diff --git a/g_mlp_gpt/g_mlp_gpt.py b/g_mlp_gpt/g_mlp_gpt.py
index d984fd1..3813719 100644
--- a/g_mlp_gpt/g_mlp_gpt.py
+++ b/g_mlp_gpt/g_mlp_gpt.py
@@ -5,7 +5,7 @@
 import torch.nn.functional as F
 from torch import nn, einsum
 
-from einops import rearrange
+from einops import rearrange, repeat
 
 from g_mlp_gpt.reversible import ReversibleSequence, SequentialSequence
 
@@ -99,6 +99,43 @@ def forward(self, x):
         out = einsum('b i j, b j d -> b i d', attn, v)
         return self.to_out(out)
 
+class LocalAttention(nn.Module):
+    def __init__(self, dim_in, dim_inner, dim_out, window = 128):
+        super().__init__()
+        self.scale = dim_inner ** -0.5
+        self.window = window
+
+        self.to_qkv = nn.Linear(dim_in, dim_inner * 3, bias = False)
+        self.to_out = nn.Linear(dim_inner, dim_out)
+
+    def forward(self, x):
+        b, n, *_, device, w = *x.shape, x.device, self.window
+
+        x = pad_to_multiple(x, w, dim = -2, value = 0.)
+        q, k, v = self.to_qkv(x).chunk(3, dim = -1)
+
+        window_fn = lambda t: rearrange(t, 'b (w n) d -> b w n d', n = w)
+        q, k, v = map(window_fn, (q, k, v))
+
+        k, v = map(lambda t: F.pad(t, (0, 0, 0, 0, 1, 0)), (k, v))
+        k, v = map(lambda t: torch.cat((k[:, :-1], k[:, 1:]), dim = 2), (k, v))
+
+        sim = einsum('b w i d, b w j d -> b w i j', q, k) * self.scale
+        buckets, i, j = sim.shape[-3:]
+
+        mask_value = -torch.finfo(sim.dtype).max
+        mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
+        mask = repeat(mask, 'i j -> () u i j', u = buckets)
+
+        sim.masked_fill_(mask, mask_value)
+
+        attn = sim.softmax(dim = -1)
+
+        out = einsum('b w i j, b w j d -> b w i d', attn, v)
+        out = rearrange(out, 'b w n d -> b (w n) d')
+        out = self.to_out(out[:, :n])
+        return out
+
 class CausalSGU(nn.Module):
     def __init__(
         self,
@@ -172,7 +209,7 @@ def __init__(
         self.act = act
         self.register_buffer('mask', ~torch.ones(window, window * 2).triu_(window + 1).bool())
 
-    def forward(self, x, **kwargs):
+    def forward(self, x, gate_res = None):
         device, n, h, w = x.device, x.shape[1], self.heads, self.window
 
         res, gate = x.chunk(2, dim = -1)
@@ -198,6 +235,9 @@ def forward(self, x, **kwargs):
         gate = rearrange(gate, 'b w n d -> b (w n) d')
         gate = gate[:, :n]
 
+        if exists(gate_res):
+            gate = gate + gate_res
+
         return self.act(gate) * res
 
 class AxiallyFold(nn.Module):
@@ -237,15 +277,18 @@ def __init__(
         act = nn.Identity()
     ):
         super().__init__()
-        SGU = partial(CausalLocalSGU, window = window) if exists(window) and window < seq_len else CausalSGU
+        is_windowed = exists(window) and window < seq_len
+
+        SGU_klass = partial(CausalLocalSGU, window = window) if is_windowed else CausalSGU
+        Attention_klass = partial(LocalAttention, window = window) if is_windowed else Attention
 
-        self.attn = Attention(dim_in = dim, dim_inner = attn_dim, dim_out = dim_ff // 2) if exists(attn_dim) else None
+        self.attn = Attention_klass(dim_in = dim, dim_inner = attn_dim, dim_out = dim_ff // 2) if exists(attn_dim) else None
 
         self.proj_in = nn.Sequential(
             nn.Linear(dim, dim_ff),
             nn.GELU()
         )
-        self.sgu =  SGU(dim_ff, seq_len, causal, heads = heads, act = act)
+        self.sgu =  SGU_klass(dim_ff, seq_len, causal, heads = heads, act = act)
         self.proj_out = nn.Linear(dim_ff // 2, dim)
 
     def forward(self, x):
diff --git a/setup.py b/setup.py
index f95b719..ac5bdb9 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'g-mlp-gpt',
   packages = find_packages(),
-  version = '0.0.14',
+  version = '0.0.15',
   license='MIT',
   description = 'gMLP - GPT',
   author = 'Phil Wang',
diff --git a/train.py b/train.py
index be6ec24..7d77bfd 100644
--- a/train.py
+++ b/train.py
@@ -41,7 +41,8 @@ def decode_tokens(tokens):
     dim = 512,
     seq_len = SEQ_LEN,
     depth = 8,
-    window = (16, 32, 64, 128, 256, 512, 768, SEQ_LEN)
+    window = (16, 32, 64, 128, 256, 512, 768, SEQ_LEN),
+    attn_dim = 16
 )
 
 model = AutoregressiveWrapper(model)