1// RUN: mlir-opt %s -test-linalg-transform-patterns=test-linalg-to-vector-patterns \
2// RUN: -linalg-init-tensor-to-alloc-tensor -linalg-bufferize -arith-bufferize \
3// RUN: -bufferization-bufferize -tensor-bufferize -func-bufferize \
4// RUN: -finalizing-bufferize -buffer-deallocation \
5// RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-linalg-to-llvm -convert-memref-to-llvm -convert-func-to-llvm -reconcile-unrealized-casts | \
6// RUN: mlir-cpu-runner -e main -entry-point-result=void \
7// RUN:   -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext,%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
8// RUN: | FileCheck %s
9
10
11func.func @main() {
12  %const = arith.constant dense<[[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]]]> : tensor<1x2x3xf32>
13  %dynamic = tensor.cast %const: tensor<1x2x3xf32> to tensor<1x?x3xf32>
14  %offset = arith.constant 2 : index
15  %cst = arith.constant 2.3 : f32
16  %c0 = arith.constant 0 : index
17  %out = tensor.pad %dynamic low[%c0, %offset, %c0] high[%c0, %c0, %offset]  {
18  ^bb0(%gen_arg1: index, %gen_arg2: index, %gen_arg3: index):
19    tensor.yield %cst : f32
20  } : tensor<1x?x3xf32> to tensor<1x?x?xf32>
21  %unranked = tensor.cast %out: tensor<1x?x?xf32> to tensor<*xf32>
22  call @printMemrefF32(%unranked) : (tensor<*xf32>) -> ()
23
24  //      CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
25  // CHECK-SAME: rank = 3 offset = 0 sizes = [1, 4, 5] strides = [20, 5, 1] data =
26  // CHECK-NEXT{LITERAL}: [[[2.3,    2.3,    2.3,    2.3,    2.3],
27  // CHECK-NEXT: [2.3,    2.3,    2.3,    2.3,    2.3],
28  // CHECK-NEXT: [1,    2,    3,    2.3,    2.3],
29  // CHECK-NEXT: [2,    3,    4,    2.3,    2.3]]]
30
31  return
32}
33
34func.func private @printMemrefF32(%ptr : tensor<*xf32>)
35