X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=9437136ce1a45b066d6884e205540083bfb4d2d6;hb=HEAD;hp=58e80462609bcda7cb8bc43f88d5dfec9022b8d4;hpb=8e23dd068df00df61c690ffa89ecc8cb9db4b32d;p=picoclvr.git

diff --git a/main.py b/main.py
index 58e8046..3ff64b7 100755
--- a/main.py
+++ b/main.py
@@ -5,16 +5,14 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
-# torch.backends.cuda.matmul.allow_tf23
-# torch.autocast(torch.bfloat16)
-
-import math, sys, argparse, time, tqdm, 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 mygpt, tasks
+import ffutils
+import mygpt, tasks, problems
 
 ######################################################################
 
@@ -34,8 +32,8 @@ parser = argparse.ArgumentParser(
 parser.add_argument(
     "--task",
     type=str,
-    default="picoclvr",
-    help="picoclvr, mnist, maze, snake, stack, expr, world",
+    default="twotargets",
+    help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, greed",
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
@@ -44,10 +42,16 @@ parser.add_argument("--result_dir", type=str, default=None)
 
 parser.add_argument("--seed", type=int, default=0)
 
-parser.add_argument("--nb_epochs", type=int, default=None)
+parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
+
+########################################
+
+parser.add_argument("--nb_epochs", type=int, default=50)
 
 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)
@@ -58,26 +62,59 @@ parser.add_argument("--learning_rate", type=float, default=1e-4)
 
 parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
 
-parser.add_argument("--dim_model", type=int, default=512)
+########################################
+
+parser.add_argument("--model", type=str, default=None)
+
+parser.add_argument("--dim_model", type=int, default=None)
 
-parser.add_argument("--dim_keys", type=int, default=64)
+parser.add_argument("--dim_keys", type=int, default=None)
 
-parser.add_argument("--dim_hidden", type=int, default=2048)
+parser.add_argument("--dim_hidden", type=int, default=None)
 
-parser.add_argument("--nb_heads", type=int, default=8)
+parser.add_argument("--nb_heads", type=int, default=None)
 
-parser.add_argument("--nb_blocks", type=int, default=12)
+parser.add_argument("--nb_blocks", type=int, default=None)
 
 parser.add_argument("--dropout", type=float, default=0.1)
 
+########################################
+
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
 parser.add_argument("--no_checkpoint", action="store_true", default=False)
 
-parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--resume", action="store_true", default=False)
 
 parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
 
+##############################
+# filetask
+
+parser.add_argument("--filetask_train_file", type=str, default=None)
+
+parser.add_argument("--filetask_test_file", type=str, default=None)
+
+##############################
+# rpl options
+
+parser.add_argument("--rpl_nb_starting_values", type=int, default=3)
+
+parser.add_argument("--rpl_max_input", type=int, default=9)
+
+parser.add_argument("--rpl_prog_len", type=int, default=8)
+
+parser.add_argument("--rpl_nb_runs", type=int, default=5)
+
+parser.add_argument("--rpl_no_prog", action="store_true", default=False)
+
+##############################
+# grid options
+
+parser.add_argument("--grid_size", type=int, default=6)
+
+parser.add_argument("--grid_fraction_play", type=float, default=0)
+
 ##############################
 # picoclvr options
 
@@ -92,23 +129,28 @@ parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
 ##############################
 # Maze options
 
-parser.add_argument("--maze_height", type=int, default=23)
+parser.add_argument("--maze_height", type=int, default=13)
 
-parser.add_argument("--maze_width", type=int, default=39)
+parser.add_argument("--maze_width", type=int, default=21)
 
-parser.add_argument("--maze_nb_walls", type=int, default=45)
+parser.add_argument("--maze_nb_walls", type=int, default=15)
 
 ##############################
 # Snake options
 
-parser.add_argument("--snake_height", type=int, default=6)
+parser.add_argument("--snake_height", type=int, default=9)
 
-parser.add_argument("--snake_width", type=int, default=8)
+parser.add_argument("--snake_width", type=int, default=12)
 
 parser.add_argument("--snake_nb_colors", type=int, default=5)
 
 parser.add_argument("--snake_length", type=int, default=200)
 
+##############################
+# ByHeart options
+
+parser.add_argument("--byheart_separation", type=int, default=1)
+
 ##############################
 # Stack options
 
@@ -118,7 +160,7 @@ parser.add_argument("--stack_nb_stacks", type=int, default=3)
 
 parser.add_argument("--stack_nb_digits", type=int, default=3)
 
-parser.add_argument("--stack_fraction_values_for_train", type=float, default=0.75)
+parser.add_argument("--stack_fraction_values_for_train", type=float, default=None)
 
 ##############################
 # Expr options
@@ -134,9 +176,24 @@ parser.add_argument("--expr_result_max", type=int, default=99)
 parser.add_argument("--expr_input_file", type=str, default=None)
 
 ##############################
-# World options
+# Mixing
+
+parser.add_argument("--mixing_hard", action="store_true", default=False)
+
+parser.add_argument("--mixing_deterministic_start", action="store_true", default=False)
+
+##############################
+# greed options
+
+parser.add_argument("--greed_height", type=int, default=5)
 
-parser.add_argument("--world_vqae_nb_epochs", type=int, default=10)
+parser.add_argument("--greed_width", type=int, default=7)
+
+parser.add_argument("--greed_T", type=int, default=25)
+
+parser.add_argument("--greed_nb_walls", type=int, default=5)
+
+parser.add_argument("--greed_nb_coins", type=int, default=2)
 
 ######################################################################
 
@@ -149,62 +206,175 @@ if args.result_dir is None:
 
 ######################################################################
 
-default_args = {
-    "picoclvr": {
-        "nb_epochs": 25,
+default_task_args = {
+    "file": {
+        "model": "37M",
         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
-    "mnist": {
-        "nb_epochs": 25,
-        "batch_size": 10,
+    "addition": {
+        "model": "352M",
+        "batch_size": 25,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
+    },
+    "byheart": {
+        "model": "37M",
+        "batch_size": 25,
+        "nb_train_samples": 50000,
+        "nb_test_samples": 10000,
+    },
+    "expr": {
+        "model": "352M",
+        "batch_size": 25,
+        "nb_train_samples": 2500000,
+        "nb_test_samples": 10000,
+    },
+    "grid": {
+        "model": "37M",
+        "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
+    "qmlp": {
+        "model": "37M",
+        "batch_size": 10,
+        "nb_train_samples": 100000,
+        "nb_test_samples": 1000,
+    },
+    "guessop": {
+        "model": "352M",
+        "batch_size": 25,
+        "nb_train_samples": 1000000,
+        "nb_test_samples": 10000,
+    },
+    "learnop": {
+        "model": "37M",
+        "batch_size": 25,
+        "nb_train_samples": 50000,
+        "nb_test_samples": 10000,
+    },
     "maze": {
-        "nb_epochs": 25,
+        "model": "37M",
         "batch_size": 5,
+        "nb_train_samples": 100000,
+        "nb_test_samples": 10000,
+    },
+    "picoclvr": {
+        "model": "37M",
+        "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
+    "rpl": {
+        "model": "352M",
+        "batch_size": 5,
+        "nb_train_samples": 2500000,
+        "nb_test_samples": 10000,
+    },
     "snake": {
-        "nb_epochs": 5,
+        "model": "37M",
         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
     "stack": {
-        "nb_epochs": 5,
+        "model": "37M",
         "batch_size": 25,
         "nb_train_samples": 100000,
         "nb_test_samples": 1000,
     },
-    "expr": {
-        "nb_epochs": 40,
+    "twotargets": {
+        "model": "37M",
         "batch_size": 25,
-        "nb_train_samples": 1000000,
+        "nb_train_samples": 50000,
         "nb_test_samples": 10000,
     },
-    "world": {
-        "nb_epochs": 5,
-        "batch_size": 25,
-        "nb_train_samples": 10000,
+    "memory": {
+        "model": "37M",
+        "batch_size": 100,
+        "nb_train_samples": 25000,
         "nb_test_samples": 1000,
     },
+    "mixing": {
+        "model": "37M",
+        "batch_size": 25,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
+    },
+    "mnist": {
+        "model": "37M",
+        "batch_size": 10,
+        "nb_train_samples": 60000,
+        "nb_test_samples": 10000,
+    },
+    "greed": {
+        "model": "37M",
+        "batch_size": 25,
+        "nb_train_samples": 25000,
+        "nb_test_samples": 10000,
+    },
 }
 
-if args.task in default_args:
-    for k, v in default_args[args.task].items():
+if args.task in default_task_args:
+    for k, v in default_task_args[args.task].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:
-    if not args.overwrite_results:
+    if not args.resume:
         print(f"result directory {args.result_dir} already exists")
         exit(1)
 
@@ -232,6 +402,8 @@ def log_string(s):
     sys.stdout.flush()
 
 
+log_string(f"argv {' '.join(sys.argv)}")
+
 for n in vars(args):
     log_string(f"args.{n} {getattr(args, n)}")
 
@@ -257,11 +429,109 @@ picoclvr_pruner_eval = (
 
 ######################################################################
 
-if args.task == "picoclvr":
+if args.physical_batch_size is None:
+    args.physical_batch_size = args.batch_size
+else:
+    assert args.batch_size % args.physical_batch_size == 0
+
+assert args.nb_train_samples % args.batch_size == 0
+assert args.nb_test_samples % args.batch_size == 0
+
+if args.task == "file":
+    assert (
+        args.filetask_train_file is not None and args.filetask_test_file is not None
+    ), "You have to specify the task train and test files"
+    task = tasks.TaskFromFile(
+        args.filetask_train_file,
+        args.filetask_test_file,
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        shuffle=True,
+        device=device,
+    )
+    args.max_percents_of_test_in_train = 0
+
+elif args.task == "byheart":
+    task = tasks.SandBox(
+        problem=problems.ProblemByHeart(separation=args.byheart_separation),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+    args.max_percents_of_test_in_train = -1
+
+elif args.task == "learnop":
+    task = tasks.SandBox(
+        problem=problems.ProblemLearnOperator(),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+
+elif args.task == "guessop":
+    task = tasks.SandBox(
+        problem=problems.ProblemGuessOperator(),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+
+elif args.task == "twotargets":
+    task = tasks.SandBox(
+        problem=problems.ProblemTwoTargets(),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "memory":
+    task = tasks.SandBox(
+        problem=problems.ProblemMemory(),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "mixing":
+    task = tasks.SandBox(
+        problem=problems.ProblemMixing(
+            hard=args.mixing_hard, random_start=not args.mixing_deterministic_start
+        ),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "addition":
+    task = tasks.SandBox(
+        problem=problems.ProblemAddition(),
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "picoclvr":
     task = tasks.PicoCLVR(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.picoclvr_height,
         width=args.picoclvr_width,
         nb_colors=args.picoclvr_nb_colors,
@@ -275,7 +545,7 @@ elif args.task == "mnist":
     task = tasks.MNIST(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         device=device,
     )
 
@@ -283,18 +553,18 @@ elif args.task == "maze":
     task = tasks.Maze(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.maze_height,
         width=args.maze_width,
         nb_walls=args.maze_nb_walls,
-        device=device,
+        device="cpu",
     )
 
 elif args.task == "snake":
     task = tasks.Snake(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         height=args.snake_height,
         width=args.snake_width,
         nb_colors=args.snake_nb_colors,
@@ -307,7 +577,7 @@ elif args.task == "stack":
     task = tasks.Stack(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
         logger=log_string,
         nb_steps=args.stack_nb_steps,
         nb_stacks=args.stack_nb_stacks,
@@ -324,16 +594,56 @@ elif args.task == "expr":
         sequence_length=args.expr_sequence_length,
         operand_max=args.expr_operand_max,
         result_max=args.expr_result_max,
-        batch_size=args.batch_size,
+        batch_size=args.physical_batch_size,
+        device=device,
+    )
+
+elif args.task == "rpl":
+    task = tasks.RPL(
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        nb_starting_values=args.rpl_nb_starting_values,
+        max_input=args.rpl_max_input,
+        prog_len=args.rpl_prog_len,
+        nb_runs=args.rpl_nb_runs,
+        no_prog=args.rpl_no_prog,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "grid":
+    task = tasks.Grid(
+        nb_train_samples=args.nb_train_samples,
+        nb_test_samples=args.nb_test_samples,
+        batch_size=args.physical_batch_size,
+        size=args.grid_size,
+        fraction_play=args.grid_fraction_play,
+        logger=log_string,
+        device=device,
+    )
+
+elif args.task == "qmlp":
+    task = tasks.QMLP(
+        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,
     )
 
-elif args.task == "world":
-    task = tasks.World(
+elif args.task == "greed":
+    task = tasks.Greed(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
-        batch_size=args.batch_size,
-        vqae_nb_epochs=args.world_vqae_nb_epochs,
+        batch_size=args.physical_batch_size,
+        height=args.greed_height,
+        width=args.greed_width,
+        T=args.greed_T,
+        nb_walls=args.greed_nb_walls,
+        nb_coins=args.greed_nb_coins,
+        logger=log_string,
         device=device,
     )
 
@@ -396,56 +706,61 @@ else:
 
 if args.task == "expr" and args.expr_input_file is not None:
     task.produce_results(
-        nb_epochs_finished,
-        model,
-        args.result_dir,
-        log_string,
-        args.deterministic_synthesis,
-        args.expr_input_file,
+        n_epoch=nb_epochs_finished,
+        model=model,
+        result_dir=args.result_dir,
+        logger=log_string,
+        deterministic_synthesis=args.deterministic_synthesis,
+        input_file=args.expr_input_file,
     )
 
     exit(0)
 
 ######################################################################
 
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-
 # Compute the entropy of the training tokens
 
 token_count = 0
-for input in task.batches(split="train"):
+for input in task.batches(split="train", desc="train-entropy"):
     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)
 
-##############################
-
+######################################################################
 # A bit of paranoia never hurts
 
-train_examples = {}
-
-
-for input in task.batches(split="train"):
-    assert input.dim() == 2 and input.dtype == torch.int64
-    for x in input:
-        train_examples[x.sum().item()] = x
-
-nb_total, nb_collisions = 0, 0
-for input in task.batches(split="test"):
-    assert input.dim() == 2 and input.dtype == torch.int64
-    for x in input:
-        nb_total += 1
-        y = train_examples.get(x.sum().item())
-        if y is not None:
-            if x.size() == y.size() and (x - y).abs().sum() == 0:
-                nb_collisions += 1
-
-del train_examples
+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(
+        task.batches(split="test", desc="test-check"), 25000
+    ):
+        in_train = set()
+        for train_subset in subsets_as_tuples(
+            task.batches(split="train", desc="train-check"), 25000
+        ):
+            in_train.update(test_subset.intersection(train_subset))
+        nb_in_train += len(in_train)
+        nb_test += len(test_subset)
+
+    log_string(
+        f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
+    )
 
-log_string(
-    f"data_check {nb_collisions*100/nb_total:.02f}% ({nb_collisions}/{nb_total}) of test samples are in the train set"
-)
+    assert (
+        nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
+    ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
 
 ##############################
 
@@ -473,18 +788,18 @@ log_string(f"learning_rate_schedule {learning_rate_schedule}")
 
 ##############################
 
-nb_samples_seen = 0
-
-if nb_epochs_finished >= nb_epochs:
+if nb_epochs_finished >= args.nb_epochs:
     task.produce_results(
-        nb_epochs_finished,
-        model,
-        args.result_dir,
-        log_string,
-        args.deterministic_synthesis,
+        n_epoch=nb_epochs_finished,
+        model=model,
+        result_dir=args.result_dir,
+        logger=log_string,
+        deterministic_synthesis=args.deterministic_synthesis,
     )
 
-for n_epoch in range(nb_epochs_finished, nb_epochs):
+time_pred_result = None
+
+for n_epoch in range(nb_epochs_finished, args.nb_epochs):
     learning_rate = learning_rate_schedule[n_epoch]
 
     log_string(f"learning_rate {learning_rate}")
@@ -504,27 +819,37 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
 
     for input in task.batches(split="train"):
         input = input.to(device)
+
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.zero_grad()
+
         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()
+
+        if nb_train_samples % args.batch_size == 0:
+            optimizer.step()
 
     with torch.autograd.no_grad():
         model.eval()
 
         nb_test_samples, acc_test_loss = 0, 0.0
+        nb_samples_accumulated = 0
 
         for input in task.batches(split="test"):
             input = input.to(device)
 
-            output = model(mygpt.BracketedSequence(input)).x
+            bs = model(mygpt.BracketedSequence(input))
+            output = bs.x
+
             loss = F.cross_entropy(output.transpose(1, 2), input)
+
             acc_test_loss += loss.item() * input.size(0)
+
             nb_test_samples += input.size(0)
 
         train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
@@ -535,9 +860,20 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
         )
 
         task.produce_results(
-            n_epoch, model, args.result_dir, log_string, args.deterministic_synthesis
+            n_epoch=n_epoch,
+            model=model,
+            result_dir=args.result_dir,
+            logger=log_string,
+            deterministic_synthesis=args.deterministic_synthesis,
         )
 
+        time_current_result = datetime.datetime.now()
+        if time_pred_result is not None:
+            log_string(
+                f"next_result {time_current_result + (time_current_result - time_pred_result)}"
+            )
+        time_pred_result = time_current_result
+
     checkpoint = {
         "nb_epochs_finished": n_epoch + 1,
         "model_state": model.state_dict(),