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Add NVTX annotations around training phases and buffer computations RFC/discussion: https://dev-discuss.pytorch.org/t/rfc-performance-profiling-at-scale-with-details-nvtx-annotations/2224 <img width="2160" alt="Screenshot 2024-07-10 at 11 48 04" src="https://github.com/pytorch/pytorch/assets/1175576/9ade139c-d393-473f-9b68-6c25da367dc4"> Pull Request resolved: https://github.com/pytorch/pytorch/pull/130429 Approved by: https://github.com/aorenste, https://github.com/eellison, https://github.com/albanD Co-authored-by: Cedric GESTES <cedric.gestes@flex.ai>
Note [TH abstraction violation] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TH/THC provide some hpp headers, which are proper C++ headers rather than C headers. These headers serve double duty as *internal implementation detail* headers, whose contents should largely not be used by external clients. Ideally, we would not install these headers at all; instead, you should use public functions (in headers like `THTensor.h`, NOT `THTensor.hpp`) to manipulate these structs. However, there are a few places in torch/csrc where we violate this abstraction. They are marked with a pointer to this note. Each of those sites will have to be refactored when we refactor the guts of THTensor and related structures.