| // RUN: mlir-opt %s -arith-bufferize -linalg-bufferize \ |
| // RUN: -tensor-bufferize -func-bufferize -finalizing-bufferize -buffer-deallocation-pipeline -convert-bufferization-to-memref -convert-linalg-to-loops \ |
| // RUN: -convert-arith-to-llvm -convert-scf-to-cf -convert-cf-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>) |
