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[TorchToLinalg] Fix outType inconsistency in AtenTransposeInt op lowering #4403

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[TorchToLinalg] Fix outType inconsistency in AtenTransposeInt op lowering #4403

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1097006
[TorchToLinalg] Handle output vector type inconsistency in AtenTransp…
vivekkhandelwal1 Dec 11, 2025
f7a2015
Fix depreceation error
vivekkhandelwal1 Dec 11, 2025
ae95f61
Fix Inputtype inconsistency in unflatten int and add canonicalization…
vivekkhandelwal1 Dec 18, 2025
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6 changes: 6 additions & 0 deletions lib/Conversion/TorchToLinalg/DataMovement.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1740,6 +1740,12 @@ class ConvertAtenTransposeIntOp
Value outVector = tensor::EmptyOp::create(
rewriter, loc, getAsOpFoldResult(outputDims), elementType);

// Note: The empty tensor type may not match `outType` due to folding
// performed by `getAsOpFoldResult` of `tensor::DimOp`.
// Cast to `outType` if needed to ensure type consistency.
if (outVector.getType() != outType)
outVector = tensor::CastOp::create(rewriter, loc, outType, outVector);

SmallVector<int64_t> permutation(inputRank);
std::iota(permutation.begin(), permutation.end(), 0);
permutation[dim0] = dim1;
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25 changes: 25 additions & 0 deletions test/Conversion/TorchToLinalg/datamovement.mlir
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Expand Up @@ -71,3 +71,28 @@ func.func @torch.aten.reflection_pad2d(%arg0: !torch.vtensor<[1,1,4,4],f32>) ->
%1 = torch.aten.reflection_pad2d %arg0, %0 : !torch.vtensor<[1,1,4,4],f32>, !torch.list<int> -> !torch.vtensor<[1,1,8,9],f32>
return %1 : !torch.vtensor<[1,1,8,9],f32>
}

// -----

// CHECK-LABEL: func.func @torch.aten.transpose.int$dynamic_dims(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[1,56,56,96],f32>) -> !torch.vtensor<[?,?,?,?,?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[1,56,56,96],f32> -> tensor<1x56x56x96xf32>
// CHECK: %[[VAL_9:.*]] = tensor.expand_shape %[[VAL_1]] {{\[\[}}0], [1, 2], [3, 4], [5]] output_shape [1, 8, 7, 8, 7, 96] : tensor<1x56x56x96xf32> into tensor<1x8x7x8x7x96xf32>
// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<1x8x8x7x7x96xf32>
// CHECK: %[[TRANSPOSE:.*]] = linalg.transpose ins(%[[VAL_9]] {{.*}} outs(%[[EMPTY]] {{.*}} permutation = [0, 1, 3, 2, 4, 5]
// CHECK: %[[RESULT_CAST:.*]] = tensor.cast %[[TRANSPOSE]] : tensor<1x8x8x7x7x96xf32> to tensor<?x?x?x?x?x?xf32>
// CHECK: %[[VAL_10:.*]] = torch_c.from_builtin_tensor %[[RESULT_CAST]] : tensor<?x?x?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?,?,?],f32>
// CHECK: return %[[VAL_10]] : !torch.vtensor<[?,?,?,?,?,?],f32>
// CHECK: }
func.func @torch.aten.transpose.int$dynamic_dims(%arg0: !torch.vtensor<[1,56,56,96],f32>) -> !torch.vtensor<[?,?,?,?,?,?],f32> {
%int1 = torch.constant.int 1
%int8 = torch.constant.int 8
%int7 = torch.constant.int 7
%int96 = torch.constant.int 96
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%0 = torch.prim.ListConstruct %int1, %int8, %int7, %int8, %int7, %int96 : (!torch.int, !torch.int, !torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[1,56,56,96],f32>, !torch.list<int> -> !torch.vtensor<[?,?,?,?,?,?],f32>
%2 = torch.aten.transpose.int %1, %int2, %int3 : !torch.vtensor<[?,?,?,?,?,?],f32>, !torch.int, !torch.int -> !torch.vtensor<[?,?,?,?,?,?],f32>
return %2 : !torch.vtensor<[?,?,?,?,?,?],f32>
}
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