parser.add_argument("--nb_gpts", type=int, default=5)
-parser.add_argument("--min_to_validate", type=int, default=None)
-
-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)
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"
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"):
)
-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 valid_quizzes_and_logprobas(recorded, criteria):
+ validated_quizzes, validated_logprobas = [], []
+ for q, lp in recorded:
+ validated_indices = criteria(lp)
+ validated_quizzes.append(q[validated_indices])
+ validated_logprobas.append(lp[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:
+ logproba = quiz_machine.logproba_of_solutions(models, c_quizzes)
+ recorded_quizzes_logprobas.append((c_quizzes, 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 = valid_quizzes_and_logprobas(
+ recorded_quizzes_logprobas, standard_validity
+ )
- 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(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.reverse_random_half_in_place(new_c_quizzes)
+ ######################################################################
+ # save the log probas
- 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)
+ file_name = os.path.join(
+ args.result_dir, f"culture_c_quiz_all_{n_epoch:04d}_logp.dat"
+ )
- # save images
+ with open(file_name, "w") as logp_file:
+ for _, ll in recorded_quizzes_logprobas:
+ for l in ll:
+ s = " ".join([str(x.item()) for x in l])
+ logp_file.write(s + "\n")
- q = new_c_quizzes[:72]
+ ######################################################################
+ # save images with their logprobas
- if q.size(0) > 0:
- quiz_machine.save_quiz_illustrations(
- args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q
- )
+ vq = validated_quizzes[:72]
+ vl = validated_logprobas[:72]
+
+ if vq.size(0) > 0:
+ prefix = f"culture_c_quiz_{n_epoch:04d}"
+
+ file_name = os.path.join(args.result_dir, prefix + "_logp.dat")
+ 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")
+
+ quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
######################################################################
filename = f"gpt_{model.id:03d}.pth"
try:
- model.load_state_dict(
- torch.load(os.path.join(args.result_dir, filename))
- )
+ 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}")
for t in threads:
t.join()
+ # Save the models to disk
+
for model in weakest_models:
filename = f"gpt_{model.id:03d}.pth"
- torch.save(model.state_dict(), os.path.join(args.result_dir, filename))
+ torch.save(
+ (model.state_dict(), model.main_test_accuracy),
+ os.path.join(args.result_dir, filename),
+ )
log_string(f"wrote {filename}")
- ##################################################
- # Replace a fraction of the w_quizzes with fresh ones
-
- log_string(
- f"cache_w_quizzes contains {quiz_machine.problem.nb_cached_quizzes()} quizzes"
- )
-
- # Renew entirely the train set
+ # Renew the training samples
for model in weakest_models:
quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
nb_for_test=nb_new_c_quizzes_for_test,
)
- quiz_machine.save_c_quizzes(os.path.join(args.result_dir, "c_quizzes.pth"))
+ filename = "c_quizzes.pth"
+ quiz_machine.save_c_quizzes(os.path.join(args.result_dir, filename))
+ log_string(f"wrote {filename}")
######################################################################
projects / culture.git / blobdiff