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"""Fine-tuning RegFM for gene expression prediction."""
import argparse
import logging
import os
from datetime import timedelta
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["NCCL_BLOCKING_WAIT"] = "1"
os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "1"
os.environ["NCCL_DEBUG"] = "INFO"
os.environ["NCCL_TIMEOUT"] = "1800"
import torch
torch.cuda.empty_cache()
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from transformers import BertConfig, DNATokenizer
from transformers import glue_output_modes as output_modes
from transformers import glue_processors as processors
from dataset import load_and_cache_examples
from utils import (
build_dna_tokenizer,
build_regfm,
evaluate,
load_finetuned_checkpoint,
predict,
set_seed,
train,
visual_cross,
)
logger = logging.getLogger(__name__)
def main():
parser = argparse.ArgumentParser()
# data / model paths
parser.add_argument("--tfcr_dir", default=None, type=str, required=True, help="TF chromatin region input data dir.")
parser.add_argument("--dna_dir", default=None, type=str, required=True, help="DNA input data dir.")
parser.add_argument("--exp_dir", default=None, type=str, required=True, help="Expression input data dir.")
parser.add_argument("--cis_model_name_or_path", default=None, type=str, required=True, help="Path to cis-DNA pretrained model.")
parser.add_argument("--trans_model_name_or_path", default=None, type=str, required=True, help="Path to TF/trans pretrained model.")
parser.add_argument("--exp_config_name", default="", type=str, required=True, help="Expression config name or path.")
parser.add_argument("--dna_config_name", default="", type=str, required=True, help="DNA config name or path.")
parser.add_argument("--trans_tokenizer_name", default="", type=str, required=True, help="TF/trans tokenizer name or path.")
parser.add_argument("--exp_tokenizer_name", default="", type=str, required=True, help="Expression tokenizer name or path.")
parser.add_argument("--dna_tokenizer_name", default="", type=str, required=True, help="DNA tokenizer name or path.")
parser.add_argument("--output_dir", default=None, type=str, required=True, help="Output directory.")
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="Task name selected in the list: " + ", ".join(processors.keys()),
)
# modes
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run evaluation.")
parser.add_argument("--do_predict", action="store_true", help="Whether to run prediction.")
parser.add_argument("--do_visualcross", action="store_true", help="Whether to extract cross-attention.")
parser.add_argument("--evaluate_during_training", action="store_true", help="Evaluate during training.")
parser.add_argument("--overwrite_output_dir", action="store_true", help="Overwrite the output directory.")
# sequence / data processing
parser.add_argument("--max_seq_length", default=128, type=int, help="Max TF/expression sequence length.")
parser.add_argument("--max_dna_seq_length", default=128, type=int, help="Max DNA sequence length.")
parser.add_argument("--n_process", default=2, type=int, help="Number of processes used for data processing.")
# training hyperparameters
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Train batch size per GPU.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int, help="Eval batch size per GPU.")
parser.add_argument("--per_gpu_pred_batch_size", default=8, type=int, help="Predict batch size per GPU.")
parser.add_argument("--gradient_accumulation_steps", type=int, default=1, help="Gradient accumulation steps.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="Initial learning rate for Adam.")
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay.")
parser.add_argument("--hidden_dropout_prob", default=0.1, type=float, help="Hidden dropout.")
parser.add_argument("--num_train_epochs", default=3.0, type=float, help="Total training epochs.")
parser.add_argument("--max_steps", default=-1, type=int, help="Override num_train_epochs if > 0.")
parser.add_argument("--warmup_percent", default=0, type=float, help="Linear warmup percent of total steps.")
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X update steps.")
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X update steps.")
parser.add_argument("--save_total_limit", type=int, default=None, help="Max number of checkpoints to keep.")
parser.add_argument("--early_stop", default=0, type=int, help="Early stop patience (0 disables).")
# predict
parser.add_argument("--predict_dir", default=None, type=str, help="Output directory for prediction.")
parser.add_argument("--save_name", type=str, default="", help="Name for saving prediction result.")
# distributed
parser.add_argument("--local-rank", type=int, default=-1, help="Distributed training local rank.")
args = parser.parse_args()
if (
os.path.exists(args.output_dir)
and os.listdir(args.output_dir)
and args.do_train
and not args.overwrite_output_dir
):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir
)
)
if args.local_rank == -1:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl", timeout=timedelta(hours=480))
args.n_gpu = 1
args.device = device
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
)
set_seed(args)
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier()
config = BertConfig.from_pretrained(
args.exp_config_name,
num_labels=num_labels,
finetuning_task=args.task_name,
)
config.hidden_dropout_prob = args.hidden_dropout_prob
config.attention_probs_dropout_prob = 0.1
dna_config = BertConfig.from_pretrained(
args.dna_config_name,
num_labels=num_labels,
finetuning_task=args.task_name,
)
dna_config.vocab_size = 261
dna_config.max_position_embeddings = 512
dna_config.hidden_dropout_prob = args.hidden_dropout_prob
dna_config.attention_probs_dropout_prob = 0.1
tokenizer = DNATokenizer.from_pretrained(args.trans_tokenizer_name)
epi_tokenizer = DNATokenizer.from_pretrained(args.exp_tokenizer_name)
dna_tokenizer = build_dna_tokenizer(args)
model = None
if args.do_train:
model = build_regfm(args, config, dna_config)
print(model)
print(sum(p.numel() for p in model.parameters() if p.requires_grad))
logger.info("finish loading model")
if args.local_rank == 0:
torch.distributed.barrier()
if model is not None:
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
if args.do_train:
train_dataset = load_and_cache_examples(
args, args.task_name, tokenizer, epi_tokenizer, dna_tokenizer, 3000, evaluate=False
)
global_step, tr_loss = train(args, train_dataset, model, tokenizer, epi_tokenizer, dna_tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
tokenizer.save_pretrained(args.output_dir)
torch.save(model.state_dict(), args.output_dir + "/model.pth")
torch.save(model, args.output_dir + "/modelwhole.pth")
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
model.to(args.device)
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
logger.info("Evaluate the following checkpoint: %s", args.output_dir)
model = load_finetuned_checkpoint(args.output_dir, args.device, config=config)
model.to(args.device)
results = evaluate(args, model, tokenizer, epi_tokenizer, dna_tokenizer)
if args.do_predict and args.local_rank in [-1, 0]:
logger.info("Predict using the following checkpoint: %s", args.output_dir)
model = load_finetuned_checkpoint(args.output_dir, args.device, config=config)
model.to(args.device)
predict(args, model, tokenizer, epi_tokenizer, dna_tokenizer)
if args.do_visualcross and args.local_rank in [-1, 0]:
logger.info("Visualcross using the following checkpoint: %s", args.output_dir)
model = load_finetuned_checkpoint(args.output_dir, args.device, config=config)
model.to(args.device)
visual_cross(args, model, tokenizer, epi_tokenizer, dna_tokenizer)
return results
if __name__ == "__main__":
main()
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