Initial release of SwiftFormer
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amshaker
280
util/utils.py
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280
util/utils.py
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"""
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Misc functions, including distributed helpers.
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Mostly copy-paste from torchvision references.
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"""
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import io
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import os
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import time
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from collections import defaultdict, deque
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import datetime
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import torch
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import torch.distributed as dist
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import subprocess
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class SmoothedValue(object):
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"""Track a series of values and provide access to smoothed values over a
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window or the global series average.
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"""
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def __init__(self, window_size=20, fmt=None):
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if fmt is None:
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fmt = "{median:.4f} ({global_avg:.4f})"
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self.deque = deque(maxlen=window_size)
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self.total = 0.0
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self.count = 0
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self.fmt = fmt
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def update(self, value, n=1):
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self.deque.append(value)
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self.count += n
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self.total += value * n
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def synchronize_between_processes(self):
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"""
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Warning: does not synchronize the deque!
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"""
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if not is_dist_avail_and_initialized():
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return
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t = torch.tensor([self.count, self.total],
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dtype=torch.float64, device='cuda')
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dist.barrier()
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dist.all_reduce(t)
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t = t.tolist()
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self.count = int(t[0])
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self.total = t[1]
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@property
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def median(self):
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d = torch.tensor(list(self.deque))
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return d.median().item()
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@property
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def avg(self):
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d = torch.tensor(list(self.deque), dtype=torch.float32)
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return d.mean().item()
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@property
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def global_avg(self):
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return self.total / self.count
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@property
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def max(self):
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return max(self.deque)
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@property
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def value(self):
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return self.deque[-1]
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def __str__(self):
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return self.fmt.format(
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median=self.median,
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avg=self.avg,
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global_avg=self.global_avg,
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max=self.max,
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value=self.value)
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class MetricLogger(object):
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def __init__(self, delimiter="\t"):
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self.meters = defaultdict(SmoothedValue)
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self.delimiter = delimiter
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def update(self, **kwargs):
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for k, v in kwargs.items():
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if isinstance(v, torch.Tensor):
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v = v.item()
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assert isinstance(v, (float, int))
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self.meters[k].update(v)
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def __getattr__(self, attr):
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if attr in self.meters:
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return self.meters[attr]
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if attr in self.__dict__:
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return self.__dict__[attr]
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raise AttributeError("'{}' object has no attribute '{}'".format(
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type(self).__name__, attr))
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def __str__(self):
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loss_str = []
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for name, meter in self.meters.items():
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loss_str.append(
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"{}: {}".format(name, str(meter))
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)
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return self.delimiter.join(loss_str)
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def synchronize_between_processes(self):
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for meter in self.meters.values():
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meter.synchronize_between_processes()
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def add_meter(self, name, meter):
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self.meters[name] = meter
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def log_every(self, iterable, print_freq, header=None):
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i = 0
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if not header:
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header = ''
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start_time = time.time()
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end = time.time()
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iter_time = SmoothedValue(fmt='{avg:.4f}')
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data_time = SmoothedValue(fmt='{avg:.4f}')
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space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
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log_msg = [
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header,
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'[{0' + space_fmt + '}/{1}]',
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'eta: {eta}',
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'{meters}',
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'time: {time}',
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'data: {data}'
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]
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if torch.cuda.is_available():
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log_msg.append('max mem: {memory:.0f}')
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log_msg = self.delimiter.join(log_msg)
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MB = 1024.0 * 1024.0
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for obj in iterable:
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data_time.update(time.time() - end)
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yield obj
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iter_time.update(time.time() - end)
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if i % print_freq == 0 or i == len(iterable) - 1:
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eta_seconds = iter_time.global_avg * (len(iterable) - i)
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
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if torch.cuda.is_available():
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time),
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memory=torch.cuda.max_memory_allocated() / MB))
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else:
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print(log_msg.format(
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i, len(iterable), eta=eta_string,
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meters=str(self),
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time=str(iter_time), data=str(data_time)))
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i += 1
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end = time.time()
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total_time = time.time() - start_time
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total_time_str = str(datetime.timedelta(seconds=int(total_time)))
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print('{} Total time: {} ({:.4f} s / it)'.format(
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header, total_time_str, total_time / len(iterable)))
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def _load_checkpoint_for_ema(model_ema, checkpoint):
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"""
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Workaround for ModelEma._load_checkpoint to accept an already-loaded object
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"""
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mem_file = io.BytesIO()
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torch.save(checkpoint, mem_file)
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mem_file.seek(0)
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model_ema._load_checkpoint(mem_file)
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def setup_for_distributed(is_master):
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"""
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This function disables printing when not in master process
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"""
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import builtins as __builtin__
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builtin_print = __builtin__.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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if is_master or force:
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builtin_print(*args, **kwargs)
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__builtin__.print = print
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def is_dist_avail_and_initialized():
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if not dist.is_available():
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return False
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if not dist.is_initialized():
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return False
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return True
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def get_world_size():
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if not is_dist_avail_and_initialized():
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return 1
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return dist.get_world_size()
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def get_rank():
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if not is_dist_avail_and_initialized():
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return 0
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return dist.get_rank()
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def is_main_process():
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return get_rank() == 0
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def save_on_master(*args, **kwargs):
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if is_main_process():
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torch.save(*args, **kwargs)
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def init_distributed_mode(args):
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if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
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args.rank = int(os.environ["RANK"])
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args.world_size = int(os.environ['WORLD_SIZE'])
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args.gpu = int(os.environ['LOCAL_RANK'])
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args.dist_url = 'env://'
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os.environ['LOCAL_SIZE'] = str(torch.cuda.device_count())
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print('Using distributed mode: 1')
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elif 'SLURM_PROCID' in os.environ:
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proc_id = int(os.environ['SLURM_PROCID'])
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ntasks = int(os.environ['SLURM_NTASKS'])
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node_list = os.environ['SLURM_NODELIST']
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num_gpus = torch.cuda.device_count()
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addr = subprocess.getoutput(
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'scontrol show hostname {} | head -n1'.format(node_list))
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os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', '29500')
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os.environ['MASTER_ADDR'] = addr
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os.environ['WORLD_SIZE'] = str(ntasks)
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os.environ['RANK'] = str(proc_id)
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os.environ['LOCAL_RANK'] = str(proc_id % num_gpus)
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os.environ['LOCAL_SIZE'] = str(num_gpus)
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args.dist_url = 'env://'
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args.world_size = ntasks
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args.rank = proc_id
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args.gpu = proc_id % num_gpus
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print('Using distributed mode: slurm')
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print(f"world: {os.environ['WORLD_SIZE']}, rank:{os.environ['RANK']},"
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f" local_rank{os.environ['LOCAL_RANK']}, local_size{os.environ['LOCAL_SIZE']}")
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else:
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print('Not using distributed mode')
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args.distributed = False
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return
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args.distributed = True
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torch.cuda.set_device(args.gpu)
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args.dist_backend = 'nccl'
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print('| distributed init (rank {}): {}'.format(
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args.rank, args.dist_url), flush=True)
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torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
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world_size=args.world_size, rank=args.rank)
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torch.distributed.barrier()
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setup_for_distributed(args.rank == 0)
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def replace_batchnorm(net):
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for child_name, child in net.named_children():
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if hasattr(child, 'fuse'):
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setattr(net, child_name, child.fuse())
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elif isinstance(child, torch.nn.Conv2d):
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child.bias = torch.nn.Parameter(torch.zeros(child.weight.size(0)))
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elif isinstance(child, torch.nn.BatchNorm2d):
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setattr(net, child_name, torch.nn.Identity())
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else:
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replace_batchnorm(child)
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def replace_layernorm(net):
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import apex
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for child_name, child in net.named_children():
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if isinstance(child, torch.nn.LayerNorm):
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setattr(net, child_name, apex.normalization.FusedLayerNorm(
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child.weight.size(0)))
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else:
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replace_layernorm(child)
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