create a cross attention only attention layer (CrossAttender)

README.md

@@ -519,22 +519,16 @@ Cross Attention
 
 ```python
 import torch
-from x_transformers import Encoder
+from x_transformers import Encoder, CrossAttender
 
 enc = Encoder(dim = 512, depth = 6)
-
-cross_attn = Encoder(
-    dim = 512,
-    depth = 6,
-    cross_attend = True,
-    only_cross = True
-)
+model = CrossAttender(dim = 512, depth = 6)
 
 nodes = torch.randn(1, 1, 512)
 neighbors = torch.randn(1, 5, 512)
 
 encoded_neighbors = enc(neighbors)
-cross_attn(nodes, context = encoded_neighbors) # (1, 1, 512)
+model(nodes, context = encoded_neighbors) # (1, 1, 512)
 ```
 
 ## Citations

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'x-transformers',
   packages = find_packages(exclude=['examples']),
-  version = '0.3.3',
+  version = '0.3.4',
   license='MIT',
   description = 'X-Transformers - Pytorch',
   author = 'Phil Wang',

x_transformers/__init__.py

@@ -1,3 +1,3 @@
-from x_transformers.x_transformers import XTransformer, Encoder, Decoder, TransformerWrapper, ViTransformerWrapper
+from x_transformers.x_transformers import XTransformer, Encoder, Decoder, CrossAttender, TransformerWrapper, ViTransformerWrapper
 from x_transformers.funnel import FunnelEncoder
 from x_transformers.autoregressive_wrapper import AutoregressiveWrapper

x_transformers/x_transformers.py

@@ -353,6 +353,10 @@ def __init__(self, **kwargs):
         assert 'causal' not in kwargs, 'cannot set causality on decoder'
         super().__init__(causal = True, **kwargs)
 
+class CrossAttender(AttentionLayers):
+    def __init__(self, **kwargs):
+        super().__init__(cross_attend = True, only_cross = True, **kwargs)
+
 class ViTransformerWrapper(nn.Module):
     def __init__(
         self,