As per title. See also
[PR](https://github.com/llvm/torch-mlir/pull/3750) for
`torch.aten.mul.float_int`.
---------
Co-authored-by: zjgarvey <47986913+zjgarvey@users.noreply.github.com>
Added lit tests since these scalar operations don't trace well through
the `fx_importer` route.
`XOR` and `NE` are equivalent binary operators, so `aten.ne.bool` is
lowered to `arith.xori`.
This PR fixes the bugs for `Torch::AtenOneHotOp` by:
1) Using `Torch::kUnknownSize` as the default value for `numClasses` in
the pattern matching stage in `DecomposeAtenOneHotOp`
2) Adding `AtenIntScalarOp` to the patterns in `TorchToArith`
3) Handling both `int` and `float` types for `off` and `on` values in
`TorchOnnxToTorch` conversion
It also includes:
1) A new test in `TorchToArith/basic.mlir`, for `torch.aten.Int.Scalar`,
and
2) A new test in `decompose-complex-ops.mlir`, for `torch.aten.one_hot`
**Dependencies**
This PR is dependent on #3334.
Now there no lowing for `aten.Int.bool` in `convert-torch-to-arith`
pass. this PR add this support.
Below is the UT.
```
func.func @torch.aten.Int.bool(%arg0: !torch.bool) -> !torch.int {
%0 = torch.aten.Int.bool %arg0 : !torch.bool -> !torch.int
return %0 : !torch.int
}
```
This commits adds the support for cases for index_put_op:
1.) where index is a 2-d tensor.
2.) where indices is a list of tensors and none, with exactly
2 non none tensors along the consecutive dimensions.
This commit also adds a utility to compute the broadcast shape
given the two input tensors.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
Giacomo Serafini
zjgarvey
Peiming Liu
Angel Zhang
Xinan Jiang(姜曦楠)
Xida Ren (Cedar)
JianzheXiao
Vivek Khandelwal
Ramana Radhakrishnan