bring in two tricks from the cogview paper for reducing the chances of overflow, for attention and layernorm

dalle2_pytorch/dalle2_pytorch.py

@@ -496,6 +496,7 @@ class LayerNorm(nn.Module):
         self.g = nn.Parameter(torch.ones(dim))
 
     def forward(self, x):
+        x = x / x.amax(dim = -1, keepdim = True).detach()
         var = torch.var(x, dim = -1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = -1, keepdim = True)
         return (x - mean) * (var + self.eps).rsqrt() * self.g
@@ -507,6 +508,7 @@ class ChanLayerNorm(nn.Module):
         self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
 
     def forward(self, x):
+        x = x / x.amax(dim = 1, keepdim = True).detach()
         var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
         return (x - mean) * (var + self.eps).rsqrt() * self.g
@@ -629,10 +631,13 @@ class Attention(nn.Module):
         heads = 8,
         dropout = 0.,
         causal = False,
-        rotary_emb = None
+        rotary_emb = None,
+        pb_relax_alpha = 32 ** 2
     ):
         super().__init__()
-        self.scale = dim_head ** -0.5
+        self.pb_relax_alpha = pb_relax_alpha
+        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
+
         self.heads = heads
         inner_dim = dim_head * heads
 
@@ -696,6 +701,9 @@ class Attention(nn.Module):
 
         # attention
 
+        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        sim = sim * self.pb_relax_alpha
+
         attn = sim.softmax(dim = -1, dtype = torch.float32)
         attn = self.dropout(attn)
 
@@ -1210,10 +1218,12 @@ class CrossAttention(nn.Module):
         dim_head = 64,
         heads = 8,
         dropout = 0.,
-        norm_context = False
+        norm_context = False,
+        pb_relax_alpha = 32 ** 2
     ):
         super().__init__()
-        self.scale = dim_head ** -0.5
+        self.pb_relax_alpha = pb_relax_alpha
+        self.scale = dim_head ** -0.5 * (pb_relax_alpha ** -1)
         self.heads = heads
         inner_dim = dim_head * heads
 
@@ -1259,6 +1269,9 @@ class CrossAttention(nn.Module):
             mask = rearrange(mask, 'b j -> b 1 1 j')
             sim = sim.masked_fill(~mask, max_neg_value)
 
+        sim = sim - sim.amax(dim = -1, keepdim = True).detach()
+        sim = sim * self.pb_relax_alpha
+
         attn = sim.softmax(dim = -1, dtype = torch.float32)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)