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Just run, torch-mlir-opt --canonicalize IR.mlir, with the below IR:
torch-mlir-opt --canonicalize IR.mlir
module { func.func @main_graph(%arg0: tensor<?x64x1xf32>) -> tensor<?x384xf32> { %collapsed = tensor.collapse_shape %arg0 [[0, 1, 2]] : tensor<?x64x1xf32> into tensor<?xf32> %c0 = arith.constant 0 : index %dim = tensor.dim %collapsed, %c0 : tensor<?xf32> %c384= arith.constant 384 : index %div = arith.divui %dim, %c384 : index %expanded = tensor.expand_shape %collapsed [[0, 1]] output_shape [%div, 384] : tensor<?xf32> into tensor<?x384xf32> return %expanded : tensor<?x384xf32> } }
Note: To see the crash, you must have a torch-mlir build with assertions enabled.
The text was updated successfully, but these errors were encountered:
@vivekkhandelwal1 This issue is present with mlir-opt too:
mlir-opt
mlir-opt --canonicalize IR.mlir
produces the crash
mlir-opt: llvm-project/llvm/include/llvm/ADT/ArrayRef.h:257: const T &llvm::ArrayRef<long>::operator[](size_t) const [T = long]: Assertion `Index < Length && "Invalid index!"' failed.
Is it possible that the issue does not lie in Torch-MLIR?
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@vivekkhandelwal1 The issue was indeed in MLIR upstream, Addressed by llvm/llvm-project#112774.
@pdhirajkumarprasad this issue should now be gone, after llvm/torch-mlir@f5d15ab
vinayakdsci
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Just run,
torch-mlir-opt --canonicalize IR.mlir
, with the below IR:Note: To see the crash, you must have a torch-mlir build with assertions enabled.
The text was updated successfully, but these errors were encountered: