// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -test-sparsification | FileCheck %s

// Example with cyclic iteration graph with sparse and dense constraints,
// but an acyclic iteration graph using sparse constraints only.
#trait_mul = {
  indexing_maps = [
    affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>,  // A
    affine_map<(i,j,k,l,m,n,o,p) -> (p,o,n,m,l,k,j,i)>,  // B
    affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>   // X
  ],
  sparse = [
    [ "D", "D", "D", "D", "D", "D", "D", "D" ],  // a
    [ "D", "D", "D", "S", "S", "D", "D", "D" ],  // b
    [ "D", "D", "D", "D", "D", "D", "D", "D" ]   // x
  ],
  iterator_types = ["parallel", "parallel", "parallel", "parallel",
                    "parallel", "parallel", "parallel", "parallel"],
  doc = "X(i,j,k,l,m,n,o,p) = A(i,j,k,l,m,n,o,p)  * B(p,o,n,m,l,k,j,i)"
}

// CHECK-LABEL:   func @mul(
// CHECK-SAME:              %[[VAL_0:.*]]: tensor<100x200x300x400x500x600x700x800xf32>,
// CHECK-SAME:              %[[VAL_1:.*]]: tensor<100x200x300x400x500x600x700x800xf32>) -> tensor<100x200x300x400x500x600x700x800xf32> {
// CHECK:           %[[VAL_2:.*]] = constant 999 : index
// CHECK:           %[[VAL_3:.*]] = constant 100 : index
// CHECK:           %[[VAL_4:.*]] = constant 200 : index
// CHECK:           %[[VAL_5:.*]] = constant 300 : index
// CHECK:           %[[VAL_6:.*]] = constant 600 : index
// CHECK:           %[[VAL_7:.*]] = constant 700 : index
// CHECK:           %[[VAL_8:.*]] = constant 800 : index
// CHECK:           %[[VAL_9:.*]] = constant 0 : index
// CHECK:           %[[VAL_10:.*]] = constant 1 : index
// CHECK:           %[[VAL_11:.*]] = alloca() : memref<100x200x300x400x500x600x700x800xf32>
// CHECK:           %[[VAL_12:.*]] = alloca(%[[VAL_2]]) : memref<?xindex>
// CHECK:           %[[VAL_13:.*]] = alloca(%[[VAL_2]]) : memref<?xindex>
// CHECK:           %[[VAL_14:.*]] = alloca(%[[VAL_2]]) : memref<?xindex>
// CHECK:           %[[VAL_15:.*]] = alloca(%[[VAL_2]]) : memref<?xindex>
// CHECK:           %[[VAL_16:.*]] = alloca(%[[VAL_2]]) : memref<?xf32>
// CHECK:           %[[VAL_17:.*]] = alloca() : memref<100x200x300x400x500x600x700x800xf32>
// CHECK:           scf.for %[[VAL_18:.*]] = %[[VAL_9]] to %[[VAL_8]] step %[[VAL_10]] {
// CHECK:             scf.for %[[VAL_19:.*]] = %[[VAL_9]] to %[[VAL_7]] step %[[VAL_10]] {
// CHECK:               %[[VAL_20:.*]] = muli %[[VAL_18]], %[[VAL_7]] : index
// CHECK:               %[[VAL_21:.*]] = addi %[[VAL_20]], %[[VAL_19]] : index
// CHECK:               scf.for %[[VAL_22:.*]] = %[[VAL_9]] to %[[VAL_6]] step %[[VAL_10]] {
// CHECK:                 %[[VAL_23:.*]] = muli %[[VAL_21]], %[[VAL_6]] : index
// CHECK:                 %[[VAL_24:.*]] = addi %[[VAL_23]], %[[VAL_22]] : index
// CHECK:                 %[[VAL_25:.*]] = load %[[VAL_12]]{{\[}}%[[VAL_24]]] : memref<?xindex>
// CHECK:                 %[[VAL_26:.*]] = addi %[[VAL_24]], %[[VAL_10]] : index
// CHECK:                 %[[VAL_27:.*]] = load %[[VAL_12]]{{\[}}%[[VAL_26]]] : memref<?xindex>
// CHECK:                 scf.for %[[VAL_28:.*]] = %[[VAL_25]] to %[[VAL_27]] step %[[VAL_10]] {
// CHECK:                   %[[VAL_29:.*]] = load %[[VAL_13]]{{\[}}%[[VAL_28]]] : memref<?xindex>
// CHECK:                   %[[VAL_30:.*]] = load %[[VAL_14]]{{\[}}%[[VAL_28]]] : memref<?xindex>
// CHECK:                   %[[VAL_31:.*]] = addi %[[VAL_28]], %[[VAL_10]] : index
// CHECK:                   %[[VAL_32:.*]] = load %[[VAL_14]]{{\[}}%[[VAL_31]]] : memref<?xindex>
// CHECK:                   scf.for %[[VAL_33:.*]] = %[[VAL_30]] to %[[VAL_32]] step %[[VAL_10]] {
// CHECK:                     %[[VAL_34:.*]] = load %[[VAL_15]]{{\[}}%[[VAL_33]]] : memref<?xindex>
// CHECK:                     scf.for %[[VAL_35:.*]] = %[[VAL_9]] to %[[VAL_5]] step %[[VAL_10]] {
// CHECK:                       %[[VAL_36:.*]] = muli %[[VAL_33]], %[[VAL_5]] : index
// CHECK:                       %[[VAL_37:.*]] = addi %[[VAL_36]], %[[VAL_35]] : index
// CHECK:                       scf.for %[[VAL_38:.*]] = %[[VAL_9]] to %[[VAL_4]] step %[[VAL_10]] {
// CHECK:                         %[[VAL_39:.*]] = muli %[[VAL_37]], %[[VAL_4]] : index
// CHECK:                         %[[VAL_40:.*]] = addi %[[VAL_39]], %[[VAL_38]] : index
// CHECK:                         scf.for %[[VAL_41:.*]] = %[[VAL_9]] to %[[VAL_3]] step %[[VAL_10]] {
// CHECK:                           %[[VAL_42:.*]] = muli %[[VAL_40]], %[[VAL_3]] : index
// CHECK:                           %[[VAL_43:.*]] = addi %[[VAL_42]], %[[VAL_41]] : index
// CHECK:                           %[[VAL_44:.*]] = load %[[VAL_11]]{{\[}}%[[VAL_41]], %[[VAL_38]], %[[VAL_35]], %[[VAL_34]], %[[VAL_29]], %[[VAL_22]], %[[VAL_19]], %[[VAL_18]]] : memref<100x200x300x400x500x600x700x800xf32>
// CHECK:                           %[[VAL_45:.*]] = load %[[VAL_16]]{{\[}}%[[VAL_43]]] : memref<?xf32>
// CHECK:                           %[[VAL_46:.*]] = mulf %[[VAL_44]], %[[VAL_45]] : f32
// CHECK:                           store %[[VAL_46]], %[[VAL_17]]{{\[}}%[[VAL_41]], %[[VAL_38]], %[[VAL_35]], %[[VAL_34]], %[[VAL_29]], %[[VAL_22]], %[[VAL_19]], %[[VAL_18]]] : memref<100x200x300x400x500x600x700x800xf32>
// CHECK:                         }
// CHECK:                       }
// CHECK:                     }
// CHECK:                   }
// CHECK:                 }
// CHECK:               }
// CHECK:             }
// CHECK:           }
// CHECK:           %[[VAL_47:.*]] = tensor_load %[[VAL_17]] : memref<100x200x300x400x500x600x700x800xf32>
// CHECK:           return %[[VAL_47]] : tensor<100x200x300x400x500x600x700x800xf32>
// CHECK:         }
func @mul(%arga: tensor<100x200x300x400x500x600x700x800xf32>,
          %argb: tensor<100x200x300x400x500x600x700x800xf32>)
	      -> tensor<100x200x300x400x500x600x700x800xf32> {
  %0 = linalg.generic #trait_mul
    ins(%arga, %argb: tensor<100x200x300x400x500x600x700x800xf32>,
                      tensor<100x200x300x400x500x600x700x800xf32>)
    outs(%arga: tensor<100x200x300x400x500x600x700x800xf32>) {
      ^bb(%a: f32, %b: f32, %s : f32):
        %0 = mulf %a, %b : f32
        linalg.yield %0 : f32
    }      -> tensor<100x200x300x400x500x600x700x800xf32>
  return %0 : tensor<100x200x300x400x500x600x700x800xf32>
}