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
|
// 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 enable-buffer-initialization=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 enable-buffer-initialization=true 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}
#DCSR = #sparse_tensor.encoding<{
lvlTypes = ["compressed", "compressed"]
}>
#sel_trait = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // C (in)
affine_map<(i,j) -> (i,j)>, // L (in)
affine_map<(i,j) -> (i,j)>, // R (in)
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"]
}
module {
func.func @sparse_select(%cond: tensor<5x5xi1>,
%arga: tensor<5x5xf64, #DCSR>,
%argb: tensor<5x5xf64, #DCSR>) -> tensor<5x5xf64, #DCSR> {
%xv = bufferization.alloc_tensor() : tensor<5x5xf64, #DCSR>
%0 = linalg.generic #sel_trait
ins(%cond, %arga, %argb: tensor<5x5xi1>, tensor<5x5xf64, #DCSR>, tensor<5x5xf64, #DCSR>)
outs(%xv: tensor<5x5xf64, #DCSR>) {
^bb(%c: i1, %a: f64, %b: f64, %x: f64):
%1 = arith.select %c, %a, %b : f64
linalg.yield %1 : f64
} -> tensor<5x5xf64, #DCSR>
return %0 : tensor<5x5xf64, #DCSR>
}
// Driver method to call and verify vector kernels.
func.func @entry() {
%c0 = arith.constant 0 : index
%f0 = arith.constant 0.0 : f64
%cond = arith.constant sparse<
[ [0, 0], [1, 1], [2, 2], [3, 3], [4, 4] ],
[ 1, 1, 1, 1, 1 ]
> : tensor<5x5xi1>
%lhs = arith.constant sparse<
[ [0, 0], [1, 1], [2, 2], [3, 3], [4, 4] ],
[ 0.1, 1.1, 2.1, 3.1, 4.1 ]
> : tensor<5x5xf64>
%rhs = arith.constant sparse<
[ [0, 1], [1, 2], [2, 3], [3, 4], [4, 4]],
[ 1.1, 2.2, 3.3, 4.4 , 5.5 ]
> : tensor<5x5xf64>
%sl = sparse_tensor.convert %lhs : tensor<5x5xf64> to tensor<5x5xf64, #DCSR>
%sr = sparse_tensor.convert %rhs : tensor<5x5xf64> to tensor<5x5xf64, #DCSR>
// Call sparse matrix kernels.
%1 = call @sparse_select(%cond, %sl, %sr) : (tensor<5x5xi1>,
tensor<5x5xf64, #DCSR>,
tensor<5x5xf64, #DCSR>) -> tensor<5x5xf64, #DCSR>
// CHECK: ( ( 0.1, 1.1, 0, 0, 0 ),
// CHECK-SAME: ( 0, 1.1, 2.2, 0, 0 ),
// CHECK-SAME: ( 0, 0, 2.1, 3.3, 0 ),
// CHECK-SAME: ( 0, 0, 0, 3.1, 4.4 ),
// CHECK-SAME: ( 0, 0, 0, 0, 4.1 ) )
%r = sparse_tensor.convert %1 : tensor<5x5xf64, #DCSR> to tensor<5x5xf64>
%v2 = vector.transfer_read %r[%c0, %c0], %f0 : tensor<5x5xf64>, vector<5x5xf64>
vector.print %v2 : vector<5x5xf64>
// Release the resources.
bufferization.dealloc_tensor %sl: tensor<5x5xf64, #DCSR>
bufferization.dealloc_tensor %sr: tensor<5x5xf64, #DCSR>
bufferization.dealloc_tensor %1: tensor<5x5xf64, #DCSR>
return
}
}
|
