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5266953443
The idea is to add a custom handler to Functionalize key in Python dispatcher that runs the functionalized version along side a non functionalized version, and checks that their outputs agree in the end. (Technically, for metadata mutation we should also check the inputs, but for now we're relying on those functions returning self.) I turned this on for test_functionalize.py (new TestCrossRefFunctionalize) and found a bunch of failures that look legit. This probably doesn't interact that nicely if you're also tracing at the same time, probably need more special logic for that (directly, just disabling tracing for when we create the nested fake tensor mode, but IDK if there's a more principled way to organize this.) There are some misc fixups which I can split if people really want. - xfail_inherited_tests moved to test common_utils - Bindings for _dispatch_tls_set_dispatch_key_included, _dispatch_tls_is_dispatch_key_included and _functionalization_reapply_views_tls - Type stubs for _enable_functionalization, _disable_functionalization - all_known_overloads utility to let you iterate over all OpOverloads in all namespaces. Iterator support on all torch._ops objects to let you iterate over their members. - suspend_functionalization lets you temporarily disable functionalization mode in a context - check_metadata_matches for easily comparing outputs of functions and see if they match (TODO: there are a few copies of this logic, consolidate!) - _fmt for easily printing the metadata of a tensor without its data - _uncache_dispatch for removing a particular dispatch key from the cache, so that we force it to regenerate - check_significant_strides new kwarg only_cuda to let you also do stride test even when inputs are not CUDA - Functionalize in torch._C.DispatchKey Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/89498 Approved by: https://github.com/malfet |
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| gen_pyi.py | ||