jnp.unravel_index: avoid overflow for large dimension sizes

jax/_src/numpy/lax_numpy.py

@@ -808,22 +808,22 @@ def ravel_multi_index(multi_index, dims, mode='raise', order='C'):
 
 _UNRAVEL_INDEX_DOC = """\
 Unlike numpy's implementation of unravel_index, negative indices are accepted
-and out-of-bounds indices are clipped.
+and out-of-bounds indices are clipped into the valid range.
 """
 
 @_wraps(np.unravel_index, lax_description=_UNRAVEL_INDEX_DOC)
 def unravel_index(indices, shape):
   _check_arraylike("unravel_index", indices)
-  sizes = append(array(shape), 1)
-  cumulative_sizes = cumprod(sizes[::-1])[::-1]
-  total_size = cumulative_sizes[0]
-  # Clip so raveling and unraveling an oob index will not change the behavior
-  clipped_indices = clip(indices, -total_size, total_size - 1)
-  # Add enough trailing dims to avoid conflict with clipped_indices
-  cumulative_sizes = expand_dims(cumulative_sizes, range(1, 1 + _ndim(indices)))
-  clipped_indices = expand_dims(clipped_indices, axis=0)
-  idx = clipped_indices % cumulative_sizes[:-1] // cumulative_sizes[1:]
-  return tuple(idx)
+  shape = atleast_1d(shape)
+  if shape.ndim != 1:
+    raise ValueError("unravel_index: shape should be a scalar or 1D sequence.")
+  out_indices = [None] * len(shape)
+  for i, s in reversed(list(enumerate(shape))):
+    indices, out_indices[i] = divmod(indices, s)
+  oob_pos = indices > 0
+  oob_neg = indices < -1
+  return tuple(where(oob_pos, s - 1, where(oob_neg, 0, i))
+               for s, i in zip(shape, out_indices))
 
 @_wraps(np.resize)
 @partial(jit, static_argnames=('new_shape',))

tests/lax_numpy_test.py

@@ -4221,7 +4221,10 @@ def jnp_fun(a, c):
     (5, (2, 1, 3)),
     (0, ()),
     (np.array([0, 1, 2]), (2, 2)),
-    (np.array([[[0, 1], [2, 3]]]), (2, 2)))
+    (np.array([[[0, 1], [2, 3]]]), (2, 2)),
+    # regression test for https://github.com/google/jax/issues/10540
+    (np.arange(5), (201_996, 201_996)),  # prod(shape) overflows int32.
+  )
   def testUnravelIndex(self, flat_index, shape):
     args_maker = lambda: (flat_index, shape)
     np_fun = jtu.with_jax_dtype_defaults(np.unravel_index, use_defaults=not hasattr(flat_index, 'dtype'))