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
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
|
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
// DEFINE: mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{command}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{command}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{command}
#SV = #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>
#trait_reduction = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> ()> // x (scalar out)
],
iterator_types = ["reduction"],
doc = "x += SUM_CUSTOM_i UNARY a(i)"
}
// Test of unary op feeding into custom sum reduction.
module {
// Contrived example for stress testing, where neither branch feeds
// a value into a subsequent custom sum reduction. The code should
// be folded into the initial value 1.
func.func @red0(%arga: tensor<8xi32, #SV>, %argx: tensor<i32>) -> tensor<i32> {
%c1 = arith.constant 1 : i32
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<8xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %b: i32):
%u = sparse_tensor.unary %a : i32 to i32
present={ }
absent={ }
%r = sparse_tensor.reduce %u, %b, %c1 : i32 {
^bb0(%x: i32, %y: i32):
%sum = arith.addi %x, %y : i32
sparse_tensor.yield %sum : i32
}
linalg.yield %r : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
// Typical example where present branch contributes a value
// into a subsequent custom sum reduction.
func.func @red1(%arga: tensor<8xi32, #SV>, %argx: tensor<i32>) -> tensor<i32> {
%c1 = arith.constant 1 : i32
%c2 = arith.constant 2 : i32
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<8xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %b: i32):
%u = sparse_tensor.unary %a : i32 to i32
present={
^bb(%p: i32):
sparse_tensor.yield %c2 : i32
}
absent={ }
%r = sparse_tensor.reduce %u, %b, %c1 : i32 {
^bb0(%x: i32, %y: i32):
%sum = arith.addi %x, %y : i32
sparse_tensor.yield %sum : i32
}
linalg.yield %r : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
// A complementing example where absent branch contributes a value
// into a subsequent custom sum reduction.
func.func @red2(%arga: tensor<8xi32, #SV>, %argx: tensor<i32>) -> tensor<i32> {
%c1 = arith.constant 1 : i32
%c3 = arith.constant 3 : i32
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<8xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %b: i32):
%u = sparse_tensor.unary %a : i32 to i32
present={ }
absent={
sparse_tensor.yield %c3 : i32
}
%r = sparse_tensor.reduce %u, %b, %c1 : i32 {
^bb0(%x: i32, %y: i32):
%sum = arith.addi %x, %y : i32
sparse_tensor.yield %sum : i32
}
linalg.yield %r : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
// An example where both present and absent branch contribute values
// into a subsequent custom sum reduction.
func.func @red3(%arga: tensor<8xi32, #SV>, %argx: tensor<i32>) -> tensor<i32> {
%c1 = arith.constant 1 : i32
%c2 = arith.constant 2 : i32
%c3 = arith.constant 3 : i32
%0 = linalg.generic #trait_reduction
ins(%arga: tensor<8xi32, #SV>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %b: i32):
%u = sparse_tensor.unary %a : i32 to i32
present={
^bb(%p: i32):
sparse_tensor.yield %c2 : i32
} absent={
sparse_tensor.yield %c3 : i32
}
%r = sparse_tensor.reduce %u, %b, %c1 : i32 {
^bb0(%x: i32, %y: i32):
%sum = arith.addi %x, %y : i32
sparse_tensor.yield %sum : i32
}
linalg.yield %r : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
func.func @dump_i32(%arg0 : tensor<i32>) {
%v = tensor.extract %arg0[] : tensor<i32>
vector.print %v : i32
return
}
func.func @entry() {
%ri = arith.constant dense<0> : tensor<i32>
// Sparse vector of length 8 with 2 stored elements (and thus 6 implicit zeros).
%v0 = arith.constant sparse< [ [4], [6] ], [ 99, 999 ] > : tensor<8xi32>
%s0 = sparse_tensor.convert %v0: tensor<8xi32> to tensor<8xi32, #SV>
// Call the kernels.
%0 = call @red0(%s0, %ri) : (tensor<8xi32, #SV>, tensor<i32>) -> tensor<i32>
%1 = call @red1(%s0, %ri) : (tensor<8xi32, #SV>, tensor<i32>) -> tensor<i32>
%2 = call @red2(%s0, %ri) : (tensor<8xi32, #SV>, tensor<i32>) -> tensor<i32>
%3 = call @red3(%s0, %ri) : (tensor<8xi32, #SV>, tensor<i32>) -> tensor<i32>
// Verify results.
// 1 + nothing
// 1 + 2 x present
// 1 + 3 x absent
// 1 + 2 x present + 3 x absent
//
// CHECK: 1
// CHECK: 5
// CHECK: 19
// CHECK: 23
//
call @dump_i32(%0) : (tensor<i32>) -> ()
call @dump_i32(%1) : (tensor<i32>) -> ()
call @dump_i32(%2) : (tensor<i32>) -> ()
call @dump_i32(%3) : (tensor<i32>) -> ()
// Release the resources.
bufferization.dealloc_tensor %s0 : tensor<8xi32, #SV>
return
}
}
|
