import io import os import math import time import json import glob from collections import defaultdict, deque, OrderedDict import datetime import numpy as np from pathlib import Path import argparse import torch from torch import optim as optim import torch.distributed as dist try: from torch._six import inf except ImportError: from torch import inf from tensorboardX import SummaryWriter def is_dist_avail_and_initialized(): if not dist.is_available(): return False if not dist.is_initialized(): return False return True def get_world_size(): if not is_dist_avail_and_initialized(): return 1 return dist.get_world_size() def get_rank(): if not is_dist_avail_and_initialized(): return 0 return dist.get_rank() def is_main_process(): return get_rank() == 0 def save_on_master(*args, **kwargs): if is_main_process(): torch.save(*args, **kwargs) def setup_for_distributed(is_master): """ This function disables printing when not in master process """ import builtins as __builtin__ builtin_print = __builtin__.print def print(*args, **kwargs): force = kwargs.pop('force', False) if is_master or force: builtin_print(*args, **kwargs) __builtin__.print = print def init_distributed_mode(args, init_pytorch_ddp=True): if int(os.getenv('OMPI_COMM_WORLD_SIZE', '0')) > 0: rank = int(os.environ['OMPI_COMM_WORLD_RANK']) local_rank = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK']) world_size = int(os.environ['OMPI_COMM_WORLD_SIZE']) os.environ["LOCAL_RANK"] = os.environ['OMPI_COMM_WORLD_LOCAL_RANK'] os.environ["RANK"] = os.environ['OMPI_COMM_WORLD_RANK'] os.environ["WORLD_SIZE"] = os.environ['OMPI_COMM_WORLD_SIZE'] args.rank = int(os.environ["RANK"]) args.world_size = int(os.environ["WORLD_SIZE"]) args.gpu = int(os.environ["LOCAL_RANK"]) elif 'RANK' in os.environ and 'WORLD_SIZE' in os.environ: args.rank = int(os.environ["RANK"]) args.world_size = int(os.environ['WORLD_SIZE']) args.gpu = int(os.environ['LOCAL_RANK']) else: print('Not using distributed mode') args.distributed = False return args.distributed = True args.dist_backend = 'nccl' args.dist_url = "env://" print('| distributed init (rank {}): {}, gpu {}'.format( args.rank, args.dist_url, args.gpu), flush=True) if init_pytorch_ddp: # Init DDP Group, for script without using accelerate framework torch.cuda.set_device(args.gpu) torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank, timeout=datetime.timedelta(days=365)) torch.distributed.barrier() setup_for_distributed(args.rank == 0) def cosine_scheduler(base_value, final_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=0, warmup_steps=-1): warmup_schedule = np.array([]) warmup_iters = warmup_epochs * niter_per_ep if warmup_steps > 0: warmup_iters = warmup_steps print("Set warmup steps = %d" % warmup_iters) if warmup_epochs > 0: warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) iters = np.arange(epochs * niter_per_ep - warmup_iters) schedule = np.array( [final_value + 0.5 * (base_value - final_value) * (1 + math.cos(math.pi * i / (len(iters)))) for i in iters]) schedule = np.concatenate((warmup_schedule, schedule)) assert len(schedule) == epochs * niter_per_ep return schedule def constant_scheduler(base_value, epochs, niter_per_ep, warmup_epochs=0, start_warmup_value=1e-6, warmup_steps=-1): warmup_schedule = np.array([]) warmup_iters = warmup_epochs * niter_per_ep if warmup_steps > 0: warmup_iters = warmup_steps print("Set warmup steps = %d" % warmup_iters) if warmup_iters > 0: warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters) iters = epochs * niter_per_ep - warmup_iters schedule = np.array([base_value] * iters) schedule = np.concatenate((warmup_schedule, schedule)) assert len(schedule) == epochs * niter_per_ep return schedule def get_parameter_groups(model, weight_decay=1e-5, base_lr=1e-4, skip_list=(), get_num_layer=None, get_layer_scale=None, **kwargs): parameter_group_names = {} parameter_group_vars = {} for name, param in model.named_parameters(): if not param.requires_grad: continue # frozen weights if len(kwargs.get('filter_name', [])) > 0: flag = False for filter_n in kwargs.get('filter_name', []): if filter_n in name: print(f"filter {name} because of the pattern {filter_n}") flag = True if flag: continue default_scale=1. if param.ndim <= 1 or name.endswith(".bias") or name in skip_list: # param.ndim <= 1 len(param.shape) == 1 group_name = "no_decay" this_weight_decay = 0. else: group_name = "decay" this_weight_decay = weight_decay if get_num_layer is not None: layer_id = get_num_layer(name) group_name = "layer_%d_%s" % (layer_id, group_name) else: layer_id = None if group_name not in parameter_group_names: if get_layer_scale is not None: scale = get_layer_scale(layer_id) else: scale = default_scale parameter_group_names[group_name] = { "weight_decay": this_weight_decay, "params": [], "lr": base_lr, "lr_scale": scale, } parameter_group_vars[group_name] = { "weight_decay": this_weight_decay, "params": [], "lr": base_lr, "lr_scale": scale, } parameter_group_vars[group_name]["params"].append(param) parameter_group_names[group_name]["params"].append(name) print("Param groups = %s" % json.dumps(parameter_group_names, indent=2)) return list(parameter_group_vars.values()) def create_optimizer(args, model, get_num_layer=None, get_layer_scale=None, filter_bias_and_bn=True, skip_list=None, **kwargs): opt_lower = args.opt.lower() weight_decay = args.weight_decay skip = {} if skip_list is not None: skip = skip_list elif hasattr(model, 'no_weight_decay'): skip = model.no_weight_decay() print(f"Skip weight decay name marked in model: {skip}") parameters = get_parameter_groups(model, weight_decay, args.lr, skip, get_num_layer, get_layer_scale, **kwargs) weight_decay = 0. 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_beta1') and args.opt_beta1 is not None: opt_args['betas'] = (args.opt_beta1, args.opt_beta2) print('Optimizer config:', 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) elif opt_lower == 'adadelta': optimizer = optim.Adadelta(parameters, **opt_args) elif opt_lower == 'rmsprop': optimizer = optim.RMSprop(parameters, alpha=0.9, momentum=args.momentum, **opt_args) else: assert False and "Invalid optimizer" raise ValueError return optimizer class SmoothedValue(object): """Track a series of values and provide access to smoothed values over a window or the global series average. """ def __init__(self, window_size=20, fmt=None): if fmt is None: fmt = "{median:.4f} ({global_avg:.4f})" self.deque = deque(maxlen=window_size) self.total = 0.0 self.count = 0 self.fmt = fmt def update(self, value, n=1): self.deque.append(value) self.count += n self.total += value * n def synchronize_between_processes(self): """ Warning: does not synchronize the deque! """ if not is_dist_avail_and_initialized(): return t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda') dist.barrier() dist.all_reduce(t) t = t.tolist() self.count = int(t[0]) self.total = t[1] @property def median(self): d = torch.tensor(list(self.deque)) return d.median().item() @property def avg(self): d = torch.tensor(list(self.deque), dtype=torch.float32) return d.mean().item() @property def global_avg(self): return self.total / self.count @property def max(self): return max(self.deque) @property def value(self): return self.deque[-1] def __str__(self): return self.fmt.format( median=self.median, avg=self.avg, global_avg=self.global_avg, max=self.max, value=self.value) class MetricLogger(object): def __init__(self, delimiter="\t"): self.meters = defaultdict(SmoothedValue) self.delimiter = delimiter def update(self, **kwargs): for k, v in kwargs.items(): if v is None: continue if isinstance(v, torch.Tensor): v = v.item() assert isinstance(v, (float, int)) self.meters[k].update(v) def __getattr__(self, attr): if attr in self.meters: return self.meters[attr] if attr in self.__dict__: return self.__dict__[attr] raise AttributeError("'{}' object has no attribute '{}'".format( type(self).__name__, attr)) def __str__(self): loss_str = [] for name, meter in self.meters.items(): loss_str.append( "{}: {}".format(name, str(meter)) ) return self.delimiter.join(loss_str) def synchronize_between_processes(self): for meter in self.meters.values(): meter.synchronize_between_processes() def add_meter(self, name, meter): self.meters[name] = meter def log_every(self, iterable, print_freq, header=None): i = 0 if not header: header = '' start_time = time.time() end = time.time() iter_time = SmoothedValue(fmt='{avg:.4f}') data_time = SmoothedValue(fmt='{avg:.4f}') space_fmt = ':' + str(len(str(len(iterable)))) + 'd' log_msg = [ header, '[{0' + space_fmt + '}/{1}]', 'eta: {eta}', '{meters}', 'time: {time}', 'data: {data}' ] if torch.cuda.is_available(): log_msg.append('max mem: {memory:.0f}') log_msg = self.delimiter.join(log_msg) MB = 1024.0 * 1024.0 for obj in iterable: data_time.update(time.time() - end) yield obj iter_time.update(time.time() - end) if i % print_freq == 0 or i == len(iterable) - 1: eta_seconds = iter_time.global_avg * (len(iterable) - i) eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) if torch.cuda.is_available(): print(log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time), memory=torch.cuda.max_memory_allocated() / MB)) else: print(log_msg.format( i, len(iterable), eta=eta_string, meters=str(self), time=str(iter_time), data=str(data_time))) i += 1 end = time.time() total_time = time.time() - start_time total_time_str = str(datetime.timedelta(seconds=int(total_time))) print('{} Total time: {} ({:.4f} s / it)'.format( header, total_time_str, total_time / len(iterable))) def auto_load_model(args, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None): output_dir = Path(args.output_dir) if args.auto_resume and len(args.resume) == 0: all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint.pth')) if len(all_checkpoints) > 0: args.resume = os.path.join(output_dir, 'checkpoint.pth') else: all_checkpoints = glob.glob(os.path.join(output_dir, 'checkpoint-*.pth')) latest_ckpt = -1 for ckpt in all_checkpoints: t = ckpt.split('-')[-1].split('.')[0] if t.isdigit(): latest_ckpt = max(int(t), latest_ckpt) if latest_ckpt >= 0: args.resume = os.path.join(output_dir, 'checkpoint-%d.pth' % latest_ckpt) print("Auto resume checkpoint: %s" % args.resume) if args.resume: if args.resume.startswith('https'): checkpoint = torch.hub.load_state_dict_from_url( args.resume, map_location='cpu', check_hash=True) else: checkpoint = torch.load(args.resume, map_location='cpu') model_without_ddp.load_state_dict(checkpoint['model']) # strict: bool=True, , strict=False print("Resume checkpoint %s" % args.resume) if ('optimizer' in checkpoint) and ('epoch' in checkpoint) and (optimizer is not None): optimizer.load_state_dict(checkpoint['optimizer']) print(f"Resume checkpoint at epoch {checkpoint['epoch']}, the global optmization step is {checkpoint['step']}") args.start_epoch = checkpoint['epoch'] + 1 args.global_step = checkpoint['step'] + 1 if model_ema is not None: if 'model_ema' in checkpoint: ema_load_res = model_ema.load_state_dict(checkpoint["model_ema"]) print(f"EMA Model Resume results: {ema_load_res}") if 'scaler' in checkpoint: loss_scaler.load_state_dict(checkpoint['scaler']) print("With optim & sched!") if ('optimizer_disc' in checkpoint) and (optimizer_disc is not None): optimizer_disc.load_state_dict(checkpoint['optimizer_disc']) def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler, model_ema=None, optimizer_disc=None, save_ckpt_freq=1): output_dir = Path(args.output_dir) epoch_name = str(epoch) checkpoint_paths = [output_dir / 'checkpoint.pth'] if epoch == 'best': checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name),] elif (epoch + 1) % save_ckpt_freq == 0: checkpoint_paths.append(output_dir / ('checkpoint-%s.pth' % epoch_name)) for checkpoint_path in checkpoint_paths: to_save = { 'model': model_without_ddp.state_dict(), 'epoch': epoch, 'step' : args.global_step, 'args': args, } if optimizer is not None: to_save['optimizer'] = optimizer.state_dict() if loss_scaler is not None: to_save['scaler'] = loss_scaler.state_dict() if model_ema is not None: to_save['model_ema'] = model_ema.state_dict() if optimizer_disc is not None: to_save['optimizer_disc'] = optimizer_disc.state_dict() save_on_master(to_save, checkpoint_path) def get_grad_norm_(parameters, norm_type: float = 2.0, layer_names=None) -> torch.Tensor: if isinstance(parameters, torch.Tensor): parameters = [parameters] parameters = [p for p in parameters if p.grad is not None] norm_type = float(norm_type) if len(parameters) == 0: return torch.tensor(0.) device = parameters[0].grad.device if norm_type == inf: total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters) else: layer_norm = torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]) total_norm = torch.norm(layer_norm, norm_type) if layer_names is not None: if torch.isnan(total_norm) or torch.isinf(total_norm) or total_norm > 1.0: value_top, name_top = torch.topk(layer_norm, k=5) print(f"Top norm value: {value_top}") print(f"Top norm name: {[layer_names[i][7:] for i in name_top.tolist()]}") return total_norm class NativeScalerWithGradNormCount: state_dict_key = "amp_scaler" def __init__(self, enabled=True): print(f"Set the loss scaled to {enabled}") self._scaler = torch.cuda.amp.GradScaler(enabled=enabled) def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True, layer_names=None): self._scaler.scale(loss).backward(create_graph=create_graph) if update_grad: if clip_grad is not None: assert parameters is not None self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad) else: self._scaler.unscale_(optimizer) norm = get_grad_norm_(parameters, layer_names=layer_names) self._scaler.step(optimizer) self._scaler.update() else: norm = None return norm def state_dict(self): return self._scaler.state_dict() def load_state_dict(self, state_dict): self._scaler.load_state_dict(state_dict)