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1// RUN: mlir-opt %s --transform-interpreter --split-input-file | FileCheck %s2 3// CHECK-LABEL: func @matmul_tensors4func.func @matmul_tensors(5 %arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)6 -> tensor<8x32xf32> {7// CHECK-NOT: linalg8// CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>9// CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>10 %0 = linalg.matmul ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)11 outs(%arg2: tensor<8x32xf32>)12 -> tensor<8x32xf32>13 return %0 : tensor<8x32xf32>14}15 16module attributes {transform.with_named_sequence} {17 transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.consumed}) {18 %0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op19 %1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [8, 4, 2]20 : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)21 %2 = transform.get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op22 transform.structured.vectorize_children_and_apply_patterns %2 : (!transform.any_op) -> !transform.any_op23 %b = transform.bufferization.one_shot_bufferize24 layout{IdentityLayoutMap} %module_op25 {bufferize_function_boundaries = true, allow_return_allocs = true}26 : (!transform.any_op) -> !transform.any_op27 28 %f = transform.structured.match ops{["func.func"]} in %b29 : (!transform.any_op) -> !transform.any_op30 31 // TODO: group these lower-level controls into various properly named vector32 // lowering TD macros.33 transform.apply_patterns to %f {34 transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct"35 } : !transform.any_op36 37 transform.apply_patterns to %f {38 transform.apply_patterns.vector.transfer_permutation_patterns39 } : !transform.any_op40 41 transform.apply_patterns to %f {42 transform.apply_patterns.vector.lower_multi_reduction lowering_strategy = "innerparallel"43 } : !transform.any_op44 45 transform.apply_patterns to %f {46 transform.apply_patterns.vector.split_transfer_full_partial split_transfer_strategy = "linalg-copy"47 } : !transform.any_op48 49 transform.apply_patterns to %f {50 transform.apply_patterns.vector.transfer_to_scf max_transfer_rank = 1 full_unroll = true51 } : !transform.any_op52 53 transform.apply_patterns to %f {54 transform.apply_patterns.vector.lower_transfer max_transfer_rank = 155 } : !transform.any_op56 57 transform.apply_patterns to %f {58 transform.apply_patterns.vector.lower_shape_cast59 } : !transform.any_op60 61 transform.apply_patterns to %f {62 transform.apply_patterns.vector.lower_transpose lowering_strategy = "shuffle_1d"63 } : !transform.any_op64 transform.yield65 }66}67 68// -----69 70// CHECK-DAG: #[[$map0:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>71// CHECK-DAG: #[[$map1:.*]] = affine_map<(d0, d1, d2) -> (d2, d1)>72// CHECK-DAG: #[[$map2:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>73// CHECK-LABEL: func.func @fold_arith_extf_into_contract74// CHECK-SAME: (%[[ARG0:.*]]: vector<64x64xf16>, %[[ARG1:.*]]: vector<64x64xf16>, %[[ARG2:.*]]: vector<64x64xf32>)75// CHECK-NEXT: %[[R:.+]] = vector.contract {indexing_maps = [#[[$map0]], #[[$map1]], #[[$map2]]],76// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>}77// CHECK-SAME: %[[ARG0]], %[[ARG1]], %[[ARG2]] : vector<64x64xf16>, vector<64x64xf16> into vector<64x64xf32>78// CHECK-NEXT: return %[[R]] : vector<64x64xf32>79func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {80 %lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>81 %rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>82 %result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs_f32, %rhs_f32, %arg2 : vector<64x64xf32>, vector<64x64xf32> into vector<64x64xf32>83 return %result : vector<64x64xf32>84}85 86module attributes {transform.with_named_sequence} {87 transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {88 %func = transform.structured.match ops{["func.func"]} in %module_op : (!transform.any_op) -> !transform.any_op89 transform.apply_patterns to %func {90 transform.apply_patterns.vector.fold_arith_extension91 } : !transform.any_op92 transform.yield93 }94}95 96// -----97 98// CHECK-LABEL: func.func @arith_to_outerproduct_scalable_i3299// CHECK-SAME: %[[LHS:.*]]: vector<[4]xi32>,100// CHECK-SAME: %[[RHS:.*]]: vector<[4]xi32>) -> vector<[4]x[4]xi32> {101// CHECK: %[[RES:.*]] = vector.outerproduct %[[LHS]], %[[RHS]] : vector<[4]xi32>, vector<[4]xi32>102// CHECK: return %[[RES]] : vector<[4]x[4]xi32>103func.func @arith_to_outerproduct_scalable_i32(%lhs: vector<[4]xi32>, %rhs: vector<[4]xi32>) -> vector<[4]x[4]xi32> {104 %lhsBcast = vector.broadcast %lhs : vector<[4]xi32> to vector<[4]x[4]xi32>105 %lhsT = vector.transpose %lhsBcast, [1, 0] : vector<[4]x[4]xi32> to vector<[4]x[4]xi32>106 %rhsBcast = vector.broadcast %rhs : vector<[4]xi32> to vector<[4]x[4]xi32>107 %mul = arith.muli %lhsT, %rhsBcast : vector<[4]x[4]xi32>108 return %mul: vector<[4]x[4]xi32>109}110 111// CHECK-LABEL: func.func @arith_to_outerproduct_trans_rhs_f32112// CHECK-SAME: %[[LHS:.*]]: vector<16xf32>,113// CHECK-SAME: %[[RHS:.*]]: vector<8xf32>) -> vector<8x16xf32> {114// CHECK: %[[RES:.*]] = vector.outerproduct %[[RHS]], %[[LHS]] : vector<8xf32>, vector<16xf32>115// CHECK: return %[[RES]] : vector<8x16xf32>116func.func @arith_to_outerproduct_trans_rhs_f32(%lhs: vector<16xf32>, %rhs: vector<8xf32>) -> vector<8x16xf32> {117 %rhsBcast = vector.broadcast %rhs : vector<8xf32> to vector<16x8xf32>118 %rhsT = vector.transpose %rhsBcast, [1, 0] : vector<16x8xf32> to vector<8x16xf32>119 %lhsBcast = vector.broadcast %lhs : vector<16xf32> to vector<8x16xf32>120 %mul = arith.mulf %lhsBcast, %rhsT : vector<8x16xf32>121 return %mul: vector<8x16xf32>122}123 124// See https://github.com/llvm/llvm-project/pull/152957125// CHECK-LABEL: func.func @negative_non_vector_type126func.func @negative_non_vector_type(%lhs: f32, %rhs: f32) -> f32 {127 %mul = arith.mulf %lhs, %rhs : f32128 return %mul: f32129}130 131module attributes {transform.with_named_sequence} {132 transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {133 %func = transform.structured.match ops{["func.func"]} in %module_op : (!transform.any_op) -> !transform.any_op134 transform.apply_patterns to %func {135 transform.apply_patterns.vector.elementwise_to_vector136 } : !transform.any_op137 transform.yield138 }139}140