Update.

[culture.git] / tasks.py

diff --git a/tasks.py b/tasks.py
index 9cd06ae..77493a8 100755 (executable)
--- a/tasks.py
+++ b/tasks.py
@@ -1,12 +1,22 @@
 #!/usr/bin/env python
 
 #!/usr/bin/env python
 

-import math, os, tqdm
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
+import math, os, tqdm, warnings

 
 import torch, torchvision
 
 from torch import nn
 from torch.nn import functional as F
 
 
 import torch, torchvision
 
 from torch import nn
 from torch.nn import functional as F
 

+from mygpt import BracketedSequence
+
+# from graph import save_attention_image
+save_attention_image = None
+

 ######################################################################
 
 
 ######################################################################
 
 


@@ -17,6 +27,7 @@ def masked_inplace_autoregression(
     ar_mask,
     deterministic_synthesis,
     forbidden_tokens=None,
     ar_mask,
     deterministic_synthesis,
     forbidden_tokens=None,

+    logit_biases=None,

     progress_bar_desc="autoregression",
     device=torch.device("cpu"),
 ):
     progress_bar_desc="autoregression",
     device=torch.device("cpu"),
 ):


@@ -29,7 +40,7 @@ def masked_inplace_autoregression(
             batches,
             dynamic_ncols=True,
             desc=progress_bar_desc,
             batches,
             dynamic_ncols=True,
             desc=progress_bar_desc,

-            # total=input.size(0) // batch_size,
+            total=(input.size(0) + batch_size - 1) // batch_size,

         )
 
     with torch.autograd.no_grad():
         )
 
     with torch.autograd.no_grad():


@@ -38,7 +49,11 @@ def masked_inplace_autoregression(
 
         for input, ar_mask in batches:
             model.masked_inplace_autoregression(
 
         for input, ar_mask in batches:
             model.masked_inplace_autoregression(

-                input, ar_mask, forbidden_tokens, deterministic_synthesis
+                input,
+                ar_mask,
+                deterministic_synthesis,
+                forbidden_tokens,
+                logit_biases,

             )
 
         model.train(t)
             )
 
         model.train(t)


@@ -48,7 +63,7 @@ def masked_inplace_autoregression(
 
 
 class Task:
 
 
 class Task:

-    def batches(self, split="train"):
+    def batches(self, split="train", nb_to_use=-1, desc=None):

         pass
 
     def vocabulary_size(self):
         pass
 
     def vocabulary_size(self):


@@ -62,424 +77,80 @@ class Task:
 
 ######################################################################
 
 
 ######################################################################
 

+import world

 
 

-class Problem:
-    def generate(nb):
-        pass
-
-    def perf(seq, logger):
-        pass
-
-
-class ProblemByheart(Problem):
-    def __init__(self):
-        nb_seq, len_prompt, len_result = 100, 5, 5
-        self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
-        self.seq[:, len_prompt] = -1
-
-    def generate_sequences(self, nb):
-        return self.seq[torch.randint(self.seq.size(0), (nb,))]
-
-
-class SandBox(Task):
-    def __init__(
-        self,
-        nb_train_samples,
-        nb_test_samples,
-        batch_size,
-        logger=None,
-        device=torch.device("cpu"),
-    ):
-        super().__init__()
-
-        self.batch_size = batch_size
-
-        problems = [ProblemByheart()]
-        nb_common_codes = 100
-
-        def generate_sequences(nb_samples):
-            problem_indexes = torch.randint(len(problems), (nb_samples,))
-            nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
-            print(f"{nb_samples_per_problem}")
-            all_seq = []
-            for nb, p in zip(nb_samples_per_problem, problems):
-                all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
-            return all_seq
-
-        train_seq = generate_sequences(nb_train_samples)
-        test_seq = generate_sequences(nb_test_samples)
-
-        for strain, stest in zip(train_seq, test_seq):
-            s = torch.cat((strain, stest), 0)
-
-        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            yield batch
-
-    def vocabulary_size(self):
-        return self.nb_codes
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        # logger(
-        # f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
-        # )
-        pass
-
-
-######################################################################

 
 

-import picoclvr
-
-
-class PicoCLVR(Task):
-    # Make a tensor from a list of strings
-    def tensorize(self, descr):
-        token_descr = [s.strip().split(" ") for s in descr]
-        l = max([len(s) for s in token_descr])
-        token_descr = [s + ["<nul>"] * (l - len(s)) for s in token_descr]
-        id_descr = [[self.token2id[u] for u in s] for s in token_descr]
-        return torch.tensor(id_descr, device=self.device)
-
-    # Make a list of strings from a tensor
-    def detensorize(self, x):
-        return [" ".join([self.id2token[t.item()] for t in r]) for r in x]
-
-    # trim all the tensors in the tuple z to remove as much token from
-    # left and right in the first tensor. If z is a tuple, all its
-    # elements are trimed according to the triming for the first
-    def trim(self, z, token="<nul>"):
-        n = self.token2id[token]
-        if type(z) == tuple:
-            x = z[0]
-            i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
-            a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
-            return tuple([t[:, a:b] for t in z])
-        else:
-            i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0)
-            a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min()
-            return z[:, a:b]
+class World(Task):
+    def save_image(self, input, result_dir, filename, logger):
+        img = world.sample2img(input.to("cpu"), self.height, self.width)
+        image_name = os.path.join(result_dir, filename)
+        torchvision.utils.save_image(img.float() / 255.0, image_name, nrow=8, padding=2)
+        logger(f"wrote {image_name}")

 
 

-    ######################
+    def make_ar_mask(self, input):
+        b = torch.arange(input.size(1), device=input.device) > input.size(1) // 2
+        return b.long()[None, :].expand_as(input)

 
     def __init__(
         self,
         nb_train_samples,
         nb_test_samples,
         batch_size,
 
     def __init__(
         self,
         nb_train_samples,
         nb_test_samples,
         batch_size,

-        height,
-        width,
-        nb_colors=5,
+        result_dir=None,

         logger=None,
         device=torch.device("cpu"),
         logger=None,
         device=torch.device("cpu"),

-        pruner_train=None,
-        pruner_eval=None,

     ):
         super().__init__()
 
     ):
         super().__init__()
 

-        def generate_descr(nb, cache_suffix, pruner):
-            return picoclvr.generate(
-                nb,
-                height=self.height,
-                width=self.width,
-                nb_colors=nb_colors,
-                pruner=pruner,
-            )
-
-        self.height = height
-        self.width = width

         self.batch_size = batch_size
         self.device = device
         self.batch_size = batch_size
         self.device = device

-        self.pruner_train = pruner_train
-        self.pruner_eval = pruner_eval
-
-        if logger is not None:
-            logger(
-                f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
-            )
+        self.height = 6
+        self.width = 8

 
 

-        self.train_descr = generate_descr(
-            nb_train_samples, "train", pruner=self.pruner_train
-        )
-        self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
-
-        # Build the tokenizer
-        tokens = {"<nul>", "<img>"}
-        for d in [self.train_descr, self.test_descr]:
-            for s in d:
-                for t in s.strip().split(" "):
-                    tokens.add(t)
-        # make this set a sorted list to get the same tensors given
-        # the same descr
-        tokens = list(tokens)
-        tokens.sort()
-        self.token2id = dict([(t, n) for n, t in enumerate(tokens)])
-        self.id2token = dict([(n, t) for n, t in enumerate(tokens)])
-        self.t_img, self.t_nul = self.token2id["<img>"], self.token2id["<nul>"]
-
-        # Tokenize the train and test sets
-        self.train_input = self.tensorize(self.train_descr)
-        self.test_input = self.tensorize(self.test_descr)
-
-    def batches(self, split="train"):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
-        ):
-            yield self.trim(batch)
+        self.train_input = world.generate(
+            nb_train_samples, height=self.height, width=self.width
+        ).to(device)

 
 

-    def vocabulary_size(self):
-        return len(self.token2id)
+        self.test_input = world.generate(
+            nb_test_samples, height=self.height, width=self.width
+        ).to(device)

 
 

-    def compute_missing_properties(
-        self, n_epoch, model, logger, deterministic_synthesis, pruner=None
-    ):
-        acc_nb_requested_properties = []
-        acc_nb_missing_properties = []
-        acc_nb_results = 0
+        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1

 
 

-        for input in tqdm.tqdm(
-            self.test_input.split(self.batch_size),
-            dynamic_ncols=True,
-            desc=f"test-properties",
-        ):
-            result = input.clone()
-            ar_mask = (result == self.t_img).long().cumsum(dim=1).clamp(max=1)
-            result = (1 - ar_mask) * result + ar_mask * self.t_nul
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                result,
-                ar_mask,
-                deterministic_synthesis,
-                progress_bar_desc=None,
-                device=self.device,
-            )
+        self.train_quizzes = []
+        self.test_quizzes = []

 
 

-            result_descr = self.detensorize(result)
-            np = picoclvr.nb_properties(
-                result_descr,
-                height=self.height,
-                width=self.width,
-                pruner=pruner,
+        if result_dir is not None:
+            self.save_image(
+                self.train_input[:96], result_dir, f"world_train.png", logger

             )
             )

-            nb_requested_properties, _, nb_missing_properties = zip(*np)
-            acc_nb_requested_properties += nb_requested_properties
-            acc_nb_missing_properties += nb_missing_properties
-            acc_nb_results += len(result_descr)
-
-        nb_requested_properties = sum(acc_nb_requested_properties)
-        nb_missing_properties = sum(acc_nb_missing_properties)
-
-        prefix = "" if pruner is None else "pruned_"
-        logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
-        logger(
-            f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
-        )
-        logger(
-            f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
-        )
-
-    ######################################################################
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        self.compute_missing_properties(n_epoch, model, logger, deterministic_synthesis)
-
-        if self.pruner_eval is not None:
-            self.compute_missing_properties(n_epoch, model, self.pruner_eval)
-
-        nb_tokens_to_generate = self.height * self.width + 3
-        result_descr = []
-        nb_per_primer = 8
-        primer = []
-
-        for primer_descr in [
-            "red above green <sep> green top <sep> blue right of red",
-            "there is red <sep> there is yellow <sep> there is blue",
-            "red below yellow <sep> yellow below green <sep> green below blue <sep> red right <sep> yellow left <sep> green right <sep> blue left",
-            "green bottom <sep> yellow bottom <sep> green left of blue <sep> yellow right of blue <sep> blue top",
-        ]:
-            primer += [primer_descr + " <img>"] * nb_per_primer
-
-        result = self.tensorize(primer)
-        fill = result.new_full(
-            result.size()[:-1] + (self.height * self.width + 1,), self.t_nul
-        )
-        result = torch.cat((result, fill), 1)
-        ar_mask = (result == self.t_nul).long()
-        masked_inplace_autoregression(
-            model,
-            self.batch_size,
-            result,
-            ar_mask,
-            deterministic_synthesis,
-            device=self.device,
-        )
-        result_descr = self.detensorize(result)
-
-        np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
-
-        acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
-        acc_nb_results = len(result_descr)
-
-        nb_requested_properties = sum(acc_nb_requested_properties)
-        nb_missing_properties = sum(acc_nb_missing_properties)
-
-        prefix = "demo_"
-        logger(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
-        logger(
-            f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
-        )
-        logger(
-            f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
-        )
-
-        img = picoclvr.descr2img(result_descr, height=self.height, width=self.width)
-
-        if img.dim() == 5:
-            if img.size(1) == 1:
-                img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64)
-            else:
-                img = torch.cat(
-                    [
-                        torchvision.utils.make_grid(x, padding=1, pad_value=64)[None]
-                        for x in img
-                    ],
-                    0,
-                )
-
-        image_name = os.path.join(result_dir, f"picoclvr_result_{n_epoch:04d}.png")
-        torchvision.utils.save_image(
-            img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0
-        )
-        logger(f"wrote {image_name}")
-
-
-######################################################################

 
 

-
-class MNIST(Task):
-    def __init__(
-        self, nb_train_samples, nb_test_samples, batch_size, device=torch.device("cpu")
-    ):
-        super().__init__()
-
-        self.nb_train_samples = (nb_train_samples,)
-        self.nb_test_samples = (nb_test_samples,)
-        self.batch_size = batch_size
-        self.device = device
-        data_set = torchvision.datasets.MNIST(root="./data", train=True, download=True)
-        self.train_input = data_set.data[:nb_train_samples].view(-1, 28 * 28).long()
-        data_set = torchvision.datasets.MNIST(root="./data", train=False, download=True)
-        self.test_input = data_set.data[:nb_test_samples].view(-1, 28 * 28).long()
-
-    def batches(self, split="train", nb_to_use=-1, desc=None):
+    def batches(self, split="train", desc=None):

         assert split in {"train", "test"}
         assert split in {"train", "test"}

-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            yield batch
-
-    def vocabulary_size(self):
-        return 256
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
-        ar_mask = torch.full_like(results, 1)
-        masked_inplace_autoregression(
-            model,
-            self.batch_size,
-            results,
-            ar_mask,
-            deterministic_synthesis,
-            device=self.device,
-        )
-        image_name = os.path.join(result_dir, f"mnist_result_{n_epoch:04d}.png")
-        torchvision.utils.save_image(
-            1 - results.reshape(-1, 1, 28, 28) / 255.0,
-            image_name,
-            nrow=16,
-            pad_value=0.8,
-        )
-        logger(f"wrote {image_name}")
-
-
-######################################################################
-
-import maze
-
-
-class Maze(Task):
-    def map2seq(self, *m):
-        return torch.cat([x.flatten(1) for x in m], 1)
-
-    def seq2map(self, s):
-        s = s.reshape(s.size(0), -1, self.height, self.width)
-        return (s[:, k] for k in range(s.size(1)))
+        if split == "train":
+            input = self.train_input
+            quizzes = self.train_quizzes
+        else:
+            input = self.test_input
+            quizzes = self.test_quizzes

 
 

-    def __init__(
-        self,
-        nb_train_samples,
-        nb_test_samples,
-        batch_size,
-        height,
-        width,
-        nb_walls,
-        device=torch.device("cpu"),
-    ):
-        super().__init__()
+        if len(quizzes) > 0:
+            quizzes = torch.cat(quizzes, dim=0)
+            if quizzes.size(0) > input.size(0) // 2:
+                i = torch.randperm(input.size(0))[: input.size(0) // 2]
+                quizzes = quizzes[i]

 
 

-        self.batch_size = batch_size
-        self.height = height
-        self.width = width
-        self.device = device
+            i = torch.randperm(input.size(0))[: input.size(0) - quizzes.size(0)]
+            input = input[i]

 
 

-        train_mazes, train_paths, _ = maze.create_maze_data(
-            nb_train_samples,
-            height=height,
-            width=width,
-            nb_walls=nb_walls,
-            progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
-        )
-        self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
-
-        test_mazes, test_paths, _ = maze.create_maze_data(
-            nb_test_samples,
-            height=height,
-            width=width,
-            nb_walls=nb_walls,
-            progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
-        )
-        self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
+            self.nb_batch_samples_world = input.size(0)
+            self.nb_batch_samples_quizzes = quizzes.size(0)

 
 

-        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+            input = torch.cat([input, quizzes], dim=0)
+        else:
+            self.nb_batch_samples_world = input.size(0)
+            self.nb_batch_samples_quizzes = 0

 
 

-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]

         if desc is None:
             desc = f"epoch-{split}"
         for batch in tqdm.tqdm(
         if desc is None:
             desc = f"epoch-{split}"
         for batch in tqdm.tqdm(


@@ -490,22 +161,14 @@ class Maze(Task):
     def vocabulary_size(self):
         return self.nb_codes
 
     def vocabulary_size(self):
         return self.nb_codes
 

-    def compute_error(
-        self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
+    def produce_results(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000

     ):
     ):

-        nb_total, nb_correct = 0, 0
-        count = torch.zeros(
-            self.width * self.height,
-            self.width * self.height,
-            device=self.device,
-            dtype=torch.int64,
-        )
+        def compute_accuracy(input, logger=None):
+            input = input[:nmax]
+            ar_mask = self.make_ar_mask(input)
+            result = input.clone() * (1 - ar_mask)

 
 

-        for input in self.batches(split, nb_to_use):
-            result = input.clone()
-            ar_mask = result.new_zeros(result.size())
-            ar_mask[:, self.height * self.width :] = 1
-            result *= 1 - ar_mask

             masked_inplace_autoregression(
                 model,
                 self.batch_size,
             masked_inplace_autoregression(
                 model,
                 self.batch_size,


@@ -515,312 +178,34 @@ class Maze(Task):
                 progress_bar_desc=None,
                 device=self.device,
             )
                 progress_bar_desc=None,
                 device=self.device,
             )

-            mazes, paths = self.seq2map(result)
-            path_correctness = maze.path_correctness(mazes, paths)
-            nb_correct += path_correctness.long().sum()
-            nb_total += mazes.size(0)

 
 

-            optimal_path_lengths = (
-                (input[:, self.height * self.width :] == maze.v_path).long().sum(1)
-            )
-            predicted_path_lengths = (
-                (result[:, self.height * self.width :] == maze.v_path).long().sum(1)
+            nb_total, nb_correct = (
+                input.size(0),
+                (input == result).long().min(dim=1).values.sum(),

             )
             )

-            optimal_path_lengths = optimal_path_lengths[path_correctness]
-            predicted_path_lengths = predicted_path_lengths[path_correctness]
-            count[optimal_path_lengths, predicted_path_lengths] += 1

 
 

-        if count.max() == 0:
-            count = None
-        else:
-            count = count[
-                : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1
-            ]
+            return nb_total, nb_correct

 
 

-        return nb_total, nb_correct, count
+        train_nb_total, train_nb_correct = compute_accuracy(self.train_input)

 
 

-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        train_nb_total, train_nb_correct, count = self.compute_error(
-            model,
-            "train",
-            nb_to_use=1000,
-            deterministic_synthesis=deterministic_synthesis,
-        )

         logger(
             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
         )
 
         logger(
             f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
         )
 

-        test_nb_total, test_nb_correct, count = self.compute_error(
-            model,
-            "test",
-            nb_to_use=1000,
-            deterministic_synthesis=deterministic_synthesis,
-        )
-        logger(
-            f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
-        )
-
-        if count is not None:
-            proportion_optimal = count.diagonal().sum().float() / count.sum()
-            logger(f"proportion_optimal_test {proportion_optimal*100:.02f}%")
-            with open(
-                os.path.join(result_dir, f"maze_result_{n_epoch:04d}.txt"), "w"
-            ) as f:
-                for i in range(count.size(0)):
-                    for j in range(count.size(1)):
-                        eol = " " if j < count.size(1) - 1 else "\n"
-                        f.write(f"{count[i,j]}{eol}")
-
-        input = self.test_input[:48]
-        result = input.clone()
-        ar_mask = result.new_zeros(result.size())
-        ar_mask[:, self.height * self.width :] = 1
-        result *= 1 - ar_mask
-        masked_inplace_autoregression(
-            model,
-            self.batch_size,
-            result,
-            ar_mask,
-            deterministic_synthesis,
-            device=self.device,
-        )
-
-        mazes, paths = self.seq2map(input)
-        _, predicted_paths = self.seq2map(result)
-
-        filename = os.path.join(result_dir, f"maze_result_{n_epoch:04d}.png")
-        maze.save_image(
-            filename,
-            mazes=mazes,
-            target_paths=paths,
-            predicted_paths=predicted_paths,
-            path_correct=maze.path_correctness(mazes, predicted_paths),
-            path_optimal=maze.path_optimality(paths, predicted_paths),
-        )
-        logger(f"wrote {filename}")
-
-
-######################################################################
-
-
-import snake
-
-
-class Snake(Task):
-    def __init__(
-        self,
-        nb_train_samples,
-        nb_test_samples,
-        batch_size,
-        height,
-        width,
-        nb_colors,
-        length,
-        prompt_length,
-        device=torch.device("cpu"),
-    ):
-        super().__init__()
-
-        self.batch_size = batch_size
-        self.height = height
-        self.width = width
-        self.device = device
-        self.prompt_length = prompt_length
-
-        self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences(
-            nb_train_samples,
-            height,
-            width,
-            nb_colors,
-            length,
-            prompt_length,
-            self.device,
-        )
-        self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences(
-            nb_test_samples,
-            height,
-            width,
-            nb_colors,
-            length,
-            prompt_length,
-            self.device,
-        )
-
-        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            yield batch
-
-    def vocabulary_size(self):
-        return self.nb_codes
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        def compute_nb_correct(input, prior_visits):
-            result = input.clone()
-            i = torch.arange(result.size(1), device=result.device)[None, :]
-            ar_mask = (
-                torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0)
-                .long()
-                .expand_as(result)
-            )
-            result *= 1 - ar_mask
-
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                result,
-                ar_mask,
-                deterministic_synthesis,
-                device=self.device,
-            )
-
-            nb_total = ((prior_visits > 0) * ar_mask).sum()
-
-            nb_correct = ((result == input).long() * (prior_visits > 0) * ar_mask).sum()
-
-            return nb_total, nb_correct
-
-        test_nb_total, test_nb_correct = compute_nb_correct(
-            self.test_input[:1000], self.test_prior_visits[:1000]
-        )
+        test_nb_total, test_nb_correct = compute_accuracy(self.test_input, logger)

 
         logger(
             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
         )
 
 
         logger(
             f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
         )
 

+        main_test_accuracy = test_nb_correct / test_nb_total
+        logger(f"main_test_accuracy {n_epoch} {main_test_accuracy}")

 
 

-######################################################################
-
-
-import stack
-
-
-class Stack(Task):
-    def __init__(
-        self,
-        nb_train_samples,
-        nb_test_samples,
-        batch_size,
-        logger,
-        nb_steps,
-        nb_stacks,
-        nb_digits,
-        fraction_values_for_train=None,
-        device=torch.device("cpu"),
-    ):
-        super().__init__()
-
-        self.batch_size = batch_size
-        self.nb_steps = nb_steps
-        self.nb_stacks = nb_stacks
-        self.nb_digits = nb_digits
-        self.device = device
-
-        if fraction_values_for_train is None:
-            values_for_train = None
-            values_for_test = None
-        else:
-            all = torch.randperm(10**nb_digits)
-            nb_for_train = int(all.size(0) * fraction_values_for_train)
-            values_for_train = all[:nb_for_train]
-            values_for_test = all[nb_for_train:]
-
-        self.train_input, self.train_stack_counts = stack.generate_sequences(
-            nb_train_samples,
-            nb_steps,
-            nb_stacks,
-            nb_digits,
-            values_for_train,
-            self.device,
-        )
-
-        self.test_input, self.test_stack_counts = stack.generate_sequences(
-            nb_test_samples,
-            nb_steps,
-            nb_stacks,
-            nb_digits,
-            values_for_test,
-            self.device,
-        )
-
-        i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks)
-        counts = self.test_stack_counts.flatten()[i.flatten()]
-        counts = F.one_hot(counts).sum(0)
-        logger(f"test_pop_stack_counts {counts}")
-
-        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            yield batch
-
-    def vocabulary_size(self):
-        return self.nb_codes
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        def compute_nb_correct(input):
-            result = input.clone()
-            stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
-            ar_mask = (result != input).long()
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                result,
-                ar_mask,
-                deterministic_synthesis,
-                device=self.device,
-            )
-
-            errors = ((result != input).long() * ar_mask).reshape(
-                -1, 1 + self.nb_digits
-            )
-            ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits)
-
-            nb_total = ar_mask.max(1).values.sum()
-            nb_correct = nb_total - errors.max(1).values.sum()
-
-            return nb_total, nb_correct
-
-        test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000])
+        ##############################

 
 

-        logger(
-            f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
-        )
-
-        ##############################################################
-        # Log a few generated sequences
-        input = self.test_input[:10, : 12 * (1 + self.nb_digits)]
-        result = input.clone()
-        stack.remove_popped_values(result, self.nb_stacks, self.nb_digits)
-        ar_mask = (result != input).long()
-
-        # for n in range(result.size(0)):
-        # logger(
-        # f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
-        # )
+        input = self.test_input[:96]
+        ar_mask = self.make_ar_mask(input)
+        result = input.clone() * (1 - ar_mask)

 
         masked_inplace_autoregression(
             model,
 
         masked_inplace_autoregression(
             model,


@@ -828,336 +213,66 @@ class Stack(Task):
             result,
             ar_mask,
             deterministic_synthesis,
             result,
             ar_mask,
             deterministic_synthesis,

+            progress_bar_desc=None,

             device=self.device,
         )
 
             device=self.device,
         )
 

-        for n in range(result.size(0)):
-            logger(
-                f"test_after  {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}"
-            )
-        ##############################################################
-
-
-######################################################################
-
-
-import expr
-
-
-class Expr(Task):
-    def tensorize(self, sequences):
-        len_max = max([len(x) for x in sequences])
-        return torch.cat(
-            [
-                torch.tensor(
-                    [
-                        [self.char2id[c] for c in s + "#" * (len_max - len(s))]
-                        for s in sequences
-                    ]
-                )
-            ],
-            0,
-        ).to(self.device)
-
-    def __init__(
-        self,
-        nb_train_samples,
-        nb_test_samples,
-        nb_variables,
-        sequence_length,
-        operand_max,
-        result_max,
-        batch_size,
-        device=torch.device("cpu"),
-    ):
-        super().__init__()
-
-        self.batch_size = batch_size
-        self.device = device
-
-        train_sequences = expr.generate_sequences(
-            nb_train_samples,
-            nb_variables=nb_variables,
-            length=sequence_length,
-            operand_max=operand_max,
-            result_max=result_max,
-        )
-
-        test_sequences = expr.generate_sequences(
-            nb_test_samples,
-            nb_variables=nb_variables,
-            length=sequence_length,
-            operand_max=operand_max,
-            result_max=result_max,
+        self.save_image(
+            result[:96],
+            result_dir,
+            f"world_result_{n_epoch:04d}_{model.id:02d}.png",
+            logger,

         )
 
         )
 

-        symbols = list(set("#" + "".join(train_sequences + test_sequences)))
-        symbols.sort()
-
-        self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
-        self.id2char = dict([(n, c) for c, n in self.char2id.items()])
-
-        self.filler, self.space = self.char2id["#"], self.char2id[" "]
-
-        self.train_input = self.tensorize(train_sequences)
-        self.test_input = self.tensorize(test_sequences)
-
-        self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
-
-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            last = (batch != self.filler).max(0).values.nonzero().max() + 3
-            batch = batch[:, :last]
-            yield batch
-
-    def vocabulary_size(self):
-        return self.nb_codes
+        return main_test_accuracy

 
 

-    def seq2str(self, s):
-        return "".join([self.id2char[k.item()] for k in s])
+    def store_new_quizzes(self, new_quizzes, for_train=True):
+        if for_train:
+            self.train_quizzes.append(new_quizzes)
+        else:
+            self.test_quizzes.append(new_quizzes)

 
 

-    def produce_results(
+    def create_new_quizzes(

         self,
         n_epoch,
         self,
         n_epoch,

-        model,

         result_dir,
         logger,
         result_dir,
         logger,

-        deterministic_synthesis,
-        input_file=None,
+        nb,
+        model,
+        other_models,

     ):
     ):

-        def compute_nb_correct(input):
-            result = input.clone()
-            s = (result == self.space).long()
-            ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
-            result = (1 - ar_mask) * result + ar_mask * self.filler
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                result,
-                ar_mask,
-                deterministic_synthesis,
-                device=self.device,
-            )
-
-            nb_total = input.size(0)
-            nb_correct = (input == result).long().min(1).values.sum()
-
-            #######################################################################
-            # Comput predicted vs. true variable values
-
-            nb_delta = torch.zeros(5, dtype=torch.int64)
-            nb_missed = 0
-
-            values_input = expr.extract_results([self.seq2str(s) for s in input])
-            values_result = expr.extract_results([self.seq2str(s) for s in result])
-
-            filename = os.path.join(result_dir, f"expr_result_{n_epoch:04d}.txt")
-
-            with open(filename, "w") as f:
-                for i, r in zip(values_input, values_result):
-                    for n, vi in i.items():
-                        vr = r.get(n)
-                        f.write(f"{vi} {-1 if vr is None else vr}\n")
-
-                        if vr is None or vr < 0:
-                            nb_missed += 1
-                        else:
-                            d = abs(vr - vi)
-                            if d >= nb_delta.size(0):
-                                nb_missed += 1
-                            else:
-                                nb_delta[d] += 1
-
-            ######################################################################
-
-            return nb_total, nb_correct, nb_delta, nb_missed
-
-        (
-            test_nb_total,
-            test_nb_correct,
-            test_nb_delta,
-            test_nb_missed,
-        ) = compute_nb_correct(self.test_input[:10000])
-
-        logger(
-            f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+        new_quizzes = torch.empty(
+            nb, self.height * self.width * 2 + 1, device=self.device, dtype=torch.int64

         )
         )

-
-        nb_total = test_nb_delta.sum() + test_nb_missed
-        for d in range(test_nb_delta.size(0)):
-            logger(
-                f"error_value {n_epoch} delta {d} {test_nb_delta[d]} {test_nb_delta[d]*100/nb_total:.02f}%"
-            )
-        logger(
-            f"error_value {n_epoch} missed {test_nb_missed} {test_nb_missed*100/nb_total:.02f}%"
-        )
-
-        ##############################################################
-        # Log a few generated sequences
-        if input_file is None:
-            input = self.test_input[:10]
-        else:
-            with open(input_file, "r") as f:
-                sequences = [e.strip() for e in f.readlines()]
-                sequences = [s + " " + "#" * 50 for s in sequences]
-                input = self.tensorize(sequences)
-
-        result = input.clone()
-        s = (result == self.space).long()
-        ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
-        result = (1 - ar_mask) * result + ar_mask * self.filler
-
-        for n in range(result.size(0)):
-            logger(f"test_before {self.seq2str(result[n])}")
+        ar_mask = torch.full(new_quizzes.size(), 1, device=self.device)

 
         masked_inplace_autoregression(
             model,
             self.batch_size,
 
         masked_inplace_autoregression(
             model,
             self.batch_size,

-            result,
+            new_quizzes,

             ar_mask,
             ar_mask,

-            deterministic_synthesis,
+            deterministic_synthesis=False,
+            progress_bar_desc="new quizzes",

             device=self.device,
         )
 
             device=self.device,
         )
 

-        correct = (1 - ar_mask) * self.space + ar_mask * input
-        for n in range(result.size(0)):
-            comment = "GOOD" if (result[n] - input[n]).abs().max() == 0 else ""
-            logger(f"test_after  {self.seq2str(result[n])} {comment}")
-            logger(f"truth       {self.seq2str(correct[n])}")
-        ##############################################################
-
-
-######################################################################
-
-import world
-
-
-class World(Task):
-    def __init__(
-        self,
-        nb_train_samples,
-        nb_test_samples,
-        batch_size,
-        vqae_nb_epochs,
-        logger=None,
-        device=torch.device("cpu"),
-        device_storage=torch.device("cpu"),
-    ):
-        super().__init__()
-
-        self.batch_size = batch_size
-        self.device = device
-
-        (
-            train_frames,
-            train_action_seq,
-            test_frames,
-            test_action_seq,
-            self.frame2seq,
-            self.seq2frame,
-        ) = world.create_data_and_processors(
-            nb_train_samples,
-            nb_test_samples,
-            mode="first_last",
-            nb_steps=30,
-            nb_epochs=vqae_nb_epochs,
-            logger=logger,
-            device=device,
-            device_storage=device_storage,
-        )
-
-        print(f"{train_action_seq.size()=}")
-
-        train_frame_seq = self.frame2seq(train_frames).to(device_storage)
-        test_frame_seq = self.frame2seq(test_frames).to(device_storage)
-
-        nb_frame_codes = max(train_frame_seq.max(), test_frame_seq.max()) + 1
-        nb_action_codes = max(train_action_seq.max(), test_action_seq.max()) + 1
-
-        self.len_frame_seq = train_frame_seq.size(1)
-        self.len_action_seq = train_action_seq.size(1)
-        self.nb_codes = nb_frame_codes + nb_action_codes
-
-        train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
-        print(f"{train_action_seq.device=} {nb_frame_codes.device=}")
-        train_action_seq += nb_frame_codes
-        self.train_input = torch.cat(
-            (train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
-        )
-
-        test_frame_seq = test_frame_seq.reshape(test_frame_seq.size(0) // 2, 2, -1)
-        test_action_seq += nb_frame_codes
-        self.test_input = torch.cat(
-            (test_frame_seq[:, 0, :], test_action_seq, test_frame_seq[:, 1, :]), 1
-        )
-
-    def batches(self, split="train", nb_to_use=-1, desc=None):
-        assert split in {"train", "test"}
-        input = self.train_input if split == "train" else self.test_input
-        if nb_to_use > 0:
-            input = input[:nb_to_use]
-        if desc is None:
-            desc = f"epoch-{split}"
-        for batch in tqdm.tqdm(
-            input.split(self.batch_size), dynamic_ncols=True, desc=desc
-        ):
-            yield batch.to(self.device)
-
-    def vocabulary_size(self):
-        return self.nb_codes
-
-    def produce_results(
-        self, n_epoch, model, result_dir, logger, deterministic_synthesis
-    ):
-        k = torch.arange(
-            2 * self.len_frame_seq + self.len_action_seq, device=self.device
-        )[None, :]
-
-        input = self.test_input[:64].to(self.device)
-        result = input.clone()
-
-        ar_mask = (
-            (k >= self.len_frame_seq + self.len_action_seq).long().expand_as(result)
-        )
-        result *= 1 - ar_mask
+        ar_mask = self.make_ar_mask(new_quizzes)

 
 

-        masked_inplace_autoregression(
-            model,
-            self.batch_size,
-            result,
-            ar_mask,
-            deterministic_synthesis,
-            device=self.device,
-        )
+        nb_correct = 0

 
 

-        seq_start = input[:, : self.len_frame_seq]
-        seq_end = input[:, self.len_frame_seq + self.len_action_seq :]
-        seq_predicted = result[:, self.len_frame_seq + self.len_action_seq :]
+        for m in other_models:
+            result = new_quizzes.clone()

 
 

-        result = torch.cat(
-            (seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
-        )
-        result = result.reshape(-1, result.size(-1))
-        print(f"{result.size()=}")
-
-        frames = self.seq2frame(result)
-        image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")
-        torchvision.utils.save_image(
-            frames.float() / (world.Box.nb_rgb_levels - 1),
-            image_name,
-            nrow=12,
-            padding=1,
-            pad_value=0.0,
-        )
-        logger(f"wrote {image_name}")
+            masked_inplace_autoregression(
+                m,
+                self.batch_size,
+                result,
+                ar_mask,
+                deterministic_synthesis=True,
+                progress_bar_desc="solving quizzes",
+                device=self.device,
+            )

 
 

+            nb_correct += (new_quizzes == result).long().min(dim=-1).values

 
 

-######################################################################
+        return new_quizzes, nb_correct