mirror of https://github.com/llvm/torch-mlir
fd759e4b1f
Supports the result with dynamic shape and scalar indices like
```
func.func @test_gather_scalar(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[], si64>) -> !torch.vtensor<[?,?],f32> attributes {torch.onnx_meta.opset_version = 13 : si64} {
%0 = torch.operator "onnx.Gather"(%arg0, %arg1) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[3,4,5],f32>, !torch.vtensor<[], si64>) -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
```
`Torch::AtenSqueezeOp` is referring to the result shape, so it will
failed on lowering if the result shape is dynamic.
|
||
|---|---|---|
| .. | ||
| TorchConversionToMLProgram | ||
| TorchOnnxToTorch | ||
| TorchToArith | ||
| TorchToLinalg | ||
| TorchToSCF | ||
| TorchToStablehlo | ||
| TorchToTMTensor | ||
| TorchToTensor | ||
| TorchToTosa | ||
| Utils | ||
| CMakeLists.txt | ||
| PassDetail.h | ||
| Passes.cpp | ||