Some slice fixes #37
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Some slice fixes #37
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The `copy_` op being replaced by `RecomposeSliceCopy_` operates on a subset of the tensor being mutated, while the `index_put` op being used to replace the `copy_` op operates on the entire tensor being mutated. This means that the result type of the `index_put` should be the type of the input to `index_put` and we need to make sure that `copy_` does not have users before replacing to avoid type conflicts. Note: this commit fixes a type conflict that only seems to arise when `use_tracing=True`, since normally the recomposition happens before type propagation takes place. Since the e2e testing framework does not do tracing, here we use a lit test to check correctness.
When `use_tracing=True` is used to import a model into Torch-MLIR, several casts get inserted in the IR to bridge the untyped inputs and outputs with the typed body of the computation. These casts create extra aliases of tensors that cause the current analysis in `maximize-value-semantics` to fail. In particular, the `maximize-value-semantics` analysis assumes that the only valid alias right after an overwrite is the overwritten alias. So, if there is a use of a casted version of the overwritten alias after the overwrite, the analysis fails. This commit improves the analysis by identifying all cast-like aliases of the overwritten alias and allowing such aliases to be used after an overwrite. Because this issue only arises when using tracing, it cannot be currently tested e2e, so only lit test is added.
…ps where end > dim size
…on non-value tensors
…es that go from 0:int_max
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This mostly comes from TOSA having very strong requirements of start/end in slices
For pytorch, start and end can be larger than the dimension size, and they are than clamped to the size of the dimension.