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1// UNSUPPORTED: asan2// 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 %s9 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 %s17 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 return37}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_op42 %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.yield44 }45}46 47func.func private @printMemrefF32(%ptr : tensor<*xf32>)48