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[mlir][linalg] Handle reassociationIndices correctly for 0D tensor #121683

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merged 1 commit into from
Jan 10, 2025
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[mlir][linalg] Handle reassociationIndices correctly for 0D tensor #121683

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25 changes: 14 additions & 11 deletions mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -600,30 +600,33 @@ static Value createLinalgBodyCalculationForElementwiseOp(
static Value expandRank(PatternRewriter &rewriter, Location loc, Value tensor,
int64_t rank) {
// No need to expand if we are already at the desired rank
auto shapedType = dyn_cast<ShapedType>(tensor.getType());
assert(shapedType && shapedType.hasRank() && "expected a ranked shaped type");
int64_t numExtraDims = rank - shapedType.getRank();
auto tensorType = dyn_cast<RankedTensorType>(tensor.getType());
assert(tensorType && "expected a ranked tensor type");
int64_t tensorRank = tensorType.getRank();
int64_t numExtraDims = rank - tensorRank;
assert(numExtraDims >= 0 && "cannot expand tensor to a lower rank");
if (!numExtraDims)
return tensor;

// Compute reassociation indices
SmallVector<SmallVector<int64_t, 2>> reassociationIndices(
shapedType.getRank());
SmallVector<ReassociationIndices> reassociationIndices(tensorRank);
int64_t index = 0;
for (index = 0; index <= numExtraDims; index++)
reassociationIndices[0].push_back(index);
for (size_t position = 1; position < reassociationIndices.size(); position++)
reassociationIndices[position].push_back(index++);
if (tensorRank != 0) {
for (index = 0; index <= numExtraDims; index++)
reassociationIndices[0].push_back(index);
for (size_t position = 1; position < reassociationIndices.size();
position++)
reassociationIndices[position].push_back(index++);
}

// Compute result type
SmallVector<int64_t> resultShape;
for (index = 0; index < numExtraDims; index++)
resultShape.push_back(1);
for (auto size : shapedType.getShape())
for (auto size : tensorType.getShape())
resultShape.push_back(size);
auto resultType =
RankedTensorType::get(resultShape, shapedType.getElementType());
RankedTensorType::get(resultShape, tensorType.getElementType());

// Emit 'tensor.expand_shape' op
return rewriter.create<tensor::ExpandShapeOp>(loc, resultType, tensor,
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23 changes: 23 additions & 0 deletions mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
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Expand Up @@ -100,6 +100,29 @@ func.func @test_add_0d(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {

// -----

// CHECK: #[[$MAP0:.+]] = affine_map<(d0, d1) -> (d0, 0)>
// CHECK: #[[$MAP1:.+]] = affine_map<(d0, d1) -> (0, 0)>
// CHECK: #[[$MAP2:.+]] = affine_map<(d0, d1) -> (d0, d1)>

// CHECK-LABEL: func.func @test_add_0d_broadcast(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x1xf32>,
// CHECK-SAME: %[[ARG1:.*]]: tensor<f32>) -> tensor<2x1xf32> {
// CHECK: %[[EXPANDED:.*]] = tensor.expand_shape %[[ARG1]] [] output_shape [1, 1] : tensor<f32> into tensor<1x1xf32>
// CHECK: %[[EMPTY_TENSOR:.*]] = tensor.empty() : tensor<2x1xf32>
// CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]], %[[EXPANDED]] : tensor<2x1xf32>, tensor<1x1xf32>) outs(%[[EMPTY_TENSOR]] : tensor<2x1xf32>) {
// CHECK: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]] : f32
// CHECK: linalg.yield %[[ADD]] : f32
// CHECK: } -> tensor<2x1xf32>
// CHECK: return %[[RESULT]] : tensor<2x1xf32>
// CHECK: }
func.func @test_add_0d_broadcast(%arg0: tensor<2x1xf32>, %arg1: tensor<f32>) -> tensor<2x1xf32> {
%0 = tosa.add %arg0, %arg1 : (tensor<2x1xf32>, tensor<f32>) -> tensor<2x1xf32>
return %0 : tensor<2x1xf32>
}

// -----

// CHECK: #[[$MAP0:.+]] = affine_map<(d0) -> (0)>
// CHECK: #[[$MAP1:.+]] = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @test_add_1d_all_dynamic
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