X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=quizz_machine.py;h=8dc23a5dc6dd17736a18a3dd1399c3835de74f7a;hb=239a52ec7face6fcd4515916e80813702fbdf49b;hp=eae256ba8fe0ee54b8dfcd1888df17f99526febf;hpb=66d210bd5e04ae58f9e1495df77f1f975ee99c56;p=culture.git

diff --git a/quizz_machine.py b/quizz_machine.py
index eae256b..8dc23a5 100755
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -17,43 +17,6 @@ from mygpt import BracketedSequence
 
 ######################################################################
 
-
-class Gang(nn.Module):
-    def __init__(self, models, nb_models_for_generation, mode="groupthink"):
-        super().__init__()
-        self.models = nn.ModuleList(models)
-        self.nb_models_for_generation = nb_models_for_generation
-        self.mode = mode
-
-    def forward(self, bs):
-        # If first = 0, we are re-starting an auto-regressive process,
-        # that's the right moment to randomize who gonna do it
-        if bs.first == 0:
-            self.models_to_use = [
-                self.models[k]
-                for k in torch.randperm(len(self.models))[
-                    : self.nb_models_for_generation
-                ]
-            ]
-
-        all_the_logits = torch.cat(
-            [model(bs).x[None] for model in self.models_to_use], dim=0
-        )
-
-        if self.mode == "groupthink":
-            y = all_the_logits.mean(dim=0)
-        elif self.mode == "groupwork":
-            m = torch.rand(all_the_logits.size(), device=all_the_logits.device)
-            m = (m.sort(dim=0).indices == 0).long()
-            y = (y * m).sum(dim=0)
-        else:
-            raise ValueError(f"Invalid mode {self.mode}")
-
-        return BracketedSequence(y, bs.first, bs.nb)
-
-
-######################################################################
-
 # 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.
@@ -374,9 +337,7 @@ class QuizzMachine:
 
     ###############################################################
 
-    def generate_quizzes(
-        self, nb, model_for_generation, min_ave_seq_logproba, reverse_cleanup=False
-    ):
+    def generate_quizzes(self, nb, model_for_generation, reverse_cleanup=False):
         c_quizzes = torch.empty(
             nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
         )
@@ -386,8 +347,11 @@ class QuizzMachine:
         ar_mask_solve = 1 - ar_mask_prompt
         seq_logproba = torch.empty(ar_mask_prompt.size(0), device=self.device)
 
-        warnings.warn("very high temperature with reversed cleanup", RuntimeWarning)
-        temperature = 10
+        if reverse_cleanup:
+            warnings.warn("very high temperature with reversed cleanup", RuntimeWarning)
+            temperature = 10.0
+        else:
+            temperature = 1.0
 
         # warnings.warn("noise injection", RuntimeWarning)
         # noise_std = torch.rand(1).item()
@@ -403,7 +367,6 @@ class QuizzMachine:
             seq_logproba=seq_logproba,
             temperature=temperature,
             deterministic_synthesis=False,
-            # progress_bar_desc="sampling c_quizzes",
             device=self.device,
         )
 
@@ -419,7 +382,6 @@ class QuizzMachine:
             seq_logproba=seq_logproba,
             temperature=temperature,
             deterministic_synthesis=True,
-            # progress_bar_desc="sampling c_quizzes",
             device=self.device,
         )
 
@@ -433,54 +395,7 @@ class QuizzMachine:
                 seq_logproba=seq_logproba,
                 temperature=temperature,
                 deterministic_synthesis=True,
-                # progress_bar_desc="sampling c_quizzes",
                 device=self.device,
             )
 
         return c_quizzes, seq_logproba.mean()
-
-    ######################################################################
-
-    def create_c_quizzes(
-        self,
-        nb,
-        model_for_generation,
-        models_for_validation,
-        min_ave_seq_logproba,
-        reverse_cleanup,
-        n_epoch,
-        result_dir,
-    ):
-        c_quizzes, ave_seq_logproba = self.generate_quizzes(
-            nb,
-            model_for_generation=model_for_generation,
-            min_ave_seq_logproba=min_ave_seq_logproba,
-            reverse_cleanup=reverse_cleanup,
-        )
-
-        nb_correct = self.comput_correctness(c_quizzes, models_for_validation)
-
-        return c_quizzes, nb_correct, ave_seq_logproba
-
-    ######################################################################
-
-    def gang_create_c_quizzes(
-        self,
-        nb,
-        nb_models_for_generation,
-        models,
-        mode,
-        min_ave_seq_logproba,
-        n_epoch,
-        result_dir,
-    ):
-        model_for_generation = Gang(models, nb_models_for_generation, mode)
-        models_for_validation = models
-        return self.create_c_quizzes(
-            nb,
-            model_for_generation,
-            models_for_validation,
-            min_ave_seq_logproba,
-            n_epoch,
-            result_dir,
-        )