X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=inline;f=mygpt.py;h=7119c7a4ef12d49d0f1e164a0839437496d955af;hb=525bd24014786b53638dea78cfb88035a2b99d97;hp=809f79032a55204abd8add007b025ca54b1ad227;hpb=60d829ba77c9769009d3d5a93a50d23c532d019a;p=culture.git

diff --git a/mygpt.py b/mygpt.py
index 809f790..7119c7a 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -271,51 +271,6 @@ class MyGPT(nn.Module):
         bs = self.readout(bs)
         return bs
 
-    # ar_mask is a tensor with 0s and 1s, of same shape as input, with
-    # 1s where tokens should be generated. The others are kept
-    # unchanged.
-
-    def masked_inplace_autoregression(
-        self,
-        input,
-        ar_mask,
-        seq_logproba,
-        temperature=1.0,
-        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
-
-            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 forced_biases is not None:
-                logits = logits + forced_biases[None, :]
-
-            if deterministic_synthesis:
-                t_next = logits.argmax(-1)
-            else:
-                dist = torch.distributions.categorical.Categorical(logits=logits)
-                t_next = dist.sample()
-
-            if seq_logproba is not None:
-                all_t = torch.arange(t_next.size(0))
-                seq_logproba += logits[all_t, t_next].sum(dim=-1)
-
-            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):