Ramiro Leal-Cavazos eae3ff7f1c Change dtype functions interface to take ints tuple for each tensor (#1965) ... 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 function for convolution op, which takes one optional tensor as an argument.		2023-03-23 11:05:39 -07:00
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Torch	Change dtype functions interface to take ints tuple for each tensor (#1965)	2023-03-23 11:05:39 -07:00
TorchConversion	build: update llvm tag to 4546397e (#1502)	2022-10-18 04:22:53 +00:00