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
|
// RUN: mlir-opt %s -sparsification="parallelization-strategy=any-storage-any-loop" | \
// RUN: FileCheck %s
#CSR = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed" ]
}>
#trait_matvec = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (j)>, // b
affine_map<(i,j) -> (i)> // x (out)
],
iterator_types = ["parallel", "reduction"],
doc = "x(i) += A(i,j) * b(j)"
}
// CHECK-LABEL: func.func @matvec(
// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>,
// CHECK-SAME: %[[TMP_arg1:.*]]: tensor<32xf32>,
// CHECK-SAME: %[[TMP_arg2:.*]]: tensor<16xf32>) -> tensor<16xf32> {
// CHECK-DAG: %[[TMP_c16:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index
// CHECK: %[[TMP_0:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index}
// CHECK: %[[TMP_1:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index}
// CHECK: %[[TMP_2:.*]] = sparse_tensor.values %[[TMP_arg0]]
// CHECK: %[[TMP_3:.*]] = bufferization.to_memref %[[TMP_arg1]] : memref<32xf32>
// CHECK: %[[TMP_4:.*]] = bufferization.to_memref %[[TMP_arg2]] : memref<16xf32>
// CHECK: scf.parallel (%[[TMP_arg3:.*]]) = (%[[TMP_c0]]) to (%[[TMP_c16]]) step (%[[TMP_c1]]) {
// CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref<16xf32>
// CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_0]][%[[TMP_arg3]]] : memref<?xindex>
// CHECK: %[[TMP_8:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
// CHECK: %[[TMP_9:.*]] = memref.load %[[TMP_0]][%[[TMP_8]]] : memref<?xindex>
// CHECK: %[[TMP_10:.*]] = scf.parallel (%[[TMP_arg4:.*]]) = (%[[TMP_7]]) to (%[[TMP_9]]) step (%[[TMP_c1]]) init (%[[TMP_6]]) -> f32 {
// CHECK: %[[TMP_11:.*]] = memref.load %[[TMP_1]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_12:.*]] = memref.load %[[TMP_2]][%[[TMP_arg4]]] : memref<?xf32>
// CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_3]][%[[TMP_11]]] : memref<32xf32>
// CHECK: %[[TMP_14:.*]] = arith.mulf %[[TMP_12]], %[[TMP_13]] : f32
// CHECK: scf.reduce(%[[TMP_14]]) : f32 {
// CHECK: ^bb0(%[[TMP_arg5:.*]]: f32, %[[TMP_arg6:.*]]: f32):
// CHECK: %[[TMP_15:.*]] = arith.addf %[[TMP_arg5]], %[[TMP_arg6]] : f32
// CHECK: scf.reduce.return %[[TMP_15]] : f32
// CHECK: }
// CHECK: scf.yield
// CHECK: }
// CHECK: memref.store %[[TMP_10]], %[[TMP_4]][%[[TMP_arg3]]] : memref<16xf32>
// CHECK: scf.yield
// CHECK: }
// CHECK: %[[TMP_5:.*]] = bufferization.to_tensor %[[TMP_4]] : memref<16xf32>
// CHECK: return %[[TMP_5]] : tensor<16xf32>
func.func @matvec(%arga: tensor<16x32xf32, #CSR>,
%argb: tensor<32xf32>,
%argx: tensor<16xf32>) -> tensor<16xf32> {
%0 = linalg.generic #trait_matvec
ins(%arga, %argb : tensor<16x32xf32, #CSR>, tensor<32xf32>)
outs(%argx: tensor<16xf32>) {
^bb(%A: f32, %b: f32, %x: f32):
%0 = arith.mulf %A, %b : f32
%1 = arith.addf %0, %x : f32
linalg.yield %1 : f32
} -> tensor<16xf32>
return %0 : tensor<16xf32>
}
|
