// RUN: mlir-opt %s --transform-interpreter --split-input-file | FileCheck %s

// CHECK-LABEL: func @matmul_tensors
func.func @matmul_tensors(
  %arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)
    -> tensor<8x32xf32> {
// CHECK-NOT: linalg
// CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>
// CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>
  %0 = linalg.matmul  ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)
                     outs(%arg2: tensor<8x32xf32>)
    -> tensor<8x32xf32>
  return %0 : tensor<8x32xf32>
}

module attributes {transform.with_named_sequence} {
  transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.consumed}) {
    %0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op
    %1, %loops:3 = transform.structured.tile_using_for %0 tile_sizes [8, 4, 2]
      : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
    %2 = transform.get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
    transform.structured.vectorize_children_and_apply_patterns %2 : (!transform.any_op) -> !transform.any_op
    %b = transform.bufferization.one_shot_bufferize
        layout{IdentityLayoutMap} %module_op
        {bufferize_function_boundaries = true, allow_return_allocs = true}
        : (!transform.any_op) -> !transform.any_op

    %f = transform.structured.match ops{["func.func"]} in %b
      : (!transform.any_op) -> !transform.any_op

    // TODO: group these lower-level controls into various properly named vector
    // lowering TD macros.
    transform.apply_patterns to %f {
      transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct"
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.transfer_permutation_patterns
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.lower_multi_reduction lowering_strategy = "innerparallel"
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.split_transfer_full_partial split_transfer_strategy = "linalg-copy"
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.transfer_to_scf max_transfer_rank = 1 full_unroll = true
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.lower_transfer max_transfer_rank = 1
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.lower_shape_cast
    } : !transform.any_op

    transform.apply_patterns to %f {
      transform.apply_patterns.vector.lower_transpose lowering_strategy = "shuffle_1d"
    } : !transform.any_op
    transform.yield
  }
}

// -----

// CHECK-DAG: #[[$map0:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
// CHECK-DAG: #[[$map1:.*]] = affine_map<(d0, d1, d2) -> (d2, d1)>
// CHECK-DAG: #[[$map2:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: func.func @fold_arith_extf_into_contract
//  CHECK-SAME: (%[[ARG0:.*]]: vector<64x64xf16>, %[[ARG1:.*]]: vector<64x64xf16>, %[[ARG2:.*]]: vector<64x64xf32>)
//  CHECK-NEXT:   %[[R:.+]] = vector.contract {indexing_maps = [#[[$map0]], #[[$map1]], #[[$map2]]],
//  CHECK-SAME:   iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>}
//  CHECK-SAME:   %[[ARG0]], %[[ARG1]], %[[ARG2]] : vector<64x64xf16>, vector<64x64xf16> into vector<64x64xf32>
//  CHECK-NEXT:   return %[[R]] : vector<64x64xf32>
func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {
    %lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>
    %rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>
    %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>
    return %result : vector<64x64xf32>
}

module attributes {transform.with_named_sequence} {
  transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {
    %func = transform.structured.match ops{["func.func"]} in %module_op : (!transform.any_op) -> !transform.any_op
    transform.apply_patterns to %func {
      transform.apply_patterns.vector.fold_arith_extension
    } : !transform.any_op
    transform.yield
  }
}

// -----

// CHECK-LABEL: func.func @arith_to_outerproduct_scalable_i32
//  CHECK-SAME:   %[[LHS:.*]]: vector<[4]xi32>,
//  CHECK-SAME:   %[[RHS:.*]]: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
//       CHECK:     %[[RES:.*]] = vector.outerproduct %[[LHS]], %[[RHS]] : vector<[4]xi32>, vector<[4]xi32>
//       CHECK:     return %[[RES]] : vector<[4]x[4]xi32>
func.func @arith_to_outerproduct_scalable_i32(%lhs: vector<[4]xi32>, %rhs: vector<[4]xi32>) -> vector<[4]x[4]xi32> {
  %lhsBcast = vector.broadcast %lhs : vector<[4]xi32> to vector<[4]x[4]xi32>
  %lhsT = vector.transpose %lhsBcast, [1, 0] : vector<[4]x[4]xi32> to vector<[4]x[4]xi32>
  %rhsBcast = vector.broadcast %rhs : vector<[4]xi32> to vector<[4]x[4]xi32>
  %mul = arith.muli %lhsT, %rhsBcast : vector<[4]x[4]xi32>
  return %mul: vector<[4]x[4]xi32>
}

// CHECK-LABEL: func.func @arith_to_outerproduct_trans_rhs_f32
//  CHECK-SAME:   %[[LHS:.*]]: vector<16xf32>,
//  CHECK-SAME:   %[[RHS:.*]]: vector<8xf32>) -> vector<8x16xf32> {
//       CHECK:     %[[RES:.*]] = vector.outerproduct %[[RHS]], %[[LHS]] : vector<8xf32>, vector<16xf32>
//       CHECK:     return %[[RES]] : vector<8x16xf32>
func.func @arith_to_outerproduct_trans_rhs_f32(%lhs: vector<16xf32>, %rhs: vector<8xf32>) -> vector<8x16xf32> {
  %rhsBcast = vector.broadcast %rhs : vector<8xf32> to vector<16x8xf32>
  %rhsT = vector.transpose %rhsBcast, [1, 0] : vector<16x8xf32> to vector<8x16xf32>
  %lhsBcast = vector.broadcast %lhs : vector<16xf32> to vector<8x16xf32>
  %mul = arith.mulf %lhsBcast, %rhsT : vector<8x16xf32>
  return %mul: vector<8x16xf32>
}

module attributes {transform.with_named_sequence} {
  transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {
    %func = transform.structured.match ops{["func.func"]} in %module_op : (!transform.any_op) -> !transform.any_op
    transform.apply_patterns to %func {
      transform.apply_patterns.vector.elementwise_to_vector
    } : !transform.any_op
    transform.yield
  }
}