Validate shapes for boolean indices

jax/_src/numpy/lax_numpy.py

@@ -4922,6 +4922,7 @@ def _unique_axis_sorted_mask(ar, axis):
   size, *out_shape = aux.shape
   aux = aux.reshape(size, _prod(out_shape)).T
   if aux.shape[0] == 0:
+    size = 1
     perm = zeros(1, dtype=int)
   else:
     perm = lexsort(aux[::-1])
@@ -5005,7 +5006,7 @@ def _rewriting_take(arr, idx, indices_are_sorted=False, unique_indices=False):
   # All supported cases of indexing can be implemented as an XLA gather,
   # followed by an optional reverse and broadcast_in_dim.
   arr = asarray(arr)
-  treedef, static_idx, dynamic_idx = _split_index_for_jit(idx)
+  treedef, static_idx, dynamic_idx = _split_index_for_jit(idx, arr.shape)
   return _gather(arr, treedef, static_idx, dynamic_idx, indices_are_sorted,
                  unique_indices)
 
@@ -5065,7 +5066,7 @@ _Indexer = collections.namedtuple("_Indexer", [
   "newaxis_dims",
 ])
 
-def _split_index_for_jit(idx):
+def _split_index_for_jit(idx, shape):
   """Splits indices into necessarily-static and dynamic parts.
 
   Used to pass indices into `jit`-ted function.
@@ -5075,7 +5076,7 @@ def _split_index_for_jit(idx):
 
   # Expand any (concrete) boolean indices. We can then use advanced integer
   # indexing logic to handle them.
-  idx = _expand_bool_indices(idx)
+  idx = _expand_bool_indices(idx, shape)
 
   leaves, treedef = tree_flatten(idx)
   dynamic = [None] * len(leaves)
@@ -5328,16 +5329,16 @@ def _eliminate_deprecated_list_indexing(idx):
       idx = (idx,)
   return idx
 
-def _expand_bool_indices(idx):
+def _expand_bool_indices(idx, shape):
   """Converts concrete bool indexes into advanced integer indexes."""
   out = []
-  for i in idx:
+  for dim_number, i in enumerate(idx):
     try:
       abstract_i = core.get_aval(i)
     except TypeError:
       abstract_i = None
     if (isinstance(abstract_i, ShapedArray) and issubdtype(abstract_i.dtype, bool_)
-        or isinstance(i, list) and _all(_is_scalar(e) and issubdtype(_dtype(e), np.bool_) for e in i)):
+        or isinstance(i, list) and i and _all(_is_scalar(e) and issubdtype(_dtype(e), np.bool_) for e in i)):
       if isinstance(i, list):
         i = array(i)
         abstract_i = core.get_aval(i)
@@ -5346,6 +5347,11 @@ def _expand_bool_indices(idx):
         # TODO(mattjj): improve this error by tracking _why_ the indices are not concrete
         raise errors.NonConcreteBooleanIndexError(abstract_i)
       else:
+        i_shape = _shape(i)
+        expected_shape = shape[len(out): len(out) + _ndim(i)]
+        if i_shape != expected_shape:
+          raise IndexError("boolean index did not match shape of indexed array in index "
+                           f"{dim_number}: got {i_shape}, expected {expected_shape}")
         out.extend(np.where(i))
     else:
       out.append(i)

jax/_src/ops/scatter.py

@@ -64,7 +64,7 @@ def _scatter_update(x, idx, y, scatter_op, indices_are_sorted,
   y = jnp.asarray(y)
   # XLA gathers and scatters are very similar in structure; the scatter logic
   # is more or less a transpose of the gather equivalent.
-  treedef, static_idx, dynamic_idx = jnp._split_index_for_jit(idx)
+  treedef, static_idx, dynamic_idx = jnp._split_index_for_jit(idx, x.shape)
   return _scatter_impl(x, y, scatter_op, treedef, static_idx, dynamic_idx,
                        indices_are_sorted, unique_indices, normalize_indices)
 

tests/lax_numpy_indexing_test.py

@@ -833,6 +833,25 @@ class IndexingTest(jtu.JaxTestCase):
     expected =  np.array([-1])[np.array([False])]
     self.assertAllClose(ans, expected, check_dtypes=False)
 
+  def testBooleanIndexingShapeMismatch(self):
+    # Regression test for https://github.com/google/jax/issues/7329
+    x = jnp.arange(4)
+    idx = jnp.array([True, False])
+    with self.assertRaisesRegex(IndexError, "boolean index did not match shape.*"):
+      x[idx]
+
+  def testNontrivialBooleanIndexing(self):
+    # Test nontrivial corner case in boolean indexing shape validation
+    rng = jtu.rand_default(self.rng())
+    index = (rng((2, 3), np.bool_), rng((6,), np.bool_))
+
+    args_maker = lambda: [rng((2, 3, 6), np.int32)]
+    np_fun = lambda x: np.asarray(x)[index]
+    jnp_fun = lambda x: jnp.asarray(x)[index]
+
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+
   def testFloatIndexingError(self):
     BAD_INDEX_TYPE_ERROR = "Indexer must have integer or boolean type, got indexer with type"
     with self.assertRaisesRegex(TypeError, BAD_INDEX_TYPE_ERROR):