From 58fb0262d026250edc9de86e6a631e7a890de31e Mon Sep 17 00:00:00 2001
From: lucidrains <lucidrains@gmail.com>
Date: Fri, 10 May 2024 09:48:20 -0700
Subject: [PATCH] default the depth to length of computed execution order of
 layers

---
 README.md                        | 7 +------
 setup.py                         | 2 +-
 x_transformers/x_transformers.py | 6 +++++-
 3 files changed, 7 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index f8466512..948109eb 100644
--- a/README.md
+++ b/README.md
@@ -693,7 +693,7 @@ model = TransformerWrapper(
 )
 ```
 
-If you wish to do something more sophisticated, say 3 layers, with each layer recurrent 4 times before onto the next, that is possible as well.
+If you wish to do something more sophisticated, say 3 layers, with each layer recurrent 4 times before onto the next, that is possible as well. Be aware the `layers_execute_order` is 0-indexed
 
 ```python
 import torch
@@ -716,11 +716,6 @@ model = TransformerWrapper(
         )
     )
 )
-
-x = torch.randint(0, 256, (1, 1024))
-
-model(x) # (1, 1024, 20000)
-
 ```
 
 ### Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
diff --git a/setup.py b/setup.py
index 5c2b1fcd..cfad771c 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'x-transformers',
   packages = find_packages(exclude=['examples']),
-  version = '1.29.0',
+  version = '1.29.2',
   license='MIT',
   description = 'X-Transformers - Pytorch',
   author = 'Phil Wang',
diff --git a/x_transformers/x_transformers.py b/x_transformers/x_transformers.py
index 56ec37e7..b492e444 100644
--- a/x_transformers/x_transformers.py
+++ b/x_transformers/x_transformers.py
@@ -1059,7 +1059,6 @@ def __init__(
         dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
 
         self.dim = dim
-        self.depth = depth
         self.causal = causal
         self.layers = ModuleList([])
 
@@ -1179,6 +1178,11 @@ def __init__(
 
         self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
 
+        # set the depth
+
+        depth = default(depth, len(self.layers_execute_order))
+        self.depth = depth
+
         # stochastic depth
 
         self.layer_dropouts = cast_tuple(layer_dropout, len(layer_types))