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main.py
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comet_support = True
try:
from comet_ml import Experiment
except ImportError as e:
print("Comet ML is not installed, ignore the comet experiment monitor")
comet_support = False
import time
import esm.pretrained as esp
import argparse, sys, warnings, os
n_layer2esp_fns = {
48: esp.esm2_t48_15B_UR50D,
36: esp.esm2_t36_3B_UR50D,
33: esp.esm2_t33_650M_UR50D,
30: esp.esm2_t30_150M_UR50D,
12: esp.esm2_t12_35M_UR50D
}
parser = argparse.ArgumentParser(description="DrugLAMP for DTI prediction") # Tid: HACK
parser.add_argument('--seed', default=42, help="which seed to use", type=int)
parser.add_argument('--no-comet', help="do not use comet.ml", action='store_true')
parser.add_argument('--data', required=True, type=str, metavar='TASK', help='dataset')
parser.add_argument('--model', required=True, help="which model to do DTI prediction", type=str)
parser.add_argument('--n-layer', default=30, help="which esp.esm2 llm to use", type=int, choices=list(n_layer2esp_fns.keys()))
parser.add_argument('--split', default='random', type=str, metavar='S', help="split task", choices=['random', 'cold', 'cluster', 'Tcpi'])
parser.add_argument('--devices', type=str, help='CUDA visible devices')
parser.add_argument('--max_epoch', type=int)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.devices if args.devices else ""
from rich import print
from model import MInterface
from trainer import ExpModule
from configs import get_cfg_defaults
from handler import MultiModalityDataset
from pytorch_lightning.loggers import CometLogger
from utils import set_seed, multimodality_collate_func
import torch
from torch.utils.data import DataLoader
torch.set_float32_matmul_precision('medium')
device = torch.device('cpu')
def main():
model_name = args.model
model_cfg = f'configs/{model_name}.yaml'
n_layer = args.n_layer
ds_name = args.data
ds_split = args.split
seed = args.seed
comet_support = not args.no_comet
max_epoch = args.max_epoch
torch.cuda.empty_cache()
cfg = get_cfg_defaults()
cfg.merge_from_file(model_cfg)
cfg.SOLVER.SEED = seed
set_seed(cfg.SOLVER.SEED)
timestamp = time.strftime("%m%d_%H%M%S") # Only used in rankzero
exp_name = f"{ds_name}-{ds_split}-"\
+ f"{model_cfg[model_cfg.rfind('/') + 1: model_cfg.rfind('.')]}-"\
+ timestamp
ds_folder = f'datasets/{ds_name}'
ds_folder = os.path.join(ds_folder, ds_split)
if ds_split == 'cluster' or ds_split == 'Tcpi':
cfg.RS.TASK = True
if max_epoch:
cfg.SOLVER.MAX_EPOCH = max_epoch
if not comet_support:
cfg.COMET.USE = False
print('Choose not to use the Comet.ml...')
esp_fn = n_layer2esp_fns[n_layer]
gen_embed = cfg.SOLVER.SEED == 40
max_drug_atoms = cfg.DRUG.MAX_NODES
try:
if cfg.RS.TASK:
train_dataset = MultiModalityDataset(ds_folder, 'source_train.csv', esp_fn, n_layer, device, gen_embed=gen_embed, max_drug_atoms=max_drug_atoms)
test_target_dataset = MultiModalityDataset(ds_folder, 'target_test.csv', esp_fn, n_layer, device, max_drug_atoms=max_drug_atoms)
else:
train_dataset = MultiModalityDataset(ds_folder, 'train.csv', esp_fn, n_layer, device, gen_embed=gen_embed, max_drug_atoms=max_drug_atoms)
val_dataset = MultiModalityDataset(ds_folder, "val.csv", esp_fn, n_layer, device, max_drug_atoms=max_drug_atoms)
test_dataset = MultiModalityDataset(ds_folder, "test.csv", esp_fn, n_layer, device, max_drug_atoms=max_drug_atoms)
except ConnectionError as e:
print(e)
sys.exit(1)
logger = None
if cfg.COMET.USE and comet_support:
# LLM specific config
cfg.COMET.TAG += f'bsz={cfg.SOLVER.BATCH_SIZE};e_i={cfg.RS.INIT_EPOCH};e_s={cfg.RS.EPOCH_STEP};m={cfg.RS.MAX_MARGIN};e_r={cfg.RS.RESET_EPOCH};lr={cfg.SOLVER.LR};ssl_lr={cfg.SOLVER.SSL_LR};cm_lr={cfg.SOLVER.CM_LR}'
logger = CometLogger(
project_name=cfg.COMET.PROJECT_NAME,
workspace=cfg.COMET.WORKSPACE,
save_dir=cfg.RESULT.OUTPUT_DIR + exp_name.replace('-', '/'),
auto_output_logging="simple",
log_graph=True,
log_code=False,
log_git_metadata=False,
log_git_patch=False,
auto_param_logging=False,
auto_metric_logging=False
)
hyper_params = {
"BATCH_SIZE": cfg.SOLVER.BATCH_SIZE,
"MAX_EPOCH": cfg.SOLVER.MAX_EPOCH,
"LR": cfg.SOLVER.LR,
"Output_dir": cfg.RESULT.OUTPUT_DIR,
"SSL_use": cfg.RS.SSL,
"CM_use": cfg.RS.CM,
"RS_task": cfg.RS.TASK,
}
if hyper_params['SSL_use']:
ssl_hyper_params = {
"SSL_epoch_step": cfg.RS.EPOCH_STEP,
"SSL_optim_lr": cfg.SOLVER.SSL_LR
}
hyper_params.update(ssl_hyper_params)
if hyper_params['CM_use']:
cm_hyper_params = {
"CM_init_epoch": cfg.RS.INIT_EPOCH,
"CM_optim_lr": cfg.SOLVER.CM_LR
}
hyper_params.update(cm_hyper_params)
logger.log_hyperparams(hyper_params)
if cfg.COMET.TAG is not None:
logger.experiment.add_tag(cfg.COMET.TAG)
logger.experiment.set_name(exp_name)
if logger.experiment.get_name():
print(f"Config yaml: {model_cfg}")
print(f"Hyperparameters: {dict(cfg)}")
params = {'batch_size': cfg.SOLVER.BATCH_SIZE, 'shuffle': True, 'num_workers': cfg.SOLVER.NUM_WORKERS,
'drop_last': True, 'collate_fn': multimodality_collate_func}
if cfg.RS.TASK:
train_generator = DataLoader(train_dataset, **params)
params['shuffle'] = False
params['drop_last'] = False
params['batch_size'] = 1
val_generator = DataLoader(test_target_dataset, **params)
test_generator = DataLoader(test_target_dataset, **params)
else:
train_generator = DataLoader(train_dataset, **params)
params['shuffle'] = False
params['drop_last'] = False
params['batch_size'] = 1
val_generator = DataLoader(val_dataset, **params)
test_generator = DataLoader(test_dataset, **params)
model_interface = MInterface(model_name, cfg)
model = model_interface.load_model(**vars(train_dataset))
opt = torch.optim.AdamW(model.parameters(), lr=cfg.SOLVER.LR)
opt_ssl = torch.optim.AdamW(model.parameters(), lr=cfg.SOLVER.SSL_LR) if cfg.RS.SSL else None
opt_cm = torch.optim.AdamW(model.parameters(), lr=cfg.SOLVER.CM_LR) if cfg.RS.CM else None
torch.backends.cudnn.benchmark = True
exp_module = ExpModule(model, opt, train_generator, val_generator, test_generator,
opt_ssl=opt_ssl,
opt_cm=opt_cm,
split=ds_split,
logger=logger, **cfg)
exp_module.run_experiment()
print('Rank: ', exp_module.global_rank)
if __name__ == '__main__':
s = time.time()
main()
e = time.time()
print(f"Total running time: {round(e - s, 2)}s")