X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=quizz_machine.py;h=470b095ce38ec23d6a034ee3a338d87b7b1e9b52;hb=08d4ba04f038318080fc2815d85843c4873c896f;hp=0d6d8f57cba918235f951333a64cb1a4c44133d2;hpb=d283cd3d46a6323fec4c6a0970ac71e553e4a486;p=culture.git

diff --git a/quizz_machine.py b/quizz_machine.py
index 0d6d8f5..470b095 100755
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -29,8 +29,6 @@ def one_batch_masked_inplace_autoregression(
     seq_logproba,
     temperature=1.0,
     deterministic_synthesis=False,
-    forbidden_tokens=None,
-    forced_biases=None,
 ):
     to_generate = (ar_mask.sum(0) > 0).nonzero()
 
@@ -45,12 +43,6 @@ def one_batch_masked_inplace_autoregression(
 
         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:
@@ -104,8 +96,6 @@ def masked_inplace_autoregression(
                 seq_logproba=seq_logproba,
                 temperature=temperature,
                 deterministic_synthesis=deterministic_synthesis,
-                forbidden_tokens=forbidden_tokens,
-                forced_biases=logit_biases,
             )
 
         model.train(t)
@@ -170,7 +160,6 @@ class QuizzMachine:
             )
 
     def save_quizzes(self, result_dir, filename_prefix, quizzes, prediction=False):
-        print(f"DEBUG {quizzes.size()=}")
         l = (quizzes.size(1) - 1) // 2
         forward = (quizzes[:, 0] == self.token_forward).long()
         backward = (quizzes[:, 0] == self.token_backward).long()
@@ -338,7 +327,11 @@ class QuizzMachine:
         reversed_c_quizzes = self.reverse_time(c_quizzes)
 
         ar_mask = self.make_ar_mask(c_quizzes)
-        seq_logproba = torch.empty(ar_mask.size(0), device=self.device)
+        seq_logproba = torch.zeros(
+            c_quizzes.size(0),
+            max([m.id for m in models_for_validation]) + 1,
+            device=self.device,
+        )
 
         # Check how many of models can solve the quizzes in both directions
 
@@ -347,12 +340,14 @@ class QuizzMachine:
         for model in models_for_validation:
             result = c_quizzes.clone()
 
+            seq_logproba[...] = 0.0
+
             masked_inplace_autoregression(
                 model=model,
                 batch_size=self.batch_size,
                 input=result,
                 ar_mask=ar_mask,
-                seq_logproba=seq_logproba,
+                seq_logproba=seq_logproba[:, model.id],
                 temperature=1.0,
                 deterministic_synthesis=True,
                 # progress_bar_desc="solving c_quizzes",
@@ -369,7 +364,7 @@ class QuizzMachine:
                     batch_size=self.batch_size,
                     input=reversed_result,
                     ar_mask=ar_mask,
-                    seq_logproba=seq_logproba,
+                    seq_logproba=seq_logproba[:, model.id],
                     temperature=1.0,
                     deterministic_synthesis=True,
                     # progress_bar_desc="solving reversed c_quizzes",
@@ -386,7 +381,7 @@ class QuizzMachine:
 
             nb_correct += correct
 
-        return nb_correct
+        return nb_correct, seq_logproba
 
     ###############################################################
 
@@ -401,7 +396,7 @@ class QuizzMachine:
         ar_mask_first[:, 0] = 0
         ar_mask_second[:, 0] = 0
 
-        seq_logproba = torch.empty(ar_mask_first.size(0), device=self.device)
+        seq_logproba = torch.zeros(ar_mask_first.size(0), device=self.device)
 
         temperature = 10.0
 
@@ -420,8 +415,6 @@ class QuizzMachine:
             device=self.device,
         )
 
-        ave_seq_logproba = seq_logproba.mean()
-
         # Then, we generate the prompt deterministically
 
         masked_inplace_autoregression(
@@ -451,4 +444,4 @@ class QuizzMachine:
             device=self.device,
         )
 
-        return c_quizzes, seq_logproba.mean()
+        return c_quizzes