import mygpt
import sky, grids, quiz_machine
-# world quizzes vs. culture quizzes
+import threading
-######################################################################
-
-if torch.cuda.is_available():
- device = torch.device("cuda")
- torch.backends.cuda.matmul.allow_tf32 = True
-else:
- device = torch.device("cpu")
+import torch.multiprocessing as mp
######################################################################
parser = argparse.ArgumentParser(
- description="An implementation of GPT with cache.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("--seed", type=int, default=0)
-parser.add_argument("--max_percents_of_test_in_train", type=int, default=1)
+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("--problem", type=str, default="grids")
-parser.add_argument("--nb_threads", type=int, default=-1)
+parser.add_argument("--nb_threads", type=int, default=1)
+
+parser.add_argument("--gpus", type=str, default="all")
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("--proba_understands", type=float, default=0.99)
-parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
+parser.add_argument("--proba_not_understands", type=float, default=0.5)
parser.add_argument("--generation_temperature", type=float, default=2.0)
-parser.add_argument("--deterministic_validation", action="store_true", default=False)
+parser.add_argument("--dirty_debug", 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)
+grids_tasks = ", ".join(
+ [x.__name__.removeprefix("task_") for x in grids.Grids().all_tasks]
+)
+
+parser.add_argument(
+ "--grids_tasks",
+ type=str,
+ default=None,
+ help="A comma-separated subset of: " + grids_tasks + ", or None for all.",
+)
######################################################################
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):
######################################################################
+if args.gpus == "all":
+ gpus_idx = range(torch.cuda.device_count())
+else:
+ gpus_idx = [int(k) for k in args.gpus.split(",")]
+
+gpus = [torch.device(f"cuda:{n}") for n in gpus_idx]
+
+if torch.cuda.is_available():
+ main_device = gpus[0]
+else:
+ assert len(gpus) == 0
+ main_device = torch.device("cpu")
+
if args.dirty_debug:
args.nb_train_samples = 2500
args.nb_test_samples = 100
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,
+ max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
chunk_size=100,
nb_threads=args.nb_threads,
)
back_accuracy = False
elif args.problem == "grids":
problem = grids.Grids(
- max_nb_cached_chunks=args.nb_train_samples // 100,
+ max_nb_cached_chunks=len(gpus) * args.nb_train_samples // 100,
chunk_size=100,
nb_threads=args.nb_threads,
+ tasks=args.grids_tasks,
)
back_accuracy = True
else:
raise ValueError
+problem.save_some_examples(args.result_dir)
+
quiz_machine = quiz_machine.QuizMachine(
problem=problem,
nb_train_samples=args.nb_train_samples,
batch_size=args.physical_batch_size,
result_dir=args.result_dir,
logger=log_string,
- device=device,
+ device=main_device,
)
######################################################################
-log_string(f"device {device}")
+log_string(f"main_device {main_device} gpus {[ str(g) for g in gpus]}")
vocabulary_size = quiz_machine.vocabulary_size()
######################################################################
-# Compute the entropy of the training tokens
-
-token_count = 0
-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()
-entropy = -torch.xlogy(token_probas, token_probas).sum()
-train_set_perplexity = math.exp(entropy)
-
-######################################################################
-# A bit of paranoia never hurts
-
-if args.max_percents_of_test_in_train >= 0:
-
- def subsets_as_tuples(batches, cs):
- s = set()
- for batch in batches:
- for x in batch:
- s.add(tuple([v.item() for v in x]))
- if len(s) == cs:
- yield s
- s = set()
- yield s
- nb_test, nb_in_train = 0, 0
- for test_subset in subsets_as_tuples(
- quiz_machine.batches(split="test", desc="test-check"), 25000
- ):
- in_train = set()
- for train_subset in subsets_as_tuples(
- quiz_machine.batches(split="train", desc="train-check"), 25000
- ):
- in_train.update(test_subset.intersection(train_subset))
- nb_in_train += len(in_train)
- nb_test += len(test_subset)
-
- log_string(
- f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
- )
-
- assert (
- nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
- ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
-
-##############################
-
-
-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
-
- for input in quiz_machine.batches(split="train"):
- input = input.to(device)
-
- if nb_train_samples % args.batch_size == 0:
- optimizer.zero_grad()
-
- output = model(mygpt.BracketedSequence(input)).x
- loss = F.cross_entropy(output.transpose(1, 2), input)
- acc_train_loss += loss.item() * input.size(0)
-
- nb_train_samples += input.size(0)
-
- loss.backward()
-
- if nb_train_samples % args.batch_size == 0:
- optimizer.step()
-
- train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
-
- log_string(f"train_perplexity {n_epoch} {train_perplexity}")
-
-
-######################################################################
-
-
-def run_tests(model, quiz_machine, deterministic_synthesis):
+def run_tests(model, quiz_machine, deterministic_synthesis, local_device=main_device):
with torch.autograd.no_grad():
- model.eval()
+ model.eval().to(local_device)
nb_test_samples, acc_test_loss = 0, 0.0
nb_samples_accumulated = 0
- for input in quiz_machine.batches(split="test"):
- input = input.to(device)
+ for input in quiz_machine.batches(model, split="test"):
+ input = input.to(local_device)
bs = model(mygpt.BracketedSequence(input))
output = bs.x
test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
- log_string(f"test_perplexity {n_epoch} {test_perplexity}")
+ log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
model.main_test_accuracy = quiz_machine.produce_results(
n_epoch=n_epoch,
)
-######################################################################
-
-
-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 one_epoch(model, quiz_machine, local_device=main_device):
+ model.to(local_device).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([])
-
-
-######################################################################
+ optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+ nb_train_samples, acc_train_loss = 0, 0.0
-def create_c_quizzes(
- models,
- quiz_machine,
- nb_for_train=1000,
- nb_for_test=100,
-):
- quizzes_and_logproba_records = []
+ for input in quiz_machine.batches(model, split="train"):
+ input = input.to(local_device)
- nb_to_create = nb_for_train + nb_for_test
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.zero_grad()
- # ------------------------------------------------------------
+ output = model(mygpt.BracketedSequence(input)).x
+ loss = F.cross_entropy(output.transpose(1, 2), input)
+ acc_train_loss += loss.item() * input.size(0)
- file_name = os.path.join(args.result_dir, f"culture_c_quiz_{n_epoch:04d}_logp.dat")
+ nb_train_samples += input.size(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
+ loss.backward()
- model_for_generation = models[torch.randint(len(models), (1,))]
+ if nb_train_samples % args.batch_size == 0:
+ optimizer.step()
- c_quizzes = quiz_machine.generate_quizzes(
- nb_to_create,
- model_for_generation=model_for_generation,
- temperature=args.generation_temperature,
- )
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
+ log_string(f"train_perplexity {n_epoch} model {model.id} {train_perplexity}")
- 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))
+ run_tests(model, quiz_machine, deterministic_synthesis=False)
- for l in logproba:
- s = " ".join([str(x.item()) for x in l])
- logp_file.write(s + "\n")
+ model.to(main_device)
- 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}"
- )
+# This is the key routine that decides what generated quizzes to keep
- # store the new c_quizzes which have been validated
- new_c_quizzes = valid_c_quizzes(quizzes_and_logproba_records, standard_validity)
+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))
- 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)
+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)
+ )
- # save a bunch of images to investigate what quizzes with a
- # certain nb of correct predictions look like
- q = new_c_quizzes[:72]
+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 q.size(0) > 0:
- quiz_machine.save_quizzes(args.result_dir, f"culture_c_quiz_{n_epoch:04d}", q)
+ 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_nb_correct_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
- # ------------------------------------------------------------
-
- standard_validity = lambda nb_correct: torch.logical_and(
- nb_correct >= args.min_to_validate, nb_correct <= args.max_to_validate
- )
-
- 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)
- < 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,
- )
-
- # if args.prediction_correctness:
+ recorded_quizzes_logprobas = []
- # 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))
+ nb_validated = 0
- c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
+ while nb_validated < nb_to_create:
+ model_for_generation = models[torch.randint(len(models), (1,))]
- 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,
- )
-
- 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 = 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
- )
+ c_quizzes = c_quizzes[quiz_machine.non_trivial(c_quizzes)]
- quizzes_and_nb_correct_records.append((c_quizzes, nb_correct))
+ if c_quizzes.size(0) > 0:
+ token_logproba = quiz_machine.solution_token_logprobas(models, c_quizzes)
+ recorded_quizzes_logprobas.append((c_quizzes, token_logproba))
- nv = F.one_hot(nb_correct, num_classes=len(models) + 1).sum(0)
- nv = " ".join([str(x.item()) for x in nv])
+ (
+ validated_quizzes,
+ validated_logprobas,
+ ) = extract_valid_quizzes_and_logprobas(recorded_quizzes_logprobas)
- nb_validated = valid_c_quizzes(
- quizzes_and_nb_correct_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} kept {nv} 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_nb_correct_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)
+ 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
+ )
- # save a bunch of images to investigate what quizzes with a
- # certain nb of correct predictions look like
+ ######################################################################
+ # save images with their logprobas
- for n in range(len(models) + 1):
- s = (
- "_validated"
- if n >= args.min_to_validate and n <= args.max_to_validate
- else ""
- )
+ vq = validated_quizzes[:72]
+ vl = validated_logprobas[:72]
- q = valid_c_quizzes(
- quizzes_and_nb_correct_records, criteria=lambda nb_correct: nb_correct == n
- )[: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")
- 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
- )
+ quiz_machine.save_quiz_illustrations(args.result_dir, prefix, vq)
######################################################################
models = []
for k in range(args.nb_gpts):
+ log_string(f"creating model {k} and its w_quizzes")
model = mygpt.MyGPT(
vocabulary_size=vocabulary_size,
dim_model=args.dim_model,
nb_blocks=args.nb_blocks,
causal=True,
dropout=args.dropout,
- ).to(device)
+ ).to(main_device)
model.main_test_accuracy = 0.0
model.id = k
+ model.train_w_quizzes = quiz_machine.generate_token_sequences(args.nb_train_samples)
+ quiz_machine.reverse_random_half_in_place(model.train_w_quizzes)
+ model.test_w_quizzes = quiz_machine.generate_token_sequences(args.nb_test_samples)
+ quiz_machine.reverse_random_half_in_place(model.test_w_quizzes)
+
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
+# Compute the entropy of the training tokens
+
+token_count = 0
+for input in quiz_machine.batches(models[0], 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()
+entropy = -torch.xlogy(token_probas, token_probas).sum()
+train_set_perplexity = math.exp(entropy)
+
+######################################################################
+# A bit of paranoia never hurts
+
+if args.max_percents_of_test_in_train >= 0:
+
+ def subsets_as_tuples(batches, cs):
+ s = set()
+ for batch in batches:
+ for x in batch:
+ s.add(tuple([v.item() for v in x]))
+ if len(s) == cs:
+ yield s
+ s = set()
+ yield s
+
+ nb_test, nb_in_train = 0, 0
+ for test_subset in subsets_as_tuples(
+ quiz_machine.batches(models[0], split="test", desc="test-check"), 25000
+ ):
+ in_train = set()
+ for train_subset in subsets_as_tuples(
+ quiz_machine.batches(models[0], split="train", desc="train-check"), 25000
+ ):
+ in_train.update(test_subset.intersection(train_subset))
+ nb_in_train += len(in_train)
+ nb_test += len(test_subset)
+
+ log_string(
+ f"data_check {nb_in_train*100/nb_test:.02f}% ({nb_in_train}/{nb_test}) of test samples are in the train set"
+ )
+
+ assert (
+ nb_in_train <= args.max_percents_of_test_in_train * nb_test / 100
+ ), f"More than {args.max_percents_of_test_in_train}% of test samples are in the train set"
+
+######################################################################
+
+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
+ 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}")
+
##################################################
# Select, improve, and eval the worst model
- weakest_model = min(models, key=lambda m: float(m.main_test_accuracy))
+ ranked_models = sorted(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_models = ranked_models[: len(gpus)]
- one_epoch(weakest_model, quiz_machine)
+ threads = []
- log_string(
- f"train_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
- )
+ for gpu, model in zip(gpus, weakest_models):
+ log_string(f"training model {model.id}")
- run_tests(weakest_model, quiz_machine, deterministic_synthesis=False)
+ t = threading.Thread(
+ target=one_epoch, daemon=True, args=(model, quiz_machine, gpu)
+ )
- log_string(
- f"test_set_composition w_quizzes {quiz_machine.nb_batch_w_quizzes} c_quizzes {quiz_machine.nb_batch_c_quizzes}"
- )
+ threads.append(t)
- ##################################################
- # Replace a fraction of the w_quizzes with fresh ones
+ t.start()
- quiz_machine.renew_w_quizzes(args.nb_train_samples // args.nb_gpts)
+ for t in threads:
+ t.join()
- ##################################################
- # If all the models are good enough, generate new quizzes and
- # re-compute the test errors
+ # Save the models to disk
- 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,
+ 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}")
- for model in models:
- run_tests(model, quiz_machine, deterministic_synthesis=False)
+ # Renew the training samples
+
+ for model in weakest_models:
+ quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
######################################################################
projects / culture.git / blobdiff