340 lines
11 KiB
Python
340 lines
11 KiB
Python
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# DeiT: https://github.com/facebookresearch/deit
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# BEiT: https://github.com/microsoft/unilm/tree/master/beit
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# --------------------------------------------------------
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import builtins
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import datetime
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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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from pathlib import Path
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import torch
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import torch.distributed as dist
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from torch._six import inf
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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], 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 v is None:
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continue
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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 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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builtin_print = builtins.print
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def print(*args, **kwargs):
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force = kwargs.pop('force', False)
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force = force or (get_world_size() > 8)
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if is_master or force:
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now = datetime.datetime.now().time()
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builtin_print('[{}] '.format(now), end='') # print with time stamp
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builtin_print(*args, **kwargs)
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builtins.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 args.dist_on_itp:
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args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
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args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
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args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
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args.dist_url = "tcp://%s:%s" % (os.environ['MASTER_ADDR'], os.environ['MASTER_PORT'])
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os.environ['LOCAL_RANK'] = str(args.gpu)
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os.environ['RANK'] = str(args.rank)
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os.environ['WORLD_SIZE'] = str(args.world_size)
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# ["RANK", "WORLD_SIZE", "MASTER_ADDR", "MASTER_PORT", "LOCAL_RANK"]
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elif '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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elif 'SLURM_PROCID' in os.environ:
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args.rank = int(os.environ['SLURM_PROCID'])
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args.gpu = args.rank % torch.cuda.device_count()
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else:
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print('Not using distributed mode')
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setup_for_distributed(is_master=True) # hack
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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 {}): {}, gpu {}'.format(
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args.rank, args.dist_url, args.gpu), 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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class NativeScalerWithGradNormCount:
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state_dict_key = "amp_scaler"
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def __init__(self):
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self._scaler = torch.cuda.amp.GradScaler()
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def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
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self._scaler.scale(loss).backward(create_graph=create_graph)
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if update_grad:
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if clip_grad is not None:
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assert parameters is not None
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self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
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norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
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else:
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self._scaler.unscale_(optimizer)
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norm = get_grad_norm_(parameters)
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self._scaler.step(optimizer)
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self._scaler.update()
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else:
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norm = None
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return norm
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def state_dict(self):
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return self._scaler.state_dict()
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def load_state_dict(self, state_dict):
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self._scaler.load_state_dict(state_dict)
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def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
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if isinstance(parameters, torch.Tensor):
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parameters = [parameters]
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parameters = [p for p in parameters if p.grad is not None]
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norm_type = float(norm_type)
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if len(parameters) == 0:
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return torch.tensor(0.)
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device = parameters[0].grad.device
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if norm_type == inf:
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total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
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else:
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total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
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return total_norm
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def save_model(args, epoch, model, model_without_ddp, optimizer, loss_scaler):
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output_dir = Path(args.output_dir)
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epoch_name = str(epoch)
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if loss_scaler is not None:
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checkpoint_paths = [output_dir / ('checkpoint-%s.pth' % epoch_name)]
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for checkpoint_path in checkpoint_paths:
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to_save = {
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'model': model_without_ddp.state_dict(),
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'optimizer': optimizer.state_dict(),
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'epoch': epoch,
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'scaler': loss_scaler.state_dict(),
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'args': args,
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}
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save_on_master(to_save, checkpoint_path)
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else:
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client_state = {'epoch': epoch}
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model.save_checkpoint(save_dir=args.output_dir, tag="checkpoint-%s" % epoch_name, client_state=client_state)
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def load_model(args, model_without_ddp, optimizer, loss_scaler):
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if args.resume:
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if args.resume.startswith('https'):
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checkpoint = torch.hub.load_state_dict_from_url(
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args.resume, map_location='cpu', check_hash=True)
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else:
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checkpoint = torch.load(args.resume, map_location='cpu')
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model_without_ddp.load_state_dict(checkpoint['model'])
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print("Resume checkpoint %s" % args.resume)
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if 'optimizer' in checkpoint and 'epoch' in checkpoint and not (hasattr(args, 'eval') and args.eval):
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optimizer.load_state_dict(checkpoint['optimizer'])
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args.start_epoch = checkpoint['epoch'] + 1
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if 'scaler' in checkpoint:
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loss_scaler.load_state_dict(checkpoint['scaler'])
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print("With optim & sched!")
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def all_reduce_mean(x):
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world_size = get_world_size()
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if world_size > 1:
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x_reduce = torch.tensor(x).cuda()
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dist.all_reduce(x_reduce)
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x_reduce /= world_size
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return x_reduce.item()
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else:
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return x
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