X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=a17849181edafb878c6c55cc45ad0422b01c1dce;hb=ac0d58ae608b02b00226e8c08c98fca7295a2b3b;hp=131c822c76620076721cdb3a7722544dd6ea70b2;hpb=41164ce7ce1d071a4eb71f72ff277933794cf316;p=culture.git

diff --git a/mygpt.py b/mygpt.py
index 131c822..a178491 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -279,10 +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(
@@ -290,7 +292,7 @@ class MyGPT(nn.Module):
             )  # Needed to initialize the model's cache
         for s in range(to_generate.min(), to_generate.max() + 1):
             output = self(BracketedSequence(input, s, 1)).x
-            logits = output[:, s]
+            logits = output[:, s] / temperature
             if forbidden_tokens is not None:
                 logits = logits.masked_fill(forbidden_tokens, float("-inf"))
             if forced_biases is not None:
@@ -300,8 +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):