diff --git a/setup.py b/setup.py
index 9e3111ab..01edba16 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'x-transformers',
   packages = find_packages(exclude=['examples']),
-  version = '1.30.22',
+  version = '1.30.23',
   license='MIT',
   description = 'X-Transformers - Pytorch',
   author = 'Phil Wang',
diff --git a/x_transformers/x_transformers.py b/x_transformers/x_transformers.py
index 5876ae37..adad800a 100644
--- a/x_transformers/x_transformers.py
+++ b/x_transformers/x_transformers.py
@@ -1201,8 +1201,8 @@ def __init__(
         rotary_xpos_scale_base = 512,
         rotary_base_rescale_factor = 1.,
         weight_tie_layers = False,
-        custom_layers: Tuple[str] | None = None,
-        layers_execute_order: Tuple[int] | None = None,
+        custom_layers: Tuple[str, ...] | None = None,
+        layers_execute_order: Tuple[int, ...] | None = None,
         sandwich_coef = None,
         par_ratio = None,
         residual_attn = False,
@@ -1484,7 +1484,8 @@ def forward(
         cache_age = 1,
         return_hiddens = False,
         rotary_pos_emb = None,
-        condition = None
+        condition = None,
+        layers_execute_order: Tuple[int, ...] | None = None
     ):
         assert not (self.cross_attend ^ exists(context)), 'context must be passed in if cross_attend is set to True'
         assert not (exists(condition) ^ self.need_condition), 'condition needs to be passed in if using adaptive layernorm or vice versa'
@@ -1576,7 +1577,11 @@ def forward(
             self.layer_dropouts
         )
 
-        layer_variables = tuple(tuple(layer_variable[i] for i in self.layers_execute_order) for layer_variable in layer_variables)
+        # able to override the layers execution order on forward, for trying to depth extrapolate
+
+        layers_execute_order = default(layers_execute_order, self.layers_execute_order)
+
+        layer_variables = tuple(tuple(layer_variable[i] for i in layers_execute_order) for layer_variable in layer_variables)
 
         # go through the attention and feedforward layers