mirror of https://github.com/llvm/torch-mlir
9c1e3b8154
# Tracking [Issue](https://github.com/nod-ai/SHARK-ModelDev/issues/848) [TorchToLinalg Op Support](https://github.com/nod-ai/SHARK-ModelDev/issues/347) # Description Aten_TrilinearOp is an implementation of a "trilinear einstein sum". Essentially, just an einsum across 3 tensors. There are a few inputs: ## Tensor Inputs - i1, i2, i3 - The three input tensors for the _trilinear op. ## Expands These inputs allow you to unsqueeze an input tensor at the specified dims as a pre-processing step to make the shapes compatible for the rest of the op: - expand1: List[int], expand2: List[int], expand3: List[int] ## sumdim - sumdim: List[int] - After applying element wise multiplication, the values in sumdim denote where to collapse a dimension by summing over it ## unroll_dim - unroll_dim: int - In the PyTorch implementation, this specifies a dimension where you could slice the input tensors, multiply and sum them, then concatenate the results in an output tensor. This complicates the implementation significantly, but doesn't change the result, so I opted against it. Along with that, a previously accepted path for solving this involved reusing the AtenEinsumOp, which also would also ignore this input. # Solution After trying a bunch of more complicated approaches for it, this op actually ended up being quite simple: [See _trilinear](https://dev-discuss.pytorch.org/t/defining-the-core-aten-opset/1464) `_trilinear = (i1.unsqueeze(expand1) * i2.unsqueeze(expand2) * i3.unsqueeze(expand3)).sum(sumdim)` Wish I saw this earlier, but watcha gonna do: 🙃 ## Not Reusing AtenEinsumOp Frankly, I found multiple cases where valid inputs would have numerical mismatches for EinsumOp, even when running tests against EinsumOp directly. I think it has something to do with the singleton dimensions. Will need to look into this further, but once I realized the simplified approach, it appeared to be more reliable and much simpler. Either way (credit to @zjgarvey), there are improvements to the einsum op here. When I was originally trying to use the op, intermediate tensors were being flattened properly, but then its 0th dimension was being cast from a static dim to a dynamic dim due to integers not folding correctly in the MLIR. Figured it's worth keeping these improvements for future reusers of EinsumOp. # The zero'd out dim "bug" For some reason, if you specify a dimension in all `expands`, ```i.e. [expand1=[0], expand2=[0], expand3=[0]], [expand1=[1], expand2=[1], expand3=[1]] ``` The _trilinear op would specify `0` for that dimension in the output shape, unless it was also included in `sumdim`. This goes against the implementation of torch.einsum: ``` >>> a, b, c = [torch.rand(1, 3, 3, 3) for i in range(3)] # Simulate expand at dim=0 for all input tensors >>> torch.einsum('abcd,abcd,abcd->abcd', a, b, c).shape torch.Size([1, 3, 3, 3]) ``` And is just straight up incorrect mathematically. I considered "replacing" singleton dims with zeroed out dims, but that seemed like carrying over a bug. Instead, I included a test for the case, verified that the singleton dimensions were handled the way that torch.einsum handles it, instead of torch._trilinear, and xfailed it with a note as to why. |
||
|---|---|---|
| .. | ||
| torch-mlir | ||
| torch-mlir-c | ||
| torch-mlir-dialects | ||
| CMakeLists.txt | ||