Update.

[culture.git] / mygpt.py

diff --git a/mygpt.py b/mygpt.py
index 131c822..ab4ccbc 100755 (executable)
--- a/mygpt.py
+++ b/mygpt.py
@@ -279,27 +279,41 @@ class MyGPT(nn.Module):
         self,
         input,
         ar_mask,
         self,
         input,
         ar_mask,

+        summed_logits,
+        temperature=1.0,

         deterministic_synthesis=False,
         forbidden_tokens=None,
         forced_biases=None,
     ):
         to_generate = (ar_mask.sum(0) > 0).nonzero()
         deterministic_synthesis=False,
         forbidden_tokens=None,
         forced_biases=None,
     ):
         to_generate = (ar_mask.sum(0) > 0).nonzero()

+

         if to_generate.min() > 0:
             self(
                 BracketedSequence(input, 0, to_generate.min())
             )  # 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
         if to_generate.min() > 0:
             self(
                 BracketedSequence(input, 0, to_generate.min())
             )  # 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]

+
+            logits = (logits / temperature).log_softmax(dim=-1)
+

             if forbidden_tokens is not None:
                 logits = logits.masked_fill(forbidden_tokens, float("-inf"))
             if forbidden_tokens is not None:
                 logits = logits.masked_fill(forbidden_tokens, float("-inf"))

+

             if forced_biases is not None:
                 logits = logits + forced_biases[None, :]
             if forced_biases is not None:
                 logits = logits + forced_biases[None, :]

+

             if deterministic_synthesis:
             if deterministic_synthesis:

-                t_next = logits.argmax(1)
+                t_next = logits.argmax(-1)

             else:
                 dist = torch.distributions.categorical.Categorical(logits=logits)
                 t_next = dist.sample()
             else:
                 dist = torch.distributions.categorical.Categorical(logits=logits)
                 t_next = dist.sample()

+                if summed_logits is not None:
+                    summed_logits += logits[torch.arange(t_next.size(0)), t_next].sum(
+                        dim=-1
+                    )
+

             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 
     def record_attention(self, v=True):
             input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 
     def record_attention(self, v=True):