pytorch

3b6588ab - Consistent compute numel/contiguous strategy with SymInts (#85858)

Consistent compute numel/contiguous strategy with SymInts (#85858) Previously, our handling for contiguity was inconsistent in the following ways: - is_strides_like 2d/3d and is_non_overlapping_and_dense always were computed based on sizes_and_strides_, even if you had symbolic ints - Furthermore, even if you set custom policy for strides, these quantities were not overridable by subclasses - Furthermore, we didn't even store these fields on ExtraMeta - We duplicate implementations of compute_contiguous (plain, channels last, channels last 3d) - We inconsistently called refresh_numel()/refresh_contiguous(), versus recomputing it ourselves This factor makes a consistent strategy for all of the boolean fields, and for numel computation. After this refactor: - All layout boolean fields are interposable via strides policy and can be overridden from Python; you will never access a garbage field - All layout boolean fields are on ExtraMeta - You can always call refresh_numel/contiguous, no matter if your Tensor is contiguous or not - The numel/layout boolean fields are always populated consistently with the sizes strides fields (either on Tensor or ExtraMeta), even if you have custom policy - There is only one implementation of the actual computation logic Signed-off-by: Edward Z. Yang <ezyang@fb.com> Differential Revision: [D39907696](https://our.internmc.facebook.com/intern/diff/D39907696) Pull Request resolved: https://github.com/pytorch/pytorch/pull/85858 Approved by: https://github.com/albanD