1// UNSUPPORTED: asan 2// RUN: mlir-opt %s -test-transform-dialect-erase-schedule \ 3// RUN: -one-shot-bufferize="bufferize-function-boundaries" \ 4// RUN: -buffer-deallocation-pipeline -convert-bufferization-to-memref -convert-linalg-to-loops -convert-scf-to-cf \ 5// RUN: -expand-strided-metadata -lower-affine -convert-arith-to-llvm -convert-scf-to-cf --finalize-memref-to-llvm -convert-func-to-llvm -convert-cf-to-llvm -reconcile-unrealized-casts | \ 6// RUN: mlir-runner -e main -entry-point-result=void \ 7// RUN: -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils \ 8// RUN: | FileCheck %s 9 10// RUN: mlir-opt %s -transform-interpreter -test-transform-dialect-erase-schedule \ 11// RUN: -one-shot-bufferize="bufferize-function-boundaries" \ 12// RUN: -convert-linalg-to-loops -convert-scf-to-cf -convert-scf-to-cf \ 13// RUN: -expand-strided-metadata -lower-affine -convert-arith-to-llvm -convert-scf-to-cf --finalize-memref-to-llvm -convert-func-to-llvm -convert-cf-to-llvm -reconcile-unrealized-casts | \ 14// RUN: mlir-runner -e main -entry-point-result=void \ 15// RUN: -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils \ 16// RUN: | FileCheck %s 17 18func.func @main() { 19 %A = arith.constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf32> 20 %B = arith.constant dense<[[1.0, 2.0, 3.0, 4.0], 21 [5.0, 6.0, 7.0, 8.0], 22 [9.0, 10.0, 11.0, 12.0]]> : tensor<3x4xf32> 23 %C = arith.constant dense<1000.0> : tensor<2x4xf32> 24 25 %D = linalg.matmul ins(%A, %B: tensor<2x3xf32>, tensor<3x4xf32>) 26 outs(%C: tensor<2x4xf32>) -> tensor<2x4xf32> 27 28 %unranked = tensor.cast %D : tensor<2x4xf32> to tensor<*xf32> 29 call @printMemrefF32(%unranked) : (tensor<*xf32>) -> () 30 31 // CHECK: Unranked Memref base@ = {{0x[-9a-f]*}} 32 // CHECK-SAME: rank = 2 offset = 0 sizes = [2, 4] strides = [4, 1] data = 33 // CHECK-NEXT: [1038, 1044, 1050, 1056] 34 // CHECK-NEXT: [1083, 1098, 1113, 1128] 35 36 return 37} 38 39module attributes {transform.with_named_sequence} { 40 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { 41 %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op 42 %1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [1, 2, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) 43 transform.yield 44 } 45} 46 47func.func private @printMemrefF32(%ptr : tensor<*xf32>) 48