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90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
import torch
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import torch.distributed
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from sat import mpu
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from ...util import default, instantiate_from_config
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class EDMSampling:
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def __init__(self, p_mean=-1.2, p_std=1.2):
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self.p_mean = p_mean
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self.p_std = p_std
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def __call__(self, n_samples, rand=None):
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log_sigma = self.p_mean + self.p_std * default(rand, torch.randn((n_samples,)))
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return log_sigma.exp()
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class DiscreteSampling:
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def __init__(
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self,
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discretization_config,
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num_idx,
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do_append_zero=False,
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flip=True,
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uniform_sampling=False,
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group_num=0,
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):
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self.num_idx = num_idx
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self.sigmas = instantiate_from_config(discretization_config)(
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num_idx, do_append_zero=do_append_zero, flip=flip
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)
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world_size = mpu.get_data_parallel_world_size()
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if world_size <= 8:
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uniform_sampling = False
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self.uniform_sampling = uniform_sampling
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self.group_num = group_num
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if self.uniform_sampling:
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assert self.group_num > 0
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assert world_size % group_num == 0
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self.group_width = world_size // group_num # the number of rank in one group
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self.sigma_interval = self.num_idx // self.group_num
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def idx_to_sigma(self, idx):
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return self.sigmas[idx]
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def __call__(self, n_samples, rand=None, return_idx=False):
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if self.uniform_sampling:
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rank = mpu.get_data_parallel_rank()
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group_index = rank // self.group_width
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idx = default(
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rand,
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torch.randint(
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group_index * self.sigma_interval,
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(group_index + 1) * self.sigma_interval,
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(n_samples,),
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),
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)
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else:
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idx = default(
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rand,
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torch.randint(0, self.num_idx, (n_samples,)),
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)
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if return_idx:
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return self.idx_to_sigma(idx), idx
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else:
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return self.idx_to_sigma(idx)
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class PartialDiscreteSampling:
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def __init__(
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self, discretization_config, total_num_idx, partial_num_idx, do_append_zero=False, flip=True
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):
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self.total_num_idx = total_num_idx
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self.partial_num_idx = partial_num_idx
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self.sigmas = instantiate_from_config(discretization_config)(
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total_num_idx, do_append_zero=do_append_zero, flip=flip
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)
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def idx_to_sigma(self, idx):
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return self.sigmas[idx]
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def __call__(self, n_samples, rand=None):
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idx = default(
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rand,
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# torch.randint(self.total_num_idx-self.partial_num_idx, self.total_num_idx, (n_samples,)),
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torch.randint(0, self.partial_num_idx, (n_samples,)),
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)
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return self.idx_to_sigma(idx)
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