import ffutils
import mygpt
-import sky, quizz_machine
+import sky, wireworld, quizz_machine
# 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
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("--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)
########################################
parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--reverse_cleanup", 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("--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("--sky_height", type=int, default=6)
+
+parser.add_argument("--sky_width", type=int, default=8)
+
+parser.add_argument("--sky_nb_birds", type=int, default=3)
+
+parser.add_argument("--sky_nb_iterations", type=int, default=2)
+
+parser.add_argument("--sky_speed", type=int, default=3)
+
######################################################################
args = parser.parse_args()
######################################################################
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
default_args = {
"model": "37M",
"batch_size": 100,
- "nb_train_samples": 250000,
+ "nb_train_samples": 100000,
"nb_test_samples": 10000,
}
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=args.sky_height,
+ width=args.sky_width,
+ nb_birds=args.sky_nb_birds,
+ 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)
+else:
+ raise ValueError
+
quizz_machine = quizz_machine.QuizzMachine(
- problem=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,
n_epoch=n_epoch,
model=model,
result_dir=args.result_dir,
- logger=log_string,
deterministic_synthesis=deterministic_synthesis,
)
######################################################################
+def valid_c_quizzes(recorded, criteria):
+ result = [q[criteria(c)] for q, c in recorded]
+ return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
+
+
+######################################################################
+
+
def create_c_quizzes(
models,
quizz_machine,
nb_for_train=1000,
nb_for_test=100,
- min_ave_seq_logproba=None,
):
- kept = []
- model_indexes = []
+ recorded = []
+
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 = nb_for_train + nb_for_test
+ nb_to_create = nb_for_train + nb_for_test
+
+ # ------------------------------------------------------------
- if len(model_indexes) == 0:
- model_indexes = [i.item() for i in torch.randperm(len(models))]
+ standard_validity = lambda nb_correct: torch.logical_and(
+ nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate
+ )
- model = models[model_indexes.pop()]
+ while valid_c_quizzes(recorded, standard_validity).size(0) < nb_to_create:
+ model_for_generation = models[torch.randint(len(models), (1,))]
- new_c_quizzes, nb_correct, ave_seq_logproba = quizz_machine.create_c_quizzes(
- nb=nb_to_generate,
- model_for_generation=model,
- models_for_validation=models,
- min_ave_seq_logproba=min_ave_seq_logproba,
- n_epoch=n_epoch,
- result_dir=args.result_dir,
- logger=log_string,
+ c_quizzes, ave_seq_logproba = quizz_machine.generate_quizzes(
+ nb_to_create,
+ model_for_generation=model_for_generation,
+ reverse_cleanup=args.reverse_cleanup,
)
- sum_logits += new_c_quizzes.size(0) * ave_seq_logproba
- sum_nb_c_quizzes += new_c_quizzes.size(0)
+ sum_logits += c_quizzes.size(0) * ave_seq_logproba
+ sum_nb_c_quizzes += c_quizzes.size(0)
- to_keep = new_c_quizzes[nb_correct == len(models) - 1]
+ nb_correct = quizz_machine.compute_correctness(c_quizzes, models)
if args.dirty_debug:
- to_keep = new_c_quizzes[
- torch.randint(3, (new_c_quizzes.size(0),), device=new_c_quizzes.device)
- == 0
- ]
+ nb_correct = torch.randint(
+ len(models) + 1, nb_correct.size(), device=c_quizzes.device
+ )
+
+ recorded.append((c_quizzes, nb_correct))
- kept.append(to_keep)
+ 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(recorded, standard_validity).size(0)
log_string(
- f"keep c_quizzes {to_keep.size(0)}/{new_c_quizzes.size(0)} ({to_keep.size(0)*100/new_c_quizzes.size(0):.02f}%) total {sum([ x.size(0) for x in kept])}/{nb_to_generate}"
+ f"keep c_quizzes kept {nv} nb_accumulated {nb_validated} / {nb_to_create}"
)
- new_c_quizzes = torch.cat(kept, dim=0)[: nb_for_train + nb_for_test]
+ # ------------------------------------------------------------
+
+ new_c_quizzes = valid_c_quizzes(recorded, 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)
- quizz_machine.problem.save_quizzes(
- new_c_quizzes[:72],
- args.result_dir,
- f"culture_c_quiz_{n_epoch:04d}_{model.id:02d}",
- )
+ for n in range(len(models) + 1):
+ s = (
+ "_validated"
+ if n >= args.min_to_validate and n <= args.max_to_validate
+ 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}",
+ )
return sum_logits / sum_nb_c_quizzes
######################################################################
-min_ave_seq_logproba = None
-
for n_epoch in range(args.nb_epochs):
log_string(f"--- epoch {n_epoch} ----------------------------------------")
- 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}")
-
- # select the model with lowest accuracy
- models.sort(key=lambda model: model.main_test_accuracy)
- model = models[0]
+ weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
log_string(
- f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
+ f"training model {weakest_model.id} main_test_accuracy {weakest_model.main_test_accuracy}"
)
# improve it
- one_epoch(model, quizz_machine)
-
- quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+ one_epoch(weakest_model, quizz_machine)
log_string(
f"train_set_composition w_quizzes {quizz_machine.nb_batch_w_quizzes} c_quizzes {quizz_machine.nb_batch_c_quizzes}"
)
# test it
- run_tests(model, quizz_machine, deterministic_synthesis=False)
+ run_tests(weakest_model, quizz_machine, deterministic_synthesis=False)
log_string(
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:
- ave_seq_logproba = create_c_quizzes(
+ cta = " ".join([f"{float(m.main_test_accuracy):.02f}" for m in models])
+ log_string(f"current_test_accuracies {cta}")
+
+ # replace a fraction of the w_quizzes with a fresh ones
+ quizz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+
+ if min([m.main_test_accuracy for m in models]) >= args.accuracy_to_make_c_quizzes:
+ create_c_quizzes(
models,
quizz_machine,
nb_for_train=nb_new_c_quizzes_for_train,
nb_for_test=nb_new_c_quizzes_for_test,
- min_ave_seq_logproba=min_ave_seq_logproba,
)
- # 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}"
- )
-
# We update everyone
for model in models:
run_tests(model, quizz_machine, deterministic_synthesis=False)
projects / culture.git / blobdiff