File size: 8,675 Bytes
705a8fd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 | import torch
import torch.distributed as dist
from torcheval.metrics import FrechetInceptionDistance
from collections import defaultdict, deque
import os
import datetime
import builtins
from logging import getLogger
import pickle
import time
logger = getLogger()
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 setup_for_distributed(is_master):
"""
This function disables printing when not in master process
"""
builtin_print = builtins.print
def print(*args, **kwargs):
force = kwargs.pop('force', False)
force = force or (get_world_size() > 8)
if is_master or force:
now = datetime.datetime.now().time()
builtin_print('[{}] '.format(now), end='') # print with time stamp
builtin_print(*args, **kwargs)
builtins.print = print
def init_distributed(port=37124, rank_and_world_size=(None, None)):
rank, world_size = rank_and_world_size
dist_url='env://'
os.environ['MASTER_PORT'] = os.environ.get('MASTER_PORT', str(port))
print("Using port", os.environ['MASTER_PORT'])
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
try:
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
gpu = int(os.environ["LOCAL_RANK"])
except Exception:
logger.info('torchrun env vars not sets')
elif "SLURM_PROCID" in os.environ:
try:
world_size = int(os.environ['SLURM_NTASKS'])
rank = int(os.environ['SLURM_PROCID'])
gpu = rank % torch.cuda.device_count()
if 'HOSTNAME' in os.environ:
os.environ['MASTER_ADDR'] = os.environ['HOSTNAME']
else:
os.environ['MASTER_ADDR'] = '127.0.0.1'
except Exception:
logger.info('SLURM vars not set')
else:
rank = 0
world_size = 1
gpu = 0
os.environ['MASTER_ADDR'] = '127.0.0.1'
torch.cuda.set_device(gpu)
torch.distributed.init_process_group(
backend='nccl',
world_size=world_size,
rank=rank,
init_method=dist_url
)
# setup_for_distributed(rank == 0)
return world_size, rank, gpu, True
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)))
self.update(total_time=total_time)
def sync_fid_loss_fns(fid_loss_fn, device="cuda"):
"""
Synchronizes FID loss function metrics across all processes.
Args:
fid_loss_fn (dict): Local FID loss function metrics on each process.
device (str): Device to move the merged FID metrics to.
Returns:
final_fid_loss_fn (dict): Merged FID loss function metrics on all processes.
"""
if not is_dist_avail_and_initialized():
return fid_loss_fn
serialized_fid_loss_fn = pickle.dumps(fid_loss_fn)
gathered_fid_loss_fn = [None] * dist.get_world_size()
dist.barrier()
dist.all_gather_object(gathered_fid_loss_fn, serialized_fid_loss_fn)
final_fid_loss_fn = {
1: FrechetInceptionDistance(feature_dim=2048).to(device),
2: FrechetInceptionDistance(feature_dim=2048).to(device),
4: FrechetInceptionDistance(feature_dim=2048).to(device),
8: FrechetInceptionDistance(feature_dim=2048).to(device),
16: FrechetInceptionDistance(feature_dim=2048).to(device),
}
for serialized_fid_loss_fn in gathered_fid_loss_fn:
curr_fid_loss_fn = pickle.loads(serialized_fid_loss_fn)
for sec in [1, 2, 4, 8, 16]:
sec_fid_loss_fn = curr_fid_loss_fn[sec]
final_fid_loss_fn[sec].merge_state([sec_fid_loss_fn])
return final_fid_loss_fn
|