pytorch

30f26c58 - Reimplement torch::flip based on advanced indexing (#56713)

Reimplement torch::flip based on advanced indexing (#56713) Summary: ## Rationale This PR improves the performance of `torch::flip` by using `TensorIterator` as the same fashion as using `AdvancedIndexing`. Which means that this implementation is semantically equivalent to indexing a tensor using reverse indices `A[dim0_size - 1:0 ..., dimN_size-1:0, ...]`. ## Benchmark results The following benchmark compares the runtime of this implementation of `flip` against the current implementation, AdvancedIndexing with reversed indices, as well as OpenCV one. The comparison scenarios consider a 4D tensor `[B, C, H, W]`, where the dimensions flipped correspond to `H` (vertical flip) and `W` (horizontal flip) under float32 and uint8 datatypes. The benchmark implementation details can be found in https://github.com/andfoy/flip-benchmarks/blob/main/5_Stable_implementation/benchmarks.py. Additionally, there are correctness tests against the current flip implementation in https://github.com/andfoy/flip-benchmarks/blob/main/5_Stable_implementation/main.cpp, which tests against different layouts, datatypes and contiguous/non-contiguous tensors. The following plots correspond to the means of the runtime of each operator after 100 samples. As it is possible to observe, the latest implementation of flip has a runtime similar to the indexing one. Also, the performance gains are up to 6X under some scenarios. ### Horizontal flip (float)  ### Horizontal flip (uint8)  ### Vertical flip (float)  ### Vertical flip (uint8)  cc fmassa vfdev-5 Pull Request resolved: https://github.com/pytorch/pytorch/pull/56713 Reviewed By: datumbox Differential Revision: D28255088 Pulled By: fmassa fbshipit-source-id: 5b8684812357c331e83a677b99cf0d78f0821678