Update.

diff --git a/main.py b/main.py
index 634363f..d400ab1 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -29,7 +29,6 @@ else:
 ######################################################################
 
 parser = argparse.ArgumentParser(
-    description="An implementation of GPT with cache.",
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
@@ -274,53 +273,6 @@ vocabulary_size = quiz_machine.vocabulary_size()
 log_string(f"vocabulary_size {vocabulary_size}")
 
 ######################################################################
-
-# Compute the entropy of the training tokens
-
-token_count = 0
-for input in quiz_machine.batches(split="train", desc="train-entropy"):
-    token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(
-        (0, 1)
-    )
-token_probas = token_count / token_count.sum()
-entropy = -torch.xlogy(token_probas, token_probas).sum()
-train_set_perplexity = math.exp(entropy)
-
-######################################################################
-# A bit of paranoia never hurts
-
-if args.max_percents_of_test_in_train >= 0:
-
-    def subsets_as_tuples(batches, cs):
-        s = set()
-        for batch in batches:
-            for x in batch:
-                s.add(tuple([v.item() for v in x]))
-                if len(s) == cs:
-                    yield s
-                    s = set()
-        yield s
-
-    nb_test, nb_in_train = 0, 0
-    for test_subset in subsets_as_tuples(
-        quiz_machine.batches(split="test", desc="test-check"), 25000
-    ):
-        in_train = set()
-        for train_subset in subsets_as_tuples(
-            quiz_machine.batches(split="train", desc="train-check"), 25000
-        ):
-            in_train.update(test_subset.intersection(train_subset))
-        nb_in_train += len(in_train)
-        nb_test += len(test_subset)
-
-    log_string(
-        f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
-    )
-
-    assert (
-        nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
-    ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
-
 ##############################
 
 
@@ -331,7 +283,7 @@ def one_epoch(model, quiz_machine):
 
     nb_train_samples, acc_train_loss = 0, 0.0
 
-    for input in quiz_machine.batches(split="train"):
+    for input in quiz_machine.batches(model, split="train"):
         input = input.to(device)
 
         if nb_train_samples % args.batch_size == 0:
@@ -363,7 +315,7 @@ def run_tests(model, quiz_machine, deterministic_synthesis):
         nb_test_samples, acc_test_loss = 0, 0.0
         nb_samples_accumulated = 0
 
-        for input in quiz_machine.batches(split="test"):
+        for input in quiz_machine.batches(model, split="test"):
             input = input.to(device)
 
             bs = model(mygpt.BracketedSequence(input))
@@ -596,6 +548,15 @@ for k in range(args.nb_gpts):
     model.main_test_accuracy = 0.0
     model.id = k
 
+    model.train_w_quizzes = quiz_machine.generate_token_sequences(
+        args.nb_train_samples
+    ).to(device)
+    quiz_machine.reverse_random_half_in_place(model.train_w_quizzes)
+    model.test_w_quizzes = quiz_machine.generate_token_sequences(
+        args.nb_test_samples
+    ).to(device)
+    quiz_machine.reverse_random_half_in_place(model.test_w_quizzes)
+
     models.append(model)
 
 
@@ -604,6 +565,54 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 
+# Compute the entropy of the training tokens
+
+token_count = 0
+for input in quiz_machine.batches(models[0], split="train", desc="train-entropy"):
+    token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(
+        (0, 1)
+    )
+token_probas = token_count / token_count.sum()
+entropy = -torch.xlogy(token_probas, token_probas).sum()
+train_set_perplexity = math.exp(entropy)
+
+######################################################################
+# A bit of paranoia never hurts
+
+if args.max_percents_of_test_in_train >= 0:
+
+    def subsets_as_tuples(batches, cs):
+        s = set()
+        for batch in batches:
+            for x in batch:
+                s.add(tuple([v.item() for v in x]))
+                if len(s) == cs:
+                    yield s
+                    s = set()
+        yield s
+
+    nb_test, nb_in_train = 0, 0
+    for test_subset in subsets_as_tuples(
+        quiz_machine.batches(models[0], split="test", desc="test-check"), 25000
+    ):
+        in_train = set()
+        for train_subset in subsets_as_tuples(
+            quiz_machine.batches(models[0], split="train", desc="train-check"), 25000
+        ):
+            in_train.update(test_subset.intersection(train_subset))
+        nb_in_train += len(in_train)
+        nb_test += len(test_subset)
+
+    log_string(
+        f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
+    )
+
+    assert (
+        nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
+    ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
+
+######################################################################
+
 nb_new_c_quizzes_for_train = args.nb_train_samples // 50
 nb_new_c_quizzes_for_test = args.nb_test_samples // 50
 
@@ -654,7 +663,7 @@ for n_epoch in range(args.nb_epochs):
     log_string(
         f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes"
     )
-    quiz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+    quiz_machine.renew_w_quizzes(model, args.nb_train_samples // args.nb_gpts)
 
     ##################################################
     # If all the models are good enough, generate new quizzes and

diff --git a/quiz_machine.py b/quiz_machine.py
index 0ae68d0..cdfba85 100755 (executable)
--- a/quiz_machine.py
+++ b/quiz_machine.py
@@ -235,23 +235,21 @@ class QuizMachine:
         self.prompt_len = None
         self.answer_len = None
 
-        self.train_w_quizzes = self.generate_token_sequences(nb_train_samples)
-        self.reverse_random_half_in_place(self.train_w_quizzes)
-        self.train_w_quizzes = self.train_w_quizzes.to(device)
+        # self.train_w_quizzes = self.generate_token_sequences(nb_train_samples)
+        # self.reverse_random_half_in_place(self.train_w_quizzes)
 
-        self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
-        self.reverse_random_half_in_place(self.test_w_quizzes)
-        self.test_w_quizzes = self.test_w_quizzes.to(device)
+        # self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
+        # self.reverse_random_half_in_place(self.test_w_quizzes)
 
         self.train_c_quizzes = []
         self.test_c_quizzes = []
 
-        if result_dir is not None:
-            self.save_quizzes(
-                result_dir,
-                "culture_w_quizzes",
-                self.train_w_quizzes[:72],
-            )
+        # if result_dir is not None:
+        # self.save_quizzes(
+        # result_dir,
+        # "culture_w_quizzes",
+        # self.train_w_quizzes[:72],
+        # )
 
     def save_quizzes(
         self,
@@ -287,13 +285,13 @@ class QuizMachine:
             predicted_answers,
         )
 
-    def batches(self, split="train", desc=None):
+    def batches(self, model, split="train", desc=None):
         assert split in {"train", "test"}
         if split == "train":
-            w_quizzes = self.train_w_quizzes
+            w_quizzes = model.train_w_quizzes
             c_quizzes = self.train_c_quizzes
         else:
-            w_quizzes = self.test_w_quizzes
+            w_quizzes = model.test_w_quizzes
             c_quizzes = self.test_c_quizzes
 
         if len(c_quizzes) > 0:
@@ -382,10 +380,10 @@ class QuizMachine:
 
             return result, correct
 
-        compute_accuracy(self.train_w_quizzes[:nmax], log_prefix="train")
+        compute_accuracy(model.train_w_quizzes[:nmax], log_prefix="train")
 
         test_result, test_correct = compute_accuracy(
-            self.test_w_quizzes[:nmax], log_prefix="test"
+            model.test_w_quizzes[:nmax], log_prefix="test"
         )
 
         main_test_accuracy = test_correct.sum() / test_correct.size(0)
@@ -402,8 +400,8 @@ class QuizMachine:
 
         return main_test_accuracy
 
-    def renew_w_quizzes(self, nb, for_train=True):
-        input = self.train_w_quizzes if for_train else self.test_w_quizzes
+    def renew_w_quizzes(self, model, nb, for_train=True):
+        input = model.train_w_quizzes if for_train else model.test_w_quizzes
         nb = min(nb, input.size(0))
         input[:-nb] = input[nb:].clone()
         fresh_w_quizzes = self.generate_token_sequences(nb)
@@ -507,7 +505,10 @@ class QuizMachine:
 
     def generate_quizzes(self, nb, model_for_generation, temperature=1.0):
         c_quizzes = torch.empty(
-            nb, self.train_w_quizzes.size(1), device=self.device, dtype=torch.int64
+            nb,
+            self.prompt_len + self.answer_len + 2,
+            device=self.device,
+            dtype=torch.int64,
         )
 
         seq_logproba = torch.zeros(nb, device=self.device)