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[uncategorized_lowerings] Add lowering for torch.aten.round.decimals #3811

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@meshtag meshtag commented Oct 22, 2024

Implement missing lowering for the op in a similar fashion as done by torch inductor. Also fix data movement and reduce op variants patterns to correctly handle explicitly declared legal ops.

Inductor decomposition ref: https://github.com/pytorch/pytorch/blob/main/torch/_inductor/decomposition.py#L223.

@meshtag meshtag force-pushed the prathamesh/aten.round.decimals branch from c7a3666 to 286443f Compare October 22, 2024 06:02
Implement missing lowering for the op in a similar fashion as done by torch
inductor. Also fix data movement and reduce op variants patterns to correctly
handle explicitly declared legal ops.

Signed-off-by: Prathamesh Tagore <[email protected]>
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I am not able to understand why the lowering for the op torch.aten.round.decimals is added as a custom op lowering, why don't do it the right way?

Please follow the steps specified here https://github.com/llvm/torch-mlir/blob/main/docs/Torch-ops-E2E-implementation.md to add the op lowering.

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I am not able to understand why the lowering for the op torch.aten.round.decimals is added as a custom op lowering, why don't do it the right way?

Please follow the steps specified here https://github.com/llvm/torch-mlir/blob/main/docs/Torch-ops-E2E-implementation.md to add the op lowering.

Hi @meshtag, is this PR still of interest to you?

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meshtag commented Nov 12, 2024

Hi @meshtag, is this PR still of interest to you?

Hey @vivekkhandelwal1, looks like I missed this PR. Apologies for that.

I am not able to understand why the lowering for the op torch.aten.round.decimals is added as a custom op lowering, why don't do it the right way?

AFAICT, we have two options here:

  1. Get this op directly in the mlir-world and then lower it from there.
  2. Register this op for decomposition (it already exists in torch, as pointed above) and then decompose it at a higher level (ideally before the fx->mlir translation layer) and then directly handle the decomposed op (which is already supported).

This PR tries to go via path 1. I am not sure if we should discard path 2 though, can you please share your thoughts on this.

Please follow the steps specified here https://github.com/llvm/torch-mlir/blob/main/docs/Torch-ops-E2E-implementation.md to add the op lowering.

Sure, I can do this (if we want to go via this path). Thanks for pointing it out.

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