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Update.
[culture.git] / main.py
diff --git a/main.py b/main.py
index c01cc8f..b88847e 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -5,91 +5,179 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, sys, argparse, time, tqdm, itertools, os
+import math, sys, argparse, time, tqdm, os, datetime, 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
 
-import mygpt, tensorstack
+import ffutils
+import mygpt
+import sky, wireworld, quizz_machine
+
+# world quizzes vs. culture quizzes
 
 ######################################################################
 
 
 ######################################################################
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+nb_new_c_quizzes_for_train = 1000
+nb_new_c_quizzes_for_test = 100
+
+######################################################################
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+    torch.backends.cuda.matmul.allow_tf32 = True
+else:
+    device = torch.device("cpu")
 
 ######################################################################
 
 parser = argparse.ArgumentParser(
 
 ######################################################################
 
 parser = argparse.ArgumentParser(
-    description="An implementation of GPT with cache to solve a toy geometric reasoning task."
+    description="An implementation of GPT with cache.",
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log")
 
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log")
 
-parser.add_argument("--result_dir", type=str, default="results_default")
+parser.add_argument("--result_dir", type=str, default=None)
 
 parser.add_argument("--seed", type=int, default=0)
 
 
 parser.add_argument("--seed", type=int, default=0)
 
-parser.add_argument("--nb_epochs", type=int, default=25)
+parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
 
 
-parser.add_argument("--batch_size", type=int, default=100)
+########################################
 
 
-parser.add_argument("--data_size", type=int, default=-1)
+parser.add_argument("--nb_epochs", type=int, default=10000)
 
 
-parser.add_argument("--optim", type=str, default="adam")
+parser.add_argument("--batch_size", type=int, default=None)
+
+parser.add_argument("--physical_batch_size", type=int, default=None)
+
+parser.add_argument("--nb_train_samples", type=int, default=None)
+
+parser.add_argument("--nb_test_samples", type=int, default=None)
 
 parser.add_argument("--learning_rate", type=float, default=1e-3)
 
 
 parser.add_argument("--learning_rate", type=float, default=1e-3)
 
-parser.add_argument(
-    "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6"
-)
+########################################
 
 
-parser.add_argument("--dim_model", type=int, default=512)
+parser.add_argument("--model", type=str, default=None)
 
 
-parser.add_argument("--dim_keys", type=int, default=64)
+parser.add_argument("--dim_model", type=int, default=None)
 
 
-parser.add_argument("--dim_hidden", type=int, default=2048)
+parser.add_argument("--dim_keys", type=int, default=None)
 
 
-parser.add_argument("--nb_heads", type=int, default=8)
+parser.add_argument("--dim_hidden", type=int, default=None)
 
 
-parser.add_argument("--nb_blocks", type=int, default=12)
+parser.add_argument("--nb_heads", type=int, default=None)
+
+parser.add_argument("--nb_blocks", type=int, default=None)
 
 parser.add_argument("--dropout", type=float, default=0.1)
 
 
 parser.add_argument("--dropout", type=float, default=0.1)
 
-parser.add_argument("--nb_oneshot_blocks", type=int, default=-1)
+########################################
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
-parser.add_argument("--no_checkpoint", action="store_true", default=False)
+parser.add_argument("--problem", type=str, default="sky")
 
 
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--nb_gpts", type=int, default=5)
 
 
-parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
+parser.add_argument("--nb_models_for_generation", type=int, default=1)
 
 
-##############################
-# picoclvr options
+parser.add_argument("--generation_mode", type=str, default="groupthink")
 
 
-parser.add_argument("--nb_colors", type=int, default=5)
+parser.add_argument("--min_to_validate", type=int, default=4)
 
 
-parser.add_argument("--height", type=int, default=12)
+parser.add_argument("--max_to_validate", type=int, default=4)
 
 
-parser.add_argument("--width", type=int, default=16)
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
 
 
-parser.add_argument("--prune_properties", type=str, default="none")
+parser.add_argument("--dirty_debug", action="store_true", default=False)
 
 ######################################################################
 
 args = parser.parse_args()
 
 
 ######################################################################
 
 args = parser.parse_args()
 
-assert args.prune_properties in {"none", "train+eval", "eval"}
+if args.result_dir is None:
+    args.result_dir = f"results_culture"
+
+######################################################################
+
+if args.dirty_debug:
+    args.accuracy_to_make_c_quizzes = 0.0
+    nb_new_c_quizzes_for_train = 100
+    nb_new_c_quizzes_for_test = 10
+
+######################################################################
+
+default_args = {
+    "model": "37M",
+    "batch_size": 100,
+    "nb_train_samples": 100000,
+    "nb_test_samples": 10000,
+}
+
+for k, v in default_args.items():
+    if getattr(args, k) is None:
+        setattr(args, k, v)
+
+######################################################################
+
+default_model_args = {
+    "17K": {
+        "dim_model": 32,
+        "dim_keys": 32,
+        "dim_hidden": 32,
+        "nb_heads": 2,
+        "nb_blocks": 2,
+    },
+    "4M": {
+        "dim_model": 256,
+        "dim_keys": 32,
+        "dim_hidden": 1024,
+        "nb_heads": 4,
+        "nb_blocks": 6,
+    },
+    "37M": {
+        "dim_model": 512,
+        "dim_keys": 64,
+        "dim_hidden": 2048,
+        "nb_heads": 8,
+        "nb_blocks": 12,
+    },
+    "122M": {
+        "dim_model": 768,
+        "dim_keys": 64,
+        "dim_hidden": 2048,
+        "nb_heads": 8,
+        "nb_blocks": 24,
+    },
+    "352M": {
+        "dim_model": 1024,
+        "dim_keys": 64,
+        "dim_hidden": 2048,
+        "nb_heads": 8,
+        "nb_blocks": 48,
+    },
+}
+
+if args.model in default_model_args:
+    for k, v in default_model_args[args.model].items():
+        if getattr(args, k) is None:
+            setattr(args, k, v)
+else:
+    raise ValueError(f"Unknown model {args.model}")
+
+######################################################################
 
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
 
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
-    if not args.overwrite_results:
-        print(f"result directory {args.result_dir} already exists")
-        exit(1)
+    print(f"result directory {args.result_dir} already exists")
+    exit(1)
 
 
-log_file = open(os.path.join(args.result_dir, args.log_filename), "w")
+log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 
 if args.seed >= 0:
     # torch.backends.cudnn.deterministic = True
 
 if args.seed >= 0:
     # torch.backends.cudnn.deterministic = True
@@ -113,516 +201,346 @@ def log_string(s):
     sys.stdout.flush()
 
 
     sys.stdout.flush()
 
 
+log_string(f"argv {' '.join(sys.argv)}")
+
 for n in vars(args):
     log_string(f"args.{n} {getattr(args, n)}")
 
 for n in vars(args):
     log_string(f"args.{n} {getattr(args, n)}")
 
+
 ######################################################################
 
 ######################################################################
 
+if args.dirty_debug:
+    args.nb_train_samples = 2500
+    args.nb_test_samples = 100
 
 
-def masked_inplace_autoregression(
-    model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu")
-):
+if args.physical_batch_size is None:
+    args.physical_batch_size = args.batch_size
+else:
+    assert args.batch_size % args.physical_batch_size == 0
 
 
-    for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
-        i = (ar_mask.sum(0) > 0).nonzero()
-        if i.min() > 0:
-            model(
-                mygpt.BracketedSequence(input, 0, i.min())
-            )  # Needed to initialize the model's cache
-        for s in range(i.min(), i.max() + 1):
-            output = model(mygpt.BracketedSequence(input, s, 1)).x
-            logits = output[:, s]
-            if forbidden_tokens is not None:
-                logits = logits.masked_fill(forbidden_tokens, float("-inf"))
-            if args.deterministic_synthesis:
-                t_next = logits.argmax(1)
-            else:
-                dist = torch.distributions.categorical.Categorical(logits=logits)
-                t_next = dist.sample()
-            input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s]
+assert args.nb_train_samples % args.batch_size == 0
+assert args.nb_test_samples % args.batch_size == 0
 
 
+if args.problem=="sky":
+    problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2, speed=2),
+elif args.problem="wireworld":
+    problem=wireworld.Wireworld(height=10, width=15, nb_iterations=4)
+else:
+    raise ValueError
+
+quizz_machine = quizz_machine.QuizzMachine(
+    problem=problem,
+    nb_train_samples=args.nb_train_samples,
+    nb_test_samples=args.nb_test_samples,
+    batch_size=args.physical_batch_size,
+    result_dir=args.result_dir,
+    logger=log_string,
+    device=device,
+)
 
 ######################################################################
 
 
 ######################################################################
 
+log_string(f"device {device}")
 
 
-class Task:
-    def batches(self, split="train"):
-        pass
-
-    def vocabulary_size(self):
-        pass
-
-    def produce_results(self, n_epoch, model):
-        pass
+vocabulary_size = quizz_machine.vocabulary_size()
 
 
+log_string(f"vocabulary_size {vocabulary_size}")
 
 ######################################################################
 
 
 ######################################################################
 
-import picoclvr
-
-
-class TaskPicoCLVR(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]
-
-    ######################
-    # Not the cleanest part of the code
-
-    # Extract the last image of each sequence, from the last <img>
-    # included, and set to <nul> all the tokens from the beginning of
-    # that image to the end
-    def excise_last_image(self, input):
-        t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
-        nb_img_tokens = self.height * self.width + 1
-
-        input = input.clone()
-        t = (input == t_img).long()
-        tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long()
-        i = (t * tail_masks).nonzero(as_tuple=True)
-        j = (
-            i[0][:, None],
-            i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
-        )
-        images = self.trim(input[j])
-        input[j] = t_nul
-        loss_masks = 1 - tail_masks
-        input, loss_masks = self.trim((input, loss_masks))
-        return input, loss_masks, images
-
-    def add_true_image(self, input, images, loss_masks):
-        t_nul = self.token2id["<nul>"]
-        nb_img_tokens = self.height * self.width + 1
-        input = F.pad(input, (0, nb_img_tokens), value=t_nul)
-        loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
-        t = (input == t_nul).long()
-        i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
-        j = (
-            i[0][:, None],
-            i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :],
-        )
-        input[j] = images
-        loss_masks[j] = 1
-        input, loss_masks = self.trim((input, loss_masks))
-        return input, loss_masks
-
-    def add_generated_image(self, input, loss_masks, model):
-        t_img, t_nul = self.token2id["<img>"], self.token2id["<nul>"]
-        nb_img_tokens = self.height * self.width + 1
-
-        input = F.pad(input, (0, nb_img_tokens), value=t_nul)
-        loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0)
-        t = (input == t_nul).long()
-        i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True)
-        input[i] = t_img
-
-        j = (
-            i[0][:, None],
-            i[1][:, None]
-            + 1
-            + torch.arange(nb_img_tokens - 1, device=input.device)[None, :],
-        )
-        ar_masks = input.new_zeros(input.size(), dtype=torch.int64)
-        ar_masks[j] = 1
-        forbidden_tokens = (
-            torch.arange(self.vocabulary_size(), device=input.device) == t_nul
-        )
-        with torch.autograd.no_grad():
-            t = model.training
-            model.eval()
-            masked_inplace_autoregression(
-                model,
-                self.batch_size,
-                input,
-                ar_masks,
-                forbidden_tokens,
-                device=self.device,
-            )
-            model.train(t)
-
-        input, loss_masks = self.trim((input, loss_masks))
-
-        return input, loss_masks
+# Compute the entropy of the training tokens
 
 
-    ######################
+token_count = 0
+for input in quizz_machine.batches(split="train", desc="train-entropy"):
+    token_count += F.one_hot(input, num_classes=quizz_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)
 
 
-    def __init__(
-        self,
-        batch_size,
-        height,
-        width,
-        nb_colors=5,
-        device=torch.device("cpu"),
-        pruner_train=None,
-        pruner_eval=None,
+######################################################################
+# 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(
+        quizz_machine.batches(split="test", desc="test-check"), 25000
     ):
     ):
-        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
-        nb = args.data_size if args.data_size > 0 else 250000
-        self.pruner_train = pruner_train
-        self.pruner_eval = pruner_eval
-
-        param = {
-            "nb": nb,
-            "height": height,
-            "width": width,
-            "nb_colors": nb_colors,
-            "batch_size": batch_size,
-            "rng_state": list(torch.get_rng_state()),
-        }
-
-        log_string(f"generating {nb} samples (can take some time)")
-        self.train_descr = generate_descr(
-            (nb * 4) // 5, "train", pruner=self.pruner_train
-        )
-        self.test_descr = generate_descr((nb * 1) // 5, "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)])
-
-        # 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}"
+        in_train = set()
+        for train_subset in subsets_as_tuples(
+            quizz_machine.batches(split="train", desc="train-check"), 25000
         ):
         ):
-            yield self.trim(batch)
+            in_train.update(test_subset.intersection(train_subset))
+        nb_in_train += len(in_train)
+        nb_test += len(test_subset)
 
 
-    def vocabulary_size(self):
-        return len(self.token2id)
+    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"
+    )
 
 
-    def compute_missing_properties(self, n_epoch, model, pruner=None):
+    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"
 
 
-        acc_nb_requested_properties = []
-        acc_nb_missing_properties = []
-        acc_nb_results = 0
+##############################
 
 
-        for input in tqdm.tqdm(
-            self.test_input.split(self.batch_size),
-            dynamic_ncols=True,
-            desc=f"test-properties",
-        ):
-            tape, loss_masks, _ = self.excise_last_image(input)
-            tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
-            result_descr = self.detensorize(tape)
-            np = picoclvr.nb_properties(
-                result_descr,
-                height=self.height,
-                width=self.width,
-                pruner=pruner,
-            )
-            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)
+def one_epoch(model, quizz_machine):
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
 
-        prefix = "" if pruner is None else "pruned_"
-        log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
-        log_string(
-            f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
-        )
-        log_string(
-            f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%"
-        )
+    model.train()
 
 
-    ######################################################################
+    nb_train_samples, acc_train_loss = 0, 0.0
 
 
-    def produce_results(self, n_epoch, model):
+    for input in quizz_machine.batches(split="train"):
+        input = input.to(device)
 
 
-        self.compute_missing_properties(n_epoch, model)
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.zero_grad()
 
 
-        if self.pruner_eval is not None:
-            self.compute_missing_properties(n_epoch, model, self.pruner_eval)
+        output = model(mygpt.BracketedSequence(input)).x
+        loss = F.cross_entropy(output.transpose(1, 2), input)
+        acc_train_loss += loss.item() * input.size(0)
 
 
-        nb_tokens_to_generate = self.height * self.width + 3
-        result_descr = []
-        nb_per_primer = 8
-        primer = []
+        nb_train_samples += input.size(0)
 
 
-        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] * nb_per_primer
+        loss.backward()
 
 
-        tape = self.tensorize(primer)
-        loss_masks = 1 - (tape == self.token2id["<nul>"]).long()
-        tape, loss_masks = self.add_generated_image(tape, loss_masks, model)
-        result_descr = self.detensorize(tape)
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.step()
 
 
-        np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width)
+    train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
 
 
-        acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np)
-        acc_nb_results = len(result_descr)
+    log_string(f"train_perplexity {n_epoch} {train_perplexity}")
 
 
-        nb_requested_properties = sum(acc_nb_requested_properties)
-        nb_missing_properties = sum(acc_nb_missing_properties)
 
 
-        prefix = "demo_"
-        log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}")
-        log_string(
-            f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}"
-        )
-        log_string(
-            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(args.result_dir, f"result_{n_epoch:04d}.png")
-        torchvision.utils.save_image(
-            img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0
-        )
-        log_string(f"wrote {image_name}")
+######################################################################
 
 
 
 
-######################################################################
+def run_tests(model, quizz_machine, deterministic_synthesis):
+    with torch.autograd.no_grad():
+        model.eval()
 
 
-log_string(f"device {device}")
+        nb_test_samples, acc_test_loss = 0, 0.0
+        nb_samples_accumulated = 0
 
 
+        for input in quizz_machine.batches(split="test"):
+            input = input.to(device)
 
 
-def pruner_horizontal_green(p):
-    return not ("green" in p and ("left" in p or "right" in p))
+            bs = model(mygpt.BracketedSequence(input))
+            output = bs.x
 
 
+            loss = F.cross_entropy(output.transpose(1, 2), input)
 
 
-task = TaskPicoCLVR(
-    batch_size=args.batch_size,
-    height=args.height,
-    width=args.width,
-    nb_colors=args.nb_colors,
-    device=device,
-    pruner_train=pruner_horizontal_green
-    if args.prune_properties in {"train+eval"}
-    else None,
-    pruner_eval=(lambda p: not pruner_horizontal_green(p))
-    if args.prune_properties in {"train+eval", "eval"}
-    else None,
-)
+            acc_test_loss += loss.item() * input.size(0)
 
 
-vocabulary_size = task.vocabulary_size()
+            nb_test_samples += input.size(0)
 
 
-log_string(f"vocabulary_size {vocabulary_size}")
+        main_test_accuracy = quizz_machine.produce_results(
+            n_epoch=n_epoch,
+            model=model,
+            result_dir=args.result_dir,
+            deterministic_synthesis=deterministic_synthesis,
+        )
 
 
-##############################
+        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
 
 
-model = mygpt.MyGPT(
-    vocabulary_size=vocabulary_size,
-    dim_model=args.dim_model,
-    dim_keys=args.dim_keys,
-    dim_hidden=args.dim_hidden,
-    nb_heads=args.nb_heads,
-    nb_blocks=args.nb_blocks,
-    causal=True,
-    dropout=args.dropout,
-)
+        log_string(f"test_perplexity {n_epoch} {test_perplexity}")
 
 
-model.to(device)
+    model.main_test_accuracy = main_test_accuracy
 
 
-nb_parameters = sum(p.numel() for p in model.parameters())
-log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 ######################################################################
 
 
 ######################################################################
 
-nb_epochs_finished = 0
 
 
-if args.no_checkpoint:
-    log_string(f"not trying to load checkpoint.")
-
-else:
-    try:
-        checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
-        checkpoint = torch.load(checkpoint_name)
-        nb_epochs_finished = checkpoint["nb_epochs_finished"]
-        model.load_state_dict(checkpoint["model_state"])
-        torch.set_rng_state(checkpoint["rng_state"])
-        if torch.cuda.is_available():
-            torch.cuda.set_rng_state(checkpoint["cuda_rng_state"])
+def create_c_quizzes(
+    models,
+    quizz_machine,
+    nb_for_train=1000,
+    nb_for_test=100,
+    min_ave_seq_logproba=None,
+):
+    # We will store the generated quizzes for each number of
+    # correct prediction
+    recorded = dict([(n, []) for n in range(len(models) + 1)])
+
+    model_indexes = []
+    sum_logits, sum_nb_c_quizzes = 0, 0
+
+    def nb_generated():
+        return sum([sum([x.size(0) for x in recorded[n]]) for n in recorded.keys()])
+
+    def nb_validated():
+        return sum(
+            [
+                sum([x.size(0) for x in recorded[n]])
+                for n in range(args.min_to_validate, args.max_to_validate + 1)
+            ]
+        )
 
 
-        log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.")
+    nb_to_create = nb_for_train + nb_for_test
+
+    while nb_validated() < nb_to_create:
+        (
+            new_c_quizzes,
+            nb_correct,
+            ave_seq_logproba,
+        ) = quizz_machine.gang_create_c_quizzes(
+            nb=nb_to_create,
+            nb_models_for_generation=args.nb_models_for_generation,
+            models=models,
+            mode=args.generation_mode,
+            min_ave_seq_logproba=min_ave_seq_logproba,
+            n_epoch=n_epoch,
+            result_dir=args.result_dir,
+        )
 
 
-    except FileNotFoundError:
-        log_string("starting from scratch.")
+        sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
+        sum_nb_c_quizzes += new_c_quizzes.size(0)
 
 
-    except:
-        log_string("error when loading the checkpoint.")
-        exit(1)
+        if args.dirty_debug:
+            nb_correct = torch.randint(
+                len(models) + 1, nb_correct.size(), device=new_c_quizzes.device
+            )
 
 
-######################################################################
+        for n in range(nb_correct.max() + 1):
+            recorded[n].append(new_c_quizzes[nb_correct == n].clone())
 
 
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
+        log_string(
+            f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}"
+        )
 
 
-token_count = 0
-for input in task.batches(split="train"):
-    token_count += F.one_hot(input, num_classes=task.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)
+    # concatenate and shuffle
+    for n in recorded.keys():
+        if len(recorded[n]) > 0:
+            q = torch.cat(recorded[n], dim=0)
+            q = q[torch.randperm(q.size(0), device=q.device)]
+            recorded[n] = q
+        else:
+            del recorded[n]
 
 
-##############################
+    new_c_quizzes = torch.cat(
+        [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)],
+        dim=0,
+    )
 
 
-if args.learning_rate_schedule == "cos":
-    learning_rate_schedule = {}
-    for n_epoch in range(args.nb_epochs):
-        u = n_epoch / args.nb_epochs * math.pi
-        learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
-else:
-    u = {
-        int(k): float(v)
-        for k, v in [
-            tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
+    new_c_quizzes = new_c_quizzes[
+        torch.randperm(new_c_quizzes.size(0), device=new_c_quizzes.device)[
+            : nb_for_train + nb_for_test
         ]
         ]
-    }
+    ]
 
 
-    learning_rate_schedule = {}
-    learning_rate = args.learning_rate
-    for n_epoch in range(args.nb_epochs):
-        if n_epoch in u:
-            learning_rate = u[n_epoch]
-        learning_rate_schedule[n_epoch] = learning_rate
+    quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
+    quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
 
 
-log_string(f"learning_rate_schedule {learning_rate_schedule}")
+    for n in recorded.keys():
+        s = (
+            "_validated"
+            if n >= args.min_to_validate and n <= args.max_to_validate
+            else ""
+        )
+        quizz_machine.problem.save_quizzes(
+            recorded[n][:72],
+            args.result_dir,
+            f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
+        )
 
 
-##############################
+    return sum_logits / sum_nb_c_quizzes
 
 
-nb_samples_seen = 0
 
 
-if nb_epochs_finished >= nb_epochs:
-    task.produce_results(nb_epochs_finished, model)
+######################################################################
 
 
-for n_epoch in range(nb_epochs_finished, nb_epochs):
+models = []
 
 
-    learning_rate = learning_rate_schedule[n_epoch]
+for k in range(args.nb_gpts):
+    model = mygpt.MyGPT(
+        vocabulary_size=vocabulary_size,
+        dim_model=args.dim_model,
+        dim_keys=args.dim_keys,
+        dim_hidden=args.dim_hidden,
+        nb_heads=args.nb_heads,
+        nb_blocks=args.nb_blocks,
+        causal=True,
+        dropout=args.dropout,
+    ).to(device)
 
 
-    log_string(f"learning_rate {learning_rate}")
+    model.main_test_accuracy = 0.0
+    model.id = k
 
 
-    if args.optim == "sgd":
-        optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
-    elif args.optim == "adam":
-        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-    elif args.optim == "adamw":
-        optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
-    else:
-        raise ValueError(f"Unknown optimizer {args.optim}.")
+    models.append(model)
 
 
-    model.train()
 
 
-    nb_train_samples, acc_train_loss = 0, 0.0
+nb_parameters = sum(p.numel() for p in models[0].parameters())
+log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 
 
-    for input in task.batches(split="train"):
-        input = input.to(device)
-        output = model(mygpt.BracketedSequence(input)).x
-        loss = F.cross_entropy(output.transpose(1, 2), input)
-        acc_train_loss += loss.item() * input.size(0)
-        nb_train_samples += input.size(0)
-        nb_samples_seen += input.size(0)
+######################################################################
 
 
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
+min_ave_seq_logproba = None
 
 
-    with torch.autograd.no_grad():
+for n_epoch in range(args.nb_epochs):
+    log_string(f"--- epoch {n_epoch} ----------------------------------------")
 
 
-        model.eval()
+    a = [(model.id, float(model.main_test_accuracy)) for model in models]
+    a.sort(key=lambda p: p[0])
+    s = " ".join([f"{p[1]*100:.02f}%" for p in a])
+    log_string(f"current accuracies {s}")
 
 
-        nb_test_samples, acc_test_loss = 0, 0.0
+    # select the model with lowest accuracy
+    models.sort(key=lambda model: model.main_test_accuracy)
+    model = models[0]
 
 
-        for input in task.batches(split="test"):
-            input = input.to(device)
+    log_string(
+        f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
+    )
 
 
-            # input, loss_masks, true_images = task.excise_last_image(input)
-            # input, loss_masks = task.add_true_image(input, true_images, loss_masks)
+    # improve it
+    one_epoch(model, quizz_machine)
 
 
-            output = model(mygpt.BracketedSequence(input)).x
-            loss = F.cross_entropy(output.transpose(1, 2), input)
-            acc_test_loss += loss.item() * input.size(0)
-            nb_test_samples += input.size(0)
+    quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
 
 
-        train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
-        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+    log_string(
+        f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
+    )
 
 
-        log_string(
-            f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
-        )
+    # test it
+    run_tests(model, quizz_machine, deterministic_synthesis=False)
+
+    log_string(
+        f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
+    )
 
 
-        task.produce_results(n_epoch, model)
+    if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
+        ave_seq_logproba = create_c_quizzes(
+            models,
+            quizz_machine,
+            nb_for_train=nb_new_c_quizzes_for_train,
+            nb_for_test=nb_new_c_quizzes_for_test,
+            min_ave_seq_logproba=min_ave_seq_logproba,
+        )
 
 
-    checkpoint = {
-        "nb_epochs_finished": n_epoch + 1,
-        "model_state": model.state_dict(),
-        "rng_state": torch.get_rng_state(),
-    }
+        # We keep the first average logits as a reference
+        # if min_ave_seq_logproba is None:
+        # min_ave_seq_logproba = ave_seq_logproba
+        # else:
+        # log_string(
+        # f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"
+        # )
 
 
-    if torch.cuda.is_available():
-        checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state()
+        # We update everyone
+        for model in models:
+            run_tests(model, quizz_machine, deterministic_synthesis=False)
 
 
-    checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name)
-    torch.save(checkpoint, checkpoint_name)
-    log_string(f"saved checkpoint {checkpoint_name}")
 
 ######################################################################
 
 ######################################################################
An attempt at having GPTs general their own quizzes.