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// RUN: mlir-opt %s -linalg-bufferize \
// RUN: -arith-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation \
// RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -expand-strided-metadata -lower-affine -convert-arith-to-llvm --finalize-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
// RUN: mlir-cpu-runner -e main -entry-point-result=void \
// RUN: -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils \
// RUN: | FileCheck %s
func.func @main() {
%const = arith.constant dense<10.0> : tensor<2xf32>
%insert_val = arith.constant dense<20.0> : tensor<1xf32>
// Both of these insert_slice ops insert into the same original tensor
// value `%const`. This can easily cause bugs if at the memref level
// we attempt to write in-place into the memref that %const has been
// converted into.
%inserted_at_position_0 = tensor.insert_slice %insert_val into %const[0][1][1] : tensor<1xf32> into tensor<2xf32>
%inserted_at_position_1 = tensor.insert_slice %insert_val into %const[1][1][1] : tensor<1xf32> into tensor<2xf32>
%unranked_at_position_0 = tensor.cast %inserted_at_position_0 : tensor<2xf32> to tensor<*xf32>
call @printMemrefF32(%unranked_at_position_0) : (tensor<*xf32>) -> ()
// CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
// CHECK-SAME: rank = 1 offset = 0 sizes = [2] strides = [1] data =
// CHECK-NEXT: [20, 10]
%unranked_at_position_1 = tensor.cast %inserted_at_position_1 : tensor<2xf32> to tensor<*xf32>
call @printMemrefF32(%unranked_at_position_1) : (tensor<*xf32>) -> ()
// CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
// CHECK-SAME: rank = 1 offset = 0 sizes = [2] strides = [1] data =
// CHECK-NEXT: [10, 20]
return
}
func.func private @printMemrefF32(%ptr : tensor<*xf32>)
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