Update.

[culture.git] / quizz_machine.py

diff --git a/quizz_machine.py b/quizz_machine.py
index 4e7576e..5f19998 100755 (executable)
--- a/quizz_machine.py
+++ b/quizz_machine.py
@@ -119,6 +119,20 @@ class QuizzMachine:
         b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
         return b.long()[None, :].expand_as(input)
 
+    def generate_token_sequences(self, nb):
+        prompts, answers = self.problem.generate_prompts_and_answers(nb)
+        result = []
+
+        for prompt, answer in zip(prompts, answers):
+            if torch.rand(1) < 0.5:
+                a = [torch.tensor([self.token_forward]), prompt, answer]
+            else:
+                a = [torch.tensor([self.token_backward]), answer, prompt]
+
+            result.append(torch.cat(a, dim=0)[None, :])
+
+        return torch.cat(result, dim=0)
+
     def __init__(
         self,
         problem,
@@ -131,28 +145,57 @@ class QuizzMachine:
     ):
         super().__init__()
 
+        v = problem.nb_token_values()
+        self.token_forward = v
+        self.token_backward = v + 1
+        self.nb_token_values = v + 2
+
         self.problem = problem
         self.batch_size = batch_size
         self.device = device
         self.logger = logger
 
-        self.train_w_quizzes = self.problem.generate_token_sequences(
-            nb_train_samples
-        ).to(device)
-        self.test_w_quizzes = self.problem.generate_token_sequences(nb_test_samples).to(
+        self.train_w_quizzes = self.generate_token_sequences(nb_train_samples).to(
             device
         )
 
-        self.nb_codes = max(self.train_w_quizzes.max(), self.test_w_quizzes.max()) + 1
+        self.test_w_quizzes = self.generate_token_sequences(nb_test_samples).to(device)
 
         self.train_c_quizzes = []
         self.test_c_quizzes = []
 
         if result_dir is not None:
-            self.problem.save_quizzes(
-                self.train_w_quizzes[:72], result_dir, "culture_w_quizzes"
+            self.save_quizzes(
+                result_dir, "culture_w_quizzes", self.train_w_quizzes[:72]
             )
 
+    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()
+        assert forward.equal(1 - backward)
+        first = quizzes[:, 1 : 1 + l]
+        second = quizzes[:, 1 + l : 1 + 2 * l]
+        prompts = forward[:, None] * first + backward[:, None] * second
+        answers = forward[:, None] * second + backward[:, None] * first
+
+        if prediction:
+            predicted_prompts = backward
+            predicted_answers = forward
+        else:
+            predicted_prompts = None
+            predicted_answers = None
+
+        self.problem.save_quizzes(
+            result_dir,
+            filename_prefix,
+            prompts,
+            answers,
+            predicted_prompts,
+            predicted_answers,
+        )
+
     def batches(self, split="train", desc=None):
         assert split in {"train", "test"}
         if split == "train":
@@ -193,7 +236,7 @@ class QuizzMachine:
             yield batch
 
     def vocabulary_size(self):
-        return self.nb_codes
+        return self.nb_token_values
 
     def produce_results(
         self, n_epoch, model, result_dir, deterministic_synthesis, nmax=1000
@@ -257,8 +300,11 @@ class QuizzMachine:
             device=self.device,
         )
 
-        self.problem.save_quizzes(
-            result[:72], result_dir, f"culture_prediction_{n_epoch:04d}_{model.id:02d}"
+        self.save_quizzes(
+            result_dir,
+            f"culture_prediction_{n_epoch:04d}_{model.id:02d}",
+            quizzes=result[:72],
+            prediction=True,
         )
 
         return main_test_accuracy
@@ -267,7 +313,7 @@ class QuizzMachine:
         input = self.train_w_quizzes if for_train else self.test_w_quizzes
         nb = min(nb, input.size(0))
         input[:-nb] = input[nb:].clone()
-        input[-nb:] = self.problem.generate_token_sequences(nb).to(self.device)
+        input[-nb:] = self.generate_token_sequences(nb).to(self.device)
 
     def store_c_quizzes(self, new_c_quizzes, for_train=True):
         if for_train:
@@ -276,15 +322,15 @@ class QuizzMachine:
             self.test_c_quizzes.append(new_c_quizzes)
 
     def reverse_time(self, c_quizzes):
-        token_forward, token_backward = self.problem.direction_tokens()
-
         l = (c_quizzes.size(1) - 1) // 2
-        direction = c_quizzes[:, l : l + 1]
-        direction = self.problem.token_forward * (
-            direction == self.problem.token_backward
-        ) + self.problem.token_backward * (direction == self.problem.token_forward)
+        direction = c_quizzes[:, 0:1]
+        direction = self.token_forward * (
+            direction == self.token_backward
+        ) + self.token_backward * (direction == self.token_forward)
 
-        return torch.cat([c_quizzes[:, l + 1 :], direction, c_quizzes[:, :l]], dim=1)
+        return torch.cat(
+            [direction, c_quizzes[:, l + 1 :], c_quizzes[:, 1 : l + 1]], dim=1
+        )
 
     def compute_correctness(
         self, c_quizzes, models_for_validation, both_directions=True