mirror of
https://github.com/ml-explore/mlx.git
synced 2025-06-24 09:21:16 +08:00
Add broadcast_shapes in python API (#2091)
This commit is contained in:
parent
1d2c9d6a07
commit
3836445241
@ -5189,4 +5189,46 @@ void init_ops(nb::module_& m) {
|
|||||||
Returns:
|
Returns:
|
||||||
array: The row or col contiguous output.
|
array: The row or col contiguous output.
|
||||||
)pbdoc");
|
)pbdoc");
|
||||||
|
m.def(
|
||||||
|
"broadcast_shapes",
|
||||||
|
[](const nb::args& shapes) {
|
||||||
|
if (shapes.size() == 0)
|
||||||
|
throw std::invalid_argument(
|
||||||
|
"[broadcast_shapes] Must provide at least one shape.");
|
||||||
|
|
||||||
|
mx::Shape result = nb::cast<mx::Shape>(shapes[0]);
|
||||||
|
for (size_t i = 1; i < shapes.size(); ++i) {
|
||||||
|
if (!nb::isinstance<mx::Shape>(shapes[i]) &&
|
||||||
|
!nb::isinstance<nb::tuple>(shapes[i]))
|
||||||
|
throw std::invalid_argument(
|
||||||
|
"[broadcast_shapes] Expects a sequence of shapes (tuple or list of ints).");
|
||||||
|
result = mx::broadcast_shapes(result, nb::cast<mx::Shape>(shapes[i]));
|
||||||
|
}
|
||||||
|
|
||||||
|
return nb::tuple(nb::cast(result));
|
||||||
|
},
|
||||||
|
nb::sig("def broadcast_shapes(*shapes: Sequence[int]) -> Tuple[int]"),
|
||||||
|
R"pbdoc(
|
||||||
|
Broadcast shapes.
|
||||||
|
|
||||||
|
Returns the shape that results from broadcasting the supplied array shapes
|
||||||
|
against each other.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
*shapes (Sequence[int]): The shapes to broadcast.
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tuple: The broadcasted shape.
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: If the shapes cannot be broadcast.
|
||||||
|
|
||||||
|
Example:
|
||||||
|
>>> mx.broadcast_shapes((1,), (3, 1))
|
||||||
|
(3, 1)
|
||||||
|
>>> mx.broadcast_shapes((6, 7), (5, 6, 1), (7,))
|
||||||
|
(5, 6, 7)
|
||||||
|
>>> mx.broadcast_shapes((5, 1, 4), (1, 3, 1))
|
||||||
|
(5, 3, 4)
|
||||||
|
)pbdoc");
|
||||||
}
|
}
|
||||||
|
@ -3043,5 +3043,45 @@ class TestOps(mlx_tests.MLXTestCase):
|
|||||||
self.assertTrue(np.allclose(mx.rsqrt(x), 1.0 / np.sqrt(x)))
|
self.assertTrue(np.allclose(mx.rsqrt(x), 1.0 / np.sqrt(x)))
|
||||||
|
|
||||||
|
|
||||||
|
class TestBroadcast(mlx_tests.MLXTestCase):
|
||||||
|
def test_broadcast_shapes(self):
|
||||||
|
# Basic broadcasting
|
||||||
|
self.assertEqual(mx.broadcast_shapes((1, 2, 3), (3,)), (1, 2, 3))
|
||||||
|
self.assertEqual(mx.broadcast_shapes((4, 1, 6), (5, 6)), (4, 5, 6))
|
||||||
|
self.assertEqual(mx.broadcast_shapes((5, 1, 4), (1, 3, 4)), (5, 3, 4))
|
||||||
|
|
||||||
|
# Multiple arguments
|
||||||
|
self.assertEqual(mx.broadcast_shapes((1, 1), (1, 8), (7, 1)), (7, 8))
|
||||||
|
self.assertEqual(
|
||||||
|
mx.broadcast_shapes((6, 1, 5), (1, 7, 1), (6, 7, 5)), (6, 7, 5)
|
||||||
|
)
|
||||||
|
|
||||||
|
# Same shapes
|
||||||
|
self.assertEqual(mx.broadcast_shapes((3, 4, 5), (3, 4, 5)), (3, 4, 5))
|
||||||
|
|
||||||
|
# Single argument
|
||||||
|
self.assertEqual(mx.broadcast_shapes((2, 3)), (2, 3))
|
||||||
|
|
||||||
|
# Empty shapes
|
||||||
|
self.assertEqual(mx.broadcast_shapes((), ()), ())
|
||||||
|
self.assertEqual(mx.broadcast_shapes((), (1,)), (1,))
|
||||||
|
self.assertEqual(mx.broadcast_shapes((1,), ()), (1,))
|
||||||
|
|
||||||
|
# Broadcasting with zeroes
|
||||||
|
self.assertEqual(mx.broadcast_shapes((0,), (0,)), (0,))
|
||||||
|
self.assertEqual(mx.broadcast_shapes((1, 0, 5), (3, 1, 5)), (3, 0, 5))
|
||||||
|
self.assertEqual(mx.broadcast_shapes((5, 0), (0, 5, 0)), (0, 5, 0))
|
||||||
|
|
||||||
|
# Error cases
|
||||||
|
with self.assertRaises(ValueError):
|
||||||
|
mx.broadcast_shapes((3, 4), (4, 3))
|
||||||
|
|
||||||
|
with self.assertRaises(ValueError):
|
||||||
|
mx.broadcast_shapes((2, 3, 4), (2, 5, 4))
|
||||||
|
|
||||||
|
with self.assertRaises(ValueError):
|
||||||
|
mx.broadcast_shapes()
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
unittest.main()
|
unittest.main()
|
||||||
|
Loading…
Reference in New Issue
Block a user