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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from typing import Generic, Mapping, Tuple, TypeVar
import torch
from tqdm.auto import tqdm
from mattergen.diffusion.corruption.multi_corruption import MultiCorruption, apply
from mattergen.diffusion.data.batched_data import BatchedData
from mattergen.diffusion.diffusion_module import DiffusionModule
from mattergen.diffusion.lightning_module import DiffusionLightningModule
from mattergen.diffusion.sampling.pc_partials import CorrectorPartial, PredictorPartial
Diffusable = TypeVar(
"Diffusable", bound=BatchedData
) # Don't use 'T' because it clashes with the 'T' for time
SampleAndMean = Tuple[Diffusable, Diffusable]
SampleAndMeanAndMaybeRecords = Tuple[Diffusable, Diffusable, list[Diffusable] | None]
SampleAndMeanAndRecords = Tuple[Diffusable, Diffusable, list[Diffusable]]
class PredictorCorrector(Generic[Diffusable]):
"""Generates samples using predictor-corrector sampling."""
def __init__(
self,
*,
diffusion_module: DiffusionModule,
predictor_partials: dict[str, PredictorPartial] | None = None,
corrector_partials: dict[str, CorrectorPartial] | None = None,
device: torch.device,
n_steps_corrector: int,
N: int,
eps_t: float = 1e-3,
max_t: float | None = None,
):
"""
Args:
diffusion_module: diffusion module
predictor_partials: partials for constructing predictors. Keys are the names of the corruptions.
corrector_partials: partials for constructing correctors. Keys are the names of the corruptions.
device: device to run on
n_steps_corrector: number of corrector steps
N: number of noise levels
eps_t: diffusion time to stop denoising at
max_t: diffusion time to start denoising at. If None, defaults to the maximum diffusion time. You may want to start at T-0.01, say, for numerical stability.
"""
self._diffusion_module = diffusion_module
self.N = N
if max_t is None:
max_t = self._multi_corruption.T
assert max_t <= self._multi_corruption.T, "Denoising cannot start from beyond T"
self._max_t = max_t
assert (
corrector_partials or predictor_partials
), "Must specify at least one predictor or corrector"
corrector_partials = corrector_partials or {}
predictor_partials = predictor_partials or {}
if self._multi_corruption.discrete_corruptions:
# These all have property 'N' because they are D3PM type
assert set(c.N for c in self._multi_corruption.discrete_corruptions.values()) == {N} # type: ignore
self._predictors = {
k: v(corruption=self._multi_corruption.corruptions[k], score_fn=None)
for k, v in predictor_partials.items()
}
self._correctors = {
k: v(
corruption=self._multi_corruption.corruptions[k],
n_steps=n_steps_corrector,
score_fn=None,
)
for k, v in corrector_partials.items()
}
self._eps_t = eps_t
self._n_steps_corrector = n_steps_corrector
self._device = device
@property
def diffusion_module(self) -> DiffusionModule:
return self._diffusion_module
@property
def _multi_corruption(self) -> MultiCorruption:
return self._diffusion_module.corruption
def _score_fn(self, x: Diffusable, t: torch.Tensor) -> Diffusable:
return self._diffusion_module.score_fn(x, t)
@classmethod
def from_pl_module(cls, pl_module: DiffusionLightningModule, **kwargs) -> PredictorCorrector:
return cls(diffusion_module=pl_module.diffusion_module, device=pl_module.device, **kwargs)
@torch.no_grad()
def sample(
self, conditioning_data: BatchedData, mask: Mapping[str, torch.Tensor] | None = None
) -> SampleAndMean:
"""Create one sample for each of a batch of conditions.
Args:
conditioning_data: batched conditioning data. Even if you think you don't want conditioning, you still need to pass a batch of conditions
because the sampler uses these to determine the shapes of things to generate.
mask: for inpainting. Keys should be a subset of the keys in `data`. 1 indicates data that should be fixed, 0 indicates data that should be replaced with sampled values.
Shapes of values in `mask` must match the shapes of values in `conditioning_data`.
Returns:
(batch, mean_batch). The difference between these is that `mean_batch` has no noise added at the final denoising step.
"""
return self._sample_maybe_record(conditioning_data, mask=mask, record=False)[:2]
@torch.no_grad()
def sample_with_record(
self, conditioning_data: BatchedData, mask: Mapping[str, torch.Tensor] | None = None
) -> SampleAndMeanAndRecords:
"""Create one sample for each of a batch of conditions.
Args:
conditioning_data: batched conditioning data. Even if you think you don't want conditioning, you still need to pass a batch of conditions
because the sampler uses these to determine the shapes of things to generate.
mask: for inpainting. Keys should be a subset of the keys in `data`. 1 indicates data that should be fixed, 0 indicates data that should be replaced with sampled values.
Shapes of values in `mask` must match the shapes of values in `conditioning_data`.
Returns:
(batch, mean_batch). The difference between these is that `mean_batch` has no noise added at the final denoising step.
"""
return self._sample_maybe_record(conditioning_data, mask=mask, record=True)
@torch.no_grad()
def _sample_maybe_record(
self,
conditioning_data: BatchedData,
mask: Mapping[str, torch.Tensor] | None = None,
record: bool = False,
) -> SampleAndMeanAndMaybeRecords:
"""Create one sample for each of a batch of conditions.
Args:
conditioning_data: batched conditioning data. Even if you think you don't want conditioning, you still need to pass a batch of conditions
because the sampler uses these to determine the shapes of things to generate.
mask: for inpainting. Keys should be a subset of the keys in `data`. 1 indicates data that should be fixed, 0 indicates data that should be replaced with sampled values.
Shapes of values in `mask` must match the shapes of values in `conditioning_data`.
Returns:
(batch, mean_batch, recorded_samples, recorded_predictions).
The difference between the former two is that `mean_batch` has no noise added at the final denoising step.
The latter two are only returned if `record` is True, and contain the samples and predictions from each step of the diffusion process.
"""
if isinstance(self._diffusion_module, torch.nn.Module):
self._diffusion_module.eval()
mask = mask or {}
conditioning_data = conditioning_data.to(self._device)
mask = {k: v.to(self._device) for k, v in mask.items()}
batch = _sample_prior(self._multi_corruption, conditioning_data, mask=mask)
return self._denoise(batch=batch, mask=mask, record=record)
@torch.no_grad()
def _denoise(
self,
batch: Diffusable,
mask: dict[str, torch.Tensor],
record: bool = False,
) -> SampleAndMeanAndMaybeRecords:
"""Denoise from a prior sample to a t=eps_t sample."""
recorded_samples = None
if record:
recorded_samples = []
for k in self._predictors:
mask.setdefault(k, None)
for k in self._correctors:
mask.setdefault(k, None)
mean_batch = batch.clone()
# Decreasing timesteps from T to eps_t
timesteps = torch.linspace(self._max_t, self._eps_t, self.N, device=self._device)
dt = -torch.tensor((self._max_t - self._eps_t) / (self.N - 1)).to(self._device)
for i in tqdm(range(self.N), miniters=50, mininterval=5):
# Set the timestep
t = torch.full((batch.get_batch_size(),), timesteps[i], device=self._device)
# Corrector updates.
if self._correctors:
for _ in range(self._n_steps_corrector):
score = self._score_fn(batch, t)
fns = {
k: corrector.step_given_score for k, corrector in self._correctors.items()
}
samples_means: dict[str, Tuple[torch.Tensor, torch.Tensor]] = apply(
fns=fns,
broadcast={"t": t},
x=batch,
score=score,
batch_idx=self._multi_corruption._get_batch_indices(batch),
)
if record:
recorded_samples.append(batch.clone().to("cpu"))
batch, mean_batch = _mask_replace(
samples_means=samples_means, batch=batch, mean_batch=mean_batch, mask=mask
)
# Predictor updates
score = self._score_fn(batch, t)
predictor_fns = {
k: predictor.update_given_score for k, predictor in self._predictors.items()
}
samples_means = apply(
fns=predictor_fns,
x=batch,
score=score,
broadcast=dict(t=t, batch=batch, dt=dt),
batch_idx=self._multi_corruption._get_batch_indices(batch),
)
if record:
recorded_samples.append(batch.clone().to("cpu"))
batch, mean_batch = _mask_replace(
samples_means=samples_means, batch=batch, mean_batch=mean_batch, mask=mask
)
return batch, mean_batch, recorded_samples
def _mask_replace(
samples_means: dict[str, Tuple[torch.Tensor, torch.Tensor]],
batch: BatchedData,
mean_batch: BatchedData,
mask: dict[str, torch.Tensor | None],
) -> SampleAndMean:
# Apply masks
samples_means = apply(
fns={k: _mask_both for k in samples_means},
broadcast={},
sample_and_mean=samples_means,
mask=mask,
old_x=batch,
)
# Put the updated values in `batch` and `mean_batch`
batch = batch.replace(**{k: v[0] for k, v in samples_means.items()})
mean_batch = mean_batch.replace(**{k: v[1] for k, v in samples_means.items()})
return batch, mean_batch
def _mask_both(
*, sample_and_mean: Tuple[torch.Tensor, torch.Tensor], old_x: torch.Tensor, mask: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
return tuple(_mask(old_x=old_x, new_x=x, mask=mask) for x in sample_and_mean) # type: ignore
def _mask(*, old_x: torch.Tensor, new_x: torch.Tensor, mask: torch.Tensor | None) -> torch.Tensor:
"""Replace new_x with old_x where mask is 1."""
if mask is None:
return new_x
else:
return new_x.lerp(old_x, mask)
def _sample_prior(
multi_corruption: MultiCorruption,
conditioning_data: BatchedData,
mask: Mapping[str, torch.Tensor] | None,
) -> BatchedData:
samples = {
k: multi_corruption.corruptions[k]
.prior_sampling(
shape=conditioning_data[k].shape,
conditioning_data=conditioning_data,
batch_idx=conditioning_data.get_batch_idx(field_name=k),
)
.to(conditioning_data[k].device)
for k in multi_corruption.corruptions
}
mask = mask or {}
for k, msk in mask.items():
if k in multi_corruption.corrupted_fields:
samples[k].lerp_(conditioning_data[k], msk)
return conditioning_data.replace(**samples)
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