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[MLIR][Linalg] Fix FlattenElementwiseOp for linalg.broadcast #1

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[MLIR][Linalg] Fix FlattenElementwiseOp for linalg.broadcast #1

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73992bf
[MLIR][Linalg] Fix FlattenElementwiseOp for linalg.broadcast
nujaa Jul 20, 2026
851168b
fixup! [MLIR][Linalg] Fix FlattenElementwiseOp for linalg.broadcast
nujaa Jul 21, 2026
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4 changes: 2 additions & 2 deletions mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4311,11 +4311,11 @@ DiagnosedSilenceableFailure transform::FlattenElementwiseLinalgOp::applyToOne(
<< "only elementwise flattening is supported";

if (!llvm::all_of(target.getIndexingMapsArray(), [](AffineMap m) {
return m.isProjectedPermutation(/*allowZeroInResults=*/false);
return m.isPermutation() || m.getNumResults() == 0;
})) {
results.push_back(target);
return mlir::emitSilenceableFailure(target->getLoc())
<< "operators with broadcasting semantics are not supported";
<< "broadcasting of non scalar operands is not supported";
}

// If rank <= 1, do nothing
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28 changes: 28 additions & 0 deletions mlir/test/Dialect/Linalg/flatten-elementwise.mlir
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Expand Up @@ -43,6 +43,34 @@ module attributes {transform.with_named_sequence} {

// -----

// CHECK-LABEL: func.func @broadcast_rank0_tensor(
// CHECK-SAME: %[[ARG0:.*]]: tensor<i32>,
// CHECK-SAME: %[[ARG1:.*]]: tensor<32x2xi32>
// CHECK-NEXT: %[[FLATTENED:.*]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0, 1]]
// CHECK-NEXT: %[[FLATTENED_RESULT:.*]] = linalg.generic {{.*}} ins(%[[ARG0]] : tensor<i32>) outs(%[[FLATTENED]] : tensor<64xi32>)
// CHECK: %[[RESULT:.*]] = tensor.expand_shape %[[FLATTENED_RESULT]] {{\[}}[0, 1]] output_shape [32, 2] : tensor<64xi32> into tensor<32x2xi32>
#map0 = affine_map<(d0, d1) -> ()>
#map1 = affine_map<(d0, d1) -> (d0, d1)>

func.func @broadcast_rank0_tensor(%arg0: tensor<i32>, %arg1: tensor<32x2xi32>) -> tensor<32x2xi32> {
%0 = linalg.generic {indexing_maps = [#map0, #map1], iterator_types = ["parallel", "parallel"]} ins(%arg0 : tensor<i32>) outs(%arg1 : tensor<32x2xi32>) {
^bb0(%in: i32, %out: i32):
linalg.yield %in : i32
} -> tensor<32x2xi32>
return %0 : tensor<32x2xi32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
%flattened = transform.structured.flatten_elementwise %0
: (!transform.any_op) -> !transform.any_op
transform.yield
}
}

// -----

// CHECK-LABEL: func.func @map_memref(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32x7xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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16 changes: 5 additions & 11 deletions mlir/test/Dialect/Linalg/flatten-unsupported.mlir
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Expand Up @@ -37,7 +37,7 @@ module attributes {transform.with_named_sequence} {
#map2 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>

func.func @unsupported_broadcasting_elementwise(%arg0: tensor<1x2x1xi32>, %arg1: tensor<32x2x2xi32>) -> tensor<32x2x2xi32> {
// expected-error @below {{operators with broadcasting semantics are not supported}}
// expected-error @below {{broadcasting of non scalar operands is not supported}}
%0 = linalg.generic {indexing_maps = [#map1, #map2], iterator_types = ["parallel", "parallel", "parallel"]} ins(%arg0 : tensor<1x2x1xi32>) outs(%arg1 : tensor<32x2x2xi32>) {
^bb0(%in: i32, %out: i32):
linalg.yield %in : i32
Expand All @@ -56,16 +56,10 @@ module attributes {transform.with_named_sequence} {

// -----

#map1 = affine_map<(d0, d1, d2) -> (d1, 0)>
#map2 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>

func.func @unsupported_rank_expanding_broadcasting_elementwise(%arg0: tensor<2x1xi32>, %arg1: tensor<32x2x2xi32>) -> tensor<32x2x2xi32> {
// expected-error @below {{operators with broadcasting semantics are not supported}}
%0 = linalg.generic {indexing_maps = [#map1, #map2], iterator_types = ["parallel", "parallel", "parallel"]} ins(%arg0 : tensor<2x1xi32>) outs(%arg1 : tensor<32x2x2xi32>) {
^bb0(%in: i32, %out: i32):
linalg.yield %in : i32
} -> tensor<32x2x2xi32>
return %0 : tensor<32x2x2xi32>
func.func @unsupported_dim_expanding_broadcast(%arg0: tensor<64xi16>, %arg1: tensor<32x64xi16>) -> tensor<32x64xi16> {
// expected-error @below {{broadcasting of non scalar operands is not supported}}
%broadcasted = linalg.broadcast ins(%arg0 : tensor<64xi16>) outs(%arg1 : tensor<32x64xi16>) dimensions = [0]
return %broadcasted : tensor<32x64xi16>
}

module attributes {transform.with_named_sequence} {
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