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Fix Metal sort of a view with a negative stride #4252

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Merged
zcbenz merged 1 commit into from
Aug 16, 2026
+28 −3
Merged

Fix Metal sort of a view with a negative stride #4252

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10 changes: 7 additions & 3 deletions mlx/backend/metal/kernels/sort.h
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Original file line number Diff line number Diff line change
Expand Up @@ -388,8 +388,11 @@ template <
using ValT = typename sort_kernel::ValT;
using IdxT = typename sort_kernel::IdxT;

auto in_block_idx = elem_to_loc(tid.y, nc_shape, in_nc_strides, nc_dim);
auto out_block_idx = elem_to_loc(tid.y, nc_shape, out_nc_strides, nc_dim);
// Signed offsets: a non-sorted axis may have a negative stride.
auto in_block_idx =
elem_to_loc<int64_t>(tid.y, nc_shape, in_nc_strides, nc_dim);
auto out_block_idx =
elem_to_loc<int64_t>(tid.y, nc_shape, out_nc_strides, nc_dim);
inp += in_block_idx;
out += out_block_idx;

Expand Down Expand Up @@ -532,7 +535,8 @@ template <
BLOCK_THREADS,
N_PER_THREAD>;

auto block_idx = elem_to_loc(tid.y, nc_shape, nc_strides, nc_dim);
// Signed offset: a non-sorted axis may have a negative stride.
auto block_idx = elem_to_loc<int64_t>(tid.y, nc_shape, nc_strides, nc_dim);
inp += block_idx;
out_vals += tid.y * size_sorted_axis;
out_idxs += tid.y * size_sorted_axis;
Expand Down
21 changes: 21 additions & 0 deletions python/tests/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2675,6 +2675,27 @@ def test_sort(self):
y_np = np.sort(np.array(a), axis=-1)
self.assertTrue(np.array_equal(y_np, y_mx))

# Negative stride on an axis that is not sorted, single and multi block
np.random.seed(0)
for dtype in ("int32", "float32"):
for size in (4, 32769):
with self.subTest(dtype=dtype, size=size):
a_np = np.random.uniform(0, 100, size=(3, size))
a_np = a_np.astype(getattr(np, dtype))
a_mx = mx.array(a_np)[::-1, :]
a_np = a_np[::-1, :]

b_np = np.sort(a_np, axis=-1)
self.assertTrue(np.array_equal(b_np, mx.sort(a_mx, axis=-1)))

idx = mx.argsort(a_mx, axis=-1)
self.assertTrue(
np.array_equal(b_np, mx.take_along_axis(a_mx, idx, axis=-1))
)

b_mx = mx.partition(a_mx, 1, axis=-1)
self.assertTrue(np.array_equal(b_np[:, 1], np.array(b_mx)[:, 1]))

def test_partition(self):
shape = (3, 4, 5)
for dtype in ("int32", "float32"):
Expand Down
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