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import argparse
import json
import os
import warnings
from typing import Dict, Any
from datetime import datetime
def parse_args() -> Dict[str, Any]:
"""Parse and validate command line arguments."""
parser = create_argument_parser()
args = parser.parse_args()
# Validate and process arguments
validate_args(args)
process_dataset_config(args)
setup_output_dirs(args)
setup_wandb_config(args)
return args
def create_argument_parser() -> argparse.ArgumentParser:
"""Create argument parser with all training arguments."""
parser = argparse.ArgumentParser()
# Model parameters
add_model_args(parser)
# Dataset parameters
add_dataset_args(parser)
# Training parameters
add_training_args(parser)
# Output parameters
add_output_args(parser)
# Wandb parameters
add_wandb_args(parser)
return parser
def add_model_args(parser: argparse.ArgumentParser):
"""Add model-related arguments."""
model_group = parser.add_argument_group('Model Configuration')
model_group.add_argument('--hidden_size', type=int, default=None)
model_group.add_argument('--num_attention_head', type=int, default=8)
model_group.add_argument('--attention_probs_dropout', type=float, default=0.1)
model_group.add_argument('--plm_model', type=str, default='facebook/esm2_t33_650M_UR50D')
model_group.add_argument('--pooling_method', type=str, default='mean',
choices=['mean', 'attention1d', 'light_attention'])
model_group.add_argument('--pooling_dropout', type=float, default=0.1)
def add_dataset_args(parser: argparse.ArgumentParser):
"""Add dataset-related arguments."""
data_group = parser.add_argument_group('Dataset Configuration')
data_group.add_argument('--dataset', type=str)
data_group.add_argument('--dataset_config', type=str)
data_group.add_argument('--normalize', type=str)
data_group.add_argument('--num_labels', type=int)
data_group.add_argument('--problem_type', type=str)
data_group.add_argument('--pdb_type', type=str)
data_group.add_argument('--train_file', type=str)
data_group.add_argument('--valid_file', type=str)
data_group.add_argument('--test_file', type=str)
data_group.add_argument('--metrics', type=str)
def add_training_args(parser: argparse.ArgumentParser):
"""Add training-related arguments."""
train_group = parser.add_argument_group('Training Configuration')
train_group.add_argument('--seed', type=int, default=3407)
train_group.add_argument('--learning_rate', type=float, default=1e-3)
train_group.add_argument('--scheduler', type=str, choices=['linear', 'cosine', 'step'])
train_group.add_argument('--warmup_steps', type=int, default=0)
train_group.add_argument('--num_workers', type=int, default=4)
train_group.add_argument('--batch_size', type=int)
train_group.add_argument('--batch_token', type=int)
train_group.add_argument('--num_epochs', type=int, default=100)
train_group.add_argument('--max_seq_len', type=int, default=-1)
train_group.add_argument('--gradient_accumulation_steps', type=int, default=1)
train_group.add_argument('--max_grad_norm', type=float, default=-1)
train_group.add_argument('--patience', type=int, default=10)
train_group.add_argument('--monitor', type=str)
train_group.add_argument('--monitor_strategy', type=str, choices=['max', 'min'])
train_group.add_argument('--training_method', type=str, default='freeze',
choices=['full', 'freeze', 'lora', 'ses-adapter', 'plm-lora', 'plm-qlora', 'plm-adalora', 'plm-dora', 'plm-ia3'])
parser.add_argument("--lora_r", type=int, default=8, help="lora r")
parser.add_argument("--lora_alpha", type=int, default=32, help="lora_alpha")
parser.add_argument("--lora_dropout", type=float, default=0.1, help="lora_dropout")
parser.add_argument("--feedforward_modules", type=str, default="w0")
parser.add_argument(
"--lora_target_modules",
nargs="+",
default=["query", "key", "value"],
help="lora target module",
)
train_group.add_argument('--structure_seq', type=str, default='')
def add_output_args(parser: argparse.ArgumentParser):
"""Add output-related arguments."""
output_group = parser.add_argument_group('Output Configuration')
output_group.add_argument('--output_model_name', type=str)
output_group.add_argument('--output_root', default="ckpt")
output_group.add_argument('--output_dir', default=None)
def add_wandb_args(parser: argparse.ArgumentParser):
"""Add wandb-related arguments."""
wandb_group = parser.add_argument_group('Wandb Configuration')
wandb_group.add_argument('--wandb', action='store_true')
wandb_group.add_argument('--wandb_entity', type=str)
wandb_group.add_argument('--wandb_project', type=str, default='VenusFactory')
wandb_group.add_argument('--wandb_run_name', type=str)
def validate_args(args: argparse.Namespace):
"""Validate command line arguments."""
if args.batch_size is None and args.batch_token is None:
raise ValueError("batch_size or batch_token must be provided")
if args.training_method == 'ses-adapter':
if args.structure_seq is None:
raise ValueError("structure_seq must be provided for ses-adapter")
args.structure_seq = args.structure_seq.split(',')
else:
args.structure_seq = []
def process_dataset_config(args: argparse.Namespace):
"""Process dataset configuration file."""
if not args.dataset_config:
return
config = json.load(open(args.dataset_config))
# Update args with dataset config values if not already set
for key in ['dataset', 'pdb_type', 'train_file', 'valid_file', 'test_file',
'num_labels', 'problem_type', 'monitor', 'monitor_strategy',
'metrics', 'normalize']:
if getattr(args, key) is None and key in config:
setattr(args, key, config[key])
# Handle metrics specially
if args.metrics:
args.metrics = args.metrics.split(',')
if args.metrics == ['None']:
args.metrics = ['loss']
warnings.warn("No metrics provided, using default metrics: loss")
def setup_output_dirs(args: argparse.Namespace):
"""Setup output directories."""
if args.output_dir is None:
current_date = strftime("%Y%m%d", localtime())
args.output_dir = os.path.join(args.output_root, current_date)
else:
args.output_dir = os.path.join(args.output_root, args.output_dir)
os.makedirs(args.output_dir, exist_ok=True)
def setup_wandb_config(args: argparse.Namespace):
"""Setup wandb configuration."""
if args.wandb:
if args.wandb_run_name is None:
args.wandb_run_name = f"VenusFactory-{args.dataset}"
if args.output_model_name is None:
args.output_model_name = f"{args.wandb_run_name}.pt"
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