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import os
# from architecture import DecoderTransformer
from builtin_architecture import make_model, make_model_custom
from dataset import fromDataset, get_dataloader, TextCorpusDataset
import torch
from tqdm import tqdm, trange
from logger import init_logger, flush
import torchvision
from trainingmanager import TrainingManager
import torch.nn as nn
def train_model(
experiment_directory,
trainset,
testset,
epochs,
additional_epochs,
model_params=None,
schedule=False,
**kwargs,
):
os.system(f"caffeinate -is -w {os.getpid()} &")
if model_params is None:
model_params = {}
device = "mps" if torch.backends.mps.is_available() else "cpu"
dataloader = get_dataloader(trainset)
testloader = get_dataloader(testset)
if model_params == {}:
net = make_model()
else:
net = make_model_custom(**model_params)
net.to(device)
trainer = TrainingManager(
net=net,
dir=experiment_directory,
dataloader=dataloader,
device=device,
trainstep_checkin_interval=100,
epochs=epochs,
val_dataloader=testloader,
)
# trainer.profile_trainstep()
for batch, attn_mask in dataloader:
init_logger(
net,
dir=os.path.join(experiment_directory, "tensorboard"),
)
break
if schedule:
trainer.train_curriculum(**kwargs)
else:
trainer.train()
if additional_epochs > 0:
print(f"Running additional {additional_epochs} epochs")
additional_trainer = TrainingManager(
net=net,
dir=experiment_directory,
dataloader=dataloader,
device=device,
trainstep_checkin_interval=100,
epochs=epochs + additional_epochs,
val_dataloader=testloader,
)
additional_trainer.train()
flush()
os.system("bash safe_cleanup.sh")
def run_experiment(experiment_directory, epochs, additional_epochs, trainset, testset, del_runs, **kwargs):
train_model(experiment_directory, trainset, testset, epochs, additional_epochs, schedule=True, **kwargs)
if del_runs:
os.system(f"rm -r {experiment_directory}/ckpt/*.pt")
if __name__ == "__main__":
del_runs = False
if del_runs:
del_runs = (
del_runs and input("Confirm that this will delete checkpoints: ") == "y"
)
if not del_runs:
print("Exiting")
exit()
parent_directory = "runs/code-decoder-v31-mega-licensed-1"
Curriculum = TrainingManager.get_curriculum_enum()
experiments = [
(
"curriculum-noloss",
{"curriculum_type": Curriculum.CURRICULUM, "loss_based": False},
),
(
"curriculum-loss",
{"curriculum_type": Curriculum.CURRICULUM, "loss_based": True},
),
("noop", {"curriculum_type": Curriculum.NOOP, "loss_based": False}),
(
"anticurriculum",
{"curriculum_type": Curriculum.ANTICURRICULUM, "loss_based": False},
),
(
"anticurriculum-loss",
{"curriculum_type": Curriculum.ANTICURRICULUM, "loss_based": True},
),
("hybrid", {"curriculum_type": Curriculum.HYBRID, "loss_based": False}),
("hybrid-loss", {"curriculum_type": Curriculum.HYBRID, "loss_based": True}),
("sequential", {"curriculum_type": Curriculum.SEQUENTIAL, "loss_based": False}),
(
"sequential-loss",
{"curriculum_type": Curriculum.SEQUENTIAL, "loss_based": True},
),
]
EPOCHS = 10
ADDITIONAL_EPOCHS = 20
trainset, testset = fromDataset(
TextCorpusDataset(
root_dir=os.path.expanduser(
"~/torch_datasets/github-python/mega_licensed_corpus"
),
vocab_size=33819,
IS_CODE=True,
IS_CUSTOM=True,
max_length=256,
sliding_window=False,
stride=10,
get_rarity_score=True,
get_entropy_score=False # change to True and change the above to false for entropy score instead
)
)
for experiment_name, params in experiments:
experiment_directory = os.path.join(parent_directory, experiment_name)
print(f"Running experiment: {experiment_name}")
print(f"Params: {params}")
# print(len(trainset), len(testset))
# print(trainset[3])
run_experiment(
experiment_directory,
EPOCHS,
ADDITIONAL_EPOCHS,
trainset,
testset,
del_runs,
**params,
)
import gc
gc.collect()
for obj in gc.get_objects():
try:
if torch.is_tensor(obj):
del obj
except:
pass
if torch.backends.mps.is_available():
torch._C._mps_emptyCache()
torch.mps.empty_cache()
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