From: François Fleuret <francois@fleuret.org>
Date: Sun, 23 Jul 2023 09:41:34 +0000 (+0200)
Subject: Update.
X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=40d0010b6b76304e340ae734cb9814e714b691cc;p=culture.git

Update.
---

diff --git a/tasks.py b/tasks.py
index a27b836..234e780 100755
--- a/tasks.py
+++ b/tasks.py
@@ -1283,31 +1283,20 @@ class RPL(Task):
         )
 
         if save_attention_image is not None:
-            input = self.test_input[:1]
-            result = input.clone()
-            s = (result == self.t_prog).long()
-            ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
-            result = (1 - ar_mask) * result + ar_mask * self.t_nul
-
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                result,
-                ar_mask,
-                deterministic_synthesis,
-                device=self.device,
-            )
+            input = self.test_input[:1].clone()
+            last = (input != self.t_nul).max(0).values.nonzero().max() + 3
+            input = input[:, :last]
 
             with torch.autograd.no_grad():
                 t = model.training
                 model.eval()
                 model.record_attention(True)
-                model(BracketedSequence(result))
+                model(BracketedSequence(input))
                 model.train(t)
                 ram = model.retrieve_attention()
                 model.record_attention(False)
 
-            tokens_output = [self.id2token[i.item()] for i in result[0]]
+            tokens_output = [self.id2token[i.item()] for i in input[0]]
             tokens_input = ["n/a"] + tokens_output[:-1]
             for n_head in range(ram[0].size(1)):
                 filename = os.path.join(