X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=tasks.py;h=c0ad5ffb08683de9027cc71e8335636f7096af64;hb=HEAD;hp=44599f7db31bd7abe2b971afeb6ff5b795875682;hpb=4aa7e109b4c712643cdddc2480b66d8799f71d3f;p=picoclvr.git

diff --git a/tasks.py b/tasks.py
index 44599f7..443419e 100755
--- a/tasks.py
+++ b/tasks.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-import math, os, tqdm
+import math, os, tqdm, warnings
 
 import torch, torchvision
 
@@ -14,10 +14,8 @@ from torch.nn import functional as F
 
 from mygpt import BracketedSequence
 
-try:
-    from graph import save_attention_image
-except ImportError:
-    save_attention_image = None
+# from graph import save_attention_image
+save_attention_image = None
 
 ######################################################################
 
@@ -29,6 +27,7 @@ def masked_inplace_autoregression(
     ar_mask,
     deterministic_synthesis,
     forbidden_tokens=None,
+    logit_biases=None,
     progress_bar_desc="autoregression",
     device=torch.device("cpu"),
 ):
@@ -50,7 +49,11 @@ def 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)
@@ -60,7 +63,7 @@ def masked_inplace_autoregression(
 
 
 class Task:
-    def batches(self, split="train"):
+    def batches(self, split="train", nb_to_use=-1, desc=None):
         pass
 
     def vocabulary_size(self):
@@ -72,6 +75,162 @@ class Task:
         pass
 
 
+class TaskFromFile(Task):
+    def tensorize(self, pairs, shuffle):
+        len_max = max([len(x[0]) for x in pairs])
+
+        input = torch.cat(
+            [
+                torch.tensor(
+                    [
+                        [self.char2id[c] for c in s[0] + "#" * (len_max - len(s[0]))]
+                        for s in pairs
+                    ]
+                )
+            ],
+            0,
+        ).to("cpu")
+
+        pred_mask = torch.cat(
+            [
+                torch.tensor(
+                    [
+                        [int(c) for c in s[1] + "0" * (len_max - len(s[1]))]
+                        for s in pairs
+                    ]
+                )
+            ],
+            0,
+        ).to("cpu")
+
+        if shuffle:
+            i = torch.randperm(input.size(0))
+            input = input[i].contiguous()
+            pred_mask = pred_mask[i].contiguous()
+
+        return input, pred_mask
+
+    # 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="#"):
+        n = self.char2id[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]
+
+    def __init__(
+        self,
+        train_filename,
+        test_filename,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        shuffle=False,
+        device=torch.device("cpu"),
+    ):
+        self.batch_size = batch_size
+        self.device = device
+
+        def read_file(filename, nb=-1):
+            pairs = []
+            with open(filename, "r") as f:
+                while True:
+                    sequence = f.readline().strip()
+                    if not sequence:
+                        break
+                    pred_mask = f.readline().strip()
+                    assert len(sequence) == len(pred_mask)
+                    assert set(pred_mask).issubset({"0", "1", "2"}), f"{set(pred_mask)}"
+                    pairs.append((sequence, pred_mask))
+                    if len(pairs) == nb:
+                        break
+
+            if nb > 0:
+                pairs = pairs[:nb]
+                assert len(pairs) == nb
+
+            return pairs
+
+        train_pairs = read_file(train_filename, nb_train_samples)
+        test_pairs = read_file(test_filename, nb_test_samples)
+
+        symbols = ["#"] + list(
+            set("".join([x[0] for x in train_pairs + test_pairs])) - set(["#"])
+        )
+        self.char2id = dict([(c, n) for n, c in enumerate(symbols)])
+        self.id2char = dict([(n, c) for c, n in self.char2id.items()])
+
+        self.train_input, self.train_pred_masks = self.tensorize(
+            train_pairs, shuffle=shuffle
+        )
+        self.test_input, self.test_pred_masks = self.tensorize(
+            test_pairs, shuffle=shuffle
+        )
+
+    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 self.trim(batch).to(self.device)
+
+    def vocabulary_size(self):
+        return len(self.char2id)
+
+    def tensor2str(self, t):
+        return ["".join([self.id2char[x.item()] for x in s]) for s in t]
+
+    def produce_results(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis
+    ):
+        correct = self.trim(self.test_input[:1000]).to(self.device)
+        result = correct.clone()
+        pred_mask = self.test_pred_masks[:1000, : result.size(1)].to(self.device)
+        ar_mask = (pred_mask > 0).long()
+        result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
+
+        logger(f"----------------------------------------------------------")
+
+        for e in self.tensor2str(result[:50]):
+            logger(f"test_before {e}")
+
+        masked_inplace_autoregression(
+            model,
+            self.batch_size,
+            result,
+            ar_mask,
+            deterministic_synthesis,
+            device=self.device,
+        )
+
+        logger(f"----------------------------------------------------------")
+
+        for e, c in zip(self.tensor2str(result[:50]), self.tensor2str(correct[:50])):
+            logger(f"test_after  {e}")
+            logger(f"correct     {c}")
+
+        logger(f"----------------------------------------------------------")
+
+        err_mask = (pred_mask == 2).long()
+        nb_total = err_mask.sum().item()
+        nb_correct = ((correct == result).long() * err_mask).sum().item()
+
+        logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
+        logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
+
+
 ####################
 
 import problems
@@ -111,13 +270,25 @@ class SandBox(Task):
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
         # A bit of paranoia never hurts
-        assert (
-            self.nb_codes <= max_nb_codes
-            and self.train_input.min() >= 0
-            and self.test_input.min() >= 0
-            and tuple(self.train_ar_mask.unique()) == (0, 1)
-            and tuple(self.test_ar_mask.unique()) == (0, 1)
-        )
+        assert self.nb_codes <= max_nb_codes
+        assert self.train_input.min() >= 0
+        assert self.test_input.min() >= 0
+        assert tuple(x.item() for x in self.train_ar_mask.unique()) in {
+            (0,),
+            (1,),
+            (0, 1),
+        }
+        assert tuple(x.item() for x in self.test_ar_mask.unique()) in {
+            (0,),
+            (1,),
+            (0, 1),
+        }
+
+        if logger is not None:
+            for s, a in zip(self.train_input[:100], self.train_ar_mask[:100]):
+                logger(f"train_sequences {self.problem.seq2str(s)}")
+                a = "".join(["01"[x.item()] for x in a])
+                logger(f"                {a}")
 
     def batches(self, split="train", nb_to_use=-1, desc=None):
         assert split in {"train", "test"}
@@ -151,17 +322,24 @@ class SandBox(Task):
                 device=self.device,
             )
 
+            log_ground_truth = ar_mask.min() == 0
+
             if logger is not None:
                 for sp, st in zip(result[:10], input[:10]):
                     logger(
                         f"test_sequences {n_epoch} prediction   {self.problem.seq2str(sp)}"
                     )
-                    logger(
-                        f"               {n_epoch} ground truth {self.problem.seq2str(st)}"
-                    )
+                    if log_ground_truth:
+                        logger(
+                            f"               {n_epoch} ground truth {self.problem.seq2str(st)}"
+                        )
+
+            nb_total, nb_correct = self.problem.compute_nb_correct(
+                input, ar_mask, result
+            )
 
-            nb_total = ar_mask.sum().item()
-            nb_correct = ((result == input).long() * ar_mask).sum().item()
+            # nb_total = ar_mask.sum().item()
+            # nb_correct = ((result == input).long() * ar_mask).sum().item()
 
             return nb_total, nb_correct
 
@@ -183,9 +361,7 @@ class SandBox(Task):
 
         logger(f"main_test_accuracy {n_epoch} {test_nb_correct/test_nb_total}")
 
-        if save_attention_image is None:
-            logger("no save_attention_image (is pycairo installed?)")
-        else:
+        if save_attention_image is not None:
             for k in range(10):
                 ns = torch.randint(self.test_input.size(0), (1,)).item()
                 input = self.test_input[ns : ns + 1].clone()
@@ -193,30 +369,30 @@ class SandBox(Task):
                 with torch.autograd.no_grad():
                     t = model.training
                     model.eval()
-                    model.record_attention(True)
+                    # model.record_attention(True)
                     model(BracketedSequence(input))
                     model.train(t)
-                    ram = model.retrieve_attention()
-                    model.record_attention(False)
-
-                tokens_output = [c for c in self.problem.seq2str(input[0])]
-                tokens_input = ["n/a"] + tokens_output[:-1]
-                for n_head in range(ram[0].size(1)):
-                    filename = os.path.join(
-                        result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
-                    )
-                    attention_matrices = [m[0, n_head] for m in ram]
-                    save_attention_image(
-                        filename,
-                        tokens_input,
-                        tokens_output,
-                        attention_matrices,
-                        k_top=10,
-                        # min_total_attention=0.9,
-                        token_gap=12,
-                        layer_gap=50,
-                    )
-                    logger(f"wrote {filename}")
+                    # ram = model.retrieve_attention()
+                    # model.record_attention(False)
+
+                # tokens_output = [c for c in self.problem.seq2str(input[0])]
+                # tokens_input = ["n/a"] + tokens_output[:-1]
+                # for n_head in range(ram[0].size(1)):
+                # filename = os.path.join(
+                # result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
+                # )
+                # attention_matrices = [m[0, n_head] for m in ram]
+                # save_attention_image(
+                # filename,
+                # tokens_input,
+                # tokens_output,
+                # attention_matrices,
+                # k_top=10,
+                ##min_total_attention=0.9,
+                # token_gap=12,
+                # layer_gap=50,
+                # )
+                # logger(f"wrote {filename}")
 
 
 ######################################################################
@@ -313,7 +489,7 @@ class PicoCLVR(Task):
         self.train_input = self.tensorize(self.train_descr)
         self.test_input = self.tensorize(self.test_descr)
 
-    def batches(self, split="train"):
+    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
         for batch in tqdm.tqdm(
@@ -578,15 +754,17 @@ class Maze(Task):
     def compute_error(
         self, model, split="train", nb_to_use=-1, deterministic_synthesis=False
     ):
+        model_device = next(model.parameters()).device
         nb_total, nb_correct = 0, 0
         count = torch.zeros(
             self.width * self.height,
             self.width * self.height,
-            device=self.device,
+            device=model_device,
             dtype=torch.int64,
         )
 
         for input in self.batches(split, nb_to_use):
+            input = input.to(model_device)
             result = input.clone()
             ar_mask = result.new_zeros(result.size())
             ar_mask[:, self.height * self.width :] = 1
@@ -660,7 +838,7 @@ class Maze(Task):
                         eol = " " if j < count.size(1) - 1 else "\n"
                         f.write(f"{count[i,j]}{eol}")
 
-        input = self.test_input[:48]
+        input = self.test_input[:48].to(next(model.parameters()).device)
         result = input.clone()
         ar_mask = result.new_zeros(result.size())
         ar_mask[:, self.height * self.width :] = 1
@@ -922,6 +1100,34 @@ class Stack(Task):
             device=self.device,
         )
 
+        #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+        for label, input in [
+            ("train", self.train_input[:32]),
+            ("test", self.test_input[:32]),
+        ]:
+            output = model(BracketedSequence(input)).x
+            output = output.log_softmax(dim=-1)
+            filename = os.path.join(
+                result_dir, f"stack_with_crossentropy_{n_epoch:04d}_{label}.txt"
+            )
+            with open(filename, "w") as f:
+                for n in range(input.size(0)):
+                    s = stack.seq_to_str(
+                        input[n], nb_stacks=self.nb_stacks, nb_digits=self.nb_digits
+                    )
+                    for t, k, w in zip(range(input[n].size(0)), input[n], s.split(" ")):
+                        u = (
+                            " " * (10 - len(w))
+                            + w
+                            + " "
+                            + str(output[n][t][k].exp().item())
+                            + "\n"
+                        )
+                        f.write(u)
+                    f.write("\n")
+            logger(f"wrote {filename}")
+        #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+
         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)}"
@@ -1460,6 +1666,7 @@ class Grid(Task):
         nb_test_samples,
         batch_size,
         size,
+        fraction_play=0.0,
         logger=None,
         device=torch.device("cpu"),
     ):
@@ -1468,6 +1675,7 @@ class Grid(Task):
         self.device = device
         self.batch_size = batch_size
         self.grid_factory = grid.GridFactory(size=size)
+        self.fraction_play = fraction_play
 
         if logger is not None:
             logger(
@@ -1475,15 +1683,25 @@ class Grid(Task):
             )
 
         self.train_descr = self.grid_factory.generate_samples(
-            nb_train_samples, lambda r: tqdm.tqdm(r)
+            nb=nb_train_samples,
+            fraction_play=fraction_play,
+            progress_bar=lambda r: tqdm.tqdm(r),
         )
+
         self.test_descr = self.grid_factory.generate_samples(
-            nb_test_samples, lambda r: tqdm.tqdm(r)
+            nb=nb_test_samples, fraction_play=0.0, progress_bar=lambda r: tqdm.tqdm(r)
         )
 
+        if fraction_play > 0:
+            self.play_descr = self.grid_factory.generate_samples(
+                nb=25, fraction_play=1.0, progress_bar=lambda r: tqdm.tqdm(r)
+            )
+        else:
+            self.play_descr = []
+
         # Build the tokenizer
         tokens = set()
-        for d in [self.train_descr, self.test_descr]:
+        for d in [self.train_descr, self.test_descr, self.play_descr]:
             for s in d:
                 for t in s.strip().split(" "):
                     tokens.add(t)
@@ -1497,12 +1715,16 @@ class Grid(Task):
         self.t_nul = self.token2id["#"]
         self.t_true = self.token2id["true"]
         self.t_false = self.token2id["false"]
+        # self.t_pipe = self.token2id["|"]
 
         # Tokenize the train and test sets
         self.train_input = self.str2tensor(self.train_descr)
         self.test_input = self.str2tensor(self.test_descr)
+        self.play_input = (
+            None if len(self.play_descr) == 0 else self.str2tensor(self.play_descr)
+        )
 
-    def batches(self, split="train"):
+    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
         for batch in tqdm.tqdm(
@@ -1548,6 +1770,32 @@ class Grid(Task):
         logger(f"test_performance {n_epoch} {nb_total=} {nb_correct=}")
         logger(f"main_test_accuracy {n_epoch} {nb_correct / nb_total}")
 
+        if self.play_input is not None:
+            result = self.play_input.clone()
+            ar_mask = (result == self.t_pipe).long().cumsum(dim=1).clamp(max=1)
+            result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
+
+            logger(f"----------------------------------------------------------")
+
+            for e in self.tensor2str(result[:10]):
+                logger(f"play_before {e}")
+
+            masked_inplace_autoregression(
+                model,
+                self.batch_size,
+                result,
+                ar_mask,
+                deterministic_synthesis,
+                device=self.device,
+            )
+
+            logger(f"----------------------------------------------------------")
+
+            for e in self.tensor2str(result[:10]):
+                logger(f"play_after  {e}")
+
+            logger(f"----------------------------------------------------------")
+
 
 ######################################################################
 
@@ -1588,18 +1836,24 @@ class QMLP(Task):
 
         self.train_input = seq[:nb_train_samples]
         self.train_q_test_set = q_test_set[:nb_train_samples]
+        self.train_ref_test_errors = test_error[:nb_train_samples]
         self.test_input = seq[nb_train_samples:]
         self.test_q_test_set = q_test_set[nb_train_samples:]
-        self.ref_test_errors = test_error
+        self.test_ref_test_errors = test_error[nb_train_samples:]
+
+        filename = os.path.join(result_dir, f"train_errors_ref.dat")
+        with open(filename, "w") as f:
+            for e in self.train_ref_test_errors:
+                f.write(f"{e}\n")
 
         filename = os.path.join(result_dir, f"test_errors_ref.dat")
         with open(filename, "w") as f:
-            for e in self.ref_test_errors:
+            for e in self.test_ref_test_errors:
                 f.write(f"{e}\n")
 
         self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
 
-    def batches(self, split="train"):
+    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
         for batch in tqdm.tqdm(
@@ -1642,3 +1896,200 @@ class QMLP(Task):
 
 
 ######################################################################
+
+import greed
+
+
+class Greed(Task):
+    def __init__(
+        self,
+        nb_train_samples,
+        nb_test_samples,
+        batch_size,
+        height,
+        width,
+        T,
+        nb_walls,
+        nb_coins,
+        logger=None,
+        device=torch.device("cpu"),
+    ):
+        super().__init__()
+
+        self.batch_size = batch_size
+        self.device = device
+
+        self.world = greed.GreedWorld(height, width, T, nb_walls, nb_coins)
+
+        states, actions, rewards = self.world.generate_episodes(
+            nb_train_samples + nb_test_samples
+        )
+        seq = self.world.episodes2seq(states, actions, rewards)
+        self.train_input = seq[:nb_train_samples].to(self.device)
+        self.test_input = seq[nb_train_samples:].to(self.device)
+
+    def wipe_lookahead_rewards(self, batch):
+        t = torch.arange(batch.size(1), device=batch.device)[None, :]
+        u = torch.randint(batch.size(1), (batch.size(0), 1), device=batch.device)
+        lr_mask = (t <= u).long() * (
+            t % self.world.it_len == self.world.index_lookahead_reward
+        ).long()
+
+        return (
+            lr_mask * self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+            + (1 - lr_mask) * batch
+        )
+
+    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 self.wipe_lookahead_rewards(batch)
+
+    def vocabulary_size(self):
+        return self.world.nb_codes
+
+    def thinking_autoregression(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+    ):
+        snapshots = []
+
+        def ar(result, ar_mask, logit_biases=None):
+            ar_mask = ar_mask.expand_as(result)
+            result *= 1 - ar_mask
+            masked_inplace_autoregression(
+                model,
+                self.batch_size,
+                result,
+                ar_mask,
+                deterministic_synthesis=deterministic_synthesis,
+                logit_biases=logit_biases,
+                device=self.device,
+                progress_bar_desc=None,
+            )
+            warnings.warn("keeping thinking snapshots", RuntimeWarning)
+            snapshots.append(result[:100].detach().clone())
+
+        # Generate iteration after iteration
+
+        result = self.test_input[:250].clone()
+        # Erase all the content but that of the first iteration
+        result[:, self.world.it_len :] = -1
+        # Set the lookahead_reward of the firs to UNKNOWN
+        result[:, self.world.index_lookahead_reward] = self.world.lookahead_reward2code(
+            greed.REWARD_UNKNOWN
+        )
+
+        t = torch.arange(result.size(1), device=result.device)[None, :]
+
+        for u in tqdm.tqdm(
+            range(0, result.size(1), self.world.it_len),
+            desc="thinking",
+        ):
+            # Generate the next state but keep the initial one, the
+            # lookahead_reward of previous iterations are set to
+            # UNKNOWN
+            if u > 0:
+                result[
+                    :, u + self.world.index_lookahead_reward
+                ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+                ar_mask = (t >= u + self.world.index_states).long() * (
+                    t < u + self.world.index_states + self.world.state_len
+                ).long()
+                ar(result, ar_mask)
+
+            # Generate the action and reward with lookahead_reward to +1
+            result[
+                :, u + self.world.index_lookahead_reward
+            ] = self.world.lookahead_reward2code(greed.REWARD_PLUS)
+            ar_mask = (t >= u + self.world.index_reward).long() * (
+                t <= u + self.world.index_action
+            ).long()
+            ar(result, ar_mask)
+
+            # Set the lookahead_reward to UNKNOWN for the next iterations
+            result[
+                :, u + self.world.index_lookahead_reward
+            ] = self.world.lookahead_reward2code(greed.REWARD_UNKNOWN)
+
+        filename = os.path.join(result_dir, f"test_thinking_compute_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            for n in range(snapshots[0].size(0)):
+                for s in snapshots:
+                    lr, s, a, r = self.world.seq2episodes(
+                        s[n : n + 1],
+                    )
+                    str = self.world.episodes2str(
+                        lr, s, a, r, unicode=True, ansi_colors=True
+                    )
+                    f.write(str)
+                f.write("\n\n")
+
+        # Saving the generated sequences
+
+        lr, s, a, r = self.world.seq2episodes(result)
+        str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+        filename = os.path.join(result_dir, f"test_thinking_seq_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            f.write(str)
+            logger(f"wrote {filename}")
+
+    def produce_results(
+        self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
+    ):
+        result = self.wipe_lookahead_rewards(self.test_input[:250].clone())
+
+        # Saving the ground truth
+
+        lr, s, a, r = self.world.seq2episodes(
+            result,
+        )
+        str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+        filename = os.path.join(result_dir, f"test_true_seq_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            f.write(str)
+            logger(f"wrote {filename}")
+
+        # Re-generating from the first frame
+
+        ar_mask = (
+            torch.arange(result.size(1), device=result.device) >= self.world.it_len
+        ).long()[None, :]
+        ar_mask = ar_mask.expand_as(result)
+        result *= 1 - ar_mask  # paraaaaanoiaaaaaaa
+
+        masked_inplace_autoregression(
+            model,
+            self.batch_size,
+            result,
+            ar_mask,
+            deterministic_synthesis,
+            device=self.device,
+        )
+
+        # Saving the generated sequences
+
+        lr, s, a, r = self.world.seq2episodes(
+            result,
+        )
+        str = self.world.episodes2str(lr, s, a, r, unicode=True, ansi_colors=True)
+
+        filename = os.path.join(result_dir, f"test_seq_{n_epoch:04d}.txt")
+        with open(filename, "w") as f:
+            f.write(str)
+            logger(f"wrote {filename}")
+
+        self.thinking_autoregression(
+            n_epoch, model, result_dir, logger, deterministic_synthesis, nmax
+        )
+
+
+######################################################################