Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41451
Since TE operates on a limited subset of ops with a well-defined
semantics, we can easily infer shapes of intermediate and output tensors
given shapes of the inputs.
There is a couple of ops that are not yet supported in the shape
inference, once we add them we could relax the shape info requirements
in the TE fuser: currently it requires all values in the fusion group to
have shapes known and we can change it to only inputs.
Test Plan: Imported from OSS
Reviewed By: eellison
Differential Revision: D22543470
Pulled By: ZolotukhinM
fbshipit-source-id: 256bae921028cb6ec3af91977f12bb870c385f40
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41507
These fields have always been a part of tensor types, this change just
makes them serializable through IR dumps.
Test Plan: Imported from OSS
Reviewed By: Krovatkin, ngimel
Differential Revision: D22563661
Pulled By: ZolotukhinM
fbshipit-source-id: f01aaa130b7e0005bf1ff21f65827fc24755b360
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37948
The input JIT graph has all the information we need to perform the
entire compilation at the construction time. We don't need to postpone
any steps until the execution time. Also, from the graph we always know
what device we will be executing on and thus we don't need to have a
CodeGen cache in TensorExprKernel - we always have one and only one
CodeGen.
Test Plan: Imported from OSS
Reviewed By: protonu
Differential Revision: D21432145
Pulled By: ZolotukhinM
fbshipit-source-id: 8dc86b891713056b2c62f30170cd4a168912f027
