X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=7117e766e3aa8347357475e77e8628850ce54942;hb=674eb2f0d02b362fbfcf8ed403b2caa329054d0a;hp=77c29ce909549fca9487e9e50564ce7e01f67932;hpb=621231cc5bb94f983c556a1b450b66067bec4165;p=culture.git

diff --git a/mygpt.py b/mygpt.py
index 77c29ce..7117e76 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -264,6 +264,7 @@ class MyGPT(nn.Module):
                     m.weight.fill_(1.0)
 
     def forward(self, bs):
+        # print(f"GENERATE {bs.first} {bs.first+bs.nb}")
         bs = BracketedSequence(F.pad(bs.x, (1, -1)), bs.first, bs.nb)
         bs = self.embedding(bs)
         bs = self.trunk(bs)
@@ -278,10 +279,12 @@ class MyGPT(nn.Module):
         self,
         input,
         ar_mask,
+        temperature=1.0,
         deterministic_synthesis=False,
         forbidden_tokens=None,
         forced_biases=None,
     ):
+        sum_logits = 0
         to_generate = (ar_mask.sum(0) > 0).nonzero()
         if to_generate.min() > 0:
             self(
@@ -299,8 +302,13 @@ class MyGPT(nn.Module):
             else:
                 dist = torch.distributions.categorical.Categorical(logits=logits)
                 t_next = dist.sample()
+                sum_logits += logits.log_softmax(dim=-1)[
+                    torch.arange(t_next.size(0)), t_next
+                ].sum()
             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 
+        return sum_logits
+
     def record_attention(self, v=True):
         for m in self.modules():
             if isinstance(m, QKVAttention):