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[mlir][linalg] Use ub.poison when vectorizing pack+unpack Ops (#159536)
This patch makes sure that in the absence of an explicit pad value in `linalg.pack`, the vectorizer will use `ub.poison` for the corresponding Xfer Op pad value (as opposed to e.g. `arith.constant 0`). Also, in the case of `linalg.unpack`, use `ub.poison` for the Xfer read operation. In this case, there is no mechanism for a user to specify the pad/pass-thru value.
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-50
lines changed

5 files changed

+47
-50
lines changed

‎mlir/include/mlir/Dialect/Vector/Utils/VectorUtils.h‎

Lines changed: 2 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -227,7 +227,8 @@ bool isLinearizableVector(VectorType type);
227227
///
228228
/// Note: all read offsets are set to 0.
229229
Value createReadOrMaskedRead(OpBuilder &builder, Location loc, Value source,
230-
ArrayRef<int64_t> inputVectorSizes, Value padValue,
230+
ArrayRef<int64_t> inputVectorSizes,
231+
std::optional<Value> padValue = std::nullopt,
231232
bool useInBoundsInsteadOfMasking = false,
232233
ArrayRef<bool> inputScalableVecDims = {});
233234

‎mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp‎

Lines changed: 4 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -1770,12 +1770,9 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
17701770
rewriter.setInsertionPoint(packOp);
17711771

17721772
Location loc = packOp.getLoc();
1773-
auto padValue = packOp.getPaddingValue();
1774-
if (!padValue) {
1775-
padValue = arith::ConstantOp::create(
1776-
rewriter, loc,
1777-
rewriter.getZeroAttr(packOp.getSourceType().getElementType()));
1778-
}
1773+
std::optional<Value> padValue = packOp.getPaddingValue()
1774+
? std::optional(packOp.getPaddingValue())
1775+
: std::nullopt;
17791776

17801777
// If the input vector sizes are not provided, then the vector sizes are
17811778
// determined by the result tensor shape. In case the vector sizes aren't
@@ -1936,11 +1933,8 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,
19361933
}
19371934

19381935
// -- Generate the read operation --
1939-
auto padValue = arith::ConstantOp::create(
1940-
rewriter, loc,
1941-
rewriter.getZeroAttr(unpackOp.getSourceType().getElementType()));
19421936
Value readResult = vector::createReadOrMaskedRead(
1943-
rewriter, loc, unpackOp.getSource(), readVectorSizes, padValue,
1937+
rewriter, loc, unpackOp.getSource(), readVectorSizes, std::nullopt,
19441938
useInBoundsInsteadOfMasking, readScalableVectorFlags);
19451939

19461940
// -- Generate the transpose operation --

‎mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp‎

Lines changed: 6 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -319,7 +319,7 @@ bool vector::isLinearizableVector(VectorType type) {
319319
Value vector::createReadOrMaskedRead(OpBuilder &builder, Location loc,
320320
Value source,
321321
ArrayRef<int64_t> inputVectorSizes,
322-
Value padValue,
322+
std::optional<Value> padValue,
323323
bool useInBoundsInsteadOfMasking,
324324
ArrayRef<bool> inputScalableVecDims) {
325325
assert(!llvm::is_contained(inputVectorSizes, ShapedType::kDynamic) &&
@@ -328,9 +328,11 @@ Value vector::createReadOrMaskedRead(OpBuilder &builder, Location loc,
328328
auto sourceShape = sourceShapedType.getShape();
329329
assert(sourceShape.size() == inputVectorSizes.size() &&
330330
"expected same ranks.");
331-
auto vectorType = VectorType::get(inputVectorSizes, padValue.getType(),
332-
inputScalableVecDims);
333-
assert(padValue.getType() == sourceShapedType.getElementType() &&
331+
auto vectorType =
332+
VectorType::get(inputVectorSizes, sourceShapedType.getElementType(),
333+
inputScalableVecDims);
334+
assert((!padValue.has_value() ||
335+
padValue.value().getType() == sourceShapedType.getElementType()) &&
334336
"expected same pad element type to match source element type");
335337
int64_t readRank = inputVectorSizes.size();
336338
auto zero = arith::ConstantIndexOp::create(builder, loc, 0);

‎mlir/test/Dialect/Linalg/vectorization/linalg-ops-with-patterns.mlir‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -339,8 +339,8 @@ module attributes {transform.with_named_sequence} {
339339
// CHECK-LABEL: func.func @test_vectorize_pack(
340340
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x8x16xf32>,
341341
// CHECK-SAME: %[[VAL_1:.*]]: tensor<4x1x32x16x2xf32>) -> tensor<4x1x32x16x2xf32> {
342-
// CHECK: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32
343-
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
342+
// CHECK-DAG: %[[VAL_2:.*]] = ub.poison : f32
343+
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
344344
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]]], %[[VAL_2]] {in_bounds = [true, true, true]} : tensor<32x8x16xf32>, vector<32x8x16xf32>
345345
// CHECK: %[[VAL_5:.*]] = vector.shape_cast %[[VAL_4]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
346346
// CHECK: %[[VAL_6:.*]] = vector.transpose %[[VAL_5]], [1, 3, 0, 4, 2] : vector<32x4x2x1x16xf32> to vector<4x1x32x16x2xf32>

‎mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir‎

Lines changed: 33 additions & 33 deletions
Original file line numberDiff line numberDiff line change
@@ -1068,16 +1068,16 @@ module attributes {transform.with_named_sequence} {
10681068
// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
10691069
// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x16x2xf32>
10701070
func.func @test_vectorize_dynamic_shapes_unpack_scalable_vec(%dest: tensor<?x?xf32>, %src: tensor<?x?x16x2xf32>) -> tensor<?x?xf32> {
1071-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
1072-
// CHECK: %[[C01:.*]] = arith.constant 0
1073-
// CHECK: %[[C02:.*]] = arith.constant 0
1071+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1072+
// CHECK-DAG: %[[C01:.*]] = arith.constant 0
1073+
// CHECK-DAG: %[[C02:.*]] = arith.constant 0
10741074
// CHECK: %[[DIM4:.*]] = tensor.dim %[[SRC]], %[[C02]] : tensor<?x?x16x2xf32>
10751075
// CHECK: %[[CNST14:.*]] = arith.constant 1
10761076
// CHECK: %[[DIM6:.*]] = tensor.dim %[[SRC]], %[[CNST14]] : tensor<?x?x16x2xf32>
10771077
// CHECK: %[[CNST16:.*]] = arith.constant 16 : index
10781078
// CHECK: %[[CNST2:.*]] = arith.constant 2 : index
10791079
// CHECK: %[[MASK_READ:.*]] = vector.create_mask %[[DIM4]], %[[DIM6]], %[[CNST16]], %[[CNST2]] : vector<2x1x[16]x2xi1>
1080-
// CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}} vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x[16]x2xf32> } : vector<2x1x[16]x2xi1> -> vector<2x1x[16]x2xf32>
1080+
// CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}} vector.transfer_read %{{.*}} %[[PAD]] {{.*}}: tensor<?x?x16x2xf32>, vector<2x1x[16]x2xf32> } : vector<2x1x[16]x2xi1> -> vector<2x1x[16]x2xf32>
10811081
// CHECK: %[[TR:.*]] = vector.transpose %[[READ]], [0, 3, 1, 2] : vector<2x1x[16]x2xf32> to vector<2x2x1x[16]xf32>
10821082
// CHECK: %[[SC:.*]] = vector.shape_cast %[[TR]] : vector<2x2x1x[16]xf32> to vector<4x[16]xf32>
10831083
// CHECK: %[[MASK_WRITE:.*]] = vector.create_mask {{.*}} : vector<4x[16]xi1>
@@ -1100,17 +1100,17 @@ module attributes {transform.with_named_sequence} {
11001100
// CHECK-SAME: %[[DEST:.*]]: tensor<?x?xf32>,
11011101
// CHECK-SAME: %[[SRC:.*]]: tensor<?x?x?x2xf32>
11021102
func.func @test_vectorize_dynamic_shapes_unpack_scalable_vec_and_tile_size(%dest: tensor<?x?xf32>, %src: tensor<?x?x?x2xf32>) -> tensor<?x?xf32> {
1103-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00
1104-
// CHECK: %[[C01:.*]] = arith.constant 0
1105-
// CHECK: %[[C02:.*]] = arith.constant 0
1103+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1104+
// CHECK-DAG: %[[C01:.*]] = arith.constant 0
1105+
// CHECK-DAG: %[[C02:.*]] = arith.constant 0
11061106
// CHECK: %[[DIM4:.*]] = tensor.dim %[[SRC]], %[[C02]] : tensor<?x?x?x2xf32>
11071107
// CHECK: %[[C1_2:.*]] = arith.constant 1
11081108
// CHECK: %[[DIM6:.*]] = tensor.dim %[[SRC]], %[[C1_2]] : tensor<?x?x?x2xf32>
11091109
// CHECK: %[[C2:.*]] = arith.constant 2 : index
11101110
// CHECK: %[[DIM_2:.*]] = tensor.dim %[[SRC]], %[[C2]] : tensor<?x?x?x2xf32>
11111111
// CHECK: %[[C2_1:.*]] = arith.constant 2 : index
11121112
// CHECK: %[[MASK_READ:.*]] = vector.create_mask %[[DIM4]], %[[DIM6]], %[[DIM_2]], %[[C2_1]] : vector<2x1x[16]x2xi1>
1113-
// CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}} vector.transfer_read %{{.*}} : tensor<?x?x?x2xf32>, vector<2x1x[16]x2xf32> } : vector<2x1x[16]x2xi1> -> vector<2x1x[16]x2xf32>
1113+
// CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {{.*}} vector.transfer_read %{{.*}} %[[PAD]] {{.*}}: tensor<?x?x?x2xf32>, vector<2x1x[16]x2xf32> } : vector<2x1x[16]x2xi1> -> vector<2x1x[16]x2xf32>
11141114
// CHECK: %[[TR:.*]] = vector.transpose %[[READ]], [0, 3, 1, 2] : vector<2x1x[16]x2xf32> to vector<2x2x1x[16]xf32>
11151115
// CHECK: %[[SC:.*]] = vector.shape_cast %[[TR]] : vector<2x2x1x[16]xf32> to vector<4x[16]xf32>
11161116
// CHECK: %[[MASK_WRITE:.*]] = vector.create_mask {{.*}} : vector<4x[16]xi1>
@@ -1138,14 +1138,14 @@ module attributes {transform.with_named_sequence} {
11381138
// CHECK-SAME: %[[SRC:.*]]: tensor<8x8x32x16xf32>
11391139
// CHECK-SAME: %[[DEST:.*]]: tensor<256x128xf32>
11401140
func.func @test_vectorize_unpack(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {
1141-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1142-
// CHECK: %[[C0:.*]]= arith.constant 0 : index
1143-
// CHECK: %[[C8:.*]] = arith.constant 8 : index
1144-
// CHECK: %[[C80:.*]] = arith.constant 8 : index
1145-
// CHECK: %[[C32:.*]] = arith.constant 32 : index
1146-
// CHECK: %[[C16:.*]] = arith.constant 16 : index
1141+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1142+
// CHECK-DAG: %[[C0:.*]]= arith.constant 0 : index
1143+
// CHECK-DAG: %[[C8:.*]] = arith.constant 8 : index
1144+
// CHECK-DAG: %[[C80:.*]] = arith.constant 8 : index
1145+
// CHECK-DAG: %[[C32:.*]] = arith.constant 32 : index
1146+
// CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index
11471147
// CHECK: %[[MSK0:.*]] = vector.create_mask %[[C8]], %[[C80]], %[[C32]], %[[C16]] : vector<16x8x32x16xi1>
1148-
// CHECK: %[[READ0:.*]] = vector.mask %[[MSK0]] { vector.transfer_read %[[SRC]]{{.*}}} : vector<16x8x32x16xi1> -> vector<16x8x32x16xf32>
1148+
// CHECK: %[[READ0:.*]] = vector.mask %[[MSK0]] { vector.transfer_read %[[SRC]]{{.*}} %[[PAD]] {{.*}} : vector<16x8x32x16xi1> -> vector<16x8x32x16xf32>
11491149
// CHECK: %[[TRANSP0:.*]] = vector.transpose %[[READ0]], [0, 2, 1, 3] : vector<16x8x32x16xf32> to vector<16x32x8x16xf32>
11501150
// CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP0]] : vector<16x32x8x16xf32> to vector<512x128xf32>
11511151
// CHECK: %[[C01:.*]] = arith.constant 0 : index
@@ -1171,9 +1171,9 @@ func.func @test_vectorize_unpack(%source: tensor<8x8x32x16xf32>, %dest: tensor<2
11711171
// CHECK-SAME: %[[SRC:.*]]: tensor<8x8x32x16xf32>
11721172
// CHECK-SAME: %[[DEST:.*]]: tensor<256x128xf32>
11731173
func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {
1174-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1175-
// CHECK: %[[C0:.*]] = arith.constant 0 : index
1176-
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>
1174+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1175+
// CHECK-AD: %[[C0:.*]] = arith.constant 0 : index
1176+
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}} %[[PAD]] {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>
11771177
// CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [0, 2, 1, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32>
11781178
// CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32>
11791179
// CHECK: %[[C00:.*]] = arith.constant 0 : index
@@ -1196,9 +1196,9 @@ func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest:
11961196
// CHECK-SAME: %[[SRC:.*]]: tensor<8x8x32x16xf32>
11971197
// CHECK-SAME: %[[DEST:.*]]: tensor<256x128xf32>
11981198
func.func @test_vectorize_unpack_with_outer_perm(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {
1199-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1200-
// CHECK: %[[C0:.*]] = arith.constant 0 : index
1201-
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>
1199+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1200+
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
1201+
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}} %[[PAD]] {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>
12021202
// CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 2, 0, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32>
12031203
// CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32>
12041204
// CHECK: %[[C00:.*]] = arith.constant 0 : index
@@ -1221,9 +1221,9 @@ func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest:
12211221
// CHECK-SAME: %[[SRC:.*]]: tensor<8x8x32x16xf32>
12221222
// CHECK-SAME: %[[DEST:.*]]: tensor<256x128xf32>
12231223
func.func @test_vectorize_unpack_no_vector_sizes(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {
1224-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1225-
// CHECK: %[[C0:.*]] = arith.constant 0 : index
1226-
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>
1224+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1225+
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
1226+
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}} %[[PAD]] {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>
12271227
// CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [0, 2, 1, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32>
12281228
// CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32>
12291229
// CHECK: %[[C00:.*]] = arith.constant 0 : index
@@ -1246,9 +1246,9 @@ func.func @test_vectorize_unpack_no_vector_sizes(%source: tensor<8x8x32x16xf32>,
12461246
// CHECK-SAME: %[[SRC:.*]]: tensor<8x4x16x16xf32>
12471247
// CHECK-SAME: %[[DEST:.*]]: tensor<64x127xf32>
12481248
func.func @test_vectorize_unpack_no_vector_sizes_slice_output(%source: tensor<8x4x16x16xf32>, %dest: tensor<64x127xf32>) -> tensor<64x127xf32> {
1249-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1250-
// CHECK: %[[C0:.*]] = arith.constant 0 : index
1251-
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x4x16x16xf32>, vector<8x4x16x16xf32>
1249+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1250+
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
1251+
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}} %[[PAD]] {{.*}} : tensor<8x4x16x16xf32>, vector<8x4x16x16xf32>
12521252
// CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 2, 0, 3] : vector<8x4x16x16xf32> to vector<4x16x8x16xf32>
12531253
// CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<4x16x8x16xf32> to vector<64x128xf32>
12541254
// CHECK: %[[C00:.*]] = arith.constant 0 : index
@@ -1275,9 +1275,9 @@ func.func @test_vectorize_unpack_no_vector_sizes_permute(%source: tensor<4x7x4xf
12751275
%0 = linalg.unpack %source outer_dims_perm=[1, 0] inner_dims_pos = [1] inner_tiles = [4] into %dest : tensor<4x7x4xf32> -> tensor<7x16xf32>
12761276
return %0 : tensor<7x16xf32>
12771277
}
1278-
// CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1279-
// CHECK: %[[C0:.*]] = arith.constant 0 : index
1280-
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<4x7x4xf32>, vector<4x7x4xf32>
1278+
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
1279+
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
1280+
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}} %[[PAD]] {{.*}} : tensor<4x7x4xf32>, vector<4x7x4xf32>
12811281
// CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 0, 2] : vector<4x7x4xf32> to vector<7x4x4xf32>
12821282
// CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<7x4x4xf32> to vector<7x16xf32>
12831283
// CHECK: %[[C00:.*]] = arith.constant 0 : index
@@ -1308,7 +1308,7 @@ func.func @test_vectorize_pack(%src: tensor<32x8x16xf32>, %dest: tensor<4x1x32x1
13081308
%pack = linalg.pack %src outer_dims_perm = [1, 2, 0] inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %dest : tensor<32x8x16xf32> -> tensor<4x1x32x16x2xf32>
13091309
return %pack : tensor<4x1x32x16x2xf32>
13101310
}
1311-
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1311+
// CHECK-DAG: %[[CST:.*]] = ub.poison : f32
13121312
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
13131313
// CHECK: %[[READ:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]], %[[C0]]], %[[CST]]
13141314
// CHECK-SAME: {in_bounds = [true, true, true]} : tensor<32x8x16xf32>, vector<32x8x16xf32>
@@ -1376,7 +1376,7 @@ func.func @test_vectorize_dynamic_pack(%src: tensor<?x?xf32>, %dest: tensor<?x?x
13761376
return %pack : tensor<?x?x16x2xf32>
13771377
}
13781378

1379-
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1379+
// CHECK-DAG: %[[CST:.*]] = ub.poison : f32
13801380
// CHECK-DAG: %[[C0_1:.*]] = arith.constant 0 : index
13811381
// CHECK-DAG: %[[C0_0:.*]] = arith.constant 0 : index
13821382
// CHECK-DAG: %[[C1_0:.*]] = arith.constant 1 : index
@@ -1417,7 +1417,7 @@ func.func @test_vectorize_pack_no_vector_sizes(%src: tensor<64x4xf32>, %dest: te
14171417
%pack = linalg.pack %src outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [16, 2] into %dest : tensor<64x4xf32> -> tensor<2x4x16x2xf32>
14181418
return %pack : tensor<2x4x16x2xf32>
14191419
}
1420-
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
1420+
// CHECK-DAG: %[[CST:.*]] = ub.poison : f32
14211421
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
14221422
// CHECK: %[[READ:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CST]]
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// CHECK-SAME: {in_bounds = [true, true]} : tensor<64x4xf32>, vector<64x4xf32>

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