xref: /llvm-project/mlir/test/Dialect/SparseTensor/sparse_index.mlir (revision 94e27c265a9aeb3659175ecee81a68d1763e0180) 
1// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s
2
3#DenseMatrix = #sparse_tensor.encoding<{
4  map = (d0, d1) -> (d0 : dense, d1 : dense)
5}>
6
7#SparseMatrix = #sparse_tensor.encoding<{
8  map = (d0, d1) -> (d0 : compressed, d1 : compressed)
9}>
10
11#trait = {
12  indexing_maps = [
13    affine_map<(i,j) -> (i,j)>,  // A
14    affine_map<(i,j) -> (i,j)>   // X (out)
15  ],
16  iterator_types = ["parallel", "parallel"],
17  doc = "X(i,j) = A(i,j) * i * j"
18}
19
20// CHECK-LABEL:   func.func @dense_index(
21// CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xi64, #sparse{{[0-9]*}}>
22// CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
23// CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
24// CHECK-DAG:       %[[VAL_3:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
25// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
26// CHECK-DAG:       %[[VAL_5:.*]] = tensor.empty(%[[VAL_3]], %[[VAL_4]]) : tensor<?x?xi64, #sparse{{[0-9]*}}>
27// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
28// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
29// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_2]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
30// CHECK-DAG:       %[[VAL_24:.*]] = sparse_tensor.lvl %[[VAL_5]], %[[VAL_2]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
31// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_5]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
32// CHECK:           scf.for %[[VAL_10:.*]] = %[[VAL_1]] to %[[VAL_7]] step %[[VAL_2]] {
33// CHECK:             %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_8]] : index
34// CHECK:             %[[VAL_14:.*]] = arith.muli %[[VAL_10]], %[[VAL_24]] : index
35// CHECK:             scf.for %[[VAL_11:.*]] = %[[VAL_1]] to %[[VAL_8]] step %[[VAL_2]] {
36// CHECK:               %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_12]] : index
37// CHECK:               %[[VAL_15:.*]] = arith.addi %[[VAL_11]], %[[VAL_14]] : index
38// CHECK:               %[[VAL_16:.*]] = arith.index_cast %[[VAL_11]] : index to i64
39// CHECK:               %[[VAL_17:.*]] = arith.index_cast %[[VAL_10]] : index to i64
40// CHECK:               %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xi64>
41// CHECK:               %[[VAL_19:.*]] = arith.muli %[[VAL_17]], %[[VAL_18]] : i64
42// CHECK:               %[[VAL_20:.*]] = arith.muli %[[VAL_16]], %[[VAL_19]] : i64
43// CHECK:               memref.store %[[VAL_20]], %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref<?xi64>
44// CHECK:             }
45// CHECK:           }
46// CHECK:           %[[VAL_21:.*]] = sparse_tensor.load %[[VAL_5]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
47// CHECK:           return %[[VAL_21]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
48// CHECK:         }
49func.func @dense_index(%arga: tensor<?x?xi64, #DenseMatrix>)
50                      -> tensor<?x?xi64, #DenseMatrix> {
51  %c0 = arith.constant 0 : index
52  %c1 = arith.constant 0 : index
53  %0 = sparse_tensor.lvl %arga, %c0 : tensor<?x?xi64, #DenseMatrix>
54  %1 = sparse_tensor.lvl %arga, %c1 : tensor<?x?xi64, #DenseMatrix>
55  %init = tensor.empty(%0, %1) : tensor<?x?xi64, #DenseMatrix>
56  %r = linalg.generic #trait
57      ins(%arga: tensor<?x?xi64, #DenseMatrix>)
58     outs(%init: tensor<?x?xi64, #DenseMatrix>) {
59      ^bb(%a: i64, %x: i64):
60        %i = linalg.index 0 : index
61        %j = linalg.index 1 : index
62        %ii = arith.index_cast %i : index to i64
63        %jj = arith.index_cast %j : index to i64
64        %m1 = arith.muli %ii, %a : i64
65        %m2 = arith.muli %jj, %m1 : i64
66        linalg.yield %m2 : i64
67  } -> tensor<?x?xi64, #DenseMatrix>
68  return %r : tensor<?x?xi64, #DenseMatrix>
69}
70
71
72// CHECK-LABEL:   func.func @sparse_index(
73// CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xi64, #sparse{{[0-9]*}}>
74// CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
75// CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
76// CHECK-DAG:       %[[VAL_3:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
77// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.lvl %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
78// CHECK-DAG:       %[[VAL_5:.*]] = tensor.empty(%[[VAL_3]], %[[VAL_4]]) : tensor<?x?xi64, #sparse{{[0-9]*}}>
79// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}>
80// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}>
81// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}>
82// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xi64, #sparse{{[0-9]*}}>
83// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
84// CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_1]]] : memref<?xindex>
85// CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex>
86// CHECK:           %[[T:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_2]] {{.*}} {
87// CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
88// CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xindex>
89// CHECK:             %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index
90// CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
91// CHECK:             %[[L:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_2]] {{.*}} {
92// CHECK:               %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex>
93// CHECK:               %[[VAL_20:.*]] = arith.index_cast %[[VAL_19]] : index to i64
94// CHECK:               %[[VAL_21:.*]] = arith.index_cast %[[VAL_14]] : index to i64
95// CHECK:               %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xi64>
96// CHECK:               %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_22]] : i64
97// CHECK:               %[[VAL_24:.*]] = arith.muli %[[VAL_20]], %[[VAL_23]] : i64
98// CHECK:               %[[Y:.*]] = tensor.insert %[[VAL_24]] into %{{.*}}[%[[VAL_14]], %[[VAL_19]]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
99// CHECK:               scf.yield %[[Y]]
100// CHECK:             }
101// CHECK:             scf.yield %[[L]]
102// CHECK:           }
103// CHECK:           %[[VAL_25:.*]] = sparse_tensor.load %[[T]] hasInserts : tensor<?x?xi64, #sparse{{[0-9]*}}>
104// CHECK:           return %[[VAL_25]] : tensor<?x?xi64, #sparse{{[0-9]*}}>
105// CHECK:         }
106func.func @sparse_index(%arga: tensor<?x?xi64, #SparseMatrix>)
107                       -> tensor<?x?xi64, #SparseMatrix> {
108  %c0 = arith.constant 0 : index
109  %c1 = arith.constant 0 : index
110  %0 = sparse_tensor.lvl %arga, %c0 : tensor<?x?xi64, #SparseMatrix>
111  %1 = sparse_tensor.lvl %arga, %c1 : tensor<?x?xi64, #SparseMatrix>
112  %init = tensor.empty(%0, %1) : tensor<?x?xi64, #SparseMatrix>
113  %r = linalg.generic #trait
114      ins(%arga: tensor<?x?xi64, #SparseMatrix>)
115     outs(%init: tensor<?x?xi64, #SparseMatrix>) {
116      ^bb(%a: i64, %x: i64):
117        %i = linalg.index 0 : index
118        %j = linalg.index 1 : index
119        %ii = arith.index_cast %i : index to i64
120        %jj = arith.index_cast %j : index to i64
121        %m1 = arith.muli %ii, %a : i64
122        %m2 = arith.muli %jj, %m1 : i64
123        linalg.yield %m2 : i64
124  } -> tensor<?x?xi64, #SparseMatrix>
125  return %r : tensor<?x?xi64, #SparseMatrix>
126}
127