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
|
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// RUN: %{compile} | %{run}
//
// 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: %{compile} | %{run}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
#trait_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) OP b(i)"
}
module {
func.func @cadd(%arga: tensor<?xcomplex<f64>, #SparseVector>,
%argb: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
%1 = complex.add %a, %b : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @cmul(%arga: tensor<?xcomplex<f64>, #SparseVector>,
%argb: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
%1 = complex.mul %a, %b : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @dump(%arg0: tensor<?xcomplex<f64>, #SparseVector>, %d: index) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%mem = sparse_tensor.values %arg0 : tensor<?xcomplex<f64>, #SparseVector> to memref<?xcomplex<f64>>
scf.for %i = %c0 to %d step %c1 {
%v = memref.load %mem[%i] : memref<?xcomplex<f64>>
%real = complex.re %v : complex<f64>
%imag = complex.im %v : complex<f64>
vector.print %real : f64
vector.print %imag : f64
}
return
}
// Driver method to call and verify complex kernels.
func.func @entry() {
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [28], [31] ],
[ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f64>>
%v2 = arith.constant sparse<
[ [1], [28], [31] ],
[ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f64>>
%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
// Call sparse vector kernels.
%0 = call @cadd(%sv1, %sv2)
: (tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%1 = call @cmul(%sv1, %sv2)
: (tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
//
// Verify the results.
//
// CHECK: 511.13
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 4
// CHECK-NEXT: 8
// CHECK-NEXT: 6
// CHECK-NEXT: 6
// CHECK-NEXT: 8
// CHECK-NEXT: 15
// CHECK-NEXT: 18
//
%d1 = arith.constant 4 : index
%d2 = arith.constant 2 : index
call @dump(%0, %d1) : (tensor<?xcomplex<f64>, #SparseVector>, index) -> ()
call @dump(%1, %d2) : (tensor<?xcomplex<f64>, #SparseVector>, index) -> ()
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %sv2 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %0 : tensor<?xcomplex<f64>, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xcomplex<f64>, #SparseVector>
return
}
}
|
