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| # Copyright 2025 ByteDance and/or its affiliates. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import torch | |
| import numpy as np | |
| class DiagonalGaussianDistribution(object): | |
| def __init__(self, parameters: torch.Tensor, deterministic: bool = False): | |
| self.parameters = parameters | |
| self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) | |
| self.logvar = torch.clamp(self.logvar, -30.0, 20.0) | |
| self.deterministic = deterministic | |
| self.std = torch.exp(0.5 * self.logvar) | |
| self.var = torch.exp(self.logvar) | |
| if self.deterministic: | |
| self.var = self.std = torch.zeros_like( | |
| self.mean, device=self.parameters.device, dtype=self.parameters.dtype | |
| ) | |
| def sample(self, generator=None) -> torch.Tensor: | |
| # make sure sample is on the same device as the parameters and has same dtype | |
| sample = torch.randn( | |
| self.mean.shape, | |
| generator=generator, | |
| device=self.parameters.device, | |
| dtype=self.parameters.dtype, | |
| ) | |
| x = self.mean + self.std * sample | |
| return x | |
| def kl(self, other: "DiagonalGaussianDistribution" = None) -> torch.Tensor: | |
| if self.deterministic: | |
| return torch.Tensor([0.0]) | |
| else: | |
| if other is None: | |
| return 0.5 * torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar | |
| else: | |
| return 0.5 * ( | |
| torch.pow(self.mean - other.mean, 2) / other.var | |
| + self.var / other.var | |
| - 1.0 | |
| - self.logvar | |
| + other.logvar | |
| ) | |
| def nll(self, sample, dims) -> torch.Tensor: | |
| if self.deterministic: | |
| return torch.Tensor([0.0]) | |
| logtwopi = np.log(2.0 * np.pi) | |
| return 0.5 * torch.sum( | |
| logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, | |
| dim=dims, | |
| ) | |
| def mode(self) -> torch.Tensor: | |
| return self.mean |