Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 524715a..b88847e 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -13,13 +13,12 @@ from torch.nn import functional as F
 
 import ffutils
 import mygpt
 
 import ffutils
 import mygpt

-import sky, quizz_machine
+import sky, wireworld, quizz_machine

 
 # world quizzes vs. culture quizzes
 
 ######################################################################
 
 
 # world quizzes vs. culture quizzes
 
 ######################################################################
 

-accuracy_to_make_c_quizzes = 0.975

 nb_new_c_quizzes_for_train = 1000
 nb_new_c_quizzes_for_test = 100
 
 nb_new_c_quizzes_for_train = 1000
 nb_new_c_quizzes_for_test = 100
 


@@ -38,7 +37,7 @@ parser = argparse.ArgumentParser(
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 
     formatter_class=argparse.ArgumentDefaultsHelpFormatter,
 )
 

-parser.add_argument("--log_filename", type=str, default="train.log", help=" ")
+parser.add_argument("--log_filename", type=str, default="train.log")

 
 parser.add_argument("--result_dir", type=str, default=None)
 
 
 parser.add_argument("--result_dir", type=str, default=None)
 


@@ -58,7 +57,7 @@ parser.add_argument("--nb_train_samples", type=int, default=None)
 
 parser.add_argument("--nb_test_samples", type=int, default=None)
 
 
 parser.add_argument("--nb_test_samples", type=int, default=None)
 

-parser.add_argument("--learning_rate", type=float, default=1e-4)
+parser.add_argument("--learning_rate", type=float, default=1e-3)

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


@@ -80,8 +79,20 @@ 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)
 

+parser.add_argument("--problem", type=str, default="sky")
+

 parser.add_argument("--nb_gpts", type=int, default=5)
 
 parser.add_argument("--nb_gpts", type=int, default=5)
 

+parser.add_argument("--nb_models_for_generation", type=int, default=1)
+
+parser.add_argument("--generation_mode", type=str, default="groupthink")
+
+parser.add_argument("--min_to_validate", type=int, default=4)
+
+parser.add_argument("--max_to_validate", type=int, default=4)
+
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
+

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


@@ -94,7 +105,7 @@ if args.result_dir is None:
 ######################################################################
 
 if args.dirty_debug:
 ######################################################################
 
 if args.dirty_debug:

-    accuracy_to_make_c_quizzes = 0.0
+    args.accuracy_to_make_c_quizzes = 0.0

     nb_new_c_quizzes_for_train = 100
     nb_new_c_quizzes_for_test = 10
 
     nb_new_c_quizzes_for_train = 100
     nb_new_c_quizzes_for_test = 10
 


@@ -103,7 +114,7 @@ if args.dirty_debug:
 default_args = {
     "model": "37M",
     "batch_size": 100,
 default_args = {
     "model": "37M",
     "batch_size": 100,

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

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


@@ -210,8 +221,15 @@ else:
 assert args.nb_train_samples % args.batch_size == 0
 assert args.nb_test_samples % args.batch_size == 0
 
 assert args.nb_train_samples % args.batch_size == 0
 assert args.nb_test_samples % args.batch_size == 0
 

+if args.problem=="sky":
+    problem=sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2, speed=2),
+elif args.problem="wireworld":
+    problem=wireworld.Wireworld(height=10, width=15, nb_iterations=4)
+else:
+    raise ValueError
+

 quizz_machine = quizz_machine.QuizzMachine(
 quizz_machine = quizz_machine.QuizzMachine(

-    sky.Sky(height=6, width=8, nb_birds=3, nb_iterations=2),
+    problem=problem,

     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     batch_size=args.physical_batch_size,
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     batch_size=args.physical_batch_size,


@@ -334,7 +352,6 @@ def run_tests(model, quizz_machine, deterministic_synthesis):
             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,
             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,

-            logger=log_string,

             deterministic_synthesis=deterministic_synthesis,
         )
 
             deterministic_synthesis=deterministic_synthesis,
         )
 


@@ -349,55 +366,96 @@ def run_tests(model, quizz_machine, deterministic_synthesis):
 
 
 def create_c_quizzes(
 
 
 def create_c_quizzes(

-    model,
-    other_models,
+    models,

     quizz_machine,
     nb_for_train=1000,
     nb_for_test=100,
     min_ave_seq_logproba=None,
 ):
     quizz_machine,
     nb_for_train=1000,
     nb_for_test=100,
     min_ave_seq_logproba=None,
 ):

-    kept = []
+    # We will store the generated quizzes for each number of
+    # correct prediction
+    recorded = dict([(n, []) for n in range(len(models) + 1)])

 
 

+    model_indexes = []

     sum_logits, sum_nb_c_quizzes = 0, 0
 
     sum_logits, sum_nb_c_quizzes = 0, 0
 

-    while sum([x.size(0) for x in kept]) < nb_for_train + nb_for_test:
-        nb_to_generate = 4 * (nb_for_train + nb_for_test)
+    def nb_generated():
+        return sum([sum([x.size(0) for x in recorded[n]]) for n in recorded.keys()])
+
+    def nb_validated():
+        return sum(
+            [
+                sum([x.size(0) for x in recorded[n]])
+                for n in range(args.min_to_validate, args.max_to_validate + 1)
+            ]
+        )

 
 

-        new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
+    nb_to_create = nb_for_train + nb_for_test
+
+    while nb_validated() < nb_to_create:
+        (
+            new_c_quizzes,
+            nb_correct,
+            ave_seq_logproba,
+        ) = quizz_machine.gang_create_c_quizzes(
+            nb=nb_to_create,
+            nb_models_for_generation=args.nb_models_for_generation,
+            models=models,
+            mode=args.generation_mode,
+            min_ave_seq_logproba=min_ave_seq_logproba,

             n_epoch=n_epoch,
             result_dir=args.result_dir,
             n_epoch=n_epoch,
             result_dir=args.result_dir,

-            logger=log_string,
-            nb=nb_to_generate,
-            model=model,
-            other_models=other_models,
-            min_ave_seq_logproba=min_ave_seq_logproba,

         )
 
         sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
         sum_nb_c_quizzes += new_c_quizzes.size(0)
 
         )
 
         sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
         sum_nb_c_quizzes += new_c_quizzes.size(0)
 

-        to_keep = new_c_quizzes[nb_correct == len(other_models) - 1]
-

         if args.dirty_debug:
         if args.dirty_debug:

-            to_keep = new_c_quizzes
+            nb_correct = torch.randint(
+                len(models) + 1, nb_correct.size(), device=new_c_quizzes.device
+            )
+
+        for n in range(nb_correct.max() + 1):
+            recorded[n].append(new_c_quizzes[nb_correct == n].clone())

 
         log_string(
 
         log_string(

-            f"keep {to_keep.size(0)}/{new_c_quizzes.size(0)} c_quizzes ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%)"
+            f"keep c_quizzes {nb_validated()*100/nb_generated():.02f}% kept total {nb_validated()} / {nb_to_create}"

         )
 
         )
 

-        kept.append(to_keep)
+    # concatenate and shuffle
+    for n in recorded.keys():
+        if len(recorded[n]) > 0:
+            q = torch.cat(recorded[n], dim=0)
+            q = q[torch.randperm(q.size(0), device=q.device)]
+            recorded[n] = q
+        else:
+            del recorded[n]
+
+    new_c_quizzes = torch.cat(
+        [recorded[n] for n in range(args.min_to_validate, args.max_to_validate + 1)],
+        dim=0,
+    )

 
 

-    new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+    new_c_quizzes = new_c_quizzes[
+        torch.randperm(new_c_quizzes.size(0), device=new_c_quizzes.device)[
+            : nb_for_train + nb_for_test
+        ]
+    ]

 
     quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
     quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
 
 
     quizz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
     quizz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)
 

-    quizz_machine.problem.save_quizzes(
-        new_c_quizzes[:72],
-        args.result_dir,
-        f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
-        log_string,
-    )
+    for n in recorded.keys():
+        s = (
+            "_validated"
+            if n >= args.min_to_validate and n <= args.max_to_validate
+            else ""
+        )
+        quizz_machine.problem.save_quizzes(
+            recorded[n][:72],
+            args.result_dir,
+            f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
+        )

 
     return sum_logits / sum_nb_c_quizzes
 
 
     return sum_logits / sum_nb_c_quizzes
 


@@ -436,7 +494,8 @@ for n_epoch in range(args.nb_epochs):
 
     a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])
 
     a = [(model.id, float(model.main_test_accuracy)) for model in models]
     a.sort(key=lambda p: p[0])

-    log_string(f"current accuracies {a}")
+    s = " ".join([f"{p[1]*100:.02f}%" for p in a])
+    log_string(f"current accuracies {s}")

 
     # select the model with lowest accuracy
     models.sort(key=lambda model: model.main_test_accuracy)
 
     # select the model with lowest accuracy
     models.sort(key=lambda model: model.main_test_accuracy)


@@ -462,13 +521,9 @@ for n_epoch in range(args.nb_epochs):
         f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
     )
 
         f"test_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
     )
 

-    if min([m.main_test_accuracy for m in models]) >= accuracy_to_make_c_quizzes:
-        other_models = models.copy()
-        other_models.remove(model)
-
+    if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:

         ave_seq_logproba = create_c_quizzes(
         ave_seq_logproba = create_c_quizzes(

-            model,
-            other_models,
+            models,

             quizz_machine,
             nb_for_train=nb_new_c_quizzes_for_train,
             nb_for_test=nb_new_c_quizzes_for_test,
             quizz_machine,
             nb_for_train=nb_new_c_quizzes_for_train,
             nb_for_test=nb_new_c_quizzes_for_test,


@@ -476,12 +531,12 @@ for n_epoch in range(args.nb_epochs):
         )
 
         # We keep the first average logits as a reference
         )
 
         # We keep the first average logits as a reference

-        if min_ave_seq_logproba is None:
-            min_ave_seq_logproba = ave_seq_logproba
-        else:
-            log_string(
-                f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"
-            )
+        # if min_ave_seq_logproba is None:
+        # min_ave_seq_logproba = ave_seq_logproba
+        # else:
+        # log_string(
+        # f"min_ave_seq_logproba {min_ave_seq_logproba} ave_seq_logproba {ave_seq_logproba}"
+        # )

 
         # We update everyone
         for model in models:
 
         # We update everyone
         for model in models: