X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;fp=mygpt.py;h=ac1c55e84d91fecb06b453533f1800aead640ed7;hb=00b2d5ed01fb523fbc4e699f0419329efbee0ea8;hp=45b7b59bc42f5f4c2ab31a92e8ad732e87226086;hpb=27dd45177a8552a8b8c9e3a4d9388844fa1d4d27;p=picoclvr.git

diff --git a/mygpt.py b/mygpt.py
index 45b7b59..ac1c55e 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -116,7 +116,13 @@ class AddPositionalEncoding(nn.Module):
 
 class QKVAttention(nn.Module):
     def __init__(
-        self, dim_in, dim_qk, dim_v, nb_heads=1, causal=False, attention_dropout=0.0
+        self,
+        dim_in,
+        dim_qk,
+        dim_v,
+        nb_heads=1,
+        causal=False,
+        attention_dropout=0.0,
     ):
         super().__init__()
 
@@ -125,6 +131,7 @@ class QKVAttention(nn.Module):
 
         self.causal = causal
         self.attention_dropout = attention_dropout
+        self.record_attention = False
 
         self.w_q = randw(nb_heads, dim_qk, dim_in)
         self.w_k = randw(nb_heads, dim_qk, dim_in)
@@ -162,6 +169,9 @@ class QKVAttention(nn.Module):
             "nhtd,nhsd->nhts", q, self.cache_k[:, :, : bs_q.first + bs_q.nb]
         ) / math.sqrt(self.w_q.size(1))
 
+        if self.record_attention:
+            self.a = a
+
         if self.causal:
             if bs_q.first == 0:
                 self.cache_attzero = (
@@ -283,6 +293,18 @@ class MyGPT(nn.Module):
                 t_next = dist.sample()
             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 
+    def record_attention(self, v=True):
+        for m in self.modules():
+            if isinstance(m, QKVAttention):
+                m.record_attention = v
+
+    def retrieve_attention(self):
+        a = []
+        for m in self.modules():
+            if isinstance(m, QKVAttention):
+                a.append(m.a)
+        return a
+
 
 ######################################################################
 
@@ -298,13 +320,12 @@ if __name__ == "__main__":
         dim_keys=2,
         dim_hidden=2,
         nb_heads=2,
-        nb_blocks=1,
+        nb_blocks=2,
         dropout=0.1,
         causal=True,
     )
 
     model.eval()
-
     y1 = model(BracketedSequence(x)).x
     y2 = torch.randn_like(y1)
     for s in range(x.size(1)):