Update.

[culture.git] / main.py

diff --git a/main.py b/main.py
index 714327d..6b46fa0 100755 (executable)
--- a/main.py
+++ b/main.py
@@ -13,7 +13,7 @@ from torch.nn import functional as F
 
 import ffutils
 import mygpt
 
 import ffutils
 import mygpt

-import sky, wireworld, quizz_machine
+import sky, grids, quiz_machine

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


@@ -57,7 +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-3)
+parser.add_argument("--learning_rate", type=float, default=5e-4)

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


@@ -79,22 +79,26 @@ 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("--reverse_cleanup", action="store_true", default=False)
-
-parser.add_argument("--validation_forward_only", action="store_true", default=False)
-
-parser.add_argument("--problem", type=str, default="sky")
+parser.add_argument("--problem", type=str, default="grids")

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

-parser.add_argument("--min_to_validate", type=int, default=4)
+parser.add_argument("--min_to_validate", type=int, default=None)

 
 

-parser.add_argument("--max_to_validate", type=int, default=4)
+parser.add_argument("--max_to_validate", type=int, default=None)

 
 parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
 
 
 parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
 

+parser.add_argument("--generation_temperature", type=float, default=2.0)
+
+parser.add_argument("--deterministic_validation", action="store_true", default=False)
+
+parser.add_argument("--bidirectional_validation", action="store_true", default=False)
+

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

+######################################################################
+

 parser.add_argument("--sky_height", type=int, default=6)
 
 parser.add_argument("--sky_width", type=int, default=8)
 parser.add_argument("--sky_height", type=int, default=6)
 
 parser.add_argument("--sky_width", type=int, default=8)


@@ -109,6 +113,12 @@ parser.add_argument("--sky_speed", type=int, default=3)
 
 args = parser.parse_args()
 
 
 args = parser.parse_args()
 

+if args.min_to_validate is None:
+    args.min_to_validate = args.nb_gpts - 1
+
+if args.max_to_validate is None:
+    args.max_to_validate = args.nb_gpts - 1
+

 if args.result_dir is None:
     args.result_dir = f"results_culture"
 
 if args.result_dir is None:
     args.result_dir = f"results_culture"
 


@@ -116,6 +126,7 @@ if args.result_dir is None:
 
 if args.dirty_debug:
     args.accuracy_to_make_c_quizzes = 0.0
 
 if args.dirty_debug:
     args.accuracy_to_make_c_quizzes = 0.0

+    args.nb_gpts = 2

     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
 


@@ -239,15 +250,18 @@ if args.problem == "sky":
         nb_iterations=args.sky_nb_iterations,
         speed=args.sky_speed,
     )
         nb_iterations=args.sky_nb_iterations,
         speed=args.sky_speed,
     )

-elif args.problem == "wireworld":
-    problem = wireworld.Wireworld(height=8, width=10, nb_iterations=2, speed=5)
+    back_accuracy = False
+elif args.problem == "grids":
+    problem = grids.Grids(device=device)
+    back_accuracy = True

 else:
     raise ValueError
 
 else:
     raise ValueError
 

-quizz_machine = quizz_machine.QuizzMachine(
+quiz_machine = quiz_machine.QuizMachine(

     problem=problem,
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,
     problem=problem,
     nb_train_samples=args.nb_train_samples,
     nb_test_samples=args.nb_test_samples,

+    back_accuracy=back_accuracy,

     batch_size=args.physical_batch_size,
     result_dir=args.result_dir,
     logger=log_string,
     batch_size=args.physical_batch_size,
     result_dir=args.result_dir,
     logger=log_string,


@@ -258,7 +272,7 @@ quizz_machine = quizz_machine.QuizzMachine(
 
 log_string(f"device {device}")
 
 
 log_string(f"device {device}")
 

-vocabulary_size = quizz_machine.vocabulary_size()
+vocabulary_size = quiz_machine.vocabulary_size()

 
 log_string(f"vocabulary_size {vocabulary_size}")
 
 
 log_string(f"vocabulary_size {vocabulary_size}")
 


@@ -267,8 +281,8 @@ log_string(f"vocabulary_size {vocabulary_size}")
 # Compute the entropy of the training tokens
 
 token_count = 0
 # Compute the entropy of the training tokens
 
 token_count = 0

-for input in quizz_machine.batches(split="train", desc="train-entropy"):
-    token_count += F.one_hot(input, num_classes=quizz_machine.vocabulary_size()).sum(
+for input in quiz_machine.batches(split="train", desc="train-entropy"):
+    token_count += F.one_hot(input, num_classes=quiz_machine.vocabulary_size()).sum(

         (0, 1)
     )
 token_probas = token_count / token_count.sum()
         (0, 1)
     )
 token_probas = token_count / token_count.sum()


@@ -292,11 +306,11 @@ if args.max_percents_of_test_in_train >= 0:
 
     nb_test, nb_in_train = 0, 0
     for test_subset in subsets_as_tuples(
 
     nb_test, nb_in_train = 0, 0
     for test_subset in subsets_as_tuples(

-        quizz_machine.batches(split="test", desc="test-check"), 25000
+        quiz_machine.batches(split="test", desc="test-check"), 25000

     ):
         in_train = set()
         for train_subset in subsets_as_tuples(
     ):
         in_train = set()
         for train_subset in subsets_as_tuples(

-            quizz_machine.batches(split="train", desc="train-check"), 25000
+            quiz_machine.batches(split="train", desc="train-check"), 25000

         ):
             in_train.update(test_subset.intersection(train_subset))
         nb_in_train += len(in_train)
         ):
             in_train.update(test_subset.intersection(train_subset))
         nb_in_train += len(in_train)


@@ -313,14 +327,14 @@ if args.max_percents_of_test_in_train >= 0:
 ##############################
 
 
 ##############################
 
 

-def one_epoch(model, quizz_machine):
+def one_epoch(model, quiz_machine):

     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
     model.train()
 
     nb_train_samples, acc_train_loss = 0, 0.0
 
     optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
 
     model.train()
 
     nb_train_samples, acc_train_loss = 0, 0.0
 

-    for input in quizz_machine.batches(split="train"):
+    for input in quiz_machine.batches(split="train"):

         input = input.to(device)
 
         if nb_train_samples % args.batch_size == 0:
         input = input.to(device)
 
         if nb_train_samples % args.batch_size == 0:


@@ -345,14 +359,14 @@ def one_epoch(model, quizz_machine):
 ######################################################################
 
 
 ######################################################################
 
 

-def run_tests(model, quizz_machine, deterministic_synthesis):
+def run_tests(model, quiz_machine, deterministic_synthesis):

     with torch.autograd.no_grad():
         model.eval()
 
         nb_test_samples, acc_test_loss = 0, 0.0
         nb_samples_accumulated = 0
 
     with torch.autograd.no_grad():
         model.eval()
 
         nb_test_samples, acc_test_loss = 0, 0.0
         nb_samples_accumulated = 0
 

-        for input in quizz_machine.batches(split="test"):
+        for input in quiz_machine.batches(split="test"):

             input = input.to(device)
 
             bs = model(mygpt.BracketedSequence(input))
             input = input.to(device)
 
             bs = model(mygpt.BracketedSequence(input))


@@ -364,19 +378,17 @@ def run_tests(model, quizz_machine, deterministic_synthesis):
 
             nb_test_samples += input.size(0)
 
 
             nb_test_samples += input.size(0)
 

-        main_test_accuracy = quizz_machine.produce_results(
+        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+
+        log_string(f"test_perplexity {n_epoch} {test_perplexity}")
+
+        model.main_test_accuracy = quiz_machine.produce_results(

             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,
             deterministic_synthesis=deterministic_synthesis,
         )
 
             n_epoch=n_epoch,
             model=model,
             result_dir=args.result_dir,
             deterministic_synthesis=deterministic_synthesis,
         )
 

-        test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
-
-        log_string(f"test_perplexity {n_epoch} {test_perplexity}")
-
-    model.main_test_accuracy = main_test_accuracy
-

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


@@ -391,13 +403,11 @@ def valid_c_quizzes(recorded, criteria):
 
 def create_c_quizzes(
     models,
 
 def create_c_quizzes(
     models,

-    quizz_machine,
+    quiz_machine,

     nb_for_train=1000,
     nb_for_test=100,
 ):
     nb_for_train=1000,
     nb_for_test=100,
 ):

-    recorded = []
-
-    sum_logits, sum_nb_c_quizzes = 0, 0
+    quizzes_and_nb_correct_records = []

 
     nb_to_create = nb_for_train + nb_for_test
 
 
     nb_to_create = nb_for_train + nb_for_test
 


@@ -407,44 +417,60 @@ def create_c_quizzes(
         nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate
     )
 
         nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate
     )
 

-    while valid_c_quizzes(recorded, standard_validity).size(0) < nb_to_create:
-        model_for_generation = models[torch.randint(len(models), (1,))]
+    file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")

 
 

-        c_quizzes, ave_seq_logproba = quizz_machine.generate_quizzes(
-            nb_to_create,
-            model_for_generation=model_for_generation,
-            reverse_cleanup=args.reverse_cleanup,
-        )
+    with open(file_name, "w") as logp_file:
+        while (
+            valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity).size(0)
+            < nb_to_create
+        ):
+            # Select a model at random to generate the new quizzes

 
 

-        sum_logits += c_quizzes.size(0) * ave_seq_logproba
-        sum_nb_c_quizzes += c_quizzes.size(0)
+            model_for_generation = models[torch.randint(len(models), (1,))]

 
 

-        nb_correct = quizz_machine.compute_correctness(
-            c_quizzes, models, both_directions=not args.validation_forward_only
-        )
+            c_quizzes = quiz_machine.generate_quizzes(
+                nb_to_create,
+                model_for_generation=model_for_generation,
+                temperature=args.generation_temperature,
+            )

 
 

-        if args.dirty_debug:
-            nb_correct = torch.randint(
-                len(models) + 1, nb_correct.size(), device=c_quizzes.device
+            nb_correct, seq_logproba = quiz_machine.compute_correctness(
+                c_quizzes,
+                models,
+                bidirectional_validation=args.bidirectional_validation,
+                deterministic_validation=args.deterministic_validation,

             )
 
             )
 

-        recorded.append((c_quizzes, nb_correct))
+            for n, l in zip(nb_correct, seq_logproba):
+                s = " ".join([str(x.item()) for x in l])
+                logp_file.write(f"{n} {s}\n")

 
 

-        nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
-        nv = " ".join([str(x.item()) for x in nv])
+            if args.dirty_debug:
+                nb_correct = torch.randint(
+                    len(models) + 1, nb_correct.size(), device=c_quizzes.device
+                )

 
 

-        nb_validated = valid_c_quizzes(recorded, standard_validity).size(0)
+            quizzes_and_nb_correct_records.append((c_quizzes, nb_correct))

 
 

-        log_string(
-            f"keep c_quizzes kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
-        )
+            nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
+            nv = " ".join([str(x.item()) for x in nv])
+
+            nb_validated = valid_c_quizzes(
+                quizzes_and_nb_correct_records, standard_validity
+            ).size(0)
+
+            log_string(
+                f"keep c_quizzes model {model_for_generation.id} kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
+            )

 
     # store the new c_quizzes which have been validated
 
 
     # store the new c_quizzes which have been validated
 

-    new_c_quizzes = valid_c_quizzes(recorded, standard_validity)
+    new_c_quizzes = valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity)

 
 

-    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)
+    quiz_machine.reverse_random_half_in_place(new_c_quizzes)
+
+    quiz_machine.store_c_quizzes(new_c_quizzes[:nb_for_train], for_train=True)
+    quiz_machine.store_c_quizzes(new_c_quizzes[nb_for_train:], for_train=False)

 
     # save a bunch of images to investigate what quizzes with a
     # certain nb of correct predictions look like
 
     # save a bunch of images to investigate what quizzes with a
     # certain nb of correct predictions look like


@@ -456,13 +482,16 @@ def create_c_quizzes(
             else ""
         )
 
             else ""
         )
 

-        quizz_machine.problem.save_quizzes(
-            valid_c_quizzes(recorded, criteria=lambda nb_correct: nb_correct == n)[:72],
-            args.result_dir,
-            f"culture_c_quiz_{n_epoch:04d}_N{n}{s}",
-        )
+        q = valid_c_quizzes(
+            quizzes_and_nb_correct_records, criteria=lambda nb_correct: nb_correct == n
+        )[:72]

 
 

-    return sum_logits / sum_nb_c_quizzes
+        quiz_machine.reverse_random_half_in_place(q)
+
+        if q.size(0) > 0:
+            quiz_machine.save_quizzes(
+                args.result_dir, f"culture_c_quiz_{n_epoch:04d}_N{n}{s}", q
+            )

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


@@ -495,43 +524,46 @@ log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
 for n_epoch in range(args.nb_epochs):
     log_string(f"--- epoch {n_epoch} ----------------------------------------")
 
 for n_epoch in range(args.nb_epochs):
     log_string(f"--- epoch {n_epoch} ----------------------------------------")
 

+    cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
+    log_string(f"current_test_accuracies {cta}")
+
+    # Select, improve, and eval the worst model
+

     weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
 
     log_string(
         f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
     )
 
     weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
 
     log_string(
         f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
     )
 

-    # improve it
-    one_epoch(weakest_model, quizz_machine)
+    one_epoch(weakest_model, quiz_machine)

 
     log_string(
 
     log_string(

-        f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
+        f"train_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"

     )
 
     )
 

-    # test it
-    run_tests(weakest_model, quizz_machine, deterministic_synthesis=False)
+    run_tests(weakest_model, quiz_machine, deterministic_synthesis=False)

 
     log_string(
 
     log_string(

-        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 {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"

     )
 
     )
 

-    cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
-    log_string(f"current_test_accuracies {cta}")
+    # Replace a fraction of the w_quizzes with fresh ones
+
+    quiz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)

 
 

-    # replace a fraction of the w_quizzes with a fresh ones
-    quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+    # If all the models are good enough, generate new quizzes and
+    # re-compute the test errors

 
     if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
         create_c_quizzes(
             models,
 
     if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
         create_c_quizzes(
             models,

-            quizz_machine,
+            quiz_machine,

             nb_for_train=nb_new_c_quizzes_for_train,
             nb_for_test=nb_new_c_quizzes_for_test,
         )
 
             nb_for_train=nb_new_c_quizzes_for_train,
             nb_for_test=nb_new_c_quizzes_for_test,
         )
 

-        # We update everyone

         for model in models:
         for model in models:

-            run_tests(model, quizz_machine, deterministic_synthesis=False)
+            run_tests(model, quiz_machine, deterministic_synthesis=False)

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