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t5_config.py
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t5_config.py
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import os
import argparse
import logging
import random
import numpy as np
import torch
def get_arguments():
parser = argparse.ArgumentParser()
## Basic parameters
parser.add_argument("--dataset_path", type=str, default="/mnt/750GB/data/Adversarial-MultiHopQA/data/hotpotqa/")
parser.add_argument("--dataset_cache", default="/mnt/750GB/data/hotpotqa/cache/")
parser.add_argument("--predict_file", default="")
parser.add_argument("--output_dir", default="/mnt/750GB/data/hotpotqa/demo_genqa", type=str)
parser.add_argument("--do_train", action='store_true', default=True)
parser.add_argument("--do_predict", action='store_true')
parser.add_argument("--train_split_name", default="demo_ans_train")
parser.add_argument("--dev_split_name", default="demo_ans_train")
parser.add_argument("--distributed", action='store_true', default=True)
parser.add_argument("--lazy", default=False)
parser.add_argument("--sf_only", default=False)
parser.add_argument("--reasoning_file", default="/mnt/750GB/data/Adversarial-MultiHopQA/data/hotpotqa/reasoning.json")
## Model parameters
parser.add_argument("--model_checkpoint", type=str, default="t5-base")
parser.add_argument("--lowercase", action='store_true', default=True)
parser.add_argument("--ans_coef", type=float, default=1.0)
parser.add_argument("--qg_coef", type=float, default=1.0)
parser.add_argument("--nce_coef", type=float, default=5.0)
parser.add_argument("--prior_coef", type=float, default=1.0)
parser.add_argument("--z_coef", type=float, default=1.0)
# Preprocessing/decoding-related parameters
parser.add_argument('--max_question_length', type=int, default=50)
parser.add_argument('--max_context_length', type=int, default=1024)
parser.add_argument('--max_output_length', type=int, default=20)
parser.add_argument('--max_num_sentences', type=int, default=8)
parser.add_argument('--num_beams', type=int, default=4)
parser.add_argument("--append_another_bos", action='store_true', default=False)
# Training-related parameters
parser.add_argument("--num_negative", default=45, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--train_batch_size", default=1, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--predict_batch_size", default=1, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--lr", default=5e-05, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--warmup_proportion", default=0.01, type=float,
help="Weight decay if we apply some.")
parser.add_argument("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--gradient_accumulation_steps", default=1, type=int,
help="Max gradient norm.")
parser.add_argument("--n_epochs", default=10, type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--max_norm", type=float, default=1.0)
parser.add_argument('--wait_step', type=int, default=10)
parser.add_argument(
# "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu"
"--device", type=str, default="cpu"
)
parser.add_argument("--fp16", type=str, default="")
parser.add_argument("--local_rank", type=int, default=-1)
# Other parameters
parser.add_argument("--verbose", action='store_true',
help="If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument('--eval_period', type=int, default=1000,
help="Evaluate & save model")
parser.add_argument('--prefix', type=str, default='',
help="Prefix for saving predictions")
parser.add_argument('--debug', action='store_true',
help="Use a subset of data for debugging")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument('--output_prob', type=int, default=1)
args = parser.parse_args()
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
print("Output directory () already exists and is not empty.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir, exist_ok=True)
##### Start writing logs
log_filename = "{}log.txt".format("" if args.do_train else "eval_")
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
handlers=[logging.FileHandler(os.path.join(args.output_dir, log_filename)),
logging.StreamHandler()])
logger = logging.getLogger(__name__)
logger.info(args)
logger.info(args.output_dir)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
args.n_gpu = torch.cuda.device_count()
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_predict:
raise ValueError("At least one of `do_train` or `do_predict` must be True.")
logger.info("Using {} gpus".format(args.n_gpu))
return args, logger