torch-mlir

Author	SHA1	Message	Date
Stella Laurenzo	fc5f10c5c5	Bump revision and fix issues. * llvm revision = 4836188ad9b3334b0c1e055d45ccaa54ed797e4b * iree revision = 091482e8fdf599d6cb5c701d5b3ccb27fc66c014	2020-06-19 10:38:51 -07:00
Stella Laurenzo	308a54c3d0	Bump llvm-project to 52cae05e087b3d4fd02849fc37c387c720055ffb (2020/6/10). * Fixes compile errors from upstream. * XFAIL several tests that are now failing to legalize (will hand off to Sean).	2020-06-11 16:10:05 -07:00
Sean Silva	3a09455540	Use upstream shape.from_extents Replace our local `tcp.shape_from_extents` op with the upstream `shape.from_extents` op.	2020-05-21 14:51:01 -07:00
Sean Silva	993338a12d	Lower to the upstream memref ABI. Specifically, we use unranked memrefs which get passed as a fixed-size set of arguments/returns. One big caveat about this is that returning results isn't going to work. See TODO in LowerTensorLoadOp. This is far from enough runtime-wise, but it starts to demarcate a plausible layering. Notice for example how this removes the runtime-dependence from LowerRankedShapes. Eventually, we want to have an `npcomp_rt` or `npcomp_hal` dialect with its own set of runtime types that will supercede this. See comments in LowerTensorLoadOp for more direction about where this is going to evolve.	2020-05-15 17:19:57 -07:00