allow each token to decide how much of input to reinject

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'x-transformers',
   packages = find_packages(exclude=['examples']),
-  version = '1.44.5',
+  version = '1.44.6',
   license='MIT',
   description = 'X-Transformers - Pytorch',
   author = 'Phil Wang',

x_transformers/x_transformers.py

@@ -1736,6 +1736,7 @@ def __init__(
         unet_skips = False,
         num_residual_streams = 1,
         reinject_input = False,              # seen first in DEQ paper https://arxiv.org/abs/1909.01377, but later used in a number of papers trying to achieve depthwise generalization https://arxiv.org/abs/2410.03020v1
+        learned_reinject_input_gate = False,
         add_value_residual = False,          # resformer from Zhou et al - https://arxiv.org/abs/2410.17897v1 - further corroboration by https://arxiv.org/abs/2412.15113 (faster emergence of ICL) - looks like this setting may becoming a necessity for every transformer soon
         learned_value_residual_mix = True,   # seeing big improvements when the value residual mix value is learned per token - credit goes to @faresobeid for taking the first step with learned scalar mix, then @Blinkdl for taking it a step further with data dependent. here we will use per token learned
         rel_pos_kwargs: dict = dict(),
@@ -1993,6 +1994,7 @@ def __init__(
 
         self.reinject_input = reinject_input
         self.reinject_input_proj = nn.Linear(dim, dim, bias = False) if reinject_input else None
+        self.learned_reinject_input_gate = nn.Linear(dim, 1, bias = False) if learned_reinject_input_gate else None
 
         # add the value from the first self attention block to all latter projected self attention values as a residual
 
@@ -2224,7 +2226,9 @@ def forward(
         layer_variables = tuple(tuple(layer_variable[i] for i in layers_execute_order) for layer_variable in layer_variables)
 
         # derived input for reinjection if needed
+
         inp_inject = None
+
         if self.reinject_input:
             assert not exists(in_attn_cond)
             inp_inject = self.reinject_input_proj(x)
@@ -2233,6 +2237,10 @@ def forward(
             # handle in-attention conditioning, which serves the same purpose of having the network learn the residual
             inp_inject = in_attn_cond if in_attn_cond.ndim == 3 else rearrange(in_attn_cond, 'b d -> b 1 d')
 
+        if exists(inp_inject) and exists(self.learned_reinject_input_gate):
+            inp_inject_gate = self.learned_reinject_input_gate(x).sigmoid()
+            inp_inject = inp_inject * inp_inject_gate
+
         # store all hiddens for skips
 
         skip_hiddens = []