| | """Modified from https://github.com/THUDM/CogVideo/blob/3710a612d8760f5cdb1741befeebb65b9e0f2fe0/sat/sgm/modules/diffusionmodules/sigma_sampling.py |
| | """ |
| | import torch |
| |
|
| | class DiscreteSampling: |
| | def __init__(self, num_idx, uniform_sampling=False): |
| | self.num_idx = num_idx |
| | self.uniform_sampling = uniform_sampling |
| | self.is_distributed = torch.distributed.is_available() and torch.distributed.is_initialized() |
| |
|
| | if self.is_distributed and self.uniform_sampling: |
| | world_size = torch.distributed.get_world_size() |
| | self.rank = torch.distributed.get_rank() |
| |
|
| | i = 1 |
| | while True: |
| | if world_size % i != 0 or num_idx % (world_size // i) != 0: |
| | i += 1 |
| | else: |
| | self.group_num = world_size // i |
| | break |
| | assert self.group_num > 0 |
| | assert world_size % self.group_num == 0 |
| | |
| | self.group_width = world_size // self.group_num |
| | self.sigma_interval = self.num_idx // self.group_num |
| | print('rank=%d world_size=%d group_num=%d group_width=%d sigma_interval=%s' % ( |
| | self.rank, world_size, self.group_num, |
| | self.group_width, self.sigma_interval)) |
| | |
| | def __call__(self, n_samples, generator=None, device=None): |
| | if self.is_distributed and self.uniform_sampling: |
| | group_index = self.rank // self.group_width |
| | idx = torch.randint( |
| | group_index * self.sigma_interval, |
| | (group_index + 1) * self.sigma_interval, |
| | (n_samples,), |
| | generator=generator, device=device, |
| | ) |
| | print('proc[%d] idx=%s' % (self.rank, idx)) |
| | else: |
| | idx = torch.randint( |
| | 0, self.num_idx, (n_samples,), |
| | generator=generator, device=device, |
| | ) |
| | return idx |
