| from enum import Enum |
|
|
| import torch |
| import torch.distributed as dist |
|
|
|
|
| class Summary(Enum): |
| NONE = 0 |
| AVERAGE = 1 |
| SUM = 2 |
| COUNT = 3 |
|
|
|
|
| class AverageMeter(object): |
| """Computes and stores the average and current value""" |
|
|
| def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE): |
| self.name = name |
| self.fmt = fmt |
| self.summary_type = summary_type |
| self.reset() |
|
|
| def reset(self): |
| self.val = 0 |
| self.avg = 0 |
| self.sum = 0 |
| self.count = 0 |
|
|
| def update(self, val, n=1): |
| self.val = val |
| self.sum += val * n |
| self.count += n |
| self.avg = self.sum / self.count |
|
|
| def all_reduce(self): |
| if torch.cuda.is_available(): |
| device = torch.device("cuda") |
| elif torch.backends.mps.is_available(): |
| device = torch.device("mps") |
| else: |
| device = torch.device("cpu") |
|
|
| total = torch.tensor([self.sum, self.count], dtype=torch.float32, device=device) |
| dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False) |
| self.sum, self.count = total.tolist() |
| self.avg = self.sum / self.count |
|
|
| def __str__(self): |
| fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' |
| return fmtstr.format(**self.__dict__) |
|
|
| def summary(self): |
| fmtstr = '' |
| if self.summary_type is Summary.NONE: |
| fmtstr = '' |
| elif self.summary_type is Summary.AVERAGE: |
| fmtstr = '{name} {avg:.3f}' |
| elif self.summary_type is Summary.SUM: |
| fmtstr = '{name} {sum:.3f}' |
| elif self.summary_type is Summary.COUNT: |
| fmtstr = '{name} {count:.3f}' |
| else: |
| raise ValueError('invalid summary type %r' % self.summary_type) |
|
|
| return fmtstr.format(**self.__dict__) |
|
|
