Ramiro Leal-Cavazos 63945a2fd4 Change dtype functions interface to take ints tuple for each tensor (#1865) ... The original design for the dtype functions outlined in https://github.com/llvm/torch-mlir/issues/1462 was unable to properly handle ops that take optional tensors as an input when the optional tensor has a value of None. By the time the op gets imported into torch-mlir, if an optional value is None, all information about the original type is lost from the op type signature, preventing torch-mlir from knowing if a value of None was from an optional tensor or not, which was crucial in the original design since each tensor argument must be turned into two separate arguments for the dtype function. This commit changes the interface to dtype functions such that each tensor turns into a tuple of two ints, the first representing the rank of the tensor and the second the dtype of the tensor. Since now there is a one-to-one correspondence between the operands of an op and the operands of its dtype function, there is no ambiguity about which operand of the op corresponds with which operand of the dtype function. To test the implementation, this commit defines dtype functions for the convolution ops, all of which take one optional tensor as an argument.		2023-02-13 17:56:09 -08:00
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main.py	[cleanup] Use `"` instead of `'` for string literals	2022-12-12 02:40:09 -08:00
xfail_sets.py	Change dtype functions interface to take ints tuple for each tensor (#1865)	2023-02-13 17:56:09 -08:00