from torch.nn import functional as F
import ffutils
+
import mygpt
import sky, grids, quiz_machine
######################################################################
-nb_new_c_quizzes_for_train = 1000
-nb_new_c_quizzes_for_test = 100
-
-######################################################################
-
if torch.cuda.is_available():
device = torch.device("cuda")
torch.backends.cuda.matmul.allow_tf32 = True
parser.add_argument("--problem", type=str, default="grids")
+parser.add_argument("--nb_threads", type=int, default=-1)
+
parser.add_argument("--nb_gpts", type=int, default=5)
parser.add_argument("--min_to_validate", type=int, default=None)
######################################################################
-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
-
-######################################################################
-
default_args = {
"model": "37M",
- "batch_size": 100,
+ "batch_size": 25,
"nb_train_samples": 100000,
"nb_test_samples": 10000,
}
nb_birds=args.sky_nb_birds,
nb_iterations=args.sky_nb_iterations,
speed=args.sky_speed,
+ max_nb_cached_chunks=args.nb_train_samples // 100,
+ chunk_size=100,
+ nb_threads=args.nb_threads,
)
back_accuracy = False
elif args.problem == "grids":
- problem = grids.Grids(device=device)
+ problem = grids.Grids(
+ device=device,
+ max_nb_cached_chunks=args.nb_train_samples // 100,
+ chunk_size=100,
+ nb_threads=args.nb_threads,
+ )
back_accuracy = True
else:
raise ValueError
######################################################################
+def standard_validity(logproba):
+ l = logproba.sort(dim=-1).values
+ return logical_and(l[0] < math.log(0.5), l[1] > math.log(0.95))
+
+
def valid_c_quizzes(recorded, criteria):
- result = [q[criteria(c)] for q, c in recorded]
+ result = [q[criteria(lp)] for q, lp in recorded]
return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
quiz_machine,
nb_for_train=1000,
nb_for_test=100,
+):
+ quizzes_and_logproba_records = []
+
+ nb_to_create = nb_for_train + nb_for_test
+
+ # ------------------------------------------------------------
+
+ file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
+
+ with open(file_name, "w") as logp_file:
+ while (
+ valid_c_quizzes(quizzes_and_logproba_records, standard_validity).size(0)
+ < nb_to_create
+ ):
+ # Select a model at random to generate the new quizzes
+
+ model_for_generation = models[torch.randint(len(models), (1,))]
+
+ c_quizzes = quiz_machine.generate_quizzes(
+ nb_to_create,
+ model_for_generation=model_for_generation,
+ temperature=args.generation_temperature,
+ )
+
+ c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
+
+ if c_quizzes.size(0) > 0:
+ logproba = c_quizzes.new(c_quizzes.size(0), len(models))
+ for q, l in zip(
+ c_quizzes.split(args.batch_size), logits.split(args.batch_size)
+ ):
+ for model in models:
+ l[model.id] = F.cross_entropy(model(q))
+
+ for l in logproba:
+ s = " ".join([str(x.item()) for x in l])
+ logp_file.write(s + "\n")
+
+ quizzes_and_logproba_records.append((c_quizzes, logproba))
+
+ nb_validated = valid_c_quizzes(
+ quizzes_and_logproba_records, standard_validity
+ ).size(0)
+
+ log_string(
+ f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
+ )
+
+ # store the new c_quizzes which have been validated
+
+ new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_records, standard_validity)
+
+ 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
+
+ q = new_c_quizzes[:72]
+
+ if q.size(0) > 0:
+ quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
+
+
+######################################################################
+
+
+def create_c_quizzes_(
+ models,
+ quiz_machine,
+ nb_for_train=1000,
+ nb_for_test=100,
):
quizzes_and_nb_correct_records = []
)
file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
+
with open(file_name, "w") as logp_file:
while (
valid_c_quizzes(quizzes_and_nb_correct_records, standard_validity).size(0)
temperature=args.generation_temperature,
)
- nb_correct, seq_logproba = quiz_machine.compute_correctness(
- c_quizzes,
- models,
- bidirectional_validation=args.bidirectional_validation,
- deterministic_validation=args.deterministic_validation,
- )
+ # if args.prediction_correctness:
+
+ # else:
+ # logproba = quiz_machine.new(quiz_machine.size(0), len(models))
+ # for q,l in zip(quizzes.split(args.batch_size), logits.split(args.batch_size)):
+ # for model in models:
+ # l[...] = F.cross_entropy(model(q))
- 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")
+ c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
- if args.dirty_debug:
- nb_correct = torch.randint(
- len(models) + 1, nb_correct.size(), device=c_quizzes.device
+ if c_quizzes.size(0) > 0:
+ nb_correct, seq_logproba = quiz_machine.compute_correctness(
+ c_quizzes,
+ models,
+ bidirectional_validation=args.bidirectional_validation,
+ deterministic_validation=args.deterministic_validation,
)
- quizzes_and_nb_correct_records.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")
+
+ if args.dirty_debug:
+ nb_correct = torch.randint(
+ len(models) + 1, nb_correct.size(), device=c_quizzes.device
+ )
+
+ quizzes_and_nb_correct_records.append((c_quizzes, nb_correct))
nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
nv = " ".join([str(x.item()) for x in nv])
######################################################################
+nb_new_c_quizzes_for_train = args.nb_train_samples // 50
+nb_new_c_quizzes_for_test = args.nb_test_samples // 50
+
+log_string(
+ f"nb_new_c_quizzes_for_train {nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {nb_new_c_quizzes_for_test}"
+)
+
+######################################################################
+
+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
+
+######################################################################
+
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))
f"test_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
)
+ ##################################################
# Replace a fraction of the w_quizzes with fresh ones
quiz_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
projects / culture.git / blobdiff