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[mlir][linalg] Use ub.poison in linalg vectorizer instead of 0 for some transfer ops #146544

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Jul 2, 2025
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[mlir][linalg] Use ub.poison in linalg vectorizer instead of 0 for some transfer ops #146544

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16 changes: 6 additions & 10 deletions mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1183,19 +1183,15 @@ vectorizeTensorExtract(RewriterBase &rewriter, VectorizationState &state,
auto srcRank = extractOp.getTensor().getType().getRank();
SmallVector<bool> inBounds(dstRank, true);

// Get the value to pad transfer reads with 0.
Value padding =
arith::getZeroConstant(rewriter, loc, resultType.getElementType());

// 2a. Handle scalar broadcast access.
if (memAccessKind == VectorMemoryAccessKind::ScalarBroadcast) {
MLIRContext *ctx = rewriter.getContext();
SmallVector<AffineExpr> exprs(dstRank, getAffineConstantExpr(0, ctx));
auto permutationMap = AffineMap::get(srcRank, 0, exprs, ctx);

auto transferReadOp = rewriter.create<vector::TransferReadOp>(
loc, resultType, extractOp.getTensor(), transferReadIdxs, padding,
permutationMap, inBounds);
loc, resultType, extractOp.getTensor(), transferReadIdxs,
/*padding=*/std::nullopt, permutationMap, inBounds);

// Mask this broadcasting xfer_read here rather than relying on the generic
// path (the generic path assumes identity masking map, which wouldn't be
Expand Down Expand Up @@ -1231,8 +1227,8 @@ vectorizeTensorExtract(RewriterBase &rewriter, VectorizationState &state,
}

auto transferReadOp = rewriter.create<vector::TransferReadOp>(
loc, resultType, extractOp.getTensor(), transferReadIdxs, padding,
permutationMap, inBounds);
loc, resultType, extractOp.getTensor(), transferReadIdxs,
/*padding=*/std::nullopt, permutationMap, inBounds);

LDBG("Vectorised as contiguous load: " << extractOp);
return VectorizationHookResult{VectorizationHookStatus::NewOp,
Expand Down Expand Up @@ -1444,7 +1440,7 @@ vectorizeAsLinalgGeneric(RewriterBase &rewriter, VectorizationState &state,

Operation *read = rewriter.create<vector::TransferReadOp>(
loc, readType, opOperand->get(), indices,
/*padding=*/arith::getZeroConstant(rewriter, loc, elemType), readMap);
/*padding=*/std::nullopt, readMap);
read = state.maskOperation(rewriter, read, linalgOp, indexingMap);
Value readValue = read->getResult(0);

Expand Down Expand Up @@ -2646,7 +2642,7 @@ LogicalResult mlir::linalg::vectorizeCopy(RewriterBase &rewriter,

Value readValue = rewriter.create<vector::TransferReadOp>(
loc, readType, copyOp.getSource(), indices,
/*padding=*/arith::getZeroConstant(rewriter, loc, srcElementType),
/*padding=*/std::nullopt,
rewriter.getMultiDimIdentityMap(srcType.getRank()));
if (cast<VectorType>(readValue.getType()).getRank() == 0) {
readValue =
Expand Down
39 changes: 20 additions & 19 deletions mlir/test/Dialect/Linalg/vectorization/extract-with-patterns.mlir
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Expand Up @@ -31,9 +31,9 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic(
// CHECK-SAME: %[[ARG1:.*]]: tensor<1x1x3xf32>

// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[CST:.+]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[PV:.+]] = ub.poison : f32

// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %[[CST]] {in_bounds = [true, true, true]} : tensor<3x3x3xf32>, vector<1x1x3xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %[[PV]] {in_bounds = [true, true, true]} : tensor<3x3x3xf32>, vector<1x1x3xf32>
// CHECK: vector.transfer_write %[[READ]], %[[ARG1]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x1x3xf32>, tensor<1x1x3xf32>

// -----
Expand Down Expand Up @@ -64,12 +64,12 @@ func.func @vectorize_nd_tensor_extract_transfer_read_complex(%6: tensor<45x80x16
// CHECK-SAME: %[[ARG5:.*]]: tensor<1x4xf32>) -> tensor<1x4xf32> {

// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[PV:.*]] = ub.poison : f32
// CHECK-DAG: %[[C79:.*]] = arith.constant 79 : index
// CHECK: %[[ADD1:.*]] = arith.addi %[[ARG1]], %[[ARG2]] : index
// CHECK: %[[ADD2:.*]] = arith.addi %[[ARG3]], %[[ARG4]] : index

// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]]{{\[}}%[[ADD1]], %[[C79]], %[[ADD2]]], %[[CST]] {in_bounds = [true, true]} : tensor<45x80x16xf32>, vector<1x4xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]]{{\[}}%[[ADD1]], %[[C79]], %[[ADD2]]], %[[PV]] {in_bounds = [true, true]} : tensor<45x80x16xf32>, vector<1x4xf32>
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[ARG5]]{{\[}}%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
// CHECK: return %[[WRITE]] : tensor<1x4xf32>
// CHECK: }
Expand Down Expand Up @@ -97,11 +97,11 @@ func.func @vectorize_nd_tensor_extract_with_affine_apply_contiguous(%6: tensor<8
// CHECK-SAME: %[[ARG1:.*]]: index,
// CHECK-SAME: %[[ARG2:.*]]: tensor<1x4xf32>) -> tensor<1x4xf32> {

// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[PV:.*]] = ub.poison : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C79:.*]] = arith.constant 79 : index

// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]]{{\[}}%[[C79]], %[[ARG1]]], %[[CST]] {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]]{{\[}}%[[C79]], %[[ARG1]]], %[[PV]] {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32>
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[ARG2]]{{\[}}%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
// CHECK: return %[[WRITE]] : tensor<1x4xf32>
// CHECK: }
Expand Down Expand Up @@ -164,9 +164,9 @@ func.func @vectorize_nd_tensor_extract_with_maxsi_contiguous(%arg0: tensor<80x16
// CHECK-SAME: %[[VAL_1:.*]]: tensor<1x4xf32>) -> tensor<1x4xf32> {
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[PV:.*]] = ub.poison : f32

// CHECK: %[[VAL_8:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[C16]], %[[C0]]], %[[CST]] {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[C16]], %[[C0]]], %[[PV]] {in_bounds = [true, true]} : tensor<80x16xf32>, vector<1x4xf32>
// CHECK: %[[VAL_9:.*]] = vector.transfer_write %[[VAL_8]], %[[VAL_1]]{{\[}}%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
// CHECK: return %[[VAL_9]] : tensor<1x4xf32>
// CHECK: }
Expand Down Expand Up @@ -229,12 +229,12 @@ func.func @vectorize_nd_tensor_extract_index_from_tensor(%arg0: tensor<3x3xf32>,
// CHECK-SAME: %[[ARG3:.*]]: tensor<4x7x2xf32>
// CHECK-SAME: %[[ARG4:.*]]: tensor<4x7x3x2xf32>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C0_i32:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[PV:.*]] = ub.poison : i32
// CHECK-DAG: %[[CST:.*]] = arith.constant dense<3> : vector<7x2x4x3xindex>
// CHECK-DAG: %[[CST_1:.*]] = arith.constant dense<true> : vector<4x7x3x2xi1>
// CHECK-DAG: %[[PASSTHRU:.*]] = arith.constant dense<0.000000e+00> : vector<4x7x3x2xf32>
// CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], %[[C0_i32]] {in_bounds = [true, true]} : tensor<4x3xi32>, vector<4x3xi32>
// CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG2]][%[[C0]], %[[C0]]], %[[C0_i32]] {in_bounds = [true, true]} : tensor<4x3xi32>, vector<4x3xi32>
// CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], %[[PV]] {in_bounds = [true, true]} : tensor<4x3xi32>, vector<4x3xi32>
// CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG2]][%[[C0]], %[[C0]]], %[[PV]] {in_bounds = [true, true]} : tensor<4x3xi32>, vector<4x3xi32>
// CHECK: %[[CAST:.*]] = arith.index_cast %[[V0]] : vector<4x3xi32> to vector<4x3xindex>
// CHECK: %[[B1:.*]] = vector.broadcast %[[CAST]] : vector<4x3xindex> to vector<7x2x4x3xindex>
// CHECK: %[[CAST_1:.*]] = arith.index_cast %[[V1]] : vector<4x3xi32> to vector<4x3xindex>
Expand Down Expand Up @@ -382,7 +382,7 @@ func.func @vectorize_nd_tensor_extract_contiguous_and_gather(%arg0: tensor<6xf32
// CHECK-SAME: %[[VAL_0:.*]]: tensor<6xf32>
// CHECK-SAME: %[[VAL_1:.*]]: tensor<5xi32>
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[VAL_3:.*]] = ub.poison : i32
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant dense<0> : vector<5xindex>
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant dense<5> : vector<5xindex>
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant dense<true> : vector<5xi1>
Expand Down Expand Up @@ -480,13 +480,14 @@ func.func @vectorize_nd_tensor_extract_block_arg(%arg0: tensor<5x6xf32>, %arg1:
// CHECK-LABEL: func.func @vectorize_nd_tensor_extract_block_arg(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x6xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<5xindex>) -> tensor<5xf32> {
// CHECK-DAG: %[[PAD:.*]] = ub.poison : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant dense<[0, 1, 2, 3, 4]> : vector<5xindex>
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant dense<true> : vector<5xi1>
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant dense<0.000000e+00> : vector<5xf32>
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant dense<6> : vector<5xindex>
// CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<5xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_read %[[VAL_1]]{{\[}}%[[VAL_2]]], %[[VAL_2]] {in_bounds = [true]} : tensor<5xindex>, vector<5xindex>
// CHECK: %[[VAL_8:.*]] = vector.transfer_read %[[VAL_1]]{{\[}}%[[VAL_2]]], %[[PAD]] {in_bounds = [true]} : tensor<5xindex>, vector<5xindex>
// CHECK: %[[VAL_9:.*]] = arith.muli %[[VAL_8]], %[[VAL_6]] : vector<5xindex>
// CHECK: %[[VAL_10:.*]] = arith.addi %[[VAL_9]], %[[VAL_3]] : vector<5xindex>
// CHECK: %[[VAL_11:.*]] = vector.gather %[[VAL_0]]{{\[}}%[[VAL_2]], %[[VAL_2]]] {{\[}}%[[VAL_10]]], %[[VAL_4]], %[[VAL_5]] : tensor<5x6xf32>, vector<5xindex>, vector<5xi1>, vector<5xf32> into vector<5xf32>
Expand Down Expand Up @@ -559,7 +560,7 @@ func.func @vectorize_nd_tensor_extract_scalar_broadcast(%src: tensor<3x3xf32>, %
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][%[[C1]], %[[C2]]], %[[PAD]] : tensor<3x3xf32>, vector<f32>
// CHECK: %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<1x1x3xf32>
// CHECK: vector.transfer_write %[[READ_BCAST]], %[[INIT]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x1x3xf32>, tensor<1x1x3xf32>
Expand All @@ -583,7 +584,7 @@ func.func @extract_scalar_from_0d_into_0d(%src: tensor<f32>, %init: tensor<f32>)
// CHECK-LABEL: func.func @extract_scalar_from_0d_into_0d(
// CHECK-SAME: %[[SRC:.*]]: tensor<f32>,
// CHECK-SAME: %[[INIT:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[PAD:.*]] = ub.poison : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: vector.transfer_write %[[READ]], %[[INIT]][] : vector<f32>, tensor<f32>

Expand All @@ -606,7 +607,7 @@ func.func @extract_scalar_from_0d_into_1d(%src: tensor<f32>, %init: tensor<1xf32
// CHECK-SAME: %[[SRC:.*]]: tensor<f32>,
// CHECK-SAME: %[[INIT:.*]]: tensor<1xf32>) -> tensor<1xf32> {
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[PAD:.*]] = ub.poison : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<1xf32>
// CHECK: vector.transfer_write %[[READ_BCAST]], %[[INIT]][%[[C0]]] {in_bounds = [true]} : vector<1xf32>, tensor<1xf32>
Expand Down Expand Up @@ -654,7 +655,7 @@ func.func @scalar_read_with_broadcast_from_column_tensor(%init: tensor<1x1x4xi32

// CHECK-LABEL: func.func @scalar_read_with_broadcast_from_column_tensor
// CHECK-SAME: %[[INIT:.*]]: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
// CHECK-DAG: %[[PAD:.*]] = arith.constant 0 : i32
// CHECK-DAG: %[[PAD:.*]] = ub.poison : i32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[SRC:.*]] = arith.constant dense<{{\[\[}}0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{\[}}%[[C0]], %[[C0]]], %[[PAD]] : tensor<15x1xi32>, vector<i32>
Expand Down Expand Up @@ -688,7 +689,7 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic_column(
// CHECK-SAME: %[[SRC:.*]]: tensor<3x3x3xf32>,
// CHECK-SAME: %[[INIT:.*]]: tensor<3x1x1xf32>)
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[CST_0:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][%[[C0]], %[[C0]], %[[C0]]], %[[CST_0]] : tensor<3x3x3xf32>, vector<f32>
// CHECK-DAG: %[[PV:.*]] = ub.poison : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]][%[[C0]], %[[C0]], %[[C0]]], %[[PV]] : tensor<3x3x3xf32>, vector<f32>
// CHECK: %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<3x1x1xf32>
// CHECK: vector.transfer_write %[[READ_BCAST]], %[[INIT]]{{\[}}%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<3x1x1xf32>, tensor<3x1x1xf32>
6 changes: 3 additions & 3 deletions mlir/test/Dialect/Linalg/vectorization/extract.mlir
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Expand Up @@ -299,7 +299,7 @@ func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(%
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_9:.*]] = ub.poison : f32
// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<1x4xi1>
// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_2]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<1x4xf32> } : vector<1x4xi1> -> vector<1x4xf32>
// CHECK: %[[VAL_12:.*]] = vector.step : vector<4xindex>
Expand Down Expand Up @@ -356,7 +356,7 @@ func.func @extract_masked_vectorize(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf3
// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_6]] : tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_9:.*]] = ub.poison : f32
// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_7]] : vector<3x3xi1>
// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_1]]{{\[}}%[[VAL_8]], %[[VAL_8]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<3x3xf32> } : vector<3x3xi1> -> vector<3x3xf32>
// CHECK: %[[VAL_12:.*]] = arith.constant dense<true> : vector<3x3xi1>
Expand Down Expand Up @@ -458,7 +458,7 @@ func.func @scalar_broadcast(%init : tensor<1x1x3xi32>, %src: tensor<1x3x2x4xi32>
// CHECK: %[[MASK_RES:.*]] = vector.create_mask %[[C1]], %[[C1_2]], %[[C3]] : vector<1x1x4xi1>

/// Read and broadcast the scalar
// CHECK: %[[PAD:.*]] = arith.constant 0 : i32
// CHECK: %[[PAD:.*]] = ub.poison : i32
// CHECK: %[[MASK_READ:.*]] = vector.constant_mask [1] : vector<1xi1>
// CHECK: %[[READ:.*]] = vector.mask %[[MASK_READ]] {
// CHECK-SAME: vector.transfer_read %[[SRC]]{{\[}}%[[IDX]], %[[IDX]], %[[IDX]], %[[IDX]]], %[[PAD]]
Expand Down
14 changes: 7 additions & 7 deletions mlir/test/Dialect/Linalg/vectorization/linalg-ops-with-patterns.mlir
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Expand Up @@ -161,7 +161,7 @@ module attributes {transform.with_named_sequence} {
// CHECK-SAME: %[[ARG_0:.*]]: tensor<f32>, %[[ARG_1:.*]]: tensor<f32>, %[[ARG_2:.*]]: tensor<f32>)
func.func @generic_0d(%arg0: tensor<f32>, %arg1: tensor<f32>,
%arg2: tensor<f32>) -> tensor<f32> {
// CHECK: %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[PAD:.*]] = ub.poison : f32
// CHECK: %[[READ_0:.*]] = vector.transfer_read %[[ARG_0]][], %[[PAD]] : tensor<f32>, vector<f32>
// CHECK: %[[ARG_0_AS_SCALAR:.*]] = vector.extract %[[READ_0]][] : f32 from vector<f32>
// CHECK: %[[READ_1:.*]] = vector.transfer_read %[[ARG_1]][], %[[PAD]] : tensor<f32>, vector<f32>
Expand Down Expand Up @@ -770,11 +770,11 @@ module attributes {transform.with_named_sequence} {
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1) -> (d1, 0, d0, 0)>
// CHECK: func @generic_vectorize_broadcast_transpose
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[CF:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[V0:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP0]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V1:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP1]]} : memref<4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V2:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP2]]} : memref<4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V3:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP3]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
// CHECK-DAG: %[[PV:.*]] = ub.poison : f32
// CHECK: %[[V0:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[PV]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP0]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V1:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[PV]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP1]]} : memref<4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V2:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[PV]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP2]]} : memref<4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V3:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[PV]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP3]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[SUB:.*]] = arith.subf %[[V0]], %[[V1]] : vector<4x4x4x4xf32>
// CHECK: %[[ADD0:.*]] = arith.addf %[[V2]], %[[SUB]] : vector<4x4x4x4xf32>
// CHECK: %[[ADD1:.*]] = arith.addf %[[V3]], %[[ADD0]] : vector<4x4x4x4xf32>
Expand Down Expand Up @@ -1702,7 +1702,7 @@ module attributes {transform.with_named_sequence} {

// CHECK-LABEL: func.func @generic_with_reduction_and_broadcast(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x12x197x197xf32>) -> tensor<1x12x197x1xf32> {
// CHECK: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_1:.*]] = ub.poison : f32
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = tensor.empty() : tensor<1x12x197x1xf32>
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]], %[[VAL_1]] {in_bounds = [true, true, true, true]} : tensor<1x12x197x197xf32>, vector<1x12x197x197xf32>
Expand Down
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