| // RUN: mlir-opt %s \ |
| // RUN: -transform-interpreter -test-transform-dialect-erase-schedule \ |
| // RUN: -one-shot-bufferize="bufferize-function-boundaries" \ |
| // RUN: -test-lower-to-arm-sme -test-lower-to-llvm | \ |
| // RUN: %mcr_aarch64_cmd \ |
| // RUN: -e=main -entry-point-result=void \ |
| // RUN: -march=aarch64 -mattr="+sve,+sme" \ |
| // RUN: -shared-libs=%mlir_runner_utils,%mlir_c_runner_utils,%arm_sme_abi_shlib | \ |
| // RUN: FileCheck %s |
| |
| func.func @matmul_transpose_a(%A : tensor<?x?xf32>, %B : tensor<?x?xf32>, %C : tensor<?x?xf32>) { |
| %res = linalg.matmul_transpose_a ins(%A, %B: tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%C: tensor<?x?xf32>) -> tensor<?x?xf32> |
| %xf = tensor.cast %res : tensor<?x?xf32> to tensor<*xf32> |
| call @printMemrefF32(%xf) : (tensor<*xf32>) -> () |
| return |
| } |
| |
| func.func @main() { |
| %c0 = arith.constant 0 : i32 |
| %c7 = arith.constant 7 : index |
| |
| %A = arith.constant dense<[ |
| [ 1., 2., 3., 4., 5., 6., 7.], |
| [ 8., 9., 10., 11., 12., 13., 14.], |
| [15., 16., 17., 18., 19., 20., 21.], |
| [22., 23., 24., 25., 26., 27., 28.], |
| [29., 30., 31., 32., 33., 34., 35.], |
| [36., 37., 38., 39., 40., 41., 42.], |
| [43., 44., 45., 46., 47., 48., 49.], |
| [50., 51., 52., 53., 54., 55., 56.], |
| [57., 58., 59., 60., 61., 62., 63.], |
| [64., 65., 66., 67., 68., 69., 70.], |
| [71., 72., 73., 74., 75., 76., 77.], |
| [78., 79., 80., 81., 82., 83., 84.], |
| [85., 86., 87., 88., 89., 90., 91.] |
| ]> : tensor<13x7xf32> |
| |
| %A_dyn = tensor.cast %A : tensor<13x7xf32> to tensor<?x?xf32> |
| |
| %C_init = bufferization.alloc_tensor(%c7, %c7) : tensor<?x?xf32> |
| %C = linalg.fill ins(%c0 : i32) outs(%C_init : tensor<?x?xf32>) -> tensor<?x?xf32> |
| |
| // CHECK: Unranked Memref {{.*}} rank = 2 offset = 0 sizes = [7, 7] strides = [7, 1] data = |
| // CHECK: [32955, 33514, 34073, 34632, 35191, 35750, 36309] |
| // CHECK: [33514, 34086, 34658, 35230, 35802, 36374, 36946] |
| // CHECK: [34073, 34658, 35243, 35828, 36413, 36998, 37583] |
| // CHECK: [34632, 35230, 35828, 36426, 37024, 37622, 38220] |
| // CHECK: [35191, 35802, 36413, 37024, 37635, 38246, 38857] |
| // CHECK: [35750, 36374, 36998, 37622, 38246, 38870, 39494] |
| // CHECK: [36309, 36946, 37583, 38220, 38857, 39494, 40131] |
| call @matmul_transpose_a(%A_dyn, %A_dyn, %C) : (tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>) -> () |
| |
| return |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%module : !transform.any_op {transform.readonly}) { |
| %matmul_transpose_a = transform.structured.match ops{["linalg.matmul_transpose_a"]} in %module |
| : (!transform.any_op) -> !transform.any_op |
| |
| // Step 1: Tile for size [4] x [4], which corresponds to SVLs x SVLs, where |
| // SVLs is the number of 32-bit elements in a vector of SVL bits. |
| %tiled_linalg_op, %loops:3 = transform.structured.tile_using_for %matmul_transpose_a[[4], [4], 1] |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| |
| // Step 2: Vectorize. |
| transform.structured.vectorize %tiled_linalg_op vector_sizes [[4], [4], 1] |
| : !transform.any_op |
| |
| %func = transform.structured.match ops{["func.func"]} in %module |
| : (!transform.any_op) -> !transform.any_op |
| |
| // Step 3: Lower vector.multi_reduction to vector.contract (+ some helpful patterns). |
| transform.apply_patterns to %func { |
| transform.apply_patterns.vector.lower_masked_transfers |
| transform.apply_patterns.vector.transfer_permutation_patterns |
| transform.apply_patterns.vector.reduction_to_contract |
| } : !transform.any_op |
| |
| // Step 4: Lower vector.contract to vector.outerproduct. |
| transform.apply_patterns to %func { |
| transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct" |
| transform.apply_patterns.vector.lower_masks |
| } : !transform.any_op |
| |
| transform.yield |
| } |
| } |
| |
| func.func private @printMemrefF32(%ptr : tensor<*xf32>) |