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import torch
from utils.manifolds import Sphere, geodesic
from torch.func import vjp, jvp, vmap, jacrev
class DDPMLoss:
def __init__(
self,
scheduler,
cond_drop_rate=0.0,
conditioning_key="label",
):
self.scheduler = scheduler
self.cond_drop_rate = cond_drop_rate
self.conditioning_key = conditioning_key
def __call__(self, preconditioning, network, batch, generator=None):
x_0 = batch["x_0"]
batch_size = x_0.shape[0]
device = x_0.device
t = torch.rand(batch_size, device=device, dtype=x_0.dtype, generator=generator)
gamma = self.scheduler(t).unsqueeze(-1)
n = torch.randn(x_0.shape, dtype=x_0.dtype, device=device, generator=generator)
y = torch.sqrt(gamma) * x_0 + torch.sqrt(1 - gamma) * n
batch["y"] = y
conditioning = batch[self.conditioning_key]
if conditioning is not None and self.cond_drop_rate > 0:
drop_mask = (
torch.rand(batch_size, device=device, generator=generator)
< self.cond_drop_rate
)
conditioning[drop_mask] = torch.zeros_like(conditioning[drop_mask])
batch[self.conditioning_key] = conditioning.detach()
batch["gamma"] = gamma.squeeze(-1).squeeze(-1).squeeze(-1)
D_n = preconditioning(network, batch)
loss = (D_n - n) ** 2
return loss
class FlowMatchingLoss:
def __init__(
self,
scheduler,
cond_drop_rate=0.0,
conditioning_key="label",
):
self.scheduler = scheduler
self.cond_drop_rate = cond_drop_rate
self.conditioning_key = conditioning_key
def __call__(self, preconditioning, network, batch, generator=None):
x_0 = batch["x_0"]
batch_size = x_0.shape[0]
device = x_0.device
t = torch.rand(batch_size, device=device, dtype=x_0.dtype, generator=generator)
gamma = self.scheduler(t).unsqueeze(-1)
n = torch.randn(x_0.shape, dtype=x_0.dtype, device=device, generator=generator)
y = gamma * x_0 + (1 - gamma) * n
batch["y"] = y
conditioning = batch[self.conditioning_key]
if conditioning is not None and self.cond_drop_rate > 0:
drop_mask = (
torch.rand(batch_size, device=device, generator=generator)
< self.cond_drop_rate
)
conditioning[drop_mask] = torch.zeros_like(conditioning[drop_mask])
batch[self.conditioning_key] = conditioning.detach()
batch["gamma"] = gamma.squeeze(-1).squeeze(-1).squeeze(-1)
D_n = preconditioning(network, batch)
loss = (D_n - (x_0 - n)) ** 2
return loss
class RiemannianFlowMatchingLoss:
def __init__(
self,
scheduler,
cond_drop_rate=0.0,
conditioning_key="label",
):
self.scheduler = scheduler
self.cond_drop_rate = cond_drop_rate
self.conditioning_key = conditioning_key
self.manifold = Sphere()
self.manifold_dim = 3
def __call__(self, preconditioning, network, batch, generator=None):
x_1 = batch["x_0"]
batch_size = x_1.shape[0]
device = x_1.device
t = torch.rand(batch_size, device=device, dtype=x_1.dtype, generator=generator)
gamma = self.scheduler(t).unsqueeze(-1)
x_0 = self.manifold.random_base(x_1.shape[0], self.manifold_dim).to(x_1)
def cond_u(x0, x1, t):
path = geodesic(self.manifold, x0, x1)
x_t, u_t = jvp(path, (t,), (torch.ones_like(t).to(t),))
return x_t, u_t
y, u_t = vmap(cond_u)(x_0, x_1, gamma)
y = y.reshape(batch_size, self.manifold_dim)
u_t = u_t.reshape(batch_size, self.manifold_dim)
batch["y"] = y
conditioning = batch[self.conditioning_key]
if conditioning is not None and self.cond_drop_rate > 0:
drop_mask = (
torch.rand(batch_size, device=device, generator=generator)
< self.cond_drop_rate
)
conditioning[drop_mask] = torch.zeros_like(conditioning[drop_mask])
batch[self.conditioning_key] = conditioning.detach()
batch["gamma"] = gamma.squeeze(-1).squeeze(-1).squeeze(-1)
D_n = preconditioning(network, batch)
diff = D_n - u_t
loss = self.manifold.inner(y, diff, diff).mean() / self.manifold_dim
return loss
class VonFisherLoss:
def __init__(self, dim=3):
self.dim = dim
def __call__(self, preconditioning, network, batch, generator=None):
x = batch["x_0"]
mu, kappa = preconditioning(network, batch)
loss = (
torch.log((kappa + 1e-8))
- torch.log(torch.tensor(4 * torch.pi, dtype=kappa.dtype))
- log_sinh(kappa)
+ kappa * (mu * x).sum(dim=-1, keepdim=True)
)
return -loss
class VonFisherMixtureLoss:
def __init__(self, dim=3):
self.dim = dim
def __call__(self, preconditioning, network, batch, generator=None):
x = batch["x_0"]
mu_mixture, kappa_mixture, weights = preconditioning(network, batch)
loss = 0
for i in range(mu_mixture.shape[1]):
mu = mu_mixture[:, i]
kappa = kappa_mixture[:, i].unsqueeze(1)
loss += weights[:, i].unsqueeze(1) * (
kappa
* torch.exp(kappa * ((mu * x).sum(dim=-1, keepdim=True) - 1))
/ (1e-8 + 2 * torch.pi * (1 - torch.exp(-2 * kappa)))
)
return -torch.log(loss)
def log_sinh(x):
return x + torch.log(1e-8 + (1 - torch.exp(-2 * x)) / 2)
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