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// RUN: mlir-opt %s -arith-bufferize -linalg-bufferize \
// RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation -convert-linalg-to-loops \
// RUN: -convert-linalg-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_runner_utils \
// RUN: | FileCheck %s
func.func @foo() -> tensor<4xf32> {
%0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32>
return %0 : tensor<4xf32>
}
func.func @main() {
%0 = call @foo() : () -> tensor<4xf32>
// Instead of relying on tensor_store which introduces aliasing, we rely on
// the conversion of printMemrefF32(tensor<*xf32>) to
// printMemrefF32(memref<*xf32>).
// Note that this is skipping a step and we would need at least some function
// attribute to declare that this conversion is valid (e.g. when we statically
// know that things will play nicely at the C ABI boundary).
%unranked = tensor.cast %0 : tensor<4xf32> to tensor<*xf32>
call @printMemrefF32(%unranked) : (tensor<*xf32>) -> ()
// CHECK: Unranked Memref base@ = {{0x[-9a-f]*}}
// CHECK-SAME: rank = 1 offset = 0 sizes = [4] strides = [1] data =
// CHECK-NEXT: [1, 2, 3, 4]
return
}
// This gets converted to a function operating on memref<*xf32>.
// Note that this is skipping a step and we would need at least some function
// attribute to declare that this conversion is valid (e.g. when we statically
// know that things will play nicely at the C ABI boundary).
func.func private @printMemrefF32(%ptr : tensor<*xf32>)
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