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Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/20372 Implement a Dict type that allows us to abstract away from the concrete implementation used. The API is similar to std::unordered_map, but behind the scenes we can switch to any map implementation we like. ska::flat_hash_map, google dense map, or any future map implementation with better performance. Switching such an implementation choice does not have to break backwards compatibility of kernel code using the Dict type. Reviewed By: zdevito Differential Revision: D15298234 fbshipit-source-id: b5ad368a9e9516030805cd8f5f1b02e3986933c0
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.