mirror of https://github.com/llvm/torch-mlir
79aade33da
This adds a pattern to MaximizeValueSemantics which does a simple abstract interpretation within a block, which handles simple cases of `torch.overwrite_tensor`, enough to remove all the unnecessary uses of non-value tensors in ResNet right now. Before/after IR: [gist](https://gist.github.com/silvasean/a3e1ef625b19dfc63579f73cd3b543b6) Also, - Split `torch.copy.tensor` into `torch.copy.to_tensor` and `torch.copy.to_vtensor` which convert between value and non-value semantic tensors. This is a much cleaner factorization as they have very separate use cases and properties (e.g. different side effects) - Remove the various canonicalization patterns they had, which were confusing because they resulted in limited forms of maximizing value semantics throughout the pipeline. We should structure our compilation pipeline such that only MaximizeValueSemantics should be maximizing value semantics. - Adjust pass pipeline to only run MaximizeValueSemantics once. - Make OverwriteTensorOp `$value` always be a value tensor and `$overwritten` be a non-value tensor. |
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|---|---|---|
| .. | ||
| GlobalizeObjectGraph | ||
| adjust-calling-conventions.mlir | ||
| canonicalize.mlir | ||
| finalizing-backend-type-conversion.mlir | ||
| func-backend-type-conversion.mlir | ||
| inline-global-slots.mlir | ||
| invalid.mlir | ||
| maximize-value-semantics.mlir | ||
| ops.mlir | ||
| prepare-for-globalize-object-graph.mlir | ||
| reduce-op-variants.mlir | ||
| refine-public-return.mlir | ||
| refine-types.mlir | ||