pytorch

f5c10fdb - Allow for heterogenous List and Dict values + Improve container typing algorithm (#57137)

Allow for heterogenous List and Dict values + Improve container typing algorithm (#57137) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/57137 This PR corrects and expands our typing algorithm for unannotated, non-empty dicts and lists. Previously, to verify type correctness for an unannotated, non-empty container, we had gotten the type of the first element in the container, then checked if each following element was a subtype of the first type. That's too restrictive--what if the first element were a subtype of the second element? Instead, we should type the container by getting the smallest common supertype of all the given elements. We need slightly different rules for keys and values in dicts, though: because the set of key types is restricted, finding two key types that cannot be unified should cause an error. On the other hand, the set of value types is not restricted, so we should be able to use `Any` as a valid supertype. We need to keep the set of keys restricted since the keys are used to generate and match schemas. This does not break backwards compatibility, because the default element type is the smallest supertype of all the given types. So, if someone creates an unannotated dict where the keys are all `str` and the values are all `torch.Tensor`, the dict will be inferred to `Dict[str, Tensor]` just like it was before. Empty lists are still typed as `List[torch.Tensor],` and empty dicts are still typed as `Dict[str, Tensor]`. This PR unblocks three engineers on an FB-internal team and improves FX-TorchScript compatibility. Test Plan: Imported from OSS Reviewed By: gmagogsfm Differential Revision: D28231839 Pulled By: ansley fbshipit-source-id: 7297bf239749daa54895add708185c75e6ca5999