/
utils_.py
125 lines (101 loc) · 4.64 KB
/
utils_.py
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import re
import numpy as np
import torch
import torch.distributed as dist
import collections
import logging
import random
import argparse
class LossMeter(object):
def __init__(self, maxlen=100):
"""Computes and stores the running average"""
self.vals = collections.deque([], maxlen=maxlen)
def __len__(self):
return len(self.vals)
def update(self, new_val):
self.vals.append(new_val)
@property
def val(self):
return sum(self.vals) / len(self.vals)
def __repr__(self):
return str(self.val)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def load_state_dict(state_dict_path, loc='cpu'):
state_dict = torch.load(state_dict_path, map_location=loc)
# Change Multi GPU to single GPU
original_keys = list(state_dict.keys())
for key in original_keys:
if key.startswith("module."):
new_key = key[len("module."):]
state_dict[new_key] = state_dict.pop(key)
return state_dict
def set_global_logging_level(level=logging.ERROR, prefices=[""]):
"""
Override logging levels of different modules based on their name as a prefix.
It needs to be invoked after the modules have been loaded so that their loggers have been initialized.
Args:
- level: desired level. e.g. logging.INFO. Optional. Default is logging.ERROR
- prefices: list of one or more str prefices to match (e.g. ["transformers", "torch"]). Optional.
Default is `[""]` to match all active loggers.
The match is a case-sensitive `module_name.startswith(prefix)`
"""
prefix_re = re.compile(fr'^(?:{ "|".join(prefices) })')
for name in logging.root.manager.loggerDict:
if re.match(prefix_re, name):
logging.getLogger(name).setLevel(level)
def parse_args():
parser = argparse.ArgumentParser()
# Model Loading
parser.add_argument('--model',
default='t5-large',
type=str, help='path to pretrained model')
parser.add_argument('--load', type=str, default=None,
help='Load the model (usually the fine-tuned model).')
parser.add_argument('--output', type=str,
default='boxbart_checkpoint',
help='Save the model (usually the fine-tuned model).')
# Training Hyper-parameters
parser.add_argument('--seed', default=42, type=int,
help='seed for initializing training. ')
parser.add_argument('-b', '--batch_size', default=8, type=int, #32
help='mini-batch size (default: 256)')
parser.add_argument('--valid_batch_size', type=int, default=8) #32
parser.add_argument('--beam_size', type=int, default=5
)
parser.add_argument('--num_predictions',type=int, default=10)
# CPU/GPU
parser.add_argument('--fp16', action='store_true')
parser.add_argument("--distributed", action='store_true')
# Data Splits
parser.add_argument('--test_only', action='store_true')
parser.add_argument('--copy', action='store_true')
parser.add_argument("--dataset_dir",
default='chemner_filter_cleaned_data',
type=str, help='which dataset')
# Quick experiments
parser.add_argument('--train_topk', type=int, default=-1)
parser.add_argument('--valid_topk', type=int, default=-1)
parser.add_argument('--neg_num', default=5, type=int,
help='number of context entities used for negatives')
# Training configuration
parser.add_argument('--clip_grad_norm', type=float, default=10.0)
parser.add_argument("--weight_decay", default=0.01, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_eps", default=1e-6, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument('--lr', type=float, default=1e-5)
parser.add_argument('--lr-scheduler', default='linear', type=str,
help='Lr scheduler to use')
parser.add_argument('--patient', type=int, default=4)
parser.add_argument('--epochs', default=100, type=int,
help='number of total epochs to run') #10
parser.add_argument('-j', '--workers', default=8, type=int,
help='number of data loading workers')
parser.add_argument('--wandb', action='store_true')
parser.add_argument('--warmup', default=400, type=int,
help='warmup steps')
args = parser.parse_args()
if args.seed is not None:
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
return args