Optimized le.Tensor_out returns b <= a for reverse-argument broadcasts

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@SofianElmotiem ya está trabajando en esto.

Desde el 3/11/2026.

  • #24486 de @SofianElmotiem — abierto

Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
35/100
Tipo de issue
Error
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
cpp, pytorch
Área
machine-learning

Línea de trabajo

Start with kernels/optimized/cpu/op_le.cpp and the reverse-argument handling described in the issue; then inspect the related tests in kernels/test/op_le_test.cpp. Add regression coverage that exercises the optimized paths with matching input and output dtypes. Done means those paths produce the expected a <= b results for the listed broadcast shapes.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Describe the bug

With the optimized CPU kernels, le.Tensor_out returns b <= a instead of a <= b when the left operand is the one being broadcast and both inputs and the output share a dtype (for example two Bool masks, or a non-Bool output).

Repro (C++ kernel test)
TensorFactory<ScalarType::Bool> tf;
Tensor a = tf.make({4}, {false, true, false, true});
Tensor b = tf.make({2, 4}, {false, false, true, true, true, true, false, false});
Tensor out = tf.zeros({2, 4});
torch::executor::aten::le_outf(context_, a, b, out);
Result
Expected (a <= b, also what the portable kernel returns) {1, 0, 1, 1, 1, 1, 1, 0}
Optimized kernel {1, 1, 0, 1, 0, 1, 1, 1} (that is b <= a)

The same happens for Float and Int inputs with a Float/Int output, on all three reverse-argument broadcast paths: (4,) vs (2, 4), (2, 1) vs (2, 3), and (2, 1, 2) vs (2, 3, 2). Float inputs with a Bool output are not affected, because select_optimized_path returns kNone when the output dtype differs and the portable comparison is used.

Cause

select_optimized_path returns kBroadcast2dBy1dReverseArguments, kBroadcastLastDimReverseArguments or kBroadcastNdByNdReverseArguments when the left operand is the broadcast one, and handle_broadcast_elementwise then calls the lambda with the operands swapped. op_div.cpp and op_add_sub_impl.h account for this by swapping their lambda on those paths, but op_le.cpp always passes x.le(y):

https://github.com/pytorch/executorch/blob/aed328df53/kernels/optimized/cpu/op_le.cpp#L57-L63

The existing BroadcastLastDimReverseTest, BroadcastNdByNdReverseTest and similar tests in kernels/test/op_le_test.cpp use a Bool output with Int inputs, so they take the portable path and never exercise the optimized broadcast code.

I have a fix with regression tests and will open a PR.

Versions

main at aed328df53, built from source with CMake (EXECUTORCH_BUILD_TESTS=ON, EXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON), Ubuntu, x86_64, GCC 15.2.0.

Lenguaje dominante
Python
Estrellas
5.1k
Forks
1.2k
Merge medio
1 d 22 h
PR fusionados (30 d)
675

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