trunk/1100802ab6ce07f48d6ad543fa87318d10bbfb49: Make topk deterministic under deterministic algorithms (#186653)

When torch.use_deterministic_algorithms(True) is enabled, make topk resolve tied values with stable index tie-breaking.

CPU topk now compares by value and then index in deterministic mode. CUDA keeps the existing top-k selection path and uses stable sorting for the selected top-k outputs so threshold ties preserve input-index order without falling back to a full sort on NVIDIA. ROCm uses stable sort in deterministic mode because its current top-k gather reserves tie slots through atomics.

This gives deterministic mode a topk behavior analogous to scatter_add: fast default kernels remain available, while deterministic mode uses stable algorithms when requested.

Test Plan:

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  Rebuilt PyTorch from source with CUDA enabled.

  
  


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  python -m py_compile test/test_sort_and_select.py torch/init.py torch/_torch_docs.py

  
  


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  python test/test_sort_and_select.py -k topk_deterministic

  
  


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  python test/test_sort_and_select.py -k topk

  
  


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  git diff --check HEAD^

  
  


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  lintrunner --config=.lintrunner.toml --skip=PYREFLY aten/src/ATen/native/TopKImpl.h aten/src/ATen/native/cuda/TensorTopK.cpp test/test_sort_and_select.py torch/init.py torch/_torch_docs.py

  
  

Pull Request resolved: