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// RUN: mlir-opt %s -test-linalg-transform-patterns=test-linalg-to-vector-patterns \
// RUN: -one-shot-bufferize="bufferize-function-boundaries" \
// RUN: -finalizing-bufferize -buffer-deallocation-pipeline -convert-bufferization-to-memref \
// RUN: -convert-linalg-to-loops -convert-scf-to-cf -expand-strided-metadata \
// RUN: -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<[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]> : tensor<1x2x3xf32>
%dynamic = tensor.cast %const: tensor<1x2x3xf32> to tensor<1x?x3xf32>
%offset = arith.constant 2 : index
%cst = arith.constant 2.3 : f32
%c0 = arith.constant 0 : index
%out = tensor.pad %dynamic low[%c0, %offset, %c0] high[%c0, %c0, %offset] {
^bb0(%gen_arg1: index, %gen_arg2: index, %gen_arg3: index):
tensor.yield %cst : f32
} : tensor<1x?x3xf32> to tensor<1x?x?xf32>
%unranked = tensor.cast %out: tensor<1x?x?xf32> to tensor<*xf32>
call @printMemrefF32(%unranked) : (tensor<*xf32>) -> ()
// CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
// CHECK-SAME: rank = 3 offset = 0 sizes = [1, 4, 5] strides = [20, 5, 1] data =
// CHECK-NEXT{LITERAL}: [[[2.3, 2.3, 2.3, 2.3, 2.3],
// CHECK-NEXT: [2.3, 2.3, 2.3, 2.3, 2.3],
// CHECK-NEXT: [1, 2, 3, 2.3, 2.3],
// CHECK-NEXT: [2, 3, 4, 2.3, 2.3]]]
return
}
func.func private @printMemrefF32(%ptr : tensor<*xf32>)
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