mirror of https://github.com/llvm/torch-mlir
0e77de996a
We can map to `tensor.reshape` for handling multiple output dynamic shapes. Later we can perform a more complex analysis for indentifying expand/collapse cases from the tensor.reshape. Initially we planned to handle this identification at the `torch` level however it will be easier to handle once converted to core mlir-dialects. |
||
|---|---|---|
| .. | ||
| basic.mlir | ||
| broadcast.mlir | ||
| convolution.mlir | ||
| elementwise.mlir | ||
| flatten.mlir | ||
| gridsampler.mlir | ||
| pooling.mlir | ||
| sparse.mlir | ||
| unsqueeze.mlir | ||
| view.mlir | ||