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
|
// RUN: mlir-opt %s \
// RUN: --sparsification --sparse-tensor-conversion \
// RUN: --convert-vector-to-scf --convert-scf-to-std \
// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
// RUN: --std-bufferize --finalizing-bufferize \
// RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \
// RUN: TENSOR1="%mlir_integration_test_dir/data/zero.mtx" \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
!Filename = type !llvm.ptr<i8>
#DenseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (i,j)>
}>
#trait_assign = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"],
doc = "X(i,j) = A(i,j)"
}
//
// Integration test that demonstrates assigning a sparse tensor
// to an all-dense annotated "sparse" tensor, which effectively
// result in inserting the nonzero elements into a linearized array.
//
// Note that there is a subtle difference between a non-annotated
// tensor and an all-dense annotated tensor. Both tensors are assumed
// dense, but the former remains an n-dimensional memref whereas the
// latter is linearized into a one-dimensional memref that is further
// lowered into a storage scheme that is backed by the runtime support
// library.
module {
//
// A kernel that assigns elements from A to an initially zero X.
//
func @dense_output(%arga: tensor<?x?xf64, #SparseMatrix>,
%argx: tensor<?x?xf64, #DenseMatrix>
{linalg.inplaceable = true})
-> tensor<?x?xf64, #DenseMatrix> {
%0 = linalg.generic #trait_assign
ins(%arga: tensor<?x?xf64, #SparseMatrix>)
outs(%argx: tensor<?x?xf64, #DenseMatrix>) {
^bb(%a: f64, %x: f64):
linalg.yield %a : f64
} -> tensor<?x?xf64, #DenseMatrix>
return %0 : tensor<?x?xf64, #DenseMatrix>
}
func private @getTensorFilename(index) -> (!Filename)
//
// Main driver that reads matrix from file and calls the kernel.
//
func @entry() {
%d0 = constant 0.0 : f64
%c0 = constant 0 : index
%c1 = constant 1 : index
// Read the sparse matrix from file, construct sparse storage.
%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
%a = sparse_tensor.new %fileName
: !llvm.ptr<i8> to tensor<?x?xf64, #SparseMatrix>
// Initialize all-dense annotated "sparse" matrix to all zeros.
%fileZero = call @getTensorFilename(%c1) : (index) -> (!Filename)
%x = sparse_tensor.new %fileZero
: !llvm.ptr<i8> to tensor<?x?xf64, #DenseMatrix>
// Call the kernel.
%0 = call @dense_output(%a, %x)
: (tensor<?x?xf64, #SparseMatrix>,
tensor<?x?xf64, #DenseMatrix>) -> tensor<?x?xf64, #DenseMatrix>
//
// Print the linearized 5x5 result for verification.
//
// CHECK: ( 1, 0, 0, 1.4, 0, 0, 2, 0, 0, 2.5, 0, 0, 3, 0, 0, 4.1, 0, 0, 4, 0, 0, 5.2, 0, 0, 5 )
//
%m = sparse_tensor.values %0
: tensor<?x?xf64, #DenseMatrix> to memref<?xf64>
%v = vector.load %m[%c0] : memref<?xf64>, vector<25xf64>
vector.print %v : vector<25xf64>
return
}
}
|
