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// RUN: mlir-opt %s -linalg-bufferize \
// RUN: -arith-bufferize -tensor-bufferize -func-bufferize \
// RUN: -finalizing-bufferize -buffer-deallocation -convert-linalg-to-llvm \
// RUN: -expand-strided-metadata -lower-affine \
// RUN: -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_runner_utils \
// RUN: | FileCheck %s
func.func @main() {
%const = arith.constant dense<[[[[-3.9058,0.9072],[-2.9470,-2.2055],[18.3946,8.2997]],[[3.4700,5.9006],[-17.2267,4.9777],[1.0450,-0.8201]]],[[[17.6996,-11.1763],[26.7775,-3.8823],[-4.2492,-5.8966]],[[2.1259,13.1794],[-10.7136,0.8428],[16.4233,9.4589]]]]> : tensor<2x2x3x2xf32>
%dynamic = tensor.cast %const: tensor<2x2x3x2xf32> to tensor<2x?x?x?xf32>
%collapsed = call @collapse_dynamic_shape(%dynamic) : (tensor<2x?x?x?xf32>) -> (tensor<2x?x?xf32>)
%unranked = tensor.cast %collapsed: tensor<2x?x?xf32> to tensor<*xf32>
call @printMemrefF32(%unranked) : (tensor<*xf32>) -> ()
// CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
// CHECK-SAME: rank = 3 offset = 0 sizes = [2, 6, 2] strides = [12, 2, 1] data =
// CHECK-NEXT{LITERAL}: [[[-3.9058, 0.9072],
// CHECK-NEXT: [-2.947, -2.2055],
// CHECK-NEXT: [18.3946, 8.2997],
// CHECK-NEXT: [3.47, 5.9006],
// CHECK-NEXT: [-17.2267, 4.9777],
// CHECK-NEXT: [1.045, -0.8201]],
// CHECK-NEXT{LITERAL}: [[17.6996, -11.1763],
// CHECK-NEXT: [26.7775, -3.8823],
// CHECK-NEXT: [-4.2492, -5.8966],
// CHECK-NEXT: [2.1259, 13.1794],
// CHECK-NEXT: [-10.7136, 0.8428],
// CHECK-NEXT: [16.4233, 9.4589]]]
return
}
func.func private @printMemrefF32(%ptr : tensor<*xf32>)
func.func @collapse_dynamic_shape(%arg0 : tensor<2x?x?x?xf32>) -> tensor<2x?x?xf32> {
%0 = tensor.collapse_shape %arg0 [[0], [1, 2], [3]]: tensor<2x?x?x?xf32> into tensor<2x?x?xf32>
return %0 : tensor<2x?x?xf32>
}
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