X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=e0588224f07ecafde8105b00c2b004e0b195e249;hb=31ed8a54992e7701eebd1c3d49bfe8dc20aa65e3;hp=22edf7b79ce01ef71eb4272c46906f6b96c79425;hpb=0cd7843a52fd6156b3fc59b1f6c2f86054948ba1;p=culture.git

diff --git a/main.py b/main.py
index 22edf7b..e058822 100755
--- a/main.py
+++ b/main.py
@@ -29,12 +29,7 @@ parser = argparse.ArgumentParser(
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
-parser.add_argument(
-    "--task",
-    type=str,
-    default="twotargets",
-    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=" ")
 
@@ -46,7 +41,7 @@ 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("--nb_epochs", type=int, default=10000)
 
 parser.add_argument("--batch_size", type=int, default=None)
 
@@ -56,12 +51,8 @@ parser.add_argument("--nb_train_samples", type=int, default=None)
 
 parser.add_argument("--nb_test_samples", type=int, default=None)
 
-parser.add_argument("--optim", type=str, default="adam")
-
 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("--model", type=str, default=None)
@@ -82,119 +73,14 @@ parser.add_argument("--dropout", type=float, default=0.1)
 
 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("--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("--nb_gpts", type=int, default=5)
 
-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()
 
-assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
-
 if args.result_dir is None:
     args.result_dir = f"results_{args.task}"
 
@@ -207,114 +93,6 @@ default_task_args = {
         "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:
@@ -374,9 +152,8 @@ else:
 try:
     os.mkdir(args.result_dir)
 except FileExistsError:
-    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)
 
 log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
 
@@ -410,24 +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
@@ -669,28 +431,6 @@ vocabulary_size = task.vocabulary_size()
 
 log_string(f"vocabulary_size {vocabulary_size}")
 
-##############################
-
-models = []
-
-for k in range(2):
-    models.append(
-        mygpt.MyGPT(
-            vocabulary_size=vocabulary_size,
-            dim_model=args.dim_model,
-            dim_keys=args.dim_keys,
-            dim_hidden=args.dim_hidden,
-            nb_heads=args.nb_heads,
-            nb_blocks=args.nb_blocks,
-            causal=True,
-            dropout=args.dropout,
-        ).to(device)
-    )
-
-
-nb_parameters = sum(p.numel() for p in models[0].parameters())
-log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
-
 ######################################################################
 
 # Compute the entropy of the training tokens
@@ -739,44 +479,9 @@ if args.max_percents_of_test_in_train >= 0:
 
 ##############################
 
-if args.learning_rate_schedule == "cos":
-    learning_rate_schedule = {}
-    for n_epoch in range(args.nb_epochs):
-        u = n_epoch / args.nb_epochs * math.pi
-        learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u))
-else:
-    u = {
-        int(k): float(v)
-        for k, v in [
-            tuple(x.split(":")) for x in args.learning_rate_schedule.split(",")
-        ]
-    }
-
-    learning_rate_schedule = {}
-    learning_rate = args.learning_rate
-    for n_epoch in range(args.nb_epochs):
-        if n_epoch in u:
-            learning_rate = u[n_epoch]
-        learning_rate_schedule[n_epoch] = learning_rate
-
-log_string(f"learning_rate_schedule {learning_rate_schedule}")
-
-time_pred_result = None
-
-######################################################################
-
-
-def one_epoch(model, task, learning_rate):
-    log_string(f"learning_rate {learning_rate}")
 
-    if args.optim == "sgd":
-        optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
-    elif args.optim == "adam":
-        optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
-    elif args.optim == "adamw":
-        optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
-    else:
-        raise ValueError(f"Unknown optimizer {args.optim}.")
+def one_epoch(model, task):
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
     model.train()
 
@@ -838,20 +543,18 @@ def run_tests(model, task, deterministic_synthesis):
 
         log_string(f"test_perplexity {n_epoch} {test_perplexity}")
 
-    return main_test_accuracy
+    model.main_test_accuracy = main_test_accuracy
 
 
 ######################################################################
 
 
 def create_quizzes(
+    model,
     other_models,
     task,
     nb_for_train=1000,
     nb_for_test=100,
-    nb_runs=10,
-    nb_min_correct=9,
-    nb_max_correct=9,
 ):
     kept = []
 
@@ -861,15 +564,11 @@ def create_quizzes(
             result_dir=args.result_dir,
             logger=log_string,
             nb=4 * (nb_for_train + nb_for_test),
-            models=other_models,
-            nb_runs=nb_runs,
+            model=model,
+            other_models=other_models,
         )
 
-        to_keep = new_quizzes[
-            torch.logical_and(
-                nb_correct >= nb_min_correct, nb_correct <= nb_max_correct
-            )
-        ]
+        to_keep = new_quizzes[nb_correct == len(other_models) - 1]
         log_string(f"keep {to_keep.size(0)} quizzes")
         kept.append(to_keep)
 
@@ -881,34 +580,77 @@ def create_quizzes(
     task.save_image(
         new_quizzes[:96],
         args.result_dir,
-        f"world_new_{n_epoch:04d}.png",
+        f"world_quiz_{n_epoch:04d}_{model.id:02d}.png",
         log_string,
     )
 
 
 ######################################################################
 
-accuracy_to_make_quizzes = 0.95
+models = []
+
+for k in range(args.nb_gpts):
+    model = mygpt.MyGPT(
+        vocabulary_size=vocabulary_size,
+        dim_model=args.dim_model,
+        dim_keys=args.dim_keys,
+        dim_hidden=args.dim_hidden,
+        nb_heads=args.nb_heads,
+        nb_blocks=args.nb_blocks,
+        causal=True,
+        dropout=args.dropout,
+    ).to(device)
+
+    model.main_test_accuracy = 0.0
+    model.id = k
+
+    models.append(model)
+
+
+nb_parameters = sum(p.numel() for p in models[0].parameters())
+log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
+
+######################################################################
+
+accuracy_to_make_quizzes = 0.975
+nb_new_quizzes_for_train = 1000
+nb_new_quizzes_for_test = 100
 
-for n_epoch in range(nb_epochs_finished, args.nb_epochs):
-    learning_rate = learning_rate_schedule[n_epoch]
+if args.check:
+    accuracy_to_make_quizzes = 0.0
+    nb_new_quizzes_for_train = 10
+    nb_new_quizzes_for_test = 10
 
-    for m in models:
-        one_epoch(m, task, learning_rate)
-        test_accuracy = run_tests(m, task, deterministic_synthesis=False)
+for n_epoch in range(args.nb_epochs):
+    # select the model with lowest accuracy
+    models.sort(key=lambda model: model.main_test_accuracy)
+    model = models[0]
 
-    if test_accuracy >= accuracy_to_make_quizzes:
+    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}"
+    )
+
+    # test it
+    run_tests(model, task, deterministic_synthesis=False)
+
+    if model.main_test_accuracy >= accuracy_to_make_quizzes:
         other_models = models.copy()
         other_models.remove(model)
-        create_quizzes(other_models, task)
 
-    # --------------------------------------------
-
-    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)}"
+        create_quizzes(
+            model,
+            other_models,
+            task,
+            nb_for_train=nb_new_quizzes_for_train,
+            nb_for_test=nb_new_quizzes_for_test,
         )
-    time_pred_result = time_current_result
+
 
 ######################################################################