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-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0

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baserev update by copy to link target

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- Add patch to fix build with oneDNN

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Remove patch leftover

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- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch

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baserev update by copy to link target

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- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.

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initialized devel package after accepting 1189413

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more conflicts

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make flavors conflicting

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- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
    - torch.compile is the main API for PyTorch 2.0, which wraps your model and
      returns a compiled model. It is a fully additive (and optional) feature
      and hence 2.0 is 100% backward compatible by definition
    - Accelerated Transformers introduce high-performance support for training
      and inference using a custom kernel architecture for scaled dot product
      attention (SPDA). The API is integrated with torch.compile() and model
      developers may also use the scaled dot product attention kernels directly
      by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
    - automatic dynamic shape support in torch.compile,
      torch.distributed.checkpoint for saving/loading distributed training jobs
      on multiple ranks in parallel, and torch.compile support for the NumPy
      API.
    - In addition, this release offers numerous performance improvements (e.g.
      CPU inductor improvements, AVX512 support, scaled-dot-product-attention
      support) as well as a prototype release of torch.export, a sound
      full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
    - 2x performance improvements to scaled_dot_product_attention via
      FlashAttention-v2 integration, as well as AOTInductor, a new
      ahead-of-time compilation and deployment tool built for non-python
      server-side deployments.
  * from 2.3.x:
    - support for user-defined Triton kernels in torch.compile, allowing for
      users to migrate their own Triton kernels from eager without
      experiencing performance complications or graph breaks. As well, Tensor
      Parallelism improves the experience for training Large Language Models
      using native PyTorch functions, which has been validated on training

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- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550
- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch

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- Add _service file to ease future update of deps
- Update sleef to fix build on aarch64

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baserev update by copy to link target

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- Don't build python36-* package (missing pandas)

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baserev update by copy to link target

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- Fix python-rpm-macros usage

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