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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
|
// 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 vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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-buffer-initialization=true reassociate-fp-reductions=true enable-index-optimizations=true 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<{dimLevelType = ["compressed"]}>
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
//
// Traits for tensor operations.
//
#trait_vec_scale = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"]
}
#trait_mat_scale = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A (in)
affine_map<(i,j) -> (i,j)> // X (out)
],
iterator_types = ["parallel", "parallel"]
}
module {
// Invert the structure of a sparse vector. Present values become missing.
// Missing values are filled with 1 (i32).
func.func @vector_complement(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector> {
%c = arith.constant 0 : index
%ci1 = arith.constant 1 : i32
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
%0 = linalg.generic #trait_vec_scale
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xi32, #SparseVector>) {
^bb(%a: f64, %x: i32):
%1 = sparse_tensor.unary %a : f64 to i32
present={}
absent={
sparse_tensor.yield %ci1 : i32
}
linalg.yield %1 : i32
} -> tensor<?xi32, #SparseVector>
return %0 : tensor<?xi32, #SparseVector>
}
// Negate existing values. Fill missing ones with +1.
func.func @vector_negation(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
%c = arith.constant 0 : index
%cf1 = arith.constant 1.0 : f64
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_vec_scale
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: f64, %x: f64):
%1 = sparse_tensor.unary %a : f64 to f64
present={
^bb0(%x0: f64):
%ret = arith.negf %x0 : f64
sparse_tensor.yield %ret : f64
}
absent={
sparse_tensor.yield %cf1 : f64
}
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Performs B[i] = i * A[i].
func.func @vector_magnify(%arga: tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xf64, #SparseVector>
%xv = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
%0 = linalg.generic #trait_vec_scale
ins(%arga: tensor<?xf64, #SparseVector>)
outs(%xv: tensor<?xf64, #SparseVector>) {
^bb(%a: f64, %x: f64):
%idx = linalg.index 0 : index
%1 = sparse_tensor.unary %a : f64 to f64
present={
^bb0(%x0: f64):
%tmp = arith.index_cast %idx : index to i64
%idxf = arith.uitofp %tmp : i64 to f64
%ret = arith.mulf %x0, %idxf : f64
sparse_tensor.yield %ret : f64
}
absent={}
linalg.yield %1 : f64
} -> tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
// Clips values to the range [3, 7].
func.func @matrix_clip(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%cfmin = arith.constant 3.0 : f64
%cfmax = arith.constant 7.0 : f64
%d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
%d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
%xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
%0 = linalg.generic #trait_mat_scale
ins(%argx: tensor<?x?xf64, #DCSR>)
outs(%xv: tensor<?x?xf64, #DCSR>) {
^bb(%a: f64, %x: f64):
%1 = sparse_tensor.unary %a: f64 to f64
present={
^bb0(%x0: f64):
%mincmp = arith.cmpf "ogt", %x0, %cfmin : f64
%x1 = arith.select %mincmp, %x0, %cfmin : f64
%maxcmp = arith.cmpf "olt", %x1, %cfmax : f64
%x2 = arith.select %maxcmp, %x1, %cfmax : f64
sparse_tensor.yield %x2 : f64
}
absent={}
linalg.yield %1 : f64
} -> tensor<?x?xf64, #DCSR>
return %0 : tensor<?x?xf64, #DCSR>
}
// Slices matrix and only keep the value of the lower-right corner of the original
// matrix (i.e., A[2/d0 ..][2/d1 ..]), and set other values to 99.
func.func @matrix_slice(%argx: tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR> {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%d0 = tensor.dim %argx, %c0 : tensor<?x?xf64, #DCSR>
%d1 = tensor.dim %argx, %c1 : tensor<?x?xf64, #DCSR>
%xv = bufferization.alloc_tensor(%d0, %d1) : tensor<?x?xf64, #DCSR>
%0 = linalg.generic #trait_mat_scale
ins(%argx: tensor<?x?xf64, #DCSR>)
outs(%xv: tensor<?x?xf64, #DCSR>) {
^bb(%a: f64, %x: f64):
%row = linalg.index 0 : index
%col = linalg.index 1 : index
%1 = sparse_tensor.unary %a: f64 to f64
present={
^bb0(%x0: f64):
%v = arith.constant 99.0 : f64
%two = arith.constant 2 : index
%r = arith.muli %two, %row : index
%c = arith.muli %two, %col : index
%cmp1 = arith.cmpi "ult", %r, %d0 : index
%tmp = arith.select %cmp1, %v, %x0 : f64
%cmp2 = arith.cmpi "ult", %c, %d1 : index
%result = arith.select %cmp2, %v, %tmp : f64
sparse_tensor.yield %result : f64
}
absent={}
linalg.yield %1 : f64
} -> tensor<?x?xf64, #DCSR>
return %0 : tensor<?x?xf64, #DCSR>
}
// Dumps a sparse vector of type f64.
func.func @dump_vec_f64(%arg0: tensor<?xf64, #SparseVector>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant 0.0 : f64
%0 = sparse_tensor.values %arg0 : tensor<?xf64, #SparseVector> to memref<?xf64>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<32xf64>
vector.print %1 : vector<32xf64>
// Dump the dense vector to verify structure is correct.
%dv = sparse_tensor.convert %arg0 : tensor<?xf64, #SparseVector> to tensor<?xf64>
%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<32xf64>
vector.print %3 : vector<32xf64>
return
}
// Dumps a sparse vector of type i32.
func.func @dump_vec_i32(%arg0: tensor<?xi32, #SparseVector>) {
// Dump the values array to verify only sparse contents are stored.
%c0 = arith.constant 0 : index
%d0 = arith.constant 0 : i32
%0 = sparse_tensor.values %arg0 : tensor<?xi32, #SparseVector> to memref<?xi32>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xi32>, vector<24xi32>
vector.print %1 : vector<24xi32>
// Dump the dense vector to verify structure is correct.
%dv = sparse_tensor.convert %arg0 : tensor<?xi32, #SparseVector> to tensor<?xi32>
%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xi32>, vector<32xi32>
vector.print %3 : vector<32xi32>
return
}
// Dump a sparse matrix.
func.func @dump_mat(%arg0: tensor<?x?xf64, #DCSR>) {
%c0 = arith.constant 0 : index
%d0 = arith.constant 0.0 : f64
%0 = sparse_tensor.values %arg0 : tensor<?x?xf64, #DCSR> to memref<?xf64>
%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<16xf64>
vector.print %1 : vector<16xf64>
%dm = sparse_tensor.convert %arg0 : tensor<?x?xf64, #DCSR> to tensor<?x?xf64>
%3 = vector.transfer_read %dm[%c0, %c0], %d0: tensor<?x?xf64>, vector<4x8xf64>
vector.print %3 : vector<4x8xf64>
return
}
// Driver method to call and verify vector kernels.
func.func @entry() {
%c0 = arith.constant 0 : index
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],
[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
> : tensor<32xf64>
%sv1 = sparse_tensor.convert %v1 : tensor<32xf64> to tensor<?xf64, #SparseVector>
// Setup sparse matrices.
%m1 = arith.constant sparse<
[ [0,0], [0,1], [1,7], [2,2], [2,4], [2,7], [3,0], [3,2], [3,3] ],
[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0 ]
> : tensor<4x8xf64>
%sm1 = sparse_tensor.convert %m1 : tensor<4x8xf64> to tensor<?x?xf64, #DCSR>
// Call sparse vector kernels.
%0 = call @vector_complement(%sv1)
: (tensor<?xf64, #SparseVector>) -> tensor<?xi32, #SparseVector>
%1 = call @vector_negation(%sv1)
: (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
%2 = call @vector_magnify(%sv1)
: (tensor<?xf64, #SparseVector>) -> tensor<?xf64, #SparseVector>
// Call sparse matrix kernels.
%3 = call @matrix_clip(%sm1)
: (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
%4 = call @matrix_slice(%sm1)
: (tensor<?x?xf64, #DCSR>) -> tensor<?x?xf64, #DCSR>
//
// Verify the results.
//
// CHECK: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 4, 0, 0, 5, 6, 0, 0, 0, 0, 0, 0, 7, 8, 0, 9 )
// CHECK-NEXT: ( 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0 )
// CHECK-NEXT: ( 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0 )
// CHECK-NEXT: ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )
// CHECK-NEXT: ( -1, 1, 1, -2, 1, 1, 1, 1, 1, 1, 1, -3, 1, 1, 1, 1, 1, -4, 1, 1, -5, -6, 1, 1, 1, 1, 1, 1, -7, -8, 1, -9 )
// CHECK-NEXT: ( 0, 6, 33, 68, 100, 126, 196, 232, 279, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 33, 0, 0, 0, 0, 0, 68, 0, 0, 100, 126, 0, 0, 0, 0, 0, 0, 196, 232, 0, 279 )
// CHECK-NEXT: ( 3, 3, 3, 4, 5, 6, 7, 7, 7, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 3, 3, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 3 ), ( 0, 0, 4, 0, 5, 0, 0, 6 ), ( 7, 0, 7, 7, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 99, 99, 99, 99, 5, 6, 99, 99, 99, 0, 0, 0, 0, 0, 0, 0 )
// CHECK-NEXT: ( ( 99, 99, 0, 0, 0, 0, 0, 0 ), ( 0, 0, 0, 0, 0, 0, 0, 99 ), ( 0, 0, 99, 0, 5, 0, 0, 6 ), ( 99, 0, 99, 99, 0, 0, 0, 0 ) )
//
call @dump_vec_f64(%sv1) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_vec_i32(%0) : (tensor<?xi32, #SparseVector>) -> ()
call @dump_vec_f64(%1) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_vec_f64(%2) : (tensor<?xf64, #SparseVector>) -> ()
call @dump_mat(%3) : (tensor<?x?xf64, #DCSR>) -> ()
call @dump_mat(%4) : (tensor<?x?xf64, #DCSR>) -> ()
// Release the resources.
bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %sm1 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %0 : tensor<?xi32, #SparseVector>
bufferization.dealloc_tensor %1 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %2 : tensor<?xf64, #SparseVector>
bufferization.dealloc_tensor %3 : tensor<?x?xf64, #DCSR>
bufferization.dealloc_tensor %4 : tensor<?x?xf64, #DCSR>
return
}
}
|
