exit 1
fi
# We also check that there are [not] cxx11 symbols in libtorch
#
echo "Checking that symbols in libtorch.so have the right gcc abi"
python3 "$(dirname ${BASH_SOURCE[0]})/smoke_test/check_binary_symbols.py"
@ -146,11 +208,35 @@ setup_link_flags () {
TEST_CODE_DIR="$(dirname $(realpath ${BASH_SOURCE[0]}))/test_example_code"
build_and_run_example_cpp () {
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
GLIBCXX_USE_CXX11_ABI=1
else
GLIBCXX_USE_CXX11_ABI=0
fi
setup_link_flags
g++ ${TEST_CODE_DIR}/$1.cpp -I${install_root}/include -I${install_root}/include/torch/csrc/api/include -std=gnu++17 -L${install_root}/lib ${REF_LIB} ${ADDITIONAL_LINKER_FLAGS} -ltorch $TORCH_CPU_LINK_FLAGS $TORCH_CUDA_LINK_FLAGS $C10_LINK_FLAGS -o $1
g++ ${TEST_CODE_DIR}/$1.cpp -I${install_root}/include -I${install_root}/include/torch/csrc/api/include -D_GLIBCXX_USE_CXX11_ABI=$GLIBCXX_USE_CXX11_ABI -std=gnu++17 -L${install_root}/lib ${REF_LIB} ${ADDITIONAL_LINKER_FLAGS} -ltorch $TORCH_CPU_LINK_FLAGS $TORCH_CUDA_LINK_FLAGS $C10_LINK_FLAGS -o $1
./$1
}
build_example_cpp_with_incorrect_abi () {
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
GLIBCXX_USE_CXX11_ABI=0
else
GLIBCXX_USE_CXX11_ABI=1
fi
set +e
setup_link_flags
g++ ${TEST_CODE_DIR}/$1.cpp -I${install_root}/include -I${install_root}/include/torch/csrc/api/include -D_GLIBCXX_USE_CXX11_ABI=$GLIBCXX_USE_CXX11_ABI -std=gnu++17 -L${install_root}/lib ${REF_LIB} ${ADDITIONAL_LINKER_FLAGS} -ltorch $TORCH_CPU_LINK_FLAGS $TORCH_CUDA_LINK_FLAGS $C10_LINK_FLAGS -o $1
ERRCODE=$?
set -e
if [ "$ERRCODE" -eq "0" ]; then
echo "Building example with incorrect ABI didn't throw error. Aborting."
exit 1
else
echo "Building example with incorrect ABI throws expected error. Proceeding."
fi
}
###############################################################################
# Check simple Python/C++ calls
###############################################################################
@ -160,6 +246,11 @@ if [[ "$PACKAGE_TYPE" == 'libtorch' ]]; then
export LD_LIBRARY_PATH=/usr/local/cuda/lib64
fi
build_and_run_example_cpp simple-torch-test
# `_GLIBCXX_USE_CXX11_ABI` is always ignored by gcc in devtoolset7, so we test
# the expected failure case for Ubuntu 16.04 + gcc 5.4 only.
if [[ "$DESIRED_DEVTOOLSET" == *"cxx11-abi"* ]]; then
build_example_cpp_with_incorrect_abi simple-torch-test
fi
else
pushd /tmp
python -c 'import torch'
@ -294,19 +385,10 @@ except RuntimeError as e:
fi
###############################################################################
# Check for C++ ABI compatibility to GCC-11
# Check for C++ ABI compatibility between gcc7 and gcc9 compiled binaries
###############################################################################
if [[ "$(uname)" == 'Linux' && "$PACKAGE_TYPE" == 'manywheel' ]]; then
if [[ "$(uname)" == 'Linux' && ("$PACKAGE_TYPE" == 'conda' || "$PACKAGE_TYPE" == 'manywheel')]]; then
pushd /tmp
# Per https://gcc.gnu.org/onlinedocs/gcc/C_002b_002b-Dialect-Options.html gcc-11 is ABI16
# Though manylinux_2.28 should have been build with gcc-14, per
# https://github.com/pypa/manylinux?tab=readme-ov-file#manylinux_2_28-almalinux-8-based
# On s390x gcc 14 is used because it contains fix for interaction
# between precompiled headers and vectorization builtins.
# This fix is not available in earlier gcc versions.
# gcc-14 uses ABI19.
if [[ "$(uname -m)" != "s390x" ]]; then
python -c "import torch; exit(0 if torch._C._PYBIND11_BUILD_ABI == '_cxxabi1016' else 1)"
fi
python -c "import torch; exit(0 if torch.compiled_with_cxx11_abi() else (0 if torch._C._PYBIND11_BUILD_ABI == '_cxxabi1011' else 1))"
popd
fi

View File

@ -3,7 +3,7 @@
# Common setup for all Jenkins scripts
# shellcheck source=./common_utils.sh
source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
set -ex -o pipefail
set -ex
# Required environment variables:
# $BUILD_ENVIRONMENT (should be set by your Docker image)

View File

@ -160,7 +160,7 @@ function install_torchvision() {
}
function install_tlparse() {
pip_install --user "tlparse==0.3.30"
pip_install --user "tlparse==0.3.25"
PATH="$(python -m site --user-base)/bin:$PATH"
}
@ -169,40 +169,30 @@ function install_torchrec_and_fbgemm() {
torchrec_commit=$(get_pinned_commit torchrec)
local fbgemm_commit
fbgemm_commit=$(get_pinned_commit fbgemm)
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]] ; then
fbgemm_commit=$(get_pinned_commit fbgemm_rocm)
fi
pip_uninstall torchrec-nightly
pip_uninstall fbgemm-gpu-nightly
pip_install setuptools-git-versioning scikit-build pyre-extensions
if [[ "$BUILD_ENVIRONMENT" == *rocm* ]] ; then
# install torchrec first because it installs fbgemm nightly on top of rocm fbgemm
pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
pip_uninstall fbgemm-gpu-nightly
# TODO (huydhn): I still have no clue on why sccache doesn't work with only fbgemm_gpu here, but it
# seems to be an sccache-related issue
if [[ "$IS_A100_RUNNER" == "1" ]]; then
unset CMAKE_CUDA_COMPILER_LAUNCHER
sudo mv /opt/cache/bin /opt/cache/bin-backup
fi
pip_install tabulate # needed for newer fbgemm
pip_install patchelf # needed for rocm fbgemm
git clone --recursive https://github.com/pytorch/fbgemm
pushd fbgemm/fbgemm_gpu
git checkout "${fbgemm_commit}"
python setup.py install \
--package_variant=rocm \
-DHIP_ROOT_DIR="${ROCM_PATH}" \
-DCMAKE_C_FLAGS="-DTORCH_USE_HIP_DSA" \
-DCMAKE_CXX_FLAGS="-DTORCH_USE_HIP_DSA"
popd
rm -rf fbgemm
else
# See https://github.com/pytorch/pytorch/issues/106971
CUDA_PATH=/usr/local/cuda-12.1 pip_install --no-use-pep517 --user "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#egg=fbgemm-gpu&subdirectory=fbgemm_gpu"
pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
# See https://github.com/pytorch/pytorch/issues/106971
CUDA_PATH=/usr/local/cuda-12.1 pip_install --no-use-pep517 --user "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#egg=fbgemm-gpu&subdirectory=fbgemm_gpu"
pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
if [[ "$IS_A100_RUNNER" == "1" ]]; then
export CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
sudo mv /opt/cache/bin-backup /opt/cache/bin
fi
}
function clone_pytorch_xla() {
if [[ ! -d ./xla ]]; then
git clone --recursive --quiet https://github.com/pytorch/xla.git
git clone --recursive -b r2.6 https://github.com/pytorch/xla.git
pushd xla
# pin the xla hash so that we don't get broken by changes to xla
git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"
@ -226,11 +216,6 @@ function checkout_install_torchbench() {
# to install and test other models
python install.py --continue_on_fail
fi
# TODO (huydhn): transformers-4.44.2 added by https://github.com/pytorch/benchmark/pull/2488
# is regressing speedup metric. This needs to be investigated further
pip install transformers==4.38.1
echo "Print all dependencies after TorchBench is installed"
python -mpip freeze
popd

View File

@ -40,7 +40,7 @@ echo "Building PyTorch C++ API docs..."
rm -rf cppdocs
git clone https://github.com/pytorch/cppdocs
set -ex -o pipefail
set -ex
# Generate ATen files
pushd "${pt_checkout}"

View File

@ -5,7 +5,7 @@ pt_checkout="/var/lib/jenkins/workspace"
source "$pt_checkout/.ci/pytorch/common_utils.sh"
echo "functorch_doc_push_script.sh: Invoked with $*"
set -ex -o pipefail
set -ex
version=${DOCS_VERSION:-nightly}
echo "version: $version"

View File

@ -6,7 +6,7 @@
# return the same thing, ex checks for for rocm, CUDA, and changing the path
# where sccache is installed, and not changing /etc/environment.
set -ex -o pipefail
set -ex
install_binary() {
echo "Downloading sccache binary from S3 repo"

View File

@ -33,11 +33,55 @@ if which sccache > /dev/null; then
export PATH="${tmp_dir}:$PATH"
fi
cross_compile_arm64() {
# Cross compilation for arm64
# Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
# that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
USE_DISTRIBUTED=0 CMAKE_OSX_ARCHITECTURES=arm64 MACOSX_DEPLOYMENT_TARGET=11.0 USE_MKLDNN=OFF USE_QNNPACK=OFF WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
}
compile_arm64() {
# Compilation for arm64
# TODO: Compile with OpenMP support (but this causes CI regressions as cross-compilation were done with OpenMP disabled)
USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
}
compile_x86_64() {
USE_DISTRIBUTED=0 WERROR=1 python setup.py bdist_wheel --plat-name=macosx_10_9_x86_64
}
build_lite_interpreter() {
echo "Testing libtorch (lite interpreter)."
CPP_BUILD="$(pwd)/../cpp_build"
# Ensure the removal of the tmp directory
trap 'rm -rfv ${CPP_BUILD}' EXIT
rm -rf "${CPP_BUILD}"
mkdir -p "${CPP_BUILD}/caffe2"
# It looks libtorch need to be built in "${CPP_BUILD}/caffe2 folder.
BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
pushd "${CPP_BUILD}/caffe2" || exit
VERBOSE=1 DEBUG=1 python "${BUILD_LIBTORCH_PY}"
popd || exit
"${CPP_BUILD}/caffe2/build/bin/test_lite_interpreter_runtime"
}
print_cmake_info
# Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
# that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
if [[ ${BUILD_ENVIRONMENT} = *arm64* ]]; then
if [[ $(uname -m) == "arm64" ]]; then
compile_arm64
else
cross_compile_arm64
fi
elif [[ ${BUILD_ENVIRONMENT} = *lite-interpreter* ]]; then
export BUILD_LITE_INTERPRETER=1
build_lite_interpreter
else
compile_x86_64
fi
if which sccache > /dev/null; then
print_sccache_stats

View File

@ -18,9 +18,6 @@ if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available(
fi
popd
# enable debug asserts in serialization
export TORCH_SERIALIZATION_DEBUG=1
setup_test_python() {
# The CircleCI worker hostname doesn't resolve to an address.
# This environment variable makes ProcessGroupGloo default to

View File

@ -8,62 +8,55 @@
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
echo "Testing pytorch"
# When adding more tests, please use HUD to see which shard is shorter
if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
# FSDP tests
for f in test/distributed/fsdp/*.py ; do time python test/run_test.py --verbose -i "${f#*/}" ; done
fi
time python test/run_test.py --include test_cuda_multigpu test_cuda_primary_ctx --verbose
if [[ "${SHARD_NUMBER:-2}" == "2" ]]; then
time python test/run_test.py --include test_cuda_multigpu test_cuda_primary_ctx --verbose
# Disabling tests to see if they solve timeout issues; see https://github.com/pytorch/pytorch/issues/70015
# python tools/download_mnist.py --quiet -d test/cpp/api/mnist
# OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" build/bin/test_api
time python test/run_test.py --verbose -i distributed/test_c10d_common
time python test/run_test.py --verbose -i distributed/test_c10d_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_nccl
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
time python test/run_test.py --verbose -i distributed/test_compute_comm_reordering
time python test/run_test.py --verbose -i distributed/test_store
time python test/run_test.py --verbose -i distributed/test_symmetric_memory
time python test/run_test.py --verbose -i distributed/test_pg_wrapper
time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
# FSDP tests
for f in test/distributed/fsdp/*.py ; do time python test/run_test.py --verbose -i "${f#*/}" ; done
# ShardedTensor tests
time python test/run_test.py --verbose -i distributed/checkpoint/test_checkpoint
time python test/run_test.py --verbose -i distributed/checkpoint/test_file_system_checkpoint
time python test/run_test.py --verbose -i distributed/_shard/sharding_spec/test_sharding_spec
time python test/run_test.py --verbose -i distributed/_shard/sharding_plan/test_sharding_plan
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor_reshard
# Disabling tests to see if they solve timeout issues; see https://github.com/pytorch/pytorch/issues/70015
# python tools/download_mnist.py --quiet -d test/cpp/api/mnist
# OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" build/bin/test_api
time python test/run_test.py --verbose -i distributed/test_c10d_common
time python test/run_test.py --verbose -i distributed/test_c10d_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_nccl
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
time python test/run_test.py --verbose -i distributed/test_compute_comm_reordering
time python test/run_test.py --verbose -i distributed/test_store
time python test/run_test.py --verbose -i distributed/test_symmetric_memory
time python test/run_test.py --verbose -i distributed/test_pg_wrapper
time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
# functional collective tests
time python test/run_test.py --verbose -i distributed/test_functional_api
# ShardedTensor tests
time python test/run_test.py --verbose -i distributed/checkpoint/test_checkpoint
time python test/run_test.py --verbose -i distributed/checkpoint/test_file_system_checkpoint
time python test/run_test.py --verbose -i distributed/_shard/sharding_spec/test_sharding_spec
time python test/run_test.py --verbose -i distributed/_shard/sharding_plan/test_sharding_plan
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor
time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor_reshard
# DTensor tests
time python test/run_test.py --verbose -i distributed/_tensor/test_random_ops
time python test/run_test.py --verbose -i distributed/_tensor/test_dtensor_compile
# functional collective tests
time python test/run_test.py --verbose -i distributed/test_functional_api
# DeviceMesh test
time python test/run_test.py --verbose -i distributed/test_device_mesh
# DTensor tests
time python test/run_test.py --verbose -i distributed/tensor/test_random_ops
time python test/run_test.py --verbose -i distributed/tensor/test_dtensor_compile
# DTensor/TP tests
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_examples
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_random_state
# DeviceMesh test
time python test/run_test.py --verbose -i distributed/test_device_mesh
# FSDP2 tests
time python test/run_test.py --verbose -i distributed/_composable/fsdp/test_fully_shard_training -- -k test_2d_mlp_with_nd_mesh
# DTensor/TP tests
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_examples
time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_random_state
# ND composability tests
time python test/run_test.py --verbose -i distributed/_composable/test_composability/test_2d_composability
time python test/run_test.py --verbose -i distributed/_composable/test_composability/test_pp_composability
# FSDP2 tests
time python test/run_test.py --verbose -i distributed/_composable/fsdp/test_fully_shard_training -- -k test_2d_mlp_with_nd_mesh
# ND composability tests
time python test/run_test.py --verbose -i distributed/_composable/test_composability/test_2d_composability
time python test/run_test.py --verbose -i distributed/_composable/test_composability/test_pp_composability
# Other tests
time python test/run_test.py --verbose -i test_cuda_primary_ctx
time python test/run_test.py --verbose -i test_optim -- -k test_forloop_goes_right_direction_multigpu
time python test/run_test.py --verbose -i test_optim -- -k test_mixed_device_dtype
time python test/run_test.py --verbose -i test_foreach -- -k test_tensors_grouping
fi
# Other tests
time python test/run_test.py --verbose -i test_cuda_primary_ctx
time python test/run_test.py --verbose -i test_optim -- -k test_forloop_goes_right_direction_multigpu
time python test/run_test.py --verbose -i test_optim -- -k test_mixed_device_dtype
time python test/run_test.py --verbose -i test_foreach -- -k test_tensors_grouping
assert_git_not_dirty

View File

@ -7,7 +7,7 @@ source "$pt_checkout/.ci/pytorch/common_utils.sh"
echo "python_doc_push_script.sh: Invoked with $*"
set -ex -o pipefail
set -ex
# for statements like ${1:-${DOCS_INSTALL_PATH:-docs/}}
# the order of operations goes:
@ -63,7 +63,7 @@ build_docs () {
echo "(tried to echo the WARNINGS above the ==== line)"
echo =========================
fi
set -ex -o pipefail
set -ex
return $code
}

View File

@ -13,7 +13,7 @@ set -eux -o pipefail
# This script expects to be in the pytorch root folder
if [[ ! -d 'test' || ! -f 'test/run_test.py' ]]; then
echo "run_tests.sh expects to be run from the Pytorch root directory " \
echo "builder/test.sh expects to be run from the Pytorch root directory " \
"but I'm actually in $(pwd)"
exit 2
fi
@ -40,7 +40,7 @@ retry () {
if [[ "$#" != 3 ]]; then
if [[ -z "${DESIRED_PYTHON:-}" || -z "${DESIRED_CUDA:-}" || -z "${PACKAGE_TYPE:-}" ]]; then
echo "USAGE: run_tests.sh PACKAGE_TYPE DESIRED_PYTHON DESIRED_CUDA"
echo "The env variable PACKAGE_TYPE must be set to 'manywheel' or 'libtorch'"
echo "The env variable PACKAGE_TYPE must be set to 'conda' or 'manywheel' or 'libtorch'"
echo "The env variable DESIRED_PYTHON must be set like '2.7mu' or '3.6m' etc"
echo "The env variable DESIRED_CUDA must be set like 'cpu' or 'cu80' etc"
exit 1

View File

@ -6,7 +6,7 @@ import itertools
import os
import re
from pathlib import Path
from typing import Any
from typing import Any, List, Tuple
# We also check that there are [not] cxx11 symbols in libtorch
@ -46,17 +46,17 @@ LIBTORCH_PRE_CXX11_PATTERNS = _apply_libtorch_symbols(PRE_CXX11_SYMBOLS)
@functools.lru_cache(100)
def get_symbols(lib: str) -> list[tuple[str, str, str]]:
def get_symbols(lib: str) -> List[Tuple[str, str, str]]:
from subprocess import check_output
lines = check_output(f'nm "{lib}"|c++filt', shell=True)
return [x.split(" ", 2) for x in lines.decode("latin1").split("\n")[:-1]]
def grep_symbols(lib: str, patterns: list[Any]) -> list[str]:
def grep_symbols(lib: str, patterns: List[Any]) -> List[str]:
def _grep_symbols(
symbols: list[tuple[str, str, str]], patterns: list[Any]
) -> list[str]:
symbols: List[Tuple[str, str, str]], patterns: List[Any]
) -> List[str]:
rc = []
for _s_addr, _s_type, s_name in symbols:
for pattern in patterns:
@ -80,7 +80,7 @@ def grep_symbols(lib: str, patterns: list[Any]) -> list[str]:
return functools.reduce(list.__add__, (x.result() for x in tasks), [])
def check_lib_symbols_for_abi_correctness(lib: str) -> None:
def check_lib_symbols_for_abi_correctness(lib: str, pre_cxx11_abi: bool = True) -> None:
print(f"lib: {lib}")
cxx11_symbols = grep_symbols(lib, LIBTORCH_CXX11_PATTERNS)
pre_cxx11_symbols = grep_symbols(lib, LIBTORCH_PRE_CXX11_PATTERNS)
@ -88,12 +88,28 @@ def check_lib_symbols_for_abi_correctness(lib: str) -> None:
num_pre_cxx11_symbols = len(pre_cxx11_symbols)
print(f"num_cxx11_symbols: {num_cxx11_symbols}")
print(f"num_pre_cxx11_symbols: {num_pre_cxx11_symbols}")
if num_pre_cxx11_symbols > 0:
raise RuntimeError(
f"Found pre-cxx11 symbols, but there shouldn't be any, see: {pre_cxx11_symbols[:100]}"
if pre_cxx11_abi:
if num_cxx11_symbols > 0:
raise RuntimeError(
f"Found cxx11 symbols, but there shouldn't be any, see: {cxx11_symbols[:100]}"
)
if num_pre_cxx11_symbols < 1000:
raise RuntimeError("Didn't find enough pre-cxx11 symbols.")
# Check for no recursive iterators, regression test for https://github.com/pytorch/pytorch/issues/133437
rec_iter_symbols = grep_symbols(
lib, [re.compile("std::filesystem::recursive_directory_iterator.*")]
)
if num_cxx11_symbols < 100:
raise RuntimeError("Didn't find enought cxx11 symbols")
if len(rec_iter_symbols) > 0:
raise RuntimeError(
f"recursive_directory_iterator in used pre-CXX11 binaries, see; {rec_iter_symbols}"
)
else:
if num_pre_cxx11_symbols > 0:
raise RuntimeError(
f"Found pre-cxx11 symbols, but there shouldn't be any, see: {pre_cxx11_symbols[:100]}"
)
if num_cxx11_symbols < 100:
raise RuntimeError("Didn't find enought cxx11 symbols")
def main() -> None:
@ -105,8 +121,9 @@ def main() -> None:
else:
install_root = Path(distutils.sysconfig.get_python_lib()) / "torch"
libtorch_cpu_path = str(install_root / "lib" / "libtorch_cpu.so")
check_lib_symbols_for_abi_correctness(libtorch_cpu_path)
libtorch_cpu_path = install_root / "lib" / "libtorch_cpu.so"
pre_cxx11_abi = "cxx11-abi" not in os.getenv("DESIRED_DEVTOOLSET", "")
check_lib_symbols_for_abi_correctness(libtorch_cpu_path, pre_cxx11_abi)
if __name__ == "__main__":

View File

@ -46,9 +46,7 @@ def train(args, model, device, train_loader, optimizer, epoch):
optimizer.step()
if batch_idx % args.log_interval == 0:
print(
f"Train Epoch: {epoch} "
f"[{batch_idx * len(data)}/{len(train_loader.dataset)} "
f"({100.0 * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}"
f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} ({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}" # noqa: B950
)
if args.dry_run:
break
@ -73,9 +71,7 @@ def test(model, device, test_loader):
test_loss /= len(test_loader.dataset)
print(
f"\nTest set: Average loss: {test_loss:.4f}, "
f"Accuracy: {correct}/{len(test_loader.dataset)} "
f"({100.0 * correct / len(test_loader.dataset):.0f}%)\n"
f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} ({100. * correct / len(test_loader.dataset):.0f}%)\n" # noqa: B950
)

View File

@ -6,8 +6,6 @@ import re
import subprocess
import sys
from pathlib import Path
from tempfile import NamedTemporaryFile
from typing import Optional
import torch
import torch._dynamo
@ -77,13 +75,10 @@ def read_release_matrix():
def test_numpy():
try:
import numpy as np
import numpy as np
x = np.arange(5)
torch.tensor(x)
except ImportError:
print("Numpy check skipped. Numpy is not installed.")
x = np.arange(5)
torch.tensor(x)
def check_version(package: str) -> None:
@ -114,10 +109,8 @@ def check_version(package: str) -> None:
{release_matrix[module['name']]} for channel {channel}. But its {module_version}"
)
else:
print(
f"{module['name']} version actual: {module_version} expected: \
{release_matrix[module['name']]} for channel {channel}."
)
print(f"{module['name']} version actual: {module_version} expected: \
{release_matrix[module['name']]} for channel {channel}.")
else:
print(f"Skip version check for channel {channel} as stable version is None")
@ -166,71 +159,8 @@ def test_cuda_runtime_errors_captured() -> None:
raise RuntimeError("Expected CUDA RuntimeError but have not received!")
def test_cuda_gds_errors_captured() -> None:
major_version = int(torch.version.cuda.split(".")[0])
minor_version = int(torch.version.cuda.split(".")[1])
if target_os == "windows":
print(f"{target_os} is not supported for GDS smoke test")
return
if major_version < 12 or (major_version == 12 and minor_version < 6):
print("CUDA version is not supported for GDS smoke test")
return
cuda_exception_missed = True
try:
print("Testing test_cuda_gds_errors_captured")
with NamedTemporaryFile() as f:
torch.cuda.gds.GdsFile(f.name, os.O_CREAT | os.O_RDWR)
except RuntimeError as e:
expected_error = "cuFileHandleRegister failed"
if re.search(expected_error, f"{e}"):
print(f"Caught CUDA exception with success: {e}")
cuda_exception_missed = False
else:
raise e
if cuda_exception_missed:
raise RuntimeError(
"Expected cuFileHandleRegister failed RuntimeError but have not received!"
)
def find_pypi_package_version(package: str) -> Optional[str]:
from importlib import metadata
dists = metadata.distributions()
for dist in dists:
if dist.metadata["Name"].startswith(package):
return dist.version
return None
def cudnn_to_version_str(cudnn_version: int) -> str:
patch = int(cudnn_version % 10)
minor = int((cudnn_version / 100) % 100)
major = int((cudnn_version / 10000) % 10000)
return f"{major}.{minor}.{patch}"
def compare_pypi_to_torch_versions(
package: str, pypi_version: str, torch_version: str
) -> None:
if pypi_version is None:
raise RuntimeError(f"Can't find {package} in PyPI for Torch: {torch_version}")
if pypi_version.startswith(torch_version):
print(f"Found matching {package}. Torch: {torch_version} PyPI {pypi_version}")
else:
raise RuntimeError(
f"Wrong {package} version. Torch: {torch_version} PyPI: {pypi_version}"
)
def smoke_test_cuda(
package: str,
runtime_error_check: str,
torch_compile_check: str,
pypi_pkg_check: str,
package: str, runtime_error_check: str, torch_compile_check: str
) -> None:
if not torch.cuda.is_available() and is_cuda_system:
raise RuntimeError(f"Expected CUDA {gpu_arch_ver}. However CUDA is not loaded.")
@ -260,30 +190,20 @@ def smoke_test_cuda(
raise RuntimeError(
f"Wrong CUDA version. Loaded: {torch.version.cuda} Expected: {gpu_arch_ver}"
)
print(f"torch cuda: {torch.version.cuda}")
# todo add cudnn version validation
print(f"torch cudnn: {torch.backends.cudnn.version()}")
print(f"cuDNN enabled? {torch.backends.cudnn.enabled}")
torch.cuda.init()
print("CUDA initialized successfully")
print(f"Number of CUDA devices: {torch.cuda.device_count()}")
for i in range(torch.cuda.device_count()):
print(f"Device {i}: {torch.cuda.get_device_name(i)}")
print(f"cuDNN enabled? {torch.backends.cudnn.enabled}")
torch_cudnn_version = cudnn_to_version_str(torch.backends.cudnn.version())
print(f"Torch cuDNN version: {torch_cudnn_version}")
# nccl is availbale only on Linux
if sys.platform in ["linux", "linux2"]:
torch_nccl_version = ".".join(str(v) for v in torch.cuda.nccl.version())
print(f"Torch nccl; version: {torch_nccl_version}")
# Pypi dependencies are installed on linux ony and nccl is availbale only on Linux.
if pypi_pkg_check == "enabled" and sys.platform in ["linux", "linux2"]:
compare_pypi_to_torch_versions(
"cudnn", find_pypi_package_version("nvidia-cudnn"), torch_cudnn_version
)
compare_pypi_to_torch_versions(
"nccl", find_pypi_package_version("nvidia-nccl"), torch_nccl_version
)
print(f"torch nccl version: {torch.cuda.nccl.version()}")
if runtime_error_check == "enabled":
test_cuda_runtime_errors_captured()
@ -419,7 +339,7 @@ def smoke_test_modules():
print(f"Output: \n{output}\n")
def parse_args():
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument(
"--package",
@ -442,41 +362,22 @@ def parse_args():
choices=["enabled", "disabled"],
default="enabled",
)
parser.add_argument(
"--pypi-pkg-check",
help="Check pypi package versions cudnn and nccl",
type=str,
choices=["enabled", "disabled"],
default="enabled",
)
return parser.parse_args()
def main() -> None:
options = parse_args()
options = parser.parse_args()
print(f"torch: {torch.__version__}")
print(torch.__config__.parallel_info())
# All PyTorch binary builds should be built with OpenMP
if not torch.backends.openmp.is_available():
raise RuntimeError("PyTorch must be built with OpenMP support")
check_version(options.package)
smoke_test_conv2d()
test_linalg()
test_numpy()
if is_cuda_system:
test_linalg("cuda")
test_cuda_gds_errors_captured()
if options.package == "all":
smoke_test_modules()
smoke_test_cuda(
options.package,
options.runtime_error_check,
options.torch_compile_check,
options.pypi_pkg_check,
options.package, options.runtime_error_check, options.torch_compile_check
)

View File

@ -4,7 +4,7 @@
# (This is set by default in the Docker images we build, so you don't
# need to set it yourself.
set -ex -o pipefail
set -ex
# Suppress ANSI color escape sequences
export TERM=vt100
@ -12,9 +12,9 @@ export TERM=vt100
# shellcheck source=./common.sh
source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
# Do not change workspace permissions for ROCm and s390x CI jobs
# Do not change workspace permissions for ROCm CI jobs
# as it can leave workspace with bad permissions for cancelled jobs
if [[ "$BUILD_ENVIRONMENT" != *rocm* && "$BUILD_ENVIRONMENT" != *s390x* && -d /var/lib/jenkins/workspace ]]; then
if [[ "$BUILD_ENVIRONMENT" != *rocm* && -d /var/lib/jenkins/workspace ]]; then
# Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
cleanup_workspace() {
@ -46,9 +46,6 @@ BUILD_BIN_DIR="$BUILD_DIR"/bin
SHARD_NUMBER="${SHARD_NUMBER:=1}"
NUM_TEST_SHARDS="${NUM_TEST_SHARDS:=1}"
# enable debug asserts in serialization
export TORCH_SERIALIZATION_DEBUG=1
export VALGRIND=ON
# export TORCH_INDUCTOR_INSTALL_GXX=ON
if [[ "$BUILD_ENVIRONMENT" == *clang9* || "$BUILD_ENVIRONMENT" == *xpu* ]]; then
@ -89,13 +86,6 @@ if [[ "$BUILD_ENVIRONMENT" == *clang9* || "$BUILD_ENVIRONMENT" == *xpu* ]]; then
export VALGRIND=OFF
fi
if [[ "$BUILD_ENVIRONMENT" == *s390x* ]]; then
# There are additional warnings on s390x, maybe due to newer gcc.
# Skip this check for now
export VALGRIND=OFF
fi
if [[ "${PYTORCH_TEST_RERUN_DISABLED_TESTS}" == "1" ]] || [[ "${CONTINUE_THROUGH_ERROR}" == "1" ]]; then
# When rerunning disable tests, do not generate core dumps as it could consume
# the runner disk space when crashed tests are run multiple times. Running out
@ -139,7 +129,7 @@ if [[ "$TEST_CONFIG" == 'default' ]]; then
fi
if [[ "$TEST_CONFIG" == 'distributed' ]] && [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
export HIP_VISIBLE_DEVICES=0,1,2,3
export HIP_VISIBLE_DEVICES=0,1
fi
if [[ "$TEST_CONFIG" == 'slow' ]]; then
@ -163,8 +153,6 @@ elif [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
export PYTORCH_TESTING_DEVICE_ONLY_FOR="xpu"
# setting PYTHON_TEST_EXTRA_OPTION
export PYTHON_TEST_EXTRA_OPTION="--xpu"
# Disable sccache for xpu test due to flaky issue https://github.com/pytorch/pytorch/issues/143585
sudo rm -rf /opt/cache
fi
if [[ "$TEST_CONFIG" == *crossref* ]]; then
@ -177,9 +165,6 @@ if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
# Print GPU info
rocminfo
rocminfo | grep -E 'Name:.*\sgfx|Marketing'
# for benchmarks/dynamo/check_accuracy.py, we need to put results in a rocm specific directory to avoid clashes with cuda
MAYBE_ROCM="rocm/"
fi
if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
@ -314,13 +299,6 @@ test_python() {
assert_git_not_dirty
}
test_lazy_tensor_meta_reference_disabled() {
export TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE=1
echo "Testing lazy tensor operations without meta reference"
time python test/run_test.py --include lazy/test_ts_opinfo.py --verbose
export -n TORCH_DISABLE_FUNCTIONALIZATION_META_REFERENCE
}
test_dynamo_wrapped_shard() {
if [[ -z "$NUM_TEST_SHARDS" ]]; then
@ -335,7 +313,6 @@ test_dynamo_wrapped_shard() {
--exclude-jit-executor \
--exclude-distributed-tests \
--exclude-torch-export-tests \
--exclude-aot-dispatch-tests \
--shard "$1" "$NUM_TEST_SHARDS" \
--verbose \
--upload-artifacts-while-running
@ -349,7 +326,7 @@ test_inductor_distributed() {
python test/run_test.py -i inductor/test_aot_inductor.py -k test_non_default_cuda_device --verbose
python test/run_test.py -i inductor/test_aot_inductor.py -k test_replicate_on_devices --verbose
python test/run_test.py -i distributed/test_c10d_functional_native.py --verbose
python test/run_test.py -i distributed/tensor/test_dtensor_compile.py --verbose
python test/run_test.py -i distributed/_tensor/test_dtensor_compile.py --verbose
python test/run_test.py -i distributed/tensor/parallel/test_micro_pipeline_tp.py --verbose
python test/run_test.py -i distributed/_composable/test_replicate_with_compiler.py --verbose
python test/run_test.py -i distributed/_composable/fsdp/test_fully_shard_comm.py --verbose
@ -402,32 +379,15 @@ test_inductor_aoti() {
CPP_TESTS_DIR="${BUILD_BIN_DIR}" LD_LIBRARY_PATH="${TORCH_LIB_DIR}" python test/run_test.py --cpp --verbose -i cpp/test_aoti_abi_check cpp/test_aoti_inference
}
test_inductor_cpp_wrapper_shard() {
if [[ -z "$NUM_TEST_SHARDS" ]]; then
echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
exit 1
fi
test_inductor_cpp_wrapper() {
export TORCHINDUCTOR_CPP_WRAPPER=1
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
if [[ "$1" -eq "2" ]]; then
# For now, manually put the opinfo tests in shard 2, and all other tests in
# shard 1. Test specific things triggering past bugs, for now.
python test/run_test.py \
--include inductor/test_torchinductor_opinfo \
-k 'linalg or to_sparse' \
--verbose
exit
fi
# Run certain inductor unit tests with cpp wrapper. In the end state, we should be able to run all the inductor
# unit tests with cpp wrapper.
python test/run_test.py --include inductor/test_torchinductor.py --verbose
# Run certain inductor unit tests with cpp wrapper. In the end state, we
# should be able to run all the inductor unit tests with cpp_wrapper.
python test/run_test.py \
--include inductor/test_torchinductor inductor/test_max_autotune inductor/test_cpu_repro \
--verbose
python test/run_test.py --inductor --include test_torch -k 'take' --verbose
# Run inductor benchmark tests with cpp wrapper.
# Skip benchmark tests if it's in rerun-disabled-mode.
@ -440,7 +400,7 @@ test_inductor_cpp_wrapper_shard() {
--output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_training.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_training.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${MAYBE_ROCM}inductor_timm_training.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_timm_training.csv"
python benchmarks/dynamo/torchbench.py --device cuda --accuracy \
--bfloat16 --inference --inductor --only hf_T5 --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
@ -450,7 +410,7 @@ test_inductor_cpp_wrapper_shard() {
--bfloat16 --inference --inductor --only moco --output "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_cpp_wrapper_inference.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${MAYBE_ROCM}inductor_torchbench_inference.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv"
fi
}
@ -483,8 +443,6 @@ elif [[ "${TEST_CONFIG}" == *aot_eager* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--backend aot_eager)
elif [[ "${TEST_CONFIG}" == *aot_inductor* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--export-aot-inductor)
elif [[ "${TEST_CONFIG}" == *max_autotune_inductor* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--inductor --inductor-compile-mode max-autotune)
elif [[ "${TEST_CONFIG}" == *inductor* && "${TEST_CONFIG}" != *perf* ]]; then
DYNAMO_BENCHMARK_FLAGS+=(--inductor)
fi
@ -499,59 +457,6 @@ else
DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
fi
test_cachebench() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
local BENCHMARK
if [[ "${SHARD_NUMBER}" == 1 ]]; then
local BENCHMARK=torchbench
elif [[ "${SHARD_NUMBER}" == 2 ]]; then
local BENCHMARK=huggingface
else
echo "invalid SHARD_NUMBER: ${SHARD_NUMBER}"
exit 1
fi
local mode_options=("training" "inference")
for mode in "${mode_options[@]}"; do
$TASKSET python "benchmarks/dynamo/cachebench.py" \
--mode "$mode" \
--device cuda \
--benchmark "$BENCHMARK" \
--repeat 3 \
--output "$TEST_REPORTS_DIR/cachebench_${BENCHMARK}_${mode}.json"
$TASKSET python "benchmarks/dynamo/cachebench.py" \
--mode "$mode" \
--dynamic \
--device cuda \
--benchmark "$BENCHMARK" \
--repeat 3 \
--output "$TEST_REPORTS_DIR/cachebench_${BENCHMARK}_${mode}_dynamic.json"
done
}
test_verify_cachebench() {
TMP_TEST_REPORTS_DIR=$(mktemp -d)
TEST_OUTPUT="$TMP_TEST_REPORTS_DIR/test.json"
$TASKSET python "benchmarks/dynamo/cachebench.py" \
--mode training \
--device cpu \
--model nanogpt \
--benchmark torchbench \
--output "$TEST_OUTPUT"
# -s checks file exists and is non empty
if [[ ! -s "$TEST_OUTPUT" ]]; then
echo "Cachebench failed to produce an output."
echo "Run 'python benchmarks/dynamo/cachebench.py' to make sure it works"
exit 1
fi
}
test_perf_for_dashboard() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
@ -580,10 +485,6 @@ test_perf_for_dashboard() {
test_inductor_set_cpu_affinity
elif [[ "${TEST_CONFIG}" == *cuda_a10g* ]]; then
device=cuda_a10g
elif [[ "${TEST_CONFIG}" == *h100* ]]; then
device=cuda_h100
elif [[ "${TEST_CONFIG}" == *rocm* ]]; then
device=rocm
fi
for mode in "${modes[@]}"; do
@ -616,7 +517,7 @@ test_perf_for_dashboard() {
--dynamic-batch-only "$@" \
--output "$TEST_REPORTS_DIR/${backend}_dynamic_${suite}_${dtype}_${mode}_${device}_${target}.csv"
fi
if [[ "$DASHBOARD_TAG" == *cppwrapper-true* ]]; then
if [[ "$DASHBOARD_TAG" == *cppwrapper-true* ]] && [[ "$mode" == "inference" ]]; then
TORCHINDUCTOR_CPP_WRAPPER=1 $TASKSET python "benchmarks/dynamo/$suite.py" \
"${target_flag[@]}" --"$mode" --"$dtype" --backend "$backend" --disable-cudagraphs "$@" \
--output "$TEST_REPORTS_DIR/${backend}_cpp_wrapper_${suite}_${dtype}_${mode}_${device}_${target}.csv"
@ -700,16 +601,16 @@ test_single_dynamo_benchmark() {
TEST_CONFIG=${TEST_CONFIG//_avx512/}
fi
python "benchmarks/dynamo/$suite.py" \
--ci --accuracy --timing --explain --print-compilation-time \
--ci --accuracy --timing --explain \
"${DYNAMO_BENCHMARK_FLAGS[@]}" \
"$@" "${partition_flags[@]}" \
--output "$TEST_REPORTS_DIR/${name}_${suite}.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/${name}_$suite.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${MAYBE_ROCM}${TEST_CONFIG}_${name}.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${TEST_CONFIG}_${name}.csv"
python benchmarks/dynamo/check_graph_breaks.py \
--actual "$TEST_REPORTS_DIR/${name}_$suite.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${MAYBE_ROCM}${TEST_CONFIG}_${name}.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/${TEST_CONFIG}_${name}.csv"
fi
}
@ -732,7 +633,7 @@ test_inductor_halide() {
}
test_inductor_triton_cpu() {
python test/run_test.py --include inductor/test_triton_cpu_backend.py inductor/test_torchinductor_strided_blocks.py --verbose
python test/run_test.py --include inductor/test_triton_cpu_backend.py --verbose
assert_git_not_dirty
}
@ -762,8 +663,6 @@ test_dynamo_benchmark() {
fi
elif [[ "${TEST_CONFIG}" == *aot_inductor* ]]; then
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
elif [[ "${TEST_CONFIG}" == *max_autotune_inductor* ]]; then
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
else
test_single_dynamo_benchmark "inference" "$suite" "$shard_id" --inference --bfloat16 "$@"
test_single_dynamo_benchmark "training" "$suite" "$shard_id" --training --amp "$@"
@ -798,7 +697,7 @@ test_inductor_torchbench_smoketest_perf() {
--only $test --output "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv"
python benchmarks/dynamo/check_accuracy.py \
--actual "$TEST_REPORTS_DIR/inductor_warm_start_smoketest_$test.csv" \
--expected "benchmarks/dynamo/ci_expected_accuracy/${MAYBE_ROCM}inductor_huggingface_training.csv"
--expected "benchmarks/dynamo/ci_expected_accuracy/inductor_huggingface_training.csv"
done
}
@ -994,20 +893,10 @@ test_libtorch_api() {
else
# Exclude IMethodTest that relies on torch::deploy, which will instead be ran in test_deploy
OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="${MNIST_DIR}" python test/run_test.py --cpp --verbose -i cpp/test_api -k "not IMethodTest"
# On s390x, pytorch is built without llvm.
# Even if it would be built with llvm, llvm currently doesn't support used features on s390x and
# test fails with errors like:
# JIT session error: Unsupported target machine architecture in ELF object pytorch-jitted-objectbuffer
# unknown file: Failure
# C++ exception with description "valOrErr INTERNAL ASSERT FAILED at "/var/lib/jenkins/workspace/torch/csrc/jit/tensorexpr/llvm_jit.h":34, please report a bug to PyTorch. Unexpected failure in LLVM JIT: Failed to materialize symbols: { (main, { func }) }
if [[ "${BUILD_ENVIRONMENT}" != *s390x* ]]; then
python test/run_test.py --cpp --verbose -i cpp/test_tensorexpr
fi
python test/run_test.py --cpp --verbose -i cpp/test_tensorexpr
fi
# quantization is not fully supported on s390x yet
if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* && "${BUILD_ENVIRONMENT}" != *asan* && "${BUILD_ENVIRONMENT}" != *s390x* ]]; then
if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* && "${BUILD_ENVIRONMENT}" != *asan* ]]; then
# NB: This test is not under TORCH_BIN_DIR but under BUILD_BIN_DIR
export CPP_TESTS_DIR="${BUILD_BIN_DIR}"
python test/run_test.py --cpp --verbose -i cpp/static_runtime_test
@ -1173,9 +1062,8 @@ build_xla() {
apply_patches
SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
# These functions are defined in .circleci/common.sh in pytorch/xla repo
retry install_pre_deps_pytorch_xla $XLA_DIR $USE_CACHE
retry install_deps_pytorch_xla $XLA_DIR $USE_CACHE
CMAKE_PREFIX_PATH="${SITE_PACKAGES}/torch:${CMAKE_PREFIX_PATH}" XLA_SANDBOX_BUILD=1 build_torch_xla $XLA_DIR
retry install_post_deps_pytorch_xla
assert_git_not_dirty
}
@ -1355,7 +1243,7 @@ EOF
}
test_bazel() {
set -e -o pipefail
set -e
# bazel test needs sccache setup.
# shellcheck source=./common-build.sh
@ -1475,13 +1363,14 @@ test_executorch() {
pushd /executorch
export PYTHON_EXECUTABLE=python
export CMAKE_ARGS="-DEXECUTORCH_BUILD_PYBIND=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
export EXECUTORCH_BUILD_PYBIND=ON
export CMAKE_ARGS="-DEXECUTORCH_BUILD_XNNPACK=ON -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON"
# For llama3
bash examples/models/llama3_2_vision/install_requirements.sh
# NB: We need to rebuild ExecuTorch runner here because it depends on PyTorch
# from the PR
bash .ci/scripts/setup-linux.sh --build-tool cmake
bash .ci/scripts/setup-linux.sh cmake
echo "Run ExecuTorch unit tests"
pytest -v -n auto
@ -1505,7 +1394,7 @@ test_executorch() {
test_linux_aarch64() {
python test/run_test.py --include test_modules test_mkldnn test_mkldnn_fusion test_openmp test_torch test_dynamic_shapes \
test_transformers test_multiprocessing test_numpy_interop test_autograd test_binary_ufuncs test_complex test_spectral_ops \
test_foreach test_reductions test_unary_ufuncs test_tensor_creation_ops test_ops \
test_foreach test_reductions test_unary_ufuncs \
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
# Dynamo tests
@ -1527,27 +1416,6 @@ test_linux_aarch64() {
--shard "$SHARD_NUMBER" "$NUM_TEST_SHARDS" --verbose
}
test_operator_benchmark() {
TEST_REPORTS_DIR=$(pwd)/test/test-reports
mkdir -p "$TEST_REPORTS_DIR"
TEST_DIR=$(pwd)
test_inductor_set_cpu_affinity
cd benchmarks/operator_benchmark/pt_extension
python setup.py install
cd "${TEST_DIR}"/benchmarks/operator_benchmark
$TASKSET python -m benchmark_all_test --device "$1" --tag-filter "$2" \
--output-dir "${TEST_REPORTS_DIR}/operator_benchmark_eager_float32_cpu.csv"
pip_install pandas
python check_perf_csv.py \
--actual "${TEST_REPORTS_DIR}/operator_benchmark_eager_float32_cpu.csv" \
--expected "expected_ci_operator_benchmark_eager_float32_cpu.csv"
}
if ! [[ "${BUILD_ENVIRONMENT}" == *libtorch* || "${BUILD_ENVIRONMENT}" == *-bazel-* ]]; then
(cd test && python -c "import torch; print(torch.__config__.show())")
(cd test && python -c "import torch; print(torch.__config__.parallel_info())")
@ -1578,19 +1446,6 @@ elif [[ "$TEST_CONFIG" == distributed ]]; then
if [[ "${SHARD_NUMBER}" == 1 ]]; then
test_rpc
fi
elif [[ "${TEST_CONFIG}" == *operator_benchmark* ]]; then
TEST_MODE="short"
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
if [[ "${TEST_CONFIG}" == *long* ]]; then
TEST_MODE="long"
elif [[ "${TEST_CONFIG}" == *all* ]]; then
TEST_MODE="all"
fi
test_operator_benchmark cpu ${TEST_MODE}
fi
elif [[ "${TEST_CONFIG}" == *inductor_distributed* ]]; then
test_inductor_distributed
elif [[ "${TEST_CONFIG}" == *inductor-halide* ]]; then
@ -1607,16 +1462,6 @@ elif [[ "${TEST_CONFIG}" == *timm* ]]; then
install_torchvision
id=$((SHARD_NUMBER-1))
test_dynamo_benchmark timm_models "$id"
elif [[ "${TEST_CONFIG}" == cachebench ]]; then
install_torchaudio cuda
install_torchvision
checkout_install_torchbench nanogpt BERT_pytorch resnet50 hf_T5 llama moco
PYTHONPATH=$(pwd)/torchbench test_cachebench
elif [[ "${TEST_CONFIG}" == verify_cachebench ]]; then
install_torchaudio cpu
install_torchvision
checkout_install_torchbench nanogpt
PYTHONPATH=$(pwd)/torchbench test_verify_cachebench
elif [[ "${TEST_CONFIG}" == *torchbench* ]]; then
if [[ "${TEST_CONFIG}" == *cpu* ]]; then
install_torchaudio cpu
@ -1652,8 +1497,7 @@ elif [[ "${TEST_CONFIG}" == *inductor_cpp_wrapper* ]]; then
install_torchaudio cuda
install_torchvision
checkout_install_torchbench hf_T5 llama moco
PYTHONPATH=$(pwd)/torchbench test_inductor_cpp_wrapper_shard "$SHARD_NUMBER"
test_inductor_aoti
PYTHONPATH=$(pwd)/torchbench test_inductor_cpp_wrapper
elif [[ "${TEST_CONFIG}" == *inductor* ]]; then
install_torchvision
test_inductor_shard "${SHARD_NUMBER}"
@ -1673,7 +1517,6 @@ elif [[ "${BUILD_ENVIRONMENT}" == *rocm* && -n "$TESTS_TO_INCLUDE" ]]; then
test_python_shard "$SHARD_NUMBER"
test_aten
elif [[ "${SHARD_NUMBER}" == 1 && $NUM_TEST_SHARDS -gt 1 ]]; then
test_lazy_tensor_meta_reference_disabled
test_without_numpy
install_torchvision
test_python_shard 1

View File

@ -1,41 +0,0 @@
r"""
It's used to check basic rnn features with cpu-only.
For example, it would throw exception if some components are missing
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
class SimpleCNN(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(1, 1, 3)
self.pool = nn.MaxPool2d(2, 2)
def forward(self, inputs):
output = self.pool(F.relu(self.conv(inputs)))
output = output.view(1)
return output
try:
# Mock one infer
net = SimpleCNN()
net_inputs = torch.rand((1, 1, 5, 5))
outputs = net(net_inputs)
print(outputs)
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.1)
# Mock one step training
label = torch.full((1,), 1.0, dtype=torch.float)
loss = criterion(outputs, label)
loss.backward()
optimizer.step()
except Exception as e:
print(f"An error occurred: {e}")

View File

@ -1,13 +0,0 @@
r"""
It's used to check basic rnn features with cpu-only.
For example, it would throw exception if missing some components are missing
"""
import torch
import torch.nn as nn
rnn = nn.RNN(10, 20, 2)
inputs = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
output, hn = rnn(inputs, h0)

View File

@ -38,7 +38,7 @@ if [[ $PYLONG_API_CHECK == 0 ]]; then
echo "PyLong_AsUnsignedLong -> THPUtils_unpackUInt32 / THPUtils_unpackUInt64"
exit 1
fi
set -ex -o pipefail
set -ex
"$SCRIPT_HELPERS_DIR"/build_pytorch.bat

View File

@ -26,8 +26,7 @@ if not errorlevel 0 goto fail
if "%USE_XPU%"=="1" (
:: Install xpu support packages
set CUDA_VERSION=xpu
call %SCRIPT_HELPERS_DIR%\..\windows\internal\xpu_install.bat
call %INSTALLER_DIR%\install_xpu.bat
if errorlevel 1 exit /b 1
)

View File

@ -0,0 +1,114 @@
@echo on
REM Description: Install Intel Support Packages on Windows
REM BKM reference: https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
set XPU_INSTALL_MODE=%~1
if "%XPU_INSTALL_MODE%"=="" goto xpu_bundle_install_start
if "%XPU_INSTALL_MODE%"=="bundle" goto xpu_bundle_install_start
if "%XPU_INSTALL_MODE%"=="driver" goto xpu_driver_install_start
if "%XPU_INSTALL_MODE%"=="all" goto xpu_driver_install_start
:arg_error
echo Illegal XPU installation mode. The value can be "bundle"/"driver"/"all"
echo If keep the value as space, will use default "bundle" mode
exit /b 1
:xpu_driver_install_start
:: TODO Need more testing for driver installation
set XPU_DRIVER_LINK=https://downloadmirror.intel.com/830975/gfx_win_101.5972.exe
curl -o xpu_driver.exe --retry 3 --retry-all-errors -k %XPU_DRIVER_LINK%
echo "XPU Driver installing..."
start /wait "Intel XPU Driver Installer" "xpu_driver.exe"
if errorlevel 1 exit /b 1
del xpu_driver.exe
if "%XPU_INSTALL_MODE%"=="driver" goto xpu_install_end
:xpu_bundle_install_start
set XPU_BUNDLE_PARENT_DIR=C:\Program Files (x86)\Intel\oneAPI
set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-for-pytorch-gpu-dev_p_0.5.3.37_offline.exe
set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.intel-for-pytorch-gpu-dev.product
set XPU_BUNDLE_VERSION=0.5.3+31
set XPU_BUNDLE_INSTALLED=0
set XPU_BUNDLE_UNINSTALL=0
set XPU_EXTRA_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/9d1a91e2-e8b8-40a5-8c7f-5db768a6a60c/w_intel-pti-dev_p_0.9.0.37_offline.exe
set XPU_EXTRA_PRODUCT_NAME=intel.oneapi.win.intel-pti-dev.product
set XPU_EXTRA_VERSION=0.9.0+36
set XPU_EXTRA_INSTALLED=0
set XPU_EXTRA_UNINSTALL=0
if not [%XPU_VERSION%]==[] if [%XPU_VERSION%]==[2025.0] (
set XPU_BUNDLE_URL=https://registrationcenter-download.intel.com/akdlm/IRC_NAS/efc86abd-cb77-452e-a03f-a741895b8ece/intel-deep-learning-essentials-2025.0.0.336_offline.exe
set XPU_BUNDLE_PRODUCT_NAME=intel.oneapi.win.deep-learning-essentials.product
set XPU_BUNDLE_VERSION=2025.0.0+335
set XPU_BUNDLE_INSTALLED=0
set XPU_BUNDLE_UNINSTALL=0
set XPU_EXTRA_URL=NULL
set XPU_EXTRA_PRODUCT_NAME=intel.oneapi.win.compiler.product
set XPU_EXTRA_VERSION=2025.0.1+1226
set XPU_EXTRA_INSTALLED=0
set XPU_EXTRA_UNINSTALL=0
)
:: Check if XPU bundle is target version or already installed
if exist "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" goto xpu_bundle_ver_check
goto xpu_bundle_install
:xpu_bundle_ver_check
"%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --list-products > xpu_bundle_installed_ver.log
for /f "tokens=1,2" %%a in (xpu_bundle_installed_ver.log) do (
if "%%a"=="%XPU_BUNDLE_PRODUCT_NAME%" (
echo %%a Installed Version: %%b
set XPU_BUNDLE_INSTALLED=1
if not "%XPU_BUNDLE_VERSION%"=="%%b" (
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %%a --product-ver %%b --log-dir uninstall_bundle
set XPU_BUNDLE_UNINSTALL=1
)
)
if "%%a"=="%XPU_EXTRA_PRODUCT_NAME%" (
echo %%a Installed Version: %%b
set XPU_EXTRA_INSTALLED=1
if not "%XPU_EXTRA_VERSION%"=="%%b" (
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %%a --product-ver %%b --log-dir uninstall_bundle
set XPU_EXTRA_UNINSTALL=1
)
)
if not "%%b" == "Version" if not [%%b]==[] if not "%%a"=="%XPU_BUNDLE_PRODUCT_NAME%" if not "%%a"=="%XPU_EXTRA_PRODUCT_NAME%" (
echo "Uninstalling...."
start /wait "Installer Title" "%XPU_BUNDLE_PARENT_DIR%\Installer\installer.exe" --action=remove --eula=accept --silent --product-id %%a --product-ver %%b --log-dir uninstall_bundle
)
)
if errorlevel 1 exit /b 1
if exist xpu_bundle_installed_ver.log del xpu_bundle_installed_ver.log
if exist uninstall_bundle rmdir /s /q uninstall_bundle
if "%XPU_BUNDLE_INSTALLED%"=="0" goto xpu_bundle_install
if "%XPU_BUNDLE_UNINSTALL%"=="1" goto xpu_bundle_install
:xpu_extra_check
if "%XPU_EXTRA_URL%"=="NULL" goto xpu_install_end
if "%XPU_EXTRA_INSTALLED%"=="0" goto xpu_extra_install
if "%XPU_EXTRA_UNINSTALL%"=="1" goto xpu_extra_install
goto xpu_install_end
:xpu_bundle_install
curl -o xpu_bundle.exe --retry 3 --retry-all-errors -k %XPU_BUNDLE_URL%
echo "XPU Bundle installing..."
start /wait "Intel Pytorch Bundle Installer" "xpu_bundle.exe" --action=install --eula=accept --silent --log-dir install_bundle
if errorlevel 1 exit /b 1
del xpu_bundle.exe
goto xpu_extra_check
:xpu_extra_install
curl -o xpu_extra.exe --retry 3 --retry-all-errors -k %XPU_EXTRA_URL%
echo "Intel XPU EXTRA installing..."
start /wait "Intel XPU EXTRA Installer" "xpu_extra.exe" --action=install --eula=accept --silent --log-dir install_bundle
if errorlevel 1 exit /b 1
del xpu_extra.exe
:xpu_install_end

View File

@ -1,5 +1,5 @@
#!/bin/bash
set -ex -o pipefail
set -ex
SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
# shellcheck source=./common.sh
@ -18,9 +18,6 @@ export PYTORCH_FINAL_PACKAGE_DIR="${PYTORCH_FINAL_PACKAGE_DIR:-/c/w/build-result
PYTORCH_FINAL_PACKAGE_DIR_WIN=$(cygpath -w "${PYTORCH_FINAL_PACKAGE_DIR}")
export PYTORCH_FINAL_PACKAGE_DIR_WIN
# enable debug asserts in serialization
export TORCH_SERIALIZATION_DEBUG=1
mkdir -p "$TMP_DIR"/build/torch
export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
@ -44,7 +41,7 @@ python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==
python -m pip install z3-solver==4.12.2.0
# Install tlparse for test\dynamo\test_structured_trace.py UTs.
python -m pip install tlparse==0.3.30
python -m pip install tlparse==0.3.25
# Install parameterized
python -m pip install parameterized==0.8.1

View File

@ -1,31 +0,0 @@
@echo off
echo Dependency ARM Performance Libraries (APL) installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
:: Set download URL for the ARM Performance Libraries (APL)
set DOWNLOAD_URL="https://developer.arm.com/-/cdn-downloads/permalink/Arm-Performance-Libraries/Version_24.10/arm-performance-libraries_24.10_Windows.msi"
set INSTALLER_FILE=%DOWNLOADS_DIR%\arm-performance-libraries.msi
:: Download installer
echo Downloading ARM Performance Libraries (APL)...
curl -L -o "%INSTALLER_FILE%" %DOWNLOAD_URL%
:: Install ARM Performance Libraries (APL)
echo Installing ARM Performance Libraries (APL)...
msiexec /i "%INSTALLER_FILE%" /qn /norestart ACCEPT_EULA=1 INSTALLFOLDER="%DEPENDENCIES_DIR%"
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install ARM Performance Libraries (APL) components. (exitcode = %errorlevel%)"
exit /b 1
)
:: Add to environment
echo ARMPL_DIR=%DEPENDENCIES_DIR%\armpl_24.10\>> %GITHUB_ENV%
echo %DEPENDENCIES_DIR%\armpl_24.10\bin\>> %GITHUB_PATH%
echo Dependency ARM Performance Libraries (APL) installation finished.

View File

@ -1,41 +0,0 @@
@echo off
echo Dependency MSVC Build Tools with C++ with ARM64/ARM64EC components installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir "%DOWNLOADS_DIR%"
if not exist "%DEPENDENCIES_DIR%" mkdir "%DEPENDENCIES_DIR%"
:: Set download URL for the Visual Studio Installer
set DOWNLOAD_URL=https://aka.ms/vs/17/release/vs_BuildTools.exe
set INSTALLER_FILE=%DOWNLOADS_DIR%\vs_BuildTools.exe
:: Download installer
echo Downloading Visual Studio Build Tools with C++ installer...
curl -L -o "%INSTALLER_FILE%" %DOWNLOAD_URL%
:: Install the Visual Studio Build Tools with C++ components
echo Installing Visual Studio Build Tools with C++ components...
echo Installing MSVC %MSVC_VERSION%
"%INSTALLER_FILE%" --norestart --quiet --wait --installPath "%DEPENDENCIES_DIR%\VSBuildTools" ^
--add Microsoft.VisualStudio.Workload.VCTools ^
--add Microsoft.VisualStudio.Component.Windows10SDK ^
--add Microsoft.VisualStudio.Component.Windows11SDK.22621 ^
--add Microsoft.VisualStudio.Component.VC.ASAN ^
--add Microsoft.VisualStudio.Component.VC.CMake.Project ^
--add Microsoft.VisualStudio.Component.VC.CoreBuildTools ^
--add Microsoft.VisualStudio.Component.VC.CoreIde ^
--add Microsoft.VisualStudio.Component.VC.Redist.14.Latest ^
--add Microsoft.VisualStudio.Component.VC.Tools.ARM64EC ^
--add Microsoft.VisualStudio.Component.VC.Tools.ARM64 ^
--add Microsoft.VisualStudio.Component.VC.Tools.x86.x64
echo exitcode = %errorlevel%
:: Check if installation was successful
if %errorlevel% neq 0 (
echo Failed to install Visual Studio Build Tools with C++ components.
exit /b 1
)
echo Dependency Visual Studio Build Tools with C++ installation finished.

View File

@ -1,37 +0,0 @@
:: we need to install newer version of Git manually as "-submodules" function is not supported in the default version of runner.
@echo off
echo Dependency Git installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
:: Set download URL for the Git
set DOWNLOAD_URL="https://github.com/git-for-windows/git/releases/download/v2.46.0.windows.1/Git-2.46.0-64-bit.exe"
set INSTALLER_FILE=%DOWNLOADS_DIR%\Git-2.46.0-64-bit.exe
:: Download installer
echo Downloading Git...
curl -L -o "%INSTALLER_FILE%" %DOWNLOAD_URL%
:: Install Git
echo Installing Git...
"%INSTALLER_FILE%" /VERYSILENT /DIR="%DEPENDENCIES_DIR%\git"
dir %DEPENDENCIES_DIR%\git
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install Git. (exitcode = %errorlevel%)"
exit /b 1
)
:: Enable long paths
call "%DEPENDENCIES_DIR%\git\cmd\git.exe" config --system core.longpaths true
:: Add to PATH
echo %DEPENDENCIES_DIR%\git\cmd\;%DEPENDENCIES_DIR%\git\bin\>> %GITHUB_PATH%
echo Dependency Git installation finished.

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@ -1,33 +0,0 @@
@echo off
echo Dependency libuv installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
:: activate visual studio
call "%DEPENDENCIES_DIR%\VSBuildTools\VC\Auxiliary\Build\vcvarsall.bat" arm64
where cl.exe
cd %DEPENDENCIES_DIR%
git clone https://github.com/libuv/libuv.git -b v1.39.0
echo Configuring libuv...
mkdir libuv\build
cd libuv\build
cmake .. -DBUILD_TESTING=OFF
echo Building libuv...
cmake --build . --config Release
echo Installing libuv...
cmake --install . --prefix ../install
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install libuv. (exitcode = %errorlevel%)"
exit /b 1
)
echo Dependency libuv installation finished.

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@ -1,46 +0,0 @@
@echo off
echo Dependency OpenBLAS installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
:: activate visual studio
call "%DEPENDENCIES_DIR%\VSBuildTools\VC\Auxiliary\Build\vcvarsall.bat" arm64
where cl.exe
:: Clone OpenBLAS
cd %DEPENDENCIES_DIR%
git clone https://github.com/OpenMathLib/OpenBLAS.git -b v0.3.29
echo Configuring OpenBLAS...
mkdir OpenBLAS\build
cd OpenBLAS\build
cmake .. -G Ninja ^
-DBUILD_TESTING=0 ^
-DBUILD_BENCHMARKS=0 ^
-DC_LAPACK=1 ^
-DNOFORTRAN=1 ^
-DDYNAMIC_ARCH=0 ^
-DARCH=arm64 ^
-DBINARY=64 ^
-DTARGET=GENERIC ^
-DUSE_OPENMP=1 ^
-DCMAKE_SYSTEM_PROCESSOR=ARM64 ^
-DCMAKE_SYSTEM_NAME=Windows ^
-DCMAKE_BUILD_TYPE=Release
echo Building OpenBLAS...
cmake --build . --config Release
echo Installing OpenBLAS...
cmake --install . --prefix ../install
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install OpenBLAS. (exitcode = %errorlevel%)"
exit /b 1
)
echo Dependency OpenBLAS installation finished.

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@ -1,44 +0,0 @@
@echo off
echo Dependency Python installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
if "%DESIRED_PYTHON%" == "3.13" (
echo Python version is set to 3.13
set DOWNLOAD_URL=https://www.python.org/ftp/python/3.13.2/python-3.13.2-arm64.exe
) else if "%DESIRED_PYTHON%" == "3.12" (
echo Python version is set to 3.12
set DOWNLOAD_URL=https://www.python.org/ftp/python/3.12.7/python-3.12.7-arm64.exe
) else if "%DESIRED_PYTHON%" == "3.11" (
echo Python version is set to 3.11
set DOWNLOAD_URL=https://www.python.org/ftp/python/3.11.9/python-3.11.9-arm64.exe
) else (
echo DESIRED_PYTHON not defined, Python version is set to 3.12
set DOWNLOAD_URL=https://www.python.org/ftp/python/3.12.7/python-3.12.7-arm64.exe
)
set INSTALLER_FILE=%DOWNLOADS_DIR%\python-installer.exe
:: Download installer
echo Downloading Python...
curl -L -o "%INSTALLER_FILE%" "%DOWNLOAD_URL%"
:: Install Python
echo Installing Python...
"%INSTALLER_FILE%" /quiet Include_debug=1 TargetDir="%DEPENDENCIES_DIR%\Python"
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install Python. (exitcode = %errorlevel%)"
exit /b 1
)
:: Add to PATH
echo %DEPENDENCIES_DIR%\Python\>> %GITHUB_PATH%
echo %DEPENDENCIES_DIR%\Python\scripts\>> %GITHUB_PATH%
echo %DEPENDENCIES_DIR%\Python\libs\>> %GITHUB_PATH%
echo Dependency Python installation finished.

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@ -1,33 +0,0 @@
@echo off
echo Dependency Rust installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
set DOWNLOAD_URL="https://static.rust-lang.org/rustup/dist/x86_64-pc-windows-msvc/rustup-init.exe"
set INSTALLER_FILE=%DOWNLOADS_DIR%\rustup-init.exe
set RUSTUP_HOME=%DEPENDENCIES_DIR%\rust
set CARGO_HOME=%DEPENDENCIES_DIR%\cargo
:: Download installer
echo Downloading Rust...
curl -L -o "%INSTALLER_FILE%" %DOWNLOAD_URL%
:: Install APL
echo Installing Rust...
"%INSTALLER_FILE%" -q -y --default-host aarch64-pc-windows-msvc --default-toolchain stable --profile default
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install Rust. (exitcode = %errorlevel%)"
exit /b 1
)
:: Add to PATH
echo %DEPENDENCIES_DIR%\cargo\bin\>> %GITHUB_PATH%
echo RUSTUP_HOME=%DEPENDENCIES_DIR%\rust>> %GITHUB_ENV%
echo CARGO_HOME=%DEPENDENCIES_DIR%\cargo>> %GITHUB_ENV%
echo Dependency Rust installation finished.

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@ -1,33 +0,0 @@
@echo off
echo Dependency sccache installation started.
:: Pre-check for downloads and dependencies folders
if not exist "%DOWNLOADS_DIR%" mkdir %DOWNLOADS_DIR%
if not exist "%DEPENDENCIES_DIR%" mkdir %DEPENDENCIES_DIR%
:: Set download URL for the sccache
set DOWNLOAD_URL="https://github.com/mozilla/sccache/releases/download/v0.8.1/sccache-v0.8.1-x86_64-pc-windows-msvc.zip"
set INSTALLER_FILE=%DOWNLOADS_DIR%\sccache.zip
:: Download installer
echo Downloading sccache.zip...
curl -L -o "%INSTALLER_FILE%" %DOWNLOAD_URL%
:: Install sccache
echo Extracting sccache.zip...
tar -xf "%INSTALLER_FILE%" -C %DEPENDENCIES_DIR%
cd %DEPENDENCIES_DIR%
ren sccache-v0.8.1-x86_64-pc-windows-msvc sccache
cd ..
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed to install sccache. (exitcode = %errorlevel%)"
exit /b 1
)
:: Add to PATH
echo %DEPENDENCIES_DIR%\sccache\>> %GITHUB_PATH%
echo Dependency sccache installation finished.

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@ -1,22 +0,0 @@
:: change to source directory
cd %PYTORCH_ROOT%
:: activate visual studio
call "%DEPENDENCIES_DIR%\VSBuildTools\VC\Auxiliary\Build\vcvarsall.bat" arm64
where cl.exe
:: create virtual environment
python -m venv .venv
echo * > .venv\.gitignore
call .\.venv\Scripts\activate
where python
:: install dependencies
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install pytest numpy protobuf expecttest hypothesis
:: find file name for pytorch wheel
for /f "delims=" %%f in ('dir /b "%PYTORCH_FINAL_PACKAGE_DIR%" ^| findstr "torch-"') do set "TORCH_WHEEL_FILENAME=%PYTORCH_FINAL_PACKAGE_DIR%\%%f"
pip install %TORCH_WHEEL_FILENAME%

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@ -1,101 +0,0 @@
@echo on
:: environment variables
set CMAKE_BUILD_TYPE=%BUILD_TYPE%
set CMAKE_C_COMPILER_LAUNCHER=sccache
set CMAKE_CXX_COMPILER_LAUNCHER=sccache
set libuv_ROOT=%DEPENDENCIES_DIR%\libuv\install
set MSSdk=1
if defined PYTORCH_BUILD_VERSION (
set PYTORCH_BUILD_VERSION=%PYTORCH_BUILD_VERSION%
set PYTORCH_BUILD_NUMBER=1
)
:: Set BLAS type
if %ENABLE_APL% == 1 (
set BLAS=APL
set USE_LAPACK=1
) else if %ENABLE_OPENBLAS% == 1 (
set BLAS=OpenBLAS
set OpenBLAS_HOME=%DEPENDENCIES_DIR%\OpenBLAS\install
)
:: activate visual studio
call "%DEPENDENCIES_DIR%\VSBuildTools\VC\Auxiliary\Build\vcvarsall.bat" arm64
where cl.exe
:: change to source directory
cd %PYTORCH_ROOT%
:: copy libuv.dll
copy %libuv_ROOT%\lib\Release\uv.dll torch\lib\uv.dll
:: create virtual environment
python -m venv .venv
echo * > .venv\.gitignore
call .\.venv\Scripts\activate
where python
:: python install dependencies
python -m pip install --upgrade pip
pip install -r requirements.txt
:: DISTUTILS_USE_SDK should be set after psutil dependency
set DISTUTILS_USE_SDK=1
:: start sccache server and reset sccache stats
sccache --start-server
sccache --zero-stats
sccache --show-stats
:: Prepare the environment
mkdir libtorch
mkdir libtorch\bin
mkdir libtorch\cmake
mkdir libtorch\include
mkdir libtorch\lib
mkdir libtorch\share
mkdir libtorch\test
:: Call LibTorch build script
python ./tools/build_libtorch.py
:: Check if there is an error
IF ERRORLEVEL 1 exit /b 1
IF NOT ERRORLEVEL 0 exit /b 1
:: Move the files to the correct location
move /Y torch\bin\*.* libtorch\bin\
move /Y torch\cmake\*.* libtorch\cmake\
robocopy /move /e torch\include\ libtorch\include\
move /Y torch\lib\*.* libtorch\lib\
robocopy /move /e torch\share\ libtorch\share\
move /Y torch\test\*.* libtorch\test\
move /Y libtorch\bin\*.dll libtorch\lib\
:: Set version
echo %PYTORCH_BUILD_VERSION% > libtorch\build-version
git rev-parse HEAD > libtorch\build-hash
:: Set LIBTORCH_PREFIX
IF "%DEBUG%" == "" (
set LIBTORCH_PREFIX=libtorch-win-arm64-shared-with-deps
) ELSE (
set LIBTORCH_PREFIX=libtorch-win-arm64-shared-with-deps-debug
)
:: Create output
C:\Windows\System32\tar.exe -cvaf %LIBTORCH_PREFIX%-%PYTORCH_BUILD_VERSION%.zip -C libtorch *
:: Copy output to target directory
if not exist ..\output mkdir ..\output
copy /Y "%LIBTORCH_PREFIX%-%PYTORCH_BUILD_VERSION%.zip" "%PYTORCH_FINAL_PACKAGE_DIR%\"
copy /Y "%LIBTORCH_PREFIX%-%PYTORCH_BUILD_VERSION%.zip" "%PYTORCH_FINAL_PACKAGE_DIR%\%LIBTORCH_PREFIX%-latest.zip"
:: Cleanup raw data to save space
rmdir /s /q libtorch
:: Check if installation was successful
if %errorlevel% neq 0 (
echo "Failed on build_libtorch. (exitcode = %errorlevel%)"
exit /b 1
)

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