Merge branch 'dev'

[culture.git] / quiz_machine.py

diff --git a/quiz_machine.py b/quiz_machine.py
index de1e8d1..f0fb408 100755 (executable)
--- a/quiz_machine.py
+++ b/quiz_machine.py
@@ -27,8 +27,8 @@ def one_batch_masked_inplace_autoregression(
     input,
     ar_mask,
     seq_logproba,
-    temperature=1.0,
-    deterministic_synthesis=False,
+    temperature,
+    deterministic_synthesis,
 ):
     to_generate = (ar_mask.sum(0) > 0).nonzero()
 
@@ -50,7 +50,8 @@ def one_batch_masked_inplace_autoregression(
             t_next = dist.sample()
 
         all_n = torch.arange(t_next.size(0))
-        seq_logproba += logits[all_n, t_next].sum(dim=-1)
+
+        seq_logproba += logits[all_n, t_next]
 
         input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
 
@@ -116,6 +117,19 @@ class QuizMachine:
         ).all()
         return i_forward, i_backward
 
+    def non_trivial(self, quizzes):
+        quizzes = quizzes.clone()
+        n_forward = quizzes[quizzes[:, 0] == self.token_forward]
+        n_backward = quizzes[:, 0] == self.token_backward
+        backward = quizzes[n_backward]
+        quizzes[n_backward] = self.reverse_time(quizzes[n_backward])
+        return torch.logical_not(
+            self.problem.trivial_prompts_and_answers(
+                quizzes[:, 1 : 1 + self.prompt_len],
+                quizzes[:, 2 + self.prompt_len :],
+            )
+        )
+
     def reverse_time(self, quizzes):
         i_forward, i_backward = self.indices_forward_and_backward(quizzes)
 
@@ -359,11 +373,11 @@ class QuizMachine:
                 backward_nb_total = correct[n_backward].size(0)
 
                 self.logger(
-                    f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total}"
+                    f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)"
                 )
 
                 self.logger(
-                    f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total}"
+                    f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)"
                 )
 
             return result, correct
@@ -420,11 +434,11 @@ class QuizMachine:
 
         nb_correct = 0
 
+        seq_logproba[...] = 0.0
+
         for model in models_for_validation:
             result = c_quizzes.clone()
 
-            seq_logproba[...] = 0.0
-
             ar_mask = self.make_ar_mask(result)
 
             masked_inplace_autoregression(