import torch.multiprocessing as mp
-# world quizzes vs. culture quizzes
-
######################################################################
parser = argparse.ArgumentParser(
parser.add_argument("--seed", type=int, default=0)
+parser.add_argument("--resume", action="store_true", default=False)
+
parser.add_argument("--max_percents_of_test_in_train", type=int, default=-1)
########################################
parser.add_argument("--nb_test_samples", type=int, default=None)
+parser.add_argument("--nb_new_c_quizzes_for_train", type=int, default=None)
+
+parser.add_argument("--nb_new_c_quizzes_for_test", type=int, default=None)
+
parser.add_argument("--learning_rate", type=float, default=5e-4)
########################################
parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--min_to_validate", type=int, default=None)
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.9)
-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("--proba_understands", type=float, default=0.99)
+parser.add_argument("--proba_understands", type=float, default=0.9)
parser.add_argument("--proba_not_understands", type=float, default=0.5)
-parser.add_argument("--generation_temperature", type=float, default=2.0)
+parser.add_argument("--generation_temperature", type=float, default=1.0)
parser.add_argument("--dirty_debug", action="store_true", default=False)
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"
######################################################################
-try:
- os.mkdir(args.result_dir)
-except FileExistsError:
- print(f"result directory {args.result_dir} already exists")
- exit(1)
+if args.resume:
+ assert os.path.isdir(args.result_dir)
+
+else:
+ try:
+ os.mkdir(args.result_dir)
+ except FileExistsError:
+ print(f"result directory {args.result_dir} already exists")
+ exit(1)
log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
sys.stdout.flush()
+now = time.strftime("%Y%m%d-%H%M%S", time.localtime())
+
+os.system(f"tar zcvf {args.result_dir}/src-{now}.tgz *.py")
+
log_string(f"argv {' '.join(sys.argv)}")
for n in vars(args):
def one_epoch(model, quiz_machine, local_device=main_device):
- optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-
model.to(local_device).train()
+ optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+
nb_train_samples, acc_train_loss = 0, 0.0
for input in quiz_machine.batches(model, split="train"):
######################################################################
+# This is the key routine that decides what generated quizzes to keep
-def standard_validity(logproba):
- l = logproba.sort(dim=-1).values
+
+# token_logprobas are NxMxT where M is the number of models
+
+
+def compute_valid_quizzes_(token_logprobas):
+ warnings.warn("validation with uniform constraints", RuntimeWarning)
+ l = token_logprobas.min(dim=-1).values.sort(dim=-1).values
+ return (l[:, 0] < math.log(0.1)) & (l[:, 1] > math.log(0.5))
+
+
+def compute_valid_quizzes(token_logprobas):
+ l = token_logprobas.sum(dim=-1).sort(dim=-1).values
return (l[:, 0] < math.log(args.proba_not_understands)) & (
l[:, 1] > math.log(args.proba_understands)
)
-def valid_c_quizzes(recorded, criteria):
- result = [q[criteria(lp)] for q, lp in recorded]
- return torch.cat(result, dim=0) if len(result) > 0 else torch.tensor([])
+def extract_valid_quizzes_and_logprobas(recorded):
+ validated_quizzes, validated_logprobas = [], []
+ for quizzes, token_logprobas in recorded:
+ validated_indices = compute_valid_quizzes(token_logprobas)
+ validated_quizzes.append(quizzes[validated_indices])
+ validated_logprobas.append(token_logprobas[validated_indices])
+
+ if len(validated_quizzes) > 0:
+ return torch.cat(validated_quizzes, dim=0), torch.cat(
+ validated_logprobas, dim=0
+ )
+ else:
+ return None, None
######################################################################
-def create_c_quizzes(
- models,
- quiz_machine,
- nb_for_train=1000,
- nb_for_test=100,
-):
- quizzes_and_logproba_records = []
-
+def create_c_quizzes(models, quiz_machine, nb_for_train=1000, nb_for_test=100):
nb_to_create = nb_for_train + nb_for_test
- # ------------------------------------------------------------
+ recorded_quizzes_logprobas = []
- file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
+ nb_validated = 0
- 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
+ while nb_validated < nb_to_create:
+ model_for_generation = models[torch.randint(len(models), (1,))]
- 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 = 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)]
- c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
+ if c_quizzes.size(0) > 0:
+ token_logproba = quiz_machine.solution_token_logprobas(models, c_quizzes)
+ recorded_quizzes_logprobas.append((c_quizzes, token_logproba))
- if c_quizzes.size(0) > 0:
- logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
- 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))
+ (
+ validated_quizzes,
+ validated_logprobas,
+ ) = extract_valid_quizzes_and_logprobas(recorded_quizzes_logprobas)
- nb_validated = valid_c_quizzes(
- quizzes_and_logproba_records, standard_validity
- ).size(0)
+ if validated_quizzes is not None:
+ nb_validated = validated_quizzes.size(0)
- log_string(
- f"keep c_quizzes model {model_for_generation.id} nb_accumulated {nb_validated} / {nb_to_create}"
- )
+ 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.reverse_random_half_in_place(validated_quizzes)
+ quiz_machine.store_c_quizzes(validated_quizzes[:nb_for_train], for_train=True)
+ quiz_machine.store_c_quizzes(
+ validated_quizzes[nb_for_train:nb_to_create], for_train=False
+ )
- 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 images with their logprobas
- # save a bunch of images to investigate what quizzes with a
- # certain nb of correct predictions look like
+ vq = validated_quizzes[:72]
+ vl = validated_logprobas[:72]
- q = new_c_quizzes[:72]
+ if vq.size(0) > 0:
+ prefix = f"culture_c_quiz_{n_epoch:04d}"
+ filename = os.path.join(args.result_dir, prefix + "_logp.pth")
+ torch.save(vl, filename)
+ # with open(file_name, "w") as logp_file:
+ # for l in vl:
+ # s = " ".join([str(x.item()) for x in l])
+ # logp_file.write(s + "\n")
- if q.size(0) > 0:
- quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
+ quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
######################################################################
models.append(model)
+######################################################################
+
+if args.resume:
+ try:
+ for model in models:
+ filename = f"gpt_{model.id:03d}.pth"
+
+ try:
+ d = torch.load(os.path.join(args.result_dir, filename))
+ model.load_state_dict(d[0])
+ model.main_test_accuracy = d[1]
+ log_string(f"successfully loaded {filename}")
+ except FileNotFoundError:
+ log_string(f"cannot find {filename}")
+ pass
+
+ try:
+ filename = "c_quizzes.pth"
+ quiz_machine.load_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"successfully loaded {filename}")
+ except FileNotFoundError:
+ log_string(f"cannot find {filename}")
+ pass
+
+ except:
+ log_string(f"error when loading {filename}.")
+ exit(1)
+
+######################################################################
nb_parameters = sum(p.numel() for p in models[0].parameters())
log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)")
######################################################################
-nb_new_c_quizzes_for_train = args.nb_train_samples // 50
-nb_new_c_quizzes_for_test = args.nb_test_samples // 50
+if args.nb_new_c_quizzes_for_train is None:
+ args.nb_new_c_quizzes_for_train = args.nb_train_samples // 50
+
+if args.nb_new_c_quizzes_for_test is None:
+ args.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}"
+ f"nb_new_c_quizzes_for_train {args.nb_new_c_quizzes_for_train} nb_new_c_quizzes_for_test {args.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
-
- def standard_validity(logproba):
- l = logproba.sort(dim=-1).values
- return l[:, 0] < math.log(0.5)
+ args.nb_new_c_quizzes_for_train = 100
+ args.nb_new_c_quizzes_for_test = 10
######################################################################
cta = " ".join([f"{float(m.main_test_accuracy):.04f}" for m in models])
log_string(f"current_test_accuracies {cta}")
+ ##################################################
+ # 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,
+ quiz_machine,
+ nb_for_train=args.nb_new_c_quizzes_for_train,
+ nb_for_test=args.nb_new_c_quizzes_for_test,
+ )
+
+ filename = "c_quizzes.pth"
+ quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
+
+ # Force one epoch of training
+ for model in models:
+ model.main_test_accuracy = 0.0
+
##################################################
# Select, improve, and eval the worst model
for t in threads:
t.join()
- ##################################################
- # Replace a fraction of the w_quizzes with fresh ones
+ # Save the models to disk
- log_string(
- f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes"
- )
+ for model in weakest_models:
+ filename = f"gpt_{model.id:03d}.pth"
+ torch.save(
+ (model.state_dict(), model.main_test_accuracy),
+ os.path.join(args.result_dir, filename),
+ )
+ log_string(f"wrote {filename}")
- # Renew entirely the train set
+ # Renew the training samples
for model in weakest_models:
quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
- ##################################################
- # 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,
- quiz_machine,
- nb_for_train=nb_new_c_quizzes_for_train,
- nb_for_test=nb_new_c_quizzes_for_test,
- )
######################################################################
projects / culture.git / blobdiff