1# RUN: %PYTHON -m mlir.dialects.linalg.opdsl.dump_oplib --file %s | FileCheck %s
2
3from mlir.dialects.linalg.opdsl.lang import *
4
5
6# Verify that simple case with iteration order defined lexically and reduction
7# dims auto discovered emits the right shape, indexing maps and iterator types.
8# CHECK: ---
9# CHECK-LABEL: matmul
10# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s1)>
11# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s1, s2)>
12# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0, s2)>
13# CHECK: static_indexing_maps:
14# CHECK-NEXT: - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d2)>
15# CHECK-NEXT: - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d2, d1)>
16# CHECK-NEXT: - affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0, d1)>
17# CHECK: iterator_types:
18# CHECK-NEXT: - parallel
19# CHECK-NEXT: - parallel
20# CHECK-NEXT: - reduction
21@linalg_structured_op
22def matmul(
23    A=TensorDef(T, S.M, S.K),
24    B=TensorDef(T, S.K, S.N),
25    C=TensorDef(U, S.M, S.N, output=True)):
26  domain(D.m, D.n, D.k)
27  C[D.m, D.n] += TypeFn.cast_signed(U, A[D.m, D.k]) * TypeFn.cast_signed(
28      U, B[D.k, D.n])
29
30
31# Verifies that assignment to a scalar (represented as [None]) is represented
32# correctly.
33# CHECK: ---
34# CHECK-LABEL: dot
35# CHECK: shape_map: affine_map<()[s0] -> (s0)>
36# CHECK: shape_map: affine_map<()[s0] -> (s0)>
37# CHECK: shape_map: affine_map<()[s0] -> ()>
38# CHECK: static_indexing_maps:
39# CHECK-NEXT: - affine_map<(d0)[s0] -> (d0)>
40# CHECK-NEXT: - affine_map<(d0)[s0] -> (d0)>
41# CHECK-NEXT: - affine_map<(d0)[s0] -> ()>
42# CHECK: iterator_types:
43# CHECK-NEXT: - reduction
44@linalg_structured_op
45def dot(A=TensorDef(T, S.M), B=TensorDef(T, S.M), C=TensorDef(U, output=True)):
46  C[None] += TypeFn.cast_signed(U, A[D.m]) * TypeFn.cast_signed(U, B[D.m])
47
48
49# Verifies that the index_dims of shape-only operands translate to correct
50# indexing maps.
51# CHECK: ---
52# CHECK-LABEL: pool
53# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s0)>
54# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s1)>
55# CHECK: shape_map: affine_map<()[s0, s1, s2] -> (s2)>
56# CHECK: static_indexing_maps:
57# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d0 * 2 + d1)>
58# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d1)>
59# CHECK-NEXT: - affine_map<(d0, d1)[s0, s1, s2] -> (d0)>
60# CHECK: iterator_types:
61# CHECK-NEXT: - parallel
62# CHECK-NEXT: - reduction
63@linalg_structured_op
64def pool(
65    I=TensorDef(T, S.I),
66    K=TensorDef(T, S.K, index_dims=[D.k]),
67    O=TensorDef(U, S.O, output=True)):
68  domain(D.o, D.k)
69  O[D.o] += TypeFn.cast_signed(U, I[D.o * 2 + D.k])
70