# -------------------------------------------------------- # Swin Transformer # Copyright (c) 2021 Microsoft # Licensed under The MIT License [see LICENSE for details] # Written by Ze Liu # -------------------------------------------------------- import os import torch import torch.distributed as dist from torch._six import inf def load_checkpoint(file_name, config, model, model_ema, optimizer, lr_scheduler, loss_scaler, logger): if config.model.params.deepspeed != '': file_name = file_name.split('/') ckptdir = '/'.join(file_name[:-1]) tag = file_name[-1] _, client_states = model.load_checkpoint(ckptdir, tag=tag) print(client_states) logger.info("Resume checkpoint %s" % file_name) checkpoint = torch.load( os.path.join(ckptdir, tag, "state.pth"), map_location="cpu" ) msg = model_ema.load_state_dict(checkpoint['model_ema']) logger.info(msg) start_epoch = checkpoint["epoch"] + 1 max_accuracy = 0.0 if loss_scaler and "grad_scale_manager" in checkpoint: loss_scaler.load_state_dict(checkpoint["grad_scale_manager"]) if 'max_accuracy' in checkpoint: max_accuracy = checkpoint['max_accuracy'] else: logger.info(f"==============> Resuming form {file_name}....................") checkpoint = torch.load(file_name, map_location='cpu') msg = model.load_state_dict(checkpoint['model'], strict=False) logger.info(msg) max_accuracy = 0.0 if 'optimizer' in checkpoint and 'epoch' in checkpoint: optimizer.load_state_dict(checkpoint['optimizer']) if 'lr_scheduler' in checkpoint: lr_scheduler.load_state_dict(checkpoint['lr_scheduler']) start_epoch = checkpoint['epoch'] + 1 if 'scaler' in checkpoint: loss_scaler.load_state_dict(checkpoint['scaler']) logger.info(f"=> loaded successfully '{file_name}' (epoch {checkpoint['epoch']})") if 'max_accuracy' in checkpoint: max_accuracy = checkpoint['max_accuracy'] del checkpoint torch.cuda.empty_cache() return max_accuracy, start_epoch def save_checkpoint(ckptdir, config, epoch, model, model_ema, max_accuracy, optimizer, lr_scheduler, loss_scaler, logger): if config.model.params.deepspeed != '': if dist.get_rank() == 0: os.makedirs(os.path.join(ckptdir, f'ckpt_epoch_{epoch}'), exist_ok=True) checkpoint_path = os.path.join(ckptdir, f'ckpt_epoch_{epoch}', f'state.pth') to_save = { 'epoch': epoch, 'config': config, 'max_accuracy': max_accuracy, 'model_ema': model_ema.state_dict(), } if loss_scaler is not None: to_save["grad_scale_manager"] = loss_scaler.state_dict() logger.info(f"Saving checkpoint to {checkpoint_path}") torch.save(to_save, checkpoint_path) model.save_checkpoint(save_dir=ckptdir, tag=f'ckpt_epoch_{epoch}') print(f"rank[{dist.get_rank()}]: {ckptdir}/{f'ckpt_epoch_{epoch}'} saved") dist.barrier() else: if dist.get_rank() == 0: save_state = {'model': model.state_dict(), 'optimizer': optimizer.state_dict(), # 'lr_scheduler': lr_scheduler.state_dict(), 'max_accuracy': max_accuracy, 'scaler': loss_scaler.state_dict(), 'epoch': epoch, 'config': config} save_path = os.path.join(ckptdir, f'ckpt_epoch_{epoch}.pth') logger.info(f"{save_path} saving......") torch.save(save_state, save_path) logger.info(f"{save_path} saved !!!") def get_grad_norm(parameters, norm_type=2): if isinstance(parameters, torch.Tensor): parameters = [parameters] parameters = list(filter(lambda p: p.grad is not None, parameters)) norm_type = float(norm_type) total_norm = 0 for p in parameters: param_norm = p.grad.data.norm(norm_type) total_norm += param_norm.item() ** norm_type total_norm = total_norm ** (1. / norm_type) return total_norm def auto_resume_helper(config, output_dir): if config.model.params.deepspeed != '': dirs = os.listdir(output_dir) dirs = [d for d in dirs if d.startswith('ckpt_epoch')] print(f"All checkpoints founded in {output_dir}: {dirs}") if len(dirs) > 0: dirs = max([int(d.split('_')[-1]) for d in dirs]) latest_checkpoint = os.path.join(output_dir, 'ckpt_epoch_{}'.format(dirs)) print(f"The latest checkpoint founded: {latest_checkpoint}") resume_file = latest_checkpoint else: resume_file = None else: checkpoints = os.listdir(output_dir) checkpoints = [ckpt for ckpt in checkpoints if ckpt.endswith('pth')] print(f"All checkpoints founded in {output_dir}: {checkpoints}") if len(checkpoints) > 0: latest_checkpoint = max([os.path.join(output_dir, d) for d in checkpoints], key=os.path.getmtime) print(f"The latest checkpoint founded: {latest_checkpoint}") resume_file = latest_checkpoint else: resume_file = None return resume_file def reduce_tensor(tensor): rt = tensor.clone() dist.all_reduce(rt, op=dist.ReduceOp.SUM) rt /= dist.get_world_size() return rt def ampscaler_get_grad_norm(parameters, norm_type: float = 2.0) -> 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: total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type) return total_norm class NativeScalerWithGradNormCount: state_dict_key = "amp_scaler" def __init__(self): self._scaler = torch.cuda.amp.GradScaler() def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True): 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 = ampscaler_get_grad_norm(parameters) 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)