# --------------------------------------------------------
# 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)