projects / culture.git / blobdiff
? search:
summary | shortlog | log | commit | commitdiff | tree
raw | inline | side by side
Update.
[culture.git] / main.py
diff --git a/main.py b/main.py
index 6c27599..549e7ea 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -29,12 +29,7 @@ parser = argparse.ArgumentParser(
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
-parser.add_argument(
-    "--task",
-    type=str,
-    default="world",
-    help="file, byheart, learnop, guessop, mixing, memory, twotargets, addition, picoclvr, mnist, maze, snake, stack, expr, rpl, grid, qmlp, greed",
-)
+parser.add_argument("--task", type=str, default="world", help="world")
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
 
 
 parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
 
@@ -78,119 +73,14 @@ parser.add_argument("--dropout", type=float, default=0.1)
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
 
 parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
 
-##############################
-# 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
-
-parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
-
-parser.add_argument("--picoclvr_height", type=int, default=12)
-
-parser.add_argument("--picoclvr_width", type=int, default=16)
-
-parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
-
-##############################
-# Maze options
-
-parser.add_argument("--maze_height", type=int, default=13)
-
-parser.add_argument("--maze_width", type=int, default=21)
-
-parser.add_argument("--maze_nb_walls", type=int, default=15)
-
-##############################
-# Snake options
-
-parser.add_argument("--snake_height", type=int, default=9)
-
-parser.add_argument("--snake_width", type=int, default=12)
-
-parser.add_argument("--snake_nb_colors", type=int, default=5)
+parser.add_argument("--nb_gpts", 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
-
-parser.add_argument("--stack_nb_steps", type=int, default=100)
-
-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=None)
-
-##############################
-# Expr options
-
-parser.add_argument("--expr_nb_variables", type=int, default=5)
-
-parser.add_argument("--expr_sequence_length", type=int, default=40)
-
-parser.add_argument("--expr_operand_max", type=int, default=9)
-
-parser.add_argument("--expr_result_max", type=int, default=99)
-
-parser.add_argument("--expr_input_file", type=str, default=None)
-
-##############################
-# 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("--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)
+parser.add_argument("--check", action="store_true", default=False)
 
 ######################################################################
 
 args = parser.parse_args()
 
 
 ######################################################################
 
 args = parser.parse_args()
 
-assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
-
 if args.result_dir is None:
     args.result_dir = f"results_{args.task}"
 
 if args.result_dir is None:
     args.result_dir = f"results_{args.task}"
 
@@ -203,114 +93,6 @@ default_task_args = {
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
         "nb_train_samples": 250000,
         "nb_test_samples": 10000,
     },
-    "file": {
-        "model": "37M",
-        "batch_size": 25,
-        "nb_train_samples": 250000,
-        "nb_test_samples": 10000,
-    },
-    "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": {
-        "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": {
-        "model": "37M",
-        "batch_size": 25,
-        "nb_train_samples": 250000,
-        "nb_test_samples": 10000,
-    },
-    "stack": {
-        "model": "37M",
-        "batch_size": 25,
-        "nb_train_samples": 100000,
-        "nb_test_samples": 1000,
-    },
-    "twotargets": {
-        "model": "37M",
-        "batch_size": 25,
-        "nb_train_samples": 50000,
-        "nb_test_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_task_args:
 }
 
 if args.task in default_task_args:
@@ -405,24 +187,9 @@ for n in vars(args):
 
 ######################################################################
 
 
 ######################################################################
 
-
-def picoclvr_pruner_horizontal_green(p):
-    return not ("green" in p and ("left" in p or "right" in p))
-
-
-picoclvr_pruner_train = (
-    picoclvr_pruner_horizontal_green
-    if args.picocvlr_prune_properties in {"train+eval"}
-    else None
-)
-
-picoclvr_pruner_eval = (
-    (lambda p: not picoclvr_pruner_horizontal_green(p))
-    if args.picocvlr_prune_properties in {"train+eval", "eval"}
-    else None
-)
-
-######################################################################
+if args.check:
+    args.nb_train_samples = 500
+    args.nb_test_samples = 100
 
 if args.physical_batch_size is None:
     args.physical_batch_size = args.batch_size
 
 if args.physical_batch_size is None:
     args.physical_batch_size = args.batch_size
@@ -801,6 +568,8 @@ def create_quizzes(
             other_models=other_models,
         )
 
             other_models=other_models,
         )
 
+        print(nb_correct)
+
         to_keep = new_quizzes[nb_correct == len(other_models) - 1]
         log_string(f"keep {to_keep.size(0)} quizzes")
         kept.append(to_keep)
         to_keep = new_quizzes[nb_correct == len(other_models) - 1]
         log_string(f"keep {to_keep.size(0)} quizzes")
         kept.append(to_keep)
@@ -813,7 +582,7 @@ def create_quizzes(
     task.save_image(
         new_quizzes[:96],
         args.result_dir,
     task.save_image(
         new_quizzes[:96],
         args.result_dir,
-        f"world_new_{n_epoch:04d}_{model.id:02d}.png",
+        f"world_quiz_{n_epoch:04d}_{model.id:02d}.png",
         log_string,
     )
 
         log_string,
     )
 
@@ -822,7 +591,7 @@ def create_quizzes(
 
 models = []
 
 
 models = []
 
-for k in range(5):
+for k in range(args.nb_gpts):
     model = mygpt.MyGPT(
         vocabulary_size=vocabulary_size,
         dim_model=args.dim_model,
     model = mygpt.MyGPT(
         vocabulary_size=vocabulary_size,
         dim_model=args.dim_model,
@@ -846,22 +615,31 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 ######################################################################
 
 accuracy_to_make_quizzes = 0.975
 ######################################################################
 
 accuracy_to_make_quizzes = 0.975
+nb_new_quizzes_for_train = 1000
+nb_new_quizzes_for_test = 100
+
+if args.check:
+    accuracy_to_make_quizzes = 0.0
+    nb_new_quizzes_for_train = 10
+    nb_new_quizzes_for_test = 10
 
 for n_epoch in range(args.nb_epochs):
 
 for n_epoch in range(args.nb_epochs):
+    # select the model with lowest accuracy
     models.sort(key=lambda model: model.main_test_accuracy)
     models.sort(key=lambda model: model.main_test_accuracy)
-
     model = models[0]
 
     log_string(
         f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
     )
 
     model = models[0]
 
     log_string(
         f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
     )
 
+    # improve it
     one_epoch(model, task)
 
     log_string(
         f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
     )
 
     one_epoch(model, task)
 
     log_string(
         f"train_set_composition world {task.nb_batch_samples_world} quizzes {task.nb_batch_samples_quizzes}"
     )
 
+    # test it
     run_tests(model, task, deterministic_synthesis=False)
 
     if model.main_test_accuracy >= accuracy_to_make_quizzes:
     run_tests(model, task, deterministic_synthesis=False)
 
     if model.main_test_accuracy >= accuracy_to_make_quizzes:
@@ -872,8 +650,8 @@ for n_epoch in range(args.nb_epochs):
             model,
             other_models,
             task,
             model,
             other_models,
             task,
-            nb_for_train=1000,
-            nb_for_test=100,
+            nb_for_train=nb_new_quizzes_for_train,
+            nb_for_test=nb_new_quizzes_for_test,
         )
 
 
         )
 
 
An attempt at having GPTs general their own quizzes.