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Running
on
Zero
| """ Optimizer Factory w/ Custom Weight Decay | |
| Hacked together by / Copyright 2020 Ross Wightman | |
| """ | |
| import re | |
| import torch | |
| from torch import optim as optim | |
| from utils.distributed import is_main_process | |
| import logging | |
| logger = logging.getLogger(__name__) | |
| try: | |
| from apex.optimizers import FusedNovoGrad, FusedAdam, FusedLAMB, FusedSGD | |
| has_apex = True | |
| except ImportError: | |
| has_apex = False | |
| def add_weight_decay(model, weight_decay, no_decay_list=(), filter_bias_and_bn=True): | |
| named_param_tuples = [] | |
| for name, param in model.named_parameters(): | |
| if not param.requires_grad: | |
| continue # frozen weights | |
| if filter_bias_and_bn and (len(param.shape) == 1 or name.endswith(".bias")): | |
| named_param_tuples.append([name, param, 0]) | |
| elif name in no_decay_list: | |
| named_param_tuples.append([name, param, 0]) | |
| else: | |
| named_param_tuples.append([name, param, weight_decay]) | |
| return named_param_tuples | |
| def add_different_lr(named_param_tuples_or_model, diff_lr_names, diff_lr, default_lr): | |
| """use lr=diff_lr for modules named found in diff_lr_names, | |
| otherwise use lr=default_lr | |
| Args: | |
| named_param_tuples_or_model: List([name, param, weight_decay]), or nn.Module | |
| diff_lr_names: List(str) | |
| diff_lr: float | |
| default_lr: float | |
| Returns: | |
| named_param_tuples_with_lr: List([name, param, weight_decay, lr]) | |
| """ | |
| named_param_tuples_with_lr = [] | |
| logger.info(f"diff_names: {diff_lr_names}, diff_lr: {diff_lr}") | |
| for name, p, wd in named_param_tuples_or_model: | |
| use_diff_lr = False | |
| for diff_name in diff_lr_names: | |
| # if diff_name in name: | |
| if re.search(diff_name, name) is not None: | |
| logger.info(f"param {name} use different_lr: {diff_lr}") | |
| use_diff_lr = True | |
| break | |
| named_param_tuples_with_lr.append( | |
| [name, p, wd, diff_lr if use_diff_lr else default_lr] | |
| ) | |
| if is_main_process(): | |
| for name, _, wd, diff_lr in named_param_tuples_with_lr: | |
| logger.info(f"param {name}: wd: {wd}, lr: {diff_lr}") | |
| return named_param_tuples_with_lr | |
| def create_optimizer_params_group(named_param_tuples_with_lr): | |
| """named_param_tuples_with_lr: List([name, param, weight_decay, lr])""" | |
| group = {} | |
| for name, p, wd, lr in named_param_tuples_with_lr: | |
| if wd not in group: | |
| group[wd] = {} | |
| if lr not in group[wd]: | |
| group[wd][lr] = [] | |
| group[wd][lr].append(p) | |
| optimizer_params_group = [] | |
| for wd, lr_groups in group.items(): | |
| for lr, p in lr_groups.items(): | |
| optimizer_params_group.append(dict( | |
| params=p, | |
| weight_decay=wd, | |
| lr=lr | |
| )) | |
| logger.info(f"optimizer -- lr={lr} wd={wd} len(p)={len(p)}") | |
| return optimizer_params_group | |
| def create_optimizer(args, model, filter_bias_and_bn=True): | |
| opt_lower = args.opt.lower() | |
| weight_decay = args.weight_decay | |
| # check for modules that requires different lr | |
| if hasattr(args, "different_lr") and args.different_lr.enable: | |
| diff_lr_module_names = args.different_lr.module_names | |
| diff_lr = args.different_lr.lr | |
| else: | |
| diff_lr_module_names = [] | |
| diff_lr = None | |
| no_decay = {} | |
| if hasattr(model, 'no_weight_decay'): | |
| no_decay = model.no_weight_decay() | |
| named_param_tuples = add_weight_decay( | |
| model, weight_decay, no_decay, filter_bias_and_bn) | |
| named_param_tuples = add_different_lr( | |
| named_param_tuples, diff_lr_module_names, diff_lr, args.lr) | |
| parameters = create_optimizer_params_group(named_param_tuples) | |
| if 'fused' in opt_lower: | |
| assert has_apex and torch.cuda.is_available(), 'APEX and CUDA required for fused optimizers' | |
| opt_args = dict(lr=args.lr, weight_decay=weight_decay) | |
| if hasattr(args, 'opt_eps') and args.opt_eps is not None: | |
| opt_args['eps'] = args.opt_eps | |
| if hasattr(args, 'opt_betas') and args.opt_betas is not None: | |
| opt_args['betas'] = args.opt_betas | |
| if hasattr(args, 'opt_args') and args.opt_args is not None: | |
| opt_args.update(args.opt_args) | |
| opt_split = opt_lower.split('_') | |
| opt_lower = opt_split[-1] | |
| if opt_lower == 'sgd' or opt_lower == 'nesterov': | |
| opt_args.pop('eps', None) | |
| optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=True, **opt_args) | |
| elif opt_lower == 'momentum': | |
| opt_args.pop('eps', None) | |
| optimizer = optim.SGD(parameters, momentum=args.momentum, nesterov=False, **opt_args) | |
| elif opt_lower == 'adam': | |
| optimizer = optim.Adam(parameters, **opt_args) | |
| elif opt_lower == 'adamw': | |
| optimizer = optim.AdamW(parameters, **opt_args) | |
| else: | |
| assert False and "Invalid optimizer" | |
| raise ValueError | |
| return optimizer | |