Update clip.py

clip.py

@@ -1,64 +1,64 @@
-import torch
-from torch import nn
-from torch.nn import functional as F
-from nanograd.models.stable_diffusion.attention import SelfAttention
-
-class CLIPEmbedding(nn.Module):
-    def __init__(self, n_vocab: int, n_embd: int, n_token: int):
-        super().__init__()
-        
-        self.token_embedding = nn.Embedding(n_vocab, n_embd)
-        self.position_embedding = nn.Parameter(torch.zeros((n_token, n_embd)))
-    
-    def forward(self, tokens):
-        x = self.token_embedding(tokens)
-        x += self.position_embedding
-        
-        return x
-
-class CLIPLayer(nn.Module):
-    def __init__(self, n_head: int, n_embd: int):
-        super().__init__()
-        self.layernorm_1 = nn.LayerNorm(n_embd)
-        self.attention = SelfAttention(n_head, n_embd)
-        self.layernorm_2 = nn.LayerNorm(n_embd)
-        self.linear_1 = nn.Linear(n_embd, 4 * n_embd)
-        self.linear_2 = nn.Linear(4 * n_embd, n_embd)
-
-    def forward(self, x):
-        residue = x
-        x = self.layernorm_1(x)
-        x = self.attention(x, causal_mask=True)
-        x += residue
-
-        residue = x
-        x = self.layernorm_2(x)
-        x = self.linear_1(x)
-        
-        x = x * torch.sigmoid(1.702 * x)  
-        x = self.linear_2(x)
-        x += residue
-
-        return x
-
-class CLIP(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.embedding = CLIPEmbedding(49408, 768, 77)
-
-        self.layers = nn.ModuleList([
-            CLIPLayer(12, 768) for i in range(12)
-        ])
-
-        self.layernorm = nn.LayerNorm(768)
-    
-    def forward(self, tokens: torch.LongTensor) -> torch.FloatTensor:
-        tokens = tokens.type(torch.long)
-        
-        state = self.embedding(tokens)
-
-        for layer in self.layers: 
-            state = layer(state)
-        output = self.layernorm(state)
-        
+import torch
+from torch import nn
+from torch.nn import functional as F
+from attention import SelfAttention
+
+class CLIPEmbedding(nn.Module):
+    def __init__(self, n_vocab: int, n_embd: int, n_token: int):
+        super().__init__()
+        
+        self.token_embedding = nn.Embedding(n_vocab, n_embd)
+        self.position_embedding = nn.Parameter(torch.zeros((n_token, n_embd)))
+    
+    def forward(self, tokens):
+        x = self.token_embedding(tokens)
+        x += self.position_embedding
+        
+        return x
+
+class CLIPLayer(nn.Module):
+    def __init__(self, n_head: int, n_embd: int):
+        super().__init__()
+        self.layernorm_1 = nn.LayerNorm(n_embd)
+        self.attention = SelfAttention(n_head, n_embd)
+        self.layernorm_2 = nn.LayerNorm(n_embd)
+        self.linear_1 = nn.Linear(n_embd, 4 * n_embd)
+        self.linear_2 = nn.Linear(4 * n_embd, n_embd)
+
+    def forward(self, x):
+        residue = x
+        x = self.layernorm_1(x)
+        x = self.attention(x, causal_mask=True)
+        x += residue
+
+        residue = x
+        x = self.layernorm_2(x)
+        x = self.linear_1(x)
+        
+        x = x * torch.sigmoid(1.702 * x)  
+        x = self.linear_2(x)
+        x += residue
+
+        return x
+
+class CLIP(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.embedding = CLIPEmbedding(49408, 768, 77)
+
+        self.layers = nn.ModuleList([
+            CLIPLayer(12, 768) for i in range(12)
+        ])
+
+        self.layernorm = nn.LayerNorm(768)
+    
+    def forward(self, tokens: torch.LongTensor) -> torch.FloatTensor:
+        tokens = tokens.type(torch.long)
+        
+        state = self.embedding(tokens)
+
+        for layer in self.layers: 
+            state = layer(state)
+        output = self.layernorm(state)
+        
         return output