mirror of https://github.com/llvm/torch-mlir
79928cd2dd
We plumb through e2e a fair number of interesting cases:
- unary, binary, ternary elementwise ops
- ops like `torch.aten.add.Tensor` that also take a scalar parameter
- static size-1 broadcasting
We allow the static size-1 broadcasting case, but emit a runtime error
in the case of dynamic size-1 broadcasting. This seems like a sweet spot
subset of things that can be lowered directly to linalg, while not being
overly constraining to users. This is consistent with what IREE is doing
for CHLO->Linalg lowering as well
([code](
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BasicpyToStd | ||
NumpyToTCF | ||
TCFToLinalg | ||
TCFToStd | ||
TCFToTCP | ||
TorchToLinalg | ||
TorchToSCF | ||
TorchToStd | ||
CMakeLists.txt | ||
PassDetail.h | ||
Passes.cpp |