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"""
General helper functions for setting up experiments
"""
import os
import random
from argparse import ArgumentParser
from omegaconf import DictConfig
import torch
import numpy as np
from .logging import _format_arg
def init_wandb(args: ArgumentParser) -> any:
"""Initialize WandB"""
if args.no_wandb:
wandb = None
else:
import wandb
wandb.init(config={},
entity=args.wandb_entity,
name=args.run_name,
project=args.project_name)
return wandb
def seed_everything(seed: int) -> None:
"""
Seed everything
"""
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def get_run_name_from_checkpoint(checkpoint_path: str) -> str:
"""
Helper function to get a condensed run name from the checkpoint path
"""
name = []
for s in checkpoint_path.split('/')[-1].split('-'):
if '.pt' in s:
name.append(f'_{s[:-3]}')
try:
s = s.split('=')
s = ''.join([c[0] for c in s[1].split('_')])
name.append(s)
except IndexError:
pass
return ''.join(name)
def get_run_name_from_args(args) -> str:
"""
Prepare a heinous identifier for the run based on args
"""
if args.load_distill_checkpoint is not None and args.load_distill_checkpoint != 'default':
distill_name = get_run_name_from_checkpoint(args.load_distill_checkpoint)
else:
distill_name = args.distill_config
if args.load_finetune_checkpoint is not None and args.finetune_config is None: # args.load_finetune_checkpoint != 'default':
finetune_name = get_run_name_from_checkpoint(args.load_finetune_checkpoint)
else:
finetune_name = args.finetune_config
args.run_name = f'dl-d={distill_name}-m={args.model_config}-f={finetune_name}'
if args.no_peft_grad_ckpt is not None:
args.run_name += f'-npgc={args.no_peft_grad_ckpt}'
args.run_name += f'-s={args.seed}'
if args.debug:
args.run_name += f'-debug'
if args.no_attention_mask is not None:
args.run_name += f'-nam=1'
return args.run_name.replace('True', '1').replace('False', '0') # concise hacks
def flatten_config(config: dict, flattened: dict, key: str) -> dict:
"""
Recursive way to flatten config args for saving to WandB
"""
for k, v in config.items():
if isinstance(v, dict):
flatten_config(v, flattened, f'{key}{k}_')
elif isinstance(v, list):
for ix, _config in enumerate(v):
if isinstance(_config, dict):
flatten_config(_config, flattened, f'{key}{k}_{ix}_')
else:
flattened[f'{key}{k}'] = v
return flattened
def update_config_from_args(config: DictConfig,
args: ArgumentParser,
ignore_args: list = None) -> DictConfig:
"""
Quick hacks to override default configs
"""
ignore_args = [] if ignore_args is None else ignore_args
# Dataset
if getattr(args, 'dataset', None):
config.dataset.name = args.dataset
args.run_name += f'-ds={args.dataset}'
# Optimizer
for arg in ['lr', 'weight_decay']:
if arg not in ignore_args:
argval = getattr(args, arg, None)
if argval is not None:
setattr(config.optimizer, arg, argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
try:
if getattr(args, 'optim', None):
config.optimizer.optim = args.optim
args.run_name += f'-o={args.optim}'
except AttributeError:
pass
# Scheduler
try:
if getattr(args, 'scheduler', None):
config.lr_scheduler.lr_scheduler_type = args.scheduler
args.run_name += f'-sc={args.scheduler}'
except AttributeError:
pass
# Dataset
for arg in [a for a in dir(args) if 'dataset_' in a]:
argval = getattr(args, arg, None)
if argval is not None:
setattr(config.dataset.dataset_config, arg[len('dataset_'):], argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
# Dataloader
for arg in ['batch_size']: # , 'num_workers']:
argval = getattr(args, arg, None)
if argval is not None:
setattr(config.dataloader, arg, argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
# Trainer
for arg in ['gradient_accumulation_steps', 'num_train_epochs',
'max_steps', 'max_finetune_steps', 'eval_steps',
'seed', 'max_eval_batches']:
argval = getattr(args, arg, None)
if argval is not None:
setattr(config.trainer, arg, argval)
if arg in ['max_steps', 'max_finetune_steps',
'gradient_accumulation_steps', 'num_train_epochs', 'seed']:
args.run_name += f'-{_format_arg(arg)}={argval}'
# Misc
for arg in ['replicate']:
argval = getattr(args, arg, None)
if argval is not None:
args.run_name += f'-{_format_arg(arg)}={argval}'
return config
def update_model_config_from_args(model_config: DictConfig,
args: ArgumentParser) -> DictConfig:
"""
Override default configs given argparse args
"""
# Overall attention
for arg in ['attention_type', 'learned_kernel', 'tie_qk_kernels',
'train_qk', 'state_chunk_len', 'no_peft_grad_ckpt',
'window_size']:
argval = getattr(args, arg, None)
if argval is not None:
setattr(model_config['attention'], arg, argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
else:
try:
getattr(model_config['attention'], arg)
except AttributeError:
setattr(model_config['attention'], arg, None)
# Learned kernel
for arg in ['lk_skip_connection', 'lk_zero_init', 'lk_normal_init']:
argval = getattr(args, arg, None)
if argval is not None:
setattr(model_config['attention']['learned_kernel_kwargs'],
arg[len('lk_'):], argval)
args.run_name += f'-{_format_arg(arg)}={argval}'
# Pretrained model
if args.pretrained_model_name_or_path is not None: # if specified
pmnop = args.pretrained_model_name_or_path
model_config.model.pretrained_model_name_or_path = pmnop
args.run_name += f'-pmnop={pmnop.split("/")[-1]}'
return model_config
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