X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=mygpt.py;h=7c4e06df1259aa4bd372987589ababf5f5afd6b1;hb=6260a3593ac09a9bbdd9c85b23d78a71fa028acd;hp=7f0c9e6b7bdd89a77de30ad5cfa9f47d1c8b6257;hpb=fc570d4ccd5d5dee36271d34ff5c672a50a82101;p=mygpt.git

diff --git a/mygpt.py b/mygpt.py
index 7f0c9e6..7c4e06d 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -36,7 +36,8 @@ class PositionalEncoding(nn.Module):
         t = torch.arange(x.size(1), dtype = x.dtype, device = x.device)[:, None]
         j = torch.arange(x.size(2), dtype = x.dtype, device = x.device)[None, :]
         k = j%2
-        return x + torch.sin(t / (self.len_max ** ((j - k) / x.size(2))) + math.pi/2 * k)[None, :, :]
+        pe = torch.sin(t / (self.len_max ** ((j - k) / x.size(2))) + math.pi/2 * k)
+        return x + pe # Let broadcasting to its job
 
 ##############################
 
@@ -57,7 +58,7 @@ class QKVAttention(nn.Module):
         self.w_q = randw(nb_heads, dim_qk, dim_in)
         self.w_k = randw(nb_heads, dim_qk, dim_in)
         self.w_v = randw(nb_heads, dim_v, dim_in)
-        self.w_o = randw(dim_in, dim_v * nb_heads)
+        self.w_o = randw(dim_v * nb_heads, dim_in)
 
     def forward(self, x_q, x_kv = None):
         if x_kv is None: x_kv = x_q
@@ -107,11 +108,11 @@ class MyGPT(nn.Module):
                     nn.LayerNorm(dim_model),
                     QKVAttention(
                         dim_in = dim_model,
-                        dim_qk = dim_keys, dim_v = dim_model // nb_heads,
+                        dim_qk = dim_keys,
+                        dim_v = dim_model // nb_heads,
                         nb_heads = nb_heads,
                         causal = True, attention_dropout = dropout
                     ),
-                    nn.Linear(in_features = dim_model, out_features = dim_model),
                 ),
                 Residual(
                     nn.LayerNorm(dim_model),
@@ -127,7 +128,7 @@ class MyGPT(nn.Module):
         self.readout = nn.Linear(in_features = dim_model, out_features = vocabulary_size)
 
     def forward(self, x):
-        x = torch.cat((x.new_zeros(x.size(0), 1), x), 1)
+        x = F.pad(x, (1, 0))
         x = self.embedding(x)
         x = self.trunk(x)
         x = self.readout(x)
@@ -143,7 +144,7 @@ if __name__ == '__main__':
 
     model = MyGPT(
         vocabulary_size = vocabulary_size,
-        dim_model = 16, dim_keys = 50, dim_hidden = 100,
+        dim_model = 18, dim_keys = 50, dim_hidden = 100,
         nb_heads = 2, nb_blocks = 3,
         dropout = 0.1
     )