import mygpt
import sky, grids, quiz_machine
-import torch.multiprocessing as mp
-
-# mp.set_start_method('spawn')
+import threading
# world quizzes vs. culture quizzes
######################################################################
-
-def log_string(s):
- t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
-
- if log_file is not None:
- log_file.write(t + s + "\n")
- log_file.flush()
-
- print(t + s)
- sys.stdout.flush()
-
-
-######################################################################
-
-
-def run_tests(model, quiz_machine, deterministic_synthesis, local_device=None):
- if local_device is None:
- local_device = device
-
- with torch.autograd.no_grad():
- model.eval().to(local_device)
-
- nb_test_samples, acc_test_loss = 0, 0.0
- nb_samples_accumulated = 0
-
- for input in quiz_machine.batches(model, split="test"):
- input = input.to(local_device)
-
- bs = model(mygpt.BracketedSequence(input))
- output = bs.x
-
- loss = F.cross_entropy(output.transpose(1, 2), input)
-
- acc_test_loss += loss.item() * input.size(0)
-
- nb_test_samples += input.size(0)
-
- test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
-
- log_string(f"test_perplexity {n_epoch} model {model.id} {test_perplexity}")
-
- model.main_test_accuracy = quiz_machine.produce_results(
- n_epoch=n_epoch,
- model=model,
- result_dir=args.result_dir,
- deterministic_synthesis=deterministic_synthesis,
- )
-
-
-def one_epoch(model, quiz_machine, local_device=None):
- if local_device is None:
- local_device = device
-
- optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-
- model.to(local_device).train()
-
- nb_train_samples, acc_train_loss = 0, 0.0
-
- for input in quiz_machine.batches(model, split="train"):
- input = input.to(local_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} model {model.id} {train_perplexity}")
-
- run_tests(model, quiz_machine, deterministic_synthesis=False)
-
-
-######################################################################
-
-
-def standard_validity(logproba):
- l = logproba.sort(dim=-1).values
- return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.99))
- # warnings.warn("TEST!!!", RuntimeWarning)
- # print(l.exp())
- # return (l[:, 0] < math.log(0.99))
-
-
-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 create_c_quizzes(
- models,
- 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 = 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))
-
- 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)
-
-
-######################################################################
-
if torch.cuda.is_available():
device = torch.device("cuda")
torch.backends.cuda.matmul.allow_tf32 = True
parser.add_argument("--max_to_validate", type=int, default=None)
-parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.9)
+parser.add_argument("--accuracy_to_make_c_quizzes", type=float, default=0.975)
parser.add_argument("--generation_temperature", type=float, default=2.0)
log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
-log_string(f"argv {' '.join(sys.argv)}")
-
-for n in vars(args):
- log_string(f"args.{n} {getattr(args, n)}")
-
if args.seed >= 0:
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
######################################################################
+
+def log_string(s):
+ t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime())
+
+ if log_file is not None:
+ log_file.write(t + s + "\n")
+ log_file.flush()
+
+ print(t + s)
+ sys.stdout.flush()
+
+
+log_string(f"argv {' '.join(sys.argv)}")
+
+for n in vars(args):
+ log_string(f"args.{n} {getattr(args, n)}")
+
+
+######################################################################
+
if args.dirty_debug:
args.nb_train_samples = 2500
args.nb_test_samples = 100
else:
raise ValueError
-problem.save_some_examples(args.result_dir)
-
quiz_machine = quiz_machine.QuizMachine(
problem=problem,
nb_train_samples=args.nb_train_samples,
######################################################################
+
+######################################################################
+
+
+def run_tests(model, quiz_machine, deterministic_synthesis, local_device=None):
+ if local_device is None:
+ local_device = device
+
+ with torch.autograd.no_grad():
+ model.eval().to(local_device)
+
+ nb_test_samples, acc_test_loss = 0, 0.0
+ nb_samples_accumulated = 0
+
+ for input in quiz_machine.batches(model, split="test"):
+ input = input.to(local_device)
+
+ bs = model(mygpt.BracketedSequence(input))
+ output = bs.x
+
+ loss = F.cross_entropy(output.transpose(1, 2), input)
+
+ acc_test_loss += loss.item() * input.size(0)
+
+ nb_test_samples += input.size(0)
+
+ test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples))
+
+ log_string(f"test_perplexity {n_epoch} {test_perplexity}")
+
+ model.main_test_accuracy = quiz_machine.produce_results(
+ n_epoch=n_epoch,
+ model=model,
+ result_dir=args.result_dir,
+ deterministic_synthesis=deterministic_synthesis,
+ )
+
+
+def one_epoch(model, quiz_machine, local_device=None):
+ if local_device is None:
+ local_device = device
+
+ optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
+
+ model.to(local_device).train()
+
+ nb_train_samples, acc_train_loss = 0, 0.0
+
+ for input in quiz_machine.batches(model, split="train"):
+ input = input.to(local_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}")
+
+ run_tests(model, quiz_machine, deterministic_synthesis=False)
+
+ model.TRAINING_LOCK.release()
+
+
+######################################################################
+
+
+def standard_validity(logproba):
+ l = logproba.sort(dim=-1).values
+ return (l[:, 0] < math.log(0.5)) & (l[:, 1] > math.log(0.99))
+ # warnings.warn("TEST!!!", RuntimeWarning)
+ # print(l.exp())
+ # return (l[:, 0] < math.log(0.99))
+
+
+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 create_c_quizzes(
+ models,
+ 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 = 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))
+
+ 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)
+
+
+######################################################################
+
models = []
for k in range(args.nb_gpts):
model.main_test_accuracy = 0.0
model.id = k
+ model.TRAINING_LOCK = threading.Lock()
model.train_w_quizzes = quiz_machine.generate_token_sequences(
args.nb_train_samples
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(0.99)
-
-
######################################################################
for n_epoch in range(args.nb_epochs):
log_string(f"current_test_accuracies {cta}")
##################################################
- # Select, improve, and eval the worst models
+ # Select, improve, and eval the worst model
ranked_models = sorted(models, key=lambda m: float(m.main_test_accuracy))
weakest_models = ranked_models[: args.nb_gpus]
- processes = []
-
for gpu_id, model in enumerate(weakest_models):
+ model.TRAINING_LOCK.acquire()
+
log_string(
f"training model {model.id} main_test_accuracy {model.main_test_accuracy}"
)
- process = mp.Process(
- target=one_epoch, args=(model, quiz_machine, f"cuda:{gpu_id}")
- )
-
- processes.append(process)
-
- for process in processes:
- process.start()
+ threading.Thread(
+ target=one_epoch, daemon=True, args=(model, quiz_machine, f"cuda:{gpu_id}")
+ ).start()
- for process in processes:
- process.join()
+ for model in weakest_models:
+ model.TRAINING_LOCK.acquire()
+ model.TRAINING_LOCK.release()
##################################################
- # Renew the train sets
+ # 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
+
for model in weakest_models:
quiz_machine.renew_w_quizzes(model, args.nb_train_samples)
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