follow some suggestions from @faresobeid

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'x-transformers',
   packages = find_packages(exclude=['examples']),
-  version = '1.41.0',
+  version = '1.41.1',
   license='MIT',
   description = 'X-Transformers - Pytorch',
   author = 'Phil Wang',

x_transformers/x_transformers.py

@@ -101,12 +101,6 @@ def log(t, eps = 1e-20):
 def max_neg_value(tensor):
     return -torch.finfo(tensor.dtype).max
 
-def reverse_cumsum(t, dim = -1):
-    t = t.flip(dims = (dim,))
-    t = t.cumsum(dim = dim)
-    t = t.flip(dims = (dim,))
-    return t
-
 def l2norm(t, groups = 1):
     t = rearrange(t, '... (g d) -> ... g d', g = groups)
     t = F.normalize(t, p = 2, dim = -1)
@@ -514,27 +508,15 @@ def __init__(
         self.to_forget_gates = nn.Sequential(
             linear,
             Rearrange('b n h -> b h n'),
-            nn.Sigmoid()
+            nn.LogSigmoid()
         )
 
         nn.init.constant_(linear.bias, 5.)
 
     def forward(self, x):
-        seq = x.shape[-2]
-
-        forget_gates = self.to_forget_gates(x).log()
-        forget_gates = repeat(forget_gates, 'b h j -> b h i j', i = seq)
-
-        # causal mask out, including diagonal (so token to itself attention is never masked out)
-
-        causal_mask = torch.ones((seq, seq), dtype = torch.bool, device = x.device).triu()
-
-        forget_gates = forget_gates.masked_fill(causal_mask, 0.)
-
-        # reverse cumulative sum in log space (equivalent to cumprod)
-
-        forget_gates = reverse_cumsum(forget_gates)
-
+        forget_gates = self.to_forget_gates(x)
+        forget_gates = forget_gates.cumsum(dim = -1)
+        forget_gates = einx.subtract('b h i, b h j -> b h i j', forget_gates, forget_gates)
         return forget_gates
 
 class RotaryEmbedding(Module):