Update.

[picoclvr.git] / main.py

diff --git a/main.py b/main.py
index 9c28e47..3ff64b7 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -5,7 +5,7 @@
 
 # Written by Francois Fleuret <francois@fleuret.org>
 
 
 # Written by Francois Fleuret <francois@fleuret.org>
 

-import math, sys, argparse, time, tqdm, os
+import math, sys, argparse, time, tqdm, os, datetime, warnings

 
 import torch, torchvision
 from torch import nn
 
 import torch, torchvision
 from torch import nn


@@ -32,8 +32,8 @@ parser = argparse.ArgumentParser(
 parser.add_argument(
     "--task",
     type=str,
 parser.add_argument(
     "--task",
     type=str,

-    default="sandbox",
-    help="sandbox, picoclvr, mnist, maze, snake, stack, expr, rpl, 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=" ")
 )
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")


@@ -46,10 +46,12 @@ parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
 
 ########################################
 
 
 ########################################
 

-parser.add_argument("--nb_epochs", type=int, default=None)
+parser.add_argument("--nb_epochs", type=int, default=50)

 
 parser.add_argument("--batch_size", type=int, default=None)
 
 
 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("--nb_train_samples", type=int, default=None)
 
 parser.add_argument("--nb_test_samples", type=int, default=None)


@@ -62,7 +64,7 @@ parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30:
 
 ########################################
 
 
 ########################################
 

-parser.add_argument("--model", type=str, default="37M")
+parser.add_argument("--model", type=str, default=None)

 
 parser.add_argument("--dim_model", type=int, default=None)
 
 
 parser.add_argument("--dim_model", type=int, default=None)
 


@@ -82,33 +84,36 @@ parser.add_argument("--deterministic_synthesis", action="store_true", default=Fa
 
 parser.add_argument("--no_checkpoint", 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")
 
 
 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
 
 ##############################
 # rpl options
 

-parser.add_argument("--rpl_nb_starting_values", type=int, default=5)
+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_max_input", type=int, default=9)
 

-parser.add_argument("--rpl_prog_len", type=int, default=10)
+parser.add_argument("--rpl_prog_len", type=int, default=8)

 
 

-parser.add_argument("--rpl_nb_runs", 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)
 
 ##############################
 
 parser.add_argument("--rpl_no_prog", action="store_true", default=False)
 
 ##############################

-# sandbox options
+# grid options

 
 

-parser.add_argument("--sandbox_level", type=int, default=0)
+parser.add_argument("--grid_size", type=int, default=6)

 
 

-parser.add_argument("--sandbox_levels_nb_items", type=int, default=25)
-
-parser.add_argument("--sandbox_levels_len_source", type=int, default=6)
-
-parser.add_argument("--sandbox_levels_len_result", type=int, default=8)
+parser.add_argument("--grid_fraction_play", type=float, default=0)

 
 ##############################
 # picoclvr options
 
 ##############################
 # picoclvr options


@@ -124,23 +129,28 @@ parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
 ##############################
 # Maze options
 
 ##############################
 # 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
 
 
 ##############################
 # 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)
 
 
 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
 
 ##############################
 # Stack options
 


@@ -150,7 +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_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
 
 ##############################
 # Expr options


@@ -166,9 +176,24 @@ parser.add_argument("--expr_result_max", type=int, default=99)
 parser.add_argument("--expr_input_file", type=str, default=None)
 
 ##############################
 parser.add_argument("--expr_input_file", type=str, default=None)
 
 ##############################

-# World options
+# Mixing

 
 

-parser.add_argument("--world_vqae_nb_epochs", type=int, default=25)
+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("--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)

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


@@ -182,59 +207,113 @@ if args.result_dir is None:
 ######################################################################
 
 default_task_args = {
 ######################################################################
 
 default_task_args = {

-    "sandbox": {
-        "nb_epochs": 50,
+    "file": {
+        "model": "37M",

         "batch_size": 25,
         "batch_size": 25,

-        "nb_train_samples": 100000,
+        "nb_train_samples": 250000,

         "nb_test_samples": 10000,
     },
         "nb_test_samples": 10000,
     },

-    "picoclvr": {
-        "nb_epochs": 25,
+    "addition": {
+        "model": "352M",

         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },

-    "mnist": {
-        "nb_epochs": 25,
-        "batch_size": 10,
+    "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,
     },
         "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": {
     "maze": {

-        "nb_epochs": 25,
+        "model": "37M",

         "batch_size": 5,
         "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,
     },
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },

+    "rpl": {
+        "model": "352M",
+        "batch_size": 5,
+        "nb_train_samples": 2500000,
+        "nb_test_samples": 10000,
+    },

     "snake": {
     "snake": {

-        "nb_epochs": 5,
+        "model": "37M",

         "batch_size": 25,
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
     "stack": {
         "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,
     },
         "batch_size": 25,
         "nb_train_samples": 100000,
         "nb_test_samples": 1000,
     },

-    "expr": {
-        "nb_epochs": 40,
+    "twotargets": {
+        "model": "37M",

         "batch_size": 25,
         "batch_size": 25,

-        "nb_train_samples": 1000000,
+        "nb_train_samples": 50000,

         "nb_test_samples": 10000,
     },
         "nb_test_samples": 10000,
     },

-    "rpl": {
-        "nb_epochs": 40,
+    "memory": {
+        "model": "37M",
+        "batch_size": 100,
+        "nb_train_samples": 25000,
+        "nb_test_samples": 1000,
+    },
+    "mixing": {
+        "model": "37M",

         "batch_size": 25,
         "batch_size": 25,

-        "nb_train_samples": 100000,
+        "nb_train_samples": 250000,
+        "nb_test_samples": 10000,
+    },
+    "mnist": {
+        "model": "37M",
+        "batch_size": 10,
+        "nb_train_samples": 60000,

         "nb_test_samples": 10000,
     },
         "nb_test_samples": 10000,
     },

-    "world": {
-        "nb_epochs": 10,
+    "greed": {
+        "model": "37M",

         "batch_size": 25,
         "nb_train_samples": 25000,
         "batch_size": 25,
         "nb_train_samples": 25000,

-        "nb_test_samples": 1000,
+        "nb_test_samples": 10000,

     },
 }
 
     },
 }
 


@@ -253,6 +332,13 @@ default_model_args = {
         "nb_heads": 2,
         "nb_blocks": 2,
     },
         "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,
     "37M": {
         "dim_model": 512,
         "dim_keys": 64,


@@ -288,7 +374,7 @@ else:
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
 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)
 
         print(f"result directory {args.result_dir} already exists")
         exit(1)
 


@@ -316,6 +402,8 @@ 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)}")
 


@@ -341,35 +429,100 @@ picoclvr_pruner_eval = (
 
 ######################################################################
 
 
 ######################################################################
 

-if args.task == "sandbox":
-    if args.sandbox_level == 0:
-        problem = problems.ProblemLevel0(
-            nb_sentences=args.sandbox_levels_nb_items,
-            len_prompt=args.sandbox_levels_len_source,
-            len_result=args.sandbox_levels_len_result,
-        )
-    elif args.sandbox_level == 1:
-        problem = problems.ProblemLevel1(
-            nb_operators=args.sandbox_levels_nb_items,
-            len_source=args.sandbox_levels_len_source,
-            len_result=args.sandbox_levels_len_result,
-        )
-    elif args.sandbox_level == 2:
-        problem = problems.ProblemLevel2(
-            len_source=args.sandbox_levels_len_source,
-            len_result=args.sandbox_levels_len_result,
-        )
-    else:
-        raise ValueError(f"Unknown sandbox level {args.sandbox_level}")
+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(
     task = tasks.SandBox(

-        # problem,
-        # problems.ProblemAddition(zero_padded=False, inverted_result=False),
-        # problems.ProblemLenId(len_max=args.sandbox_levels_len_source),
-        problems.ProblemTwoTargets(len_total=12, len_targets=4),
+        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,
         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,
+        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,
     )
         logger=log_string,
         device=device,
     )


@@ -378,7 +531,7 @@ elif args.task == "picoclvr":
     task = tasks.PicoCLVR(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
     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,
         height=args.picoclvr_height,
         width=args.picoclvr_width,
         nb_colors=args.picoclvr_nb_colors,


@@ -392,7 +545,7 @@ elif args.task == "mnist":
     task = tasks.MNIST(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
     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,
     )
 
         device=device,
     )
 


@@ -400,18 +553,18 @@ elif args.task == "maze":
     task = tasks.Maze(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
     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,
         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,
     )
 
 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,
         height=args.snake_height,
         width=args.snake_width,
         nb_colors=args.snake_nb_colors,


@@ -424,7 +577,7 @@ elif args.task == "stack":
     task = tasks.Stack(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
     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,
         logger=log_string,
         nb_steps=args.stack_nb_steps,
         nb_stacks=args.stack_nb_stacks,


@@ -441,7 +594,7 @@ elif args.task == "expr":
         sequence_length=args.expr_sequence_length,
         operand_max=args.expr_operand_max,
         result_max=args.expr_result_max,
         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,
     )
 
         device=device,
     )
 


@@ -449,7 +602,7 @@ elif args.task == "rpl":
     task = tasks.RPL(
         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
     task = tasks.RPL(
         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,

         nb_starting_values=args.rpl_nb_starting_values,
         max_input=args.rpl_max_input,
         prog_len=args.rpl_prog_len,
         nb_starting_values=args.rpl_nb_starting_values,
         max_input=args.rpl_max_input,
         prog_len=args.rpl_prog_len,


@@ -459,12 +612,37 @@ elif args.task == "rpl":
         device=device,
     )
 
         device=device,
     )
 

-elif args.task == "world":
-    task = tasks.World(
+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 == "greed":
+    task = tasks.Greed(

         nb_train_samples=args.nb_train_samples,
         nb_test_samples=args.nb_test_samples,
         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,
     )
         logger=log_string,
         device=device,
     )


@@ -540,12 +718,10 @@ if args.task == "expr" and args.expr_input_file is not None:
 
 ######################################################################
 
 
 ######################################################################
 

-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-

 # Compute the entropy of the training tokens
 
 token_count = 0
 # 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()
     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()


@@ -554,33 +730,37 @@ train_set_perplexity = math.exp(entropy)
 ######################################################################
 # A bit of paranoia never hurts
 
 ######################################################################
 # 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(
+        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"
+    )

 
 

-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"), 25000):
-    in_train = set()
-    for train_subset in subsets_as_tuples(task.batches(split="train"), 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"
-)
-
-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"
+    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"

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


@@ -608,9 +788,7 @@ 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(
         n_epoch=nb_epochs_finished,
         model=model,
     task.produce_results(
         n_epoch=nb_epochs_finished,
         model=model,


@@ -619,7 +797,9 @@ if nb_epochs_finished >= nb_epochs:
         deterministic_synthesis=args.deterministic_synthesis,
     )
 
         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}")
     learning_rate = learning_rate_schedule[n_epoch]
 
     log_string(f"learning_rate {learning_rate}")


@@ -639,27 +819,37 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
 
     for input in task.batches(split="train"):
         input = input.to(device)
 
     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)
         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_train_samples += input.size(0)

-        nb_samples_seen += input.size(0)

 
 

-        optimizer.zero_grad()

         loss.backward()
         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
 
     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)
 
 
         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)
             loss = F.cross_entropy(output.transpose(1, 2), input)

+

             acc_test_loss += loss.item() * input.size(0)
             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))
             nb_test_samples += input.size(0)
 
         train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))


@@ -677,6 +867,13 @@ for n_epoch in range(nb_epochs_finished, nb_epochs):
             deterministic_synthesis=args.deterministic_synthesis,
         )
 
             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(),
     checkpoint = {
         "nb_epochs_finished": n_epoch + 1,
         "model_state": model.state_dict(),