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""" |
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Copyright 2020 The Google Research Authors. |
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Copyright (c) Microsoft Corporation. |
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Licensed under the MIT License. |
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Based on code from https://github.com/yang-song/score_sde_pytorch/blob/main/sde_lib.py |
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which is released under Apache licence. |
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Abstract SDE classes, Reverse SDE, and VE/VP SDEs. |
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Key changes: |
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- Rename SDE => Corruption |
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- Remove several methods like .reverse(), .discretize() |
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""" |
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import abc |
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import logging |
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from typing import Optional, Tuple, Union |
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import torch |
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from mattergen.diffusion.data.batched_data import BatchedData |
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B = Optional[torch.LongTensor] |
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def _broadcast_like(x, like): |
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""" |
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add broadcast dimensions to x so that it can be broadcast over ``like`` |
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""" |
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if like is None: |
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return x |
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return x[(...,) + (None,) * (like.ndim - x.ndim)] |
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def maybe_expand(x: torch.Tensor, batch: B, like: torch.Tensor = None) -> torch.Tensor: |
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""" |
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Args: |
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x: shape (batch_size, ...) |
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batch: shape (num_thingies,) with integer entries in the range [0, batch_size), indicating which sample each thingy belongs to |
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like: shape x.shape + potential additional dimensions |
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Returns: |
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expanded x with shape (num_thingies,), or if given like.shape, containing value of x for each thingy. |
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If `batch` is None, just returns `x` unmodified, to avoid pointless work if you have exactly one thingy per sample. |
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""" |
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x = _broadcast_like(x, like) |
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if batch is None: |
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return x |
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else: |
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if x.shape[0] == batch.shape[0]: |
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logging.warn( |
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"Warning: batch shape is == x shape, are you trying to expand something that is already expanded?" |
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) |
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return x[batch] |
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class Corruption(abc.ABC): |
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"""Abstract base class for corruption processes""" |
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@property |
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@abc.abstractmethod |
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def T(self) -> float: |
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"""End time of the corruption process.""" |
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pass |
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@abc.abstractmethod |
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def marginal_prob( |
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self, |
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x: torch.Tensor, |
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t: torch.Tensor, |
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batch_idx: B = None, |
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batch: Optional[BatchedData] = None, |
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) -> Tuple[torch.Tensor, torch.Tensor]: |
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"""Parameters to determine the marginal distribution of the SDE, $p_t(x)$.""" |
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pass |
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@abc.abstractmethod |
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def prior_sampling( |
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self, |
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shape: Union[torch.Size, Tuple], |
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conditioning_data: Optional[BatchedData] = None, |
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batch_idx: B = None, |
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) -> torch.Tensor: |
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"""Generate one sample from the prior distribution, $p_T(x)$.""" |
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pass |
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@abc.abstractmethod |
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def prior_logp( |
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self, |
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z: torch.Tensor, |
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batch_idx: B = None, |
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batch: Optional[BatchedData] = None, |
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) -> torch.Tensor: |
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"""Compute log-density of the prior distribution. |
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Useful for computing the log-likelihood via probability flow ODE. |
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Args: |
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z: latent code |
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Returns: |
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log probability density |
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""" |
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pass |
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@abc.abstractmethod |
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def sample_marginal( |
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self, |
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x: torch.Tensor, |
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t: torch.Tensor, |
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batch_idx: B = None, |
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batch: Optional[BatchedData] = None, |
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) -> torch.Tensor: |
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"""Sample marginal for x(t) given x(0). |
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Returns: |
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sampled x(t) (same shape as input x). |
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""" |
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pass |
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