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Summary: Verified the changes to catch unspecialized int/floats being added as additional graph in D44037548 prior to RP(https://github.com/pytorch/pytorch/pull/95621). However, with #95621 the issue to be solved originally is no longer valid because int & float in `forward` will always be specialized in export. This RP is to add the assertion anyway *(though not be hit unless there is a regression)* to immediately catch the attempt to add unspecialized int/float to additional graphargs Test Plan: Example of the error message would look like: ``` Dynamo attempts to add additional input: value=9.999999747378752e-06, source=NNModuleSource(inner=AttrSource(base=NNModuleSource(inner=AttrSource(base=LocalInputSource(local_name='self', pos=0), member='torch_module')), member='eps')) ``` Passed all export tests ``` Buck UI: https://www.internalfb.com/buck2/fea72653-5549-47e7-a9bf-740eb86a8e26 Test UI: https://www.internalfb.com/intern/testinfra/testrun/8725724422167257 RE: reSessionID-7b3470b1-c293-4c4a-9671-dd0b7a2839b8 Up: 6.0 KiB Down: 0 B Jobs completed: 101. Time elapsed: 115.7s. Tests finished: Pass 98. Fail 0. Fatal 0. Skip 0. 0 builds failed ``` Differential Revision: D44075910 Pull Request resolved: https://github.com/pytorch/pytorch/pull/96786 Approved by: https://github.com/tugsbayasgalan, https://github.com/ezyang
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.