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vivekkhandelwal1vivekkhandelwal1
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Fix regression tests
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+21
-46
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3 files changed

+21
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‎mlir/test/Dialect/Linalg/drop-unit-extent-dims.mlir

Lines changed: 6 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -25,22 +25,18 @@ func.func @drop_one_trip_loops(%arg0 : tensor<?x1x?xf32>, %arg1 : f32, %shape: t
2525
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
2626
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2) -> ()>
2727
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
28-
// CHECK-DAG: #[[$MAP4:.*]] = affine_map<()[s0, s1] -> (s0 * s1)>
2928
// CHECK-LABEL: func @drop_one_trip_loops
3029
// CHECK: %[[C2:.*]] = arith.constant 2 : index
31-
// CHECK: %[[C1:.*]] = arith.constant 1 : index
3230
// CHECK: %[[C0:.*]] = arith.constant 0 : index
3331
// CHECK: tensor.collapse_shape %{{.*}} {{\[\[}}0, 1], [2]]
3432
// CHECK: tensor.collapse_shape %{{.*}} {{\[\[}}0, 1], [2, 3], [4]]
3533
// CHECK: linalg.generic
3634
// CHECK-SAME: indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP3]]]
3735
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
3836
// CHECK: %[[DIM:.*]] = tensor.dim %{{.*}}, %[[C0]]
39-
// CHECK: %[[VAL_1:.*]] = affine.apply #[[$MAP4]]()[%[[DIM]], %[[C1]]]
4037
// CHECK: %[[DIM_1:.*]] = tensor.dim %{{.*}}, %[[C2]]
41-
// CHECK: %[[VAL_2:.*]] = affine.apply #[[$MAP4]]()[%[[DIM_1]], %[[C1]]]
4238
// CHECK: %[[DIM_2:.*]] = tensor.dim %{{.*}}, %[[C2]]
43-
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %{{.*}} {{\[\[}}0, 1], [2, 3], [4]] output_shape [%[[VAL_1]], 1, %[[VAL_2]], 1, %[[DIM_2]]] : tensor<?x?x?xf32> into tensor<?x1x?x1x?xf32>
39+
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %{{.*}} {{\[\[}}0, 1], [2, 3], [4]] output_shape [%[[DIM]], 1, %[[DIM_1]], 1, %[[DIM_2]]] : tensor<?x?x?xf32> into tensor<?x1x?x1x?xf32>
4440

4541
// CHECK-SLICES-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
4642
// CHECK-SLICES-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2) -> ()>
@@ -79,18 +75,15 @@ func.func @drop_one_trip_loops_all_ones(%arg0 : tensor<1x1x1xf32>, %arg1 : f32,
7975
}
8076
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0) -> ()>
8177
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (d0)>
82-
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<()[s0, s1, s2, s3, s4] -> ((((s0 * s1) * s2) * s3) * s4)>
8378
// CHECK-LABEL: func @drop_one_trip_loops_all_ones
8479
// CHECK: %[[C2:.*]] = arith.constant 2 : index
85-
// CHECK: %[[C1:.*]] = arith.constant 1 : index
8680
// CHECK: tensor.collapse_shape %{{.*}} []
8781
// CHECK: tensor.collapse_shape %{{.*}} {{\[}}[0, 1, 2, 3, 4]]
8882
// CHECK: linalg.generic
8983
// CHECK-SAME: indexing_maps = [#[[$MAP1]], #[[$MAP1]], #[[$MAP2]]]
9084
// CHECK-SAME: iterator_types = ["parallel"]
9185
// CHECK: %[[DIM:.*]] = tensor.dim %{{.*}}, %[[C2]] : tensor<1x1x?x1x1xf32>
92-
// CHECK: %[[SZ:.*]] = affine.apply #[[$MAP3]]()[%[[C1]], %[[C1]], %[[DIM]], %[[C1]], %[[C1]]]
93-
// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %{{.*}} {{\[\[}}0, 1, 2, 3, 4]] output_shape [1, 1, %[[SZ]], 1, 1] : tensor<?xf32> into tensor<1x1x?x1x1xf32>
86+
// CHECK: %[[EXPAND:.*]] = tensor.expand_shape %{{.*}} {{\[\[}}0, 1, 2, 3, 4]] output_shape [1, 1, %[[DIM]], 1, 1] : tensor<?xf32> into tensor<1x1x?x1x1xf32>
9487

9588
// -----
9689

@@ -406,7 +399,6 @@ func.func @unit_dim_for_reduction(%arg0: tensor<1x?x1x?xf32>) -> tensor<1x?xf32>
406399
}
407400
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
408401
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0)>
409-
// CHECK-DAG: #[[MAP3:.+]] = affine_map<()[s0, s1, s2] -> ((s0 * s1) * s2)>
410402
// CHECK: func @unit_dim_for_reduction
411403
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x?x1x?xf32>
412404
// CHECK: %[[C1:.+]] = arith.constant 1 : index
@@ -422,8 +414,7 @@ func.func @unit_dim_for_reduction(%arg0: tensor<1x?x1x?xf32>) -> tensor<1x?xf32>
422414
// CHECK-SAME: ins(%[[RESHAPE]] : tensor<?x?xf32>)
423415
// CHECK-SAME: outs(%[[FILL]] : tensor<?xf32>)
424416
// CHECK: %[[DIM_0:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<1x?x1x?xf32>
425-
// CHECK: %[[VAL_3:.*]] = affine.apply #[[$MAP3]]()[%[[C1]], %[[DIM_0]], %[[C1]]]
426-
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0, 1]] output_shape [1, %[[VAL_3]]] : tensor<?xf32> into tensor<1x?xf32>
417+
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[GENERIC]] {{\[\[}}0, 1]] output_shape [1, %[[DIM_0]]] : tensor<?xf32> into tensor<1x?xf32>
427418
// CHECK: return %[[EXPANDED]] : tensor<1x?xf32>
428419

429420
// -----
@@ -482,10 +473,8 @@ func.func @unit_dim_for_reduction_inner(%arg0: tensor<?x1x?x1xf32>) -> tensor<?x
482473
}
483474
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
484475
// CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0, d1) -> (d0)>
485-
// CHECK-DAG: #[[MAP3:.+]] = affine_map<()[s0, s1] -> (s0 * s1)>
486476
// CHECK: func @unit_dim_for_reduction_inner
487477
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x1x?x1xf32>
488-
// CHECK: %[[C1:.*]] = arith.constant 1 : index
489478
// CHECK: %[[C0:.*]] = arith.constant 0 : index
490479
// CHECK: %[[CST:.*]] = arith.constant 1.000000e+00 : f32
491480
// CHECK: %[[C2:.*]] = arith.constant 2 : index
@@ -499,8 +488,7 @@ func.func @unit_dim_for_reduction_inner(%arg0: tensor<?x1x?x1xf32>) -> tensor<?x
499488
// CHECK-SAME: ins(%[[RESHAPE]] : tensor<?x?xf32>)
500489
// CHECK-SAME: outs(%[[FILL]] : tensor<?xf32>)
501490
// CHECK: %[[DIM_0:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x1x?x1xf32>
502-
// CHECK: %[[VAL_3:.+]] = affine.apply #[[$MAP3]]()[%[[DIM_0]], %[[C1]]]
503-
// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]] output_shape [%[[VAL_3]], 1] : tensor<?xf32> into tensor<?x1xf32>
491+
// CHECK: %[[RESULT_RESHAPE:.+]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]] output_shape [%[[DIM_0]], 1] : tensor<?xf32> into tensor<?x1xf32>
504492
// CHECK: return %[[RESULT_RESHAPE]]
505493

506494
// -----
@@ -1017,7 +1005,6 @@ func.func @drop_unit_pad_dynamic_dims(%arg0: tensor<1x?xf32>) -> tensor<1x?xf32>
10171005
return %0 : tensor<1x?xf32>
10181006
}
10191007

1020-
// CHECK-DAG: #[[$MAP:.+]] = affine_map<()[s0, s1] -> (s0 * s1)>
10211008
// CHECK-DAG: #[[$MAP1:.+]] = affine_map<()[s0] -> (s0 + 11)>
10221009
// CHECK-LABEL: func @drop_unit_pad_dynamic_dims
10231010
// CHECK: %[[C1:.*]] = arith.constant 1 : index
@@ -1027,8 +1014,7 @@ func.func @drop_unit_pad_dynamic_dims(%arg0: tensor<1x?xf32>) -> tensor<1x?xf32>
10271014
// CHECK: %[[PADDED:.+]] = tensor.pad %[[COLLAPSE]] low[5] high[6]
10281015
// CHECK: } : tensor<?xf32> to tensor<?xf32>
10291016
// CHECK: %[[DIM:.+]] = tensor.dim %{{.*}}, %[[C1]] : tensor<1x?xf32>
1030-
// CHECK: %[[VAL_0:.+]] = affine.apply #[[$MAP]]()[%[[C1]], %[[DIM]]]
1031-
// CHECK: %[[VAL_1:.+]] = affine.apply #[[$MAP1]]()[%[[VAL_0]]]
1017+
// CHECK: %[[VAL_1:.+]] = affine.apply #[[$MAP1]]()[%[[DIM]]]
10321018
// CHECK: %[[EXPANDED:.+]] = tensor.expand_shape %[[PADDED]] {{\[\[}}0, 1]] output_shape [1, %[[VAL_1]]] : tensor<?xf32> into tensor<1x?xf32>
10331019

10341020
// CHECK-SLICES: #[[$MAP:.+]] = affine_map<()[s0] -> (s0 + 11)>
@@ -1090,20 +1076,17 @@ func.func @drop_known_unit_constant_low_high(%arg0: tensor<1x383x128xf32>) -> te
10901076

10911077
// -----
10921078

1093-
// CHECK: #[[$MAP0:.+]] = affine_map<()[s0, s1] -> (s0 * s1)>
10941079
// CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (0, d0)>
10951080
// CHECK: #[[$MAP2:.+]] = affine_map<(d0) -> ()>
10961081

10971082
// CHECK-LABEL: func @drop_unit_dim_corresponding_to_dynamic_dim
10981083
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x?x?x1xf32>,
10991084
// CHECK-SAME: %[[ARG1:.*]]: index) -> tensor<?x1x61x1xf32> {
11001085
// CHECK: %[[VAL_0:.*]] = arith.constant 0 : index
1101-
// CHECK: %[[VAL_1:.*]] = arith.constant 1 : index
11021086
// CHECK: %[[VAL_2:.*]] = arith.constant dense<1.000000e+00> : tensor<f32>
11031087
// CHECK: %[[VAL_3:.*]] = tensor.collapse_shape %[[ARG0]] {{\[\[}}0, 1], [2, 3]] : tensor<1x?x?x1xf32> into tensor<?x?xf32>
11041088
// CHECK: %[[VAL_4:.*]] = tensor.empty(%[[ARG1]]) : tensor<?x61xf32>
1105-
// CHECK: %[[VAL_5:.*]] = affine.apply #[[$MAP0]](){{\[}}%[[ARG1]], %[[VAL_1]]]
1106-
// CHECK: %[[VAL_6:.*]] = tensor.empty(%[[VAL_5]]) : tensor<?x61xf32>
1089+
// CHECK: %[[VAL_6:.*]] = tensor.empty(%[[ARG1]]) : tensor<?x61xf32>
11071090
// CHECK: %[[VAL_7:.*]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel"]} ins(%[[VAL_3]], %[[VAL_2]], %[[VAL_4]] : tensor<?x?xf32>, tensor<f32>, tensor<?x61xf32>) outs(%[[VAL_6]] : tensor<?x61xf32>) {
11081091
// CHECK: ^bb0(%[[VAL_8:.*]]: f32, %[[VAL_9:.*]]: f32, %[[VAL_10:.*]]: f32, %[[VAL_11:.*]]: f32):
11091092
// CHECK: %[[VAL_12:.*]] = arith.mulf %[[VAL_8]], %[[VAL_9]] : f32

‎mlir/test/Dialect/Linalg/rank-reduce-contraction-ops.mlir

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -76,13 +76,13 @@ func.func @singleton_batch_vecmat(%arg0 : tensor<1x?xf32>, %arg1 : tensor<1x?x?x
7676
// CHECK-SAME: %[[LHS:[a-zA-Z0-9]+]]: tensor<1x?xf32>
7777
// CHECK-SAME: %[[RHS:[a-zA-Z0-9]+]]: tensor<1x?x?xf32>
7878
// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<1x?xf32>
79-
// CHECK-DAG: %[[C1:.*]] = arith.constant 1
79+
// CHECK-DAG: %[[C0:.*]] = arith.constant 0
8080
// CHECK-NEXT: %[[COLLAPSED_LHS:.*]] = tensor.collapse_shape %[[LHS]] {{\[}}[0, 1]]
8181
// CHECK-NEXT: %[[COLLAPSED_RHS:.*]] = tensor.collapse_shape %[[RHS]] {{\[}}[0, 1], [2]]
8282
// CHECK-NEXT: %[[COLLAPSED_INIT:.*]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1]]
8383
// CHECK-NEXT: %[[MATMUL:.+]] = linalg.vecmat
8484
// CHECK-SAME: ins(%[[COLLAPSED_LHS]], %[[COLLAPSED_RHS]] : tensor<?xf32>, tensor<?x?xf32>) outs(%[[COLLAPSED_INIT]] : tensor<?xf32>)
85-
// CHECK-NEXT: %[[DIM1:.*]] = tensor.dim %[[INIT]], %[[C1]]
85+
// CHECK-NEXT: %[[DIM1:.*]] = tensor.dim %[[COLLAPSED_INIT]], %[[C0]]
8686
// CHECK-NEXT: %[[RES:.*]] = tensor.expand_shape %[[MATMUL]] {{\[}}[0, 1]] output_shape [1, %[[DIM1]]]
8787
// CHECK-NEXT: return %[[RES]]
8888
%1 = linalg.batch_vecmat ins(%arg0, %arg1 : tensor<1x?xf32>, tensor<1x?x?xf32>)
@@ -134,7 +134,7 @@ func.func @matmul_to_matvec_tensor(%arg0: tensor<?x?xf32>, %arg1: tensor<?x1xf32
134134
// CHECK-NEXT: %[[COLLAPSED_INIT:.*]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1]]
135135
// CHECK-NEXT: %[[MATMUL:.+]] = linalg.matvec
136136
// CHECK-SAME: ins(%[[LHS]], %[[COLLAPSED_RHS]] : tensor<?x?xf32>, tensor<?xf32>) outs(%[[COLLAPSED_INIT]] : tensor<?xf32>)
137-
// CHECK-NEXT: %[[DIM0:.*]] = tensor.dim %[[INIT]], %[[C0]]
137+
// CHECK-NEXT: %[[DIM0:.*]] = tensor.dim %[[COLLAPSED_INIT]], %[[C0]]
138138
// CHECK-NEXT: %[[RES:.*]] = tensor.expand_shape %[[MATMUL]] {{\[}}[0, 1]] output_shape [%[[DIM0]], 1]
139139
// CHECK-NEXT: return %[[RES]]
140140
%0 = linalg.matmul ins(%arg0, %arg1: tensor<?x?xf32>, tensor<?x1xf32>) outs(%arg2: tensor<?x1xf32>) -> tensor<?x1xf32>
@@ -171,12 +171,12 @@ func.func @matmul_to_vecmat_tensor(%arg0: tensor<1x?xf32>, %arg1: tensor<?x?xf32
171171
// CHECK-SAME: %[[LHS:[a-zA-Z0-9]+]]: tensor<1x?xf32>
172172
// CHECK-SAME: %[[RHS:[a-zA-Z0-9]+]]: tensor<?x?xf32>
173173
// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<1x?xf32>
174-
// CHECK-DAG: %[[C1:.*]] = arith.constant 1
174+
// CHECK-DAG: %[[C0:.*]] = arith.constant 0
175175
// CHECK-NEXT: %[[COLLAPSED_LHS:.*]] = tensor.collapse_shape %[[LHS]] {{\[}}[0, 1]]
176176
// CHECK-NEXT: %[[COLLAPSED_INIT:.*]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1]]
177177
// CHECK-NEXT: %[[RESULT:.*]] = linalg.vecmat
178178
// CHECK-SAME: ins(%[[COLLAPSED_LHS]], %[[RHS]] : tensor<?xf32>, tensor<?x?xf32>) outs(%[[COLLAPSED_INIT]] : tensor<?xf32>)
179-
// CHECK-NEXT: %[[DIM1:.*]] = tensor.dim %[[INIT]], %[[C1]]
179+
// CHECK-NEXT: %[[DIM1:.*]] = tensor.dim %[[COLLAPSED_INIT]], %[[C0]]
180180
// CHECK-NEXT: %[[RES:.*]] = tensor.expand_shape %[[RESULT]] {{\[}}[0, 1]] output_shape [1, %[[DIM1]]]
181181
// CHECK-NEXT: return %[[RES]]
182182
%0 = linalg.matmul ins(%arg0, %arg1: tensor<1x?xf32>, tensor<?x?xf32>) outs(%arg2: tensor<1x?xf32>) -> tensor<1x?xf32>

‎mlir/test/Dialect/Tensor/canonicalize.mlir

Lines changed: 10 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -1105,15 +1105,13 @@ func.func @compose_expand_of_collapse_last_two_dims(%arg0: tensor<?x64x1xf32>) -
11051105
%expanded = tensor.expand_shape %collapsed [[0, 1]] output_shape [%div, 384] : tensor<?xf32> into tensor<?x384xf32>
11061106
return %expanded : tensor<?x384xf32>
11071107
}
1108-
// CHECK: #[[$MAP:.*]] = affine_map<()[s0] -> (s0 * 64)>
11091108
// CHECK-LABEL: @compose_expand_of_collapse_last_two_dims
11101109
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x64x1xf32>
1111-
// CHECK: %[[CONSTANT0:.+]] = arith.constant 0 : index
11121110
// CHECK: %[[CONSTANT384:.+]] = arith.constant 384 : index
1111+
// CHECK: %[[CONSTANT0:.+]] = arith.constant 0 : index
11131112
// CHECK: %[[COLLAPSE:.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1, 2]] : tensor<?x64x1xf32> into tensor<?xf32>
1114-
// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[CONSTANT0]] : tensor<?x64x1xf32>
1115-
// CHECK: %[[AFFAPPLY:.+]] = affine.apply #[[$MAP]]()[%[[DIM]]]
1116-
// CHECK: %[[DIVUI:.+]] = arith.divui %[[AFFAPPLY]], %[[CONSTANT384]] : index
1113+
// CHECK: %[[DIM:.+]] = tensor.dim %[[COLLAPSE]], %[[CONSTANT0]] : tensor<?xf32>
1114+
// CHECK: %[[DIVUI:.+]] = arith.divui %[[DIM]], %[[CONSTANT384]] : index
11171115
// CHECK: %[[RESULT:.+]] = tensor.expand_shape %[[COLLAPSE]] {{\[}}[0, 1]] output_shape [%[[DIVUI]], 384] : tensor<?xf32> into tensor<?x384xf32>
11181116
// CHECK: return %[[RESULT]]
11191117

@@ -2137,13 +2135,12 @@ func.func @empty_tensor_canonicalize(%i : index) {
21372135

21382136
// -----
21392137

2140-
// CHECK: #[[$map:.*]] = affine_map<()[s0] -> (s0 floordiv 40)>
21412138
// CHECK-LABEL: func @dim_of_expand_shape(
21422139
// CHECK-SAME: %[[t:.*]]: tensor<?x?xf32>
2143-
// CHECK: %[[c1:.*]] = arith.constant 1 : index
2144-
// CHECK: %[[dim:.*]] = tensor.dim %[[t]], %[[c1]] : tensor<?x?xf32>
2145-
// CHECK: %[[apply:.*]] = affine.apply #[[$map]]()[%[[dim]]]
2146-
// CHECK: return %[[apply]]
2140+
// CHECK: %[[c2:.*]] = arith.constant 2 : index
2141+
// CHECK: %[[expanded:.*]] = tensor.expand_shape %[[t]] {{\[\[}}0], [1, 2, 3, 4, 5]] output_shape [%arg1, 1, %arg2, 5, 1, 8] : tensor<?x?xf32> into tensor<?x1x?x5x1x8xf32>
2142+
// CHECK: %[[dim:.*]] = tensor.dim %[[expanded]], %[[c2]] : tensor<?x1x?x5x1x8xf32>
2143+
// CHECK: return %[[dim]]
21472144
func.func @dim_of_expand_shape(%t: tensor<?x?xf32>, %sz0: index, %sz1: index) -> index {
21482145
%c2 = arith.constant 2 : index
21492146
%0 = tensor.expand_shape %t [[0], [1, 2, 3, 4, 5]] output_shape [%sz0, 1, %sz1, 5, 1, 8]
@@ -2154,17 +2151,12 @@ func.func @dim_of_expand_shape(%t: tensor<?x?xf32>, %sz0: index, %sz1: index) ->
21542151

21552152
// -----
21562153

2157-
// CHECK: #[[$map:.*]] = affine_map<()[s0, s1, s2] -> (((s0 * s1) * s2) * 7)>
21582154
// CHECK-LABEL: func @dim_of_collapse_shape(
21592155
// CHECK-SAME: %[[t:.*]]: tensor<?x?x?x7x?xf32>
21602156
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
2161-
// CHECK-DAG: %[[c2:.*]] = arith.constant 2 : index
2162-
// CHECK-DAG: %[[c4:.*]] = arith.constant 4 : index
2163-
// CHECK-DAG: %[[dim1:.*]] = tensor.dim %[[t]], %[[c1]]
2164-
// CHECK-DAG: %[[dim2:.*]] = tensor.dim %[[t]], %[[c2]]
2165-
// CHECK-DAG: %[[dim4:.*]] = tensor.dim %[[t]], %[[c4]]
2166-
// CHECK: %[[apply:.*]] = affine.apply #[[$map]]()[%[[dim1]], %[[dim2]], %[[dim4]]]
2167-
// CHECK: return %[[apply]]
2157+
// CHECK-DAG: %[[collapsed:.*]] = tensor.collapse_shape %[[t]] {{\[\[}}0], [1, 2, 3, 4]] : tensor<?x?x?x7x?xf32> into tensor<?x?xf32>
2158+
// CHECK-DAG: %[[dim:.*]] = tensor.dim %[[collapsed]], %[[c1]]
2159+
// CHECK: return %[[dim]]
21682160
func.func @dim_of_collapse_shape(%t: tensor<?x?x?x7x?xf32>) -> index {
21692161
%c1 = arith.constant 1 : index
21702162
%0 = tensor.collapse_shape %t [[0], [1, 2, 3, 4]]

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