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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
import math
from typing import Callable
import torch
from torch import Tensor
from flow_matching.solver.solver import Solver
from flow_matching.utils import ModelWrapper
from flow_matching.utils.manifolds import geodesic, Manifold
try:
from tqdm import tqdm
TQDM_AVAILABLE = True
except ImportError:
TQDM_AVAILABLE = False
class RiemannianODESolver(Solver):
r"""Riemannian ODE solver
Initialize the ``RiemannianODESolver``.
Args:
manifold (Manifold): the manifold to solve on.
velocity_model (ModelWrapper): a velocity field model receiving :math:`(x,t)`
and returning :math:`u_t(x)` which is assumed to lie on the tangent plane at `x`.
"""
def __init__(self, manifold: Manifold, velocity_model: ModelWrapper):
super().__init__()
self.manifold = manifold
self.velocity_model = velocity_model
def sample(
self,
x_init: Tensor,
step_size: float,
projx: bool = True,
proju: bool = True,
method: str = "euler",
time_grid: Tensor = torch.tensor([0.0, 1.0]),
return_intermediates: bool = False,
verbose: bool = False,
enable_grad: bool = False,
**model_extras,
) -> Tensor:
r"""Solve the ODE with the `velocity_field` on the manifold.
Args:
x_init (Tensor): initial conditions (e.g., source samples :math:`X_0 \sim p`).
step_size (float): The step size.
projx (bool): Whether to project the point onto the manifold at each step. Defaults to True.
proju (bool): Whether to project the vector field onto the tangent plane at each step. Defaults to True.
method (str): One of ["euler", "midpoint", "rk4"]. Defaults to "euler".
time_grid (Tensor, optional): The process is solved in the interval [min(time_grid, max(time_grid)] and if step_size is None then time discretization is set by the time grid. Defaults to torch.tensor([0.0,1.0]).
return_intermediates (bool, optional): If True then return intermediate time steps according to time_grid. Defaults to False.
verbose (bool, optional): Whether to print progress bars. Defaults to False.
enable_grad (bool, optional): Whether to compute gradients during sampling. Defaults to False.
**model_extras: Additional input for the model.
Returns:
Tensor: The sampled sequence. Defaults to returning samples at :math:`t=1`.
Raises:
ImportError: To run in verbose mode, tqdm must be installed.
"""
step_fns = {
"euler": _euler_step,
"midpoint": _midpoint_step,
"rk4": _rk4_step,
}
assert method in step_fns.keys(), f"Unknown method {method}"
step_fn = step_fns[method]
def velocity_func(x, t):
return self.velocity_model(x=x, t=t, **model_extras)
# --- Factor this out.
time_grid = torch.sort(time_grid.to(device=x_init.device)).values
if step_size is None:
# If step_size is None then set the t discretization to time_grid.
t_discretization = time_grid
n_steps = len(time_grid) - 1
else:
# If step_size is float then t discretization is uniform with step size set by step_size.
t_init = time_grid[0].item()
t_final = time_grid[-1].item()
assert (
t_final - t_init
) > step_size, f"Time interval [min(time_grid), max(time_grid)] must be larger than step_size. Got a time interval [{t_init}, {t_final}] and step_size {step_size}."
n_steps = math.ceil((t_final - t_init) / step_size)
t_discretization = torch.tensor(
[step_size * i for i in range(n_steps)] + [t_final],
device=x_init.device,
)
# ---
t0s = t_discretization[:-1]
if verbose:
if not TQDM_AVAILABLE:
raise ImportError(
"tqdm is required for verbose mode. Please install it."
)
t0s = tqdm(t0s)
if return_intermediates:
xts = []
i_ret = 0
with torch.set_grad_enabled(enable_grad):
xt = x_init
for t0, t1 in zip(t0s, t_discretization[1:]):
dt = t1 - t0
xt_next = step_fn(
velocity_func,
xt,
t0,
dt,
manifold=self.manifold,
projx=projx,
proju=proju,
)
if return_intermediates:
while (
i_ret < len(time_grid)
and t0 <= time_grid[i_ret]
and time_grid[i_ret] <= t1
):
xts.append(
interp(self.manifold, xt, xt_next, t0, t1, time_grid[i_ret])
)
i_ret += 1
xt = xt_next
if return_intermediates:
return torch.stack(xts, dim=0)
else:
return xt
def interp(manifold, xt, xt_next, t, t_next, t_ret):
return geodesic(manifold, xt, xt_next)(
(t_ret - t) / (t_next - t).reshape(1)
).reshape_as(xt)
def _euler_step(
velocity_model: Callable,
xt: Tensor,
t0: Tensor,
dt: Tensor,
manifold: Manifold,
projx: bool = True,
proju: bool = True,
) -> Tensor:
r"""Perform an Euler step on a manifold.
Args:
velocity_model (Callable): the velocity model
xt (Tensor): tensor containing the state at time t0
t0 (Tensor): the time at which this step is taken
dt (Tensor): the step size
manifold (Manifold): a manifold object
projx (bool, optional): whether to project the state onto the manifold. Defaults to True.
proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True.
Returns:
Tensor: tensor containing the state after the step
"""
velocity_fn = lambda x, t: (
manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t)
)
projx_fn = lambda x: manifold.projx(x) if projx else x
vt = velocity_fn(xt, t0)
xt = xt + dt * vt
return projx_fn(xt)
def _midpoint_step(
velocity_model: Callable,
xt: Tensor,
t0: Tensor,
dt: Tensor,
manifold: Manifold,
projx: bool = True,
proju: bool = True,
) -> Tensor:
r"""Perform a midpoint step on a manifold.
Args:
velocity_model (Callable): the velocity model
xt (Tensor): tensor containing the state at time t0
t0 (Tensor): the time at which this step is taken
dt (Tensor): the step size
manifold (Manifold): a manifold object
projx (bool, optional): whether to project the state onto the manifold. Defaults to True.
proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True.
Returns:
Tensor: tensor containing the state after the step
"""
velocity_fn = lambda x, t: (
manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t)
)
projx_fn = lambda x: manifold.projx(x) if projx else x
half_dt = 0.5 * dt
vt = velocity_fn(xt, t0)
x_mid = xt + half_dt * vt
x_mid = projx_fn(x_mid)
xt = xt + dt * velocity_fn(x_mid, t0 + half_dt)
return projx_fn(xt)
def _rk4_step(
velocity_model: Callable,
xt: Tensor,
t0: Tensor,
dt: Tensor,
manifold: Manifold,
projx: bool = True,
proju: bool = True,
) -> Tensor:
r"""Perform an RK4 step on a manifold.
Args:
velocity_model (Callable): the velocity model
xt (Tensor): tensor containing the state at time t0
t0 (Tensor): the time at which this step is taken
dt (Tensor): the step size
manifold (Manifold): a manifold object
projx (bool, optional): whether to project the state onto the manifold. Defaults to True.
proju (bool, optional): whether to project the velocity onto the tangent plane. Defaults to True.
Returns:
Tensor: tensor containing the state after the step
"""
velocity_fn = lambda x, t: (
manifold.proju(x, velocity_model(x, t)) if proju else velocity_model(x, t)
)
projx_fn = lambda x: manifold.projx(x) if projx else x
k1 = velocity_fn(xt, t0)
k2 = velocity_fn(projx_fn(xt + dt * k1 / 3), t0 + dt / 3)
k3 = velocity_fn(projx_fn(xt + dt * (k2 - k1 / 3)), t0 + dt * 2 / 3)
k4 = velocity_fn(projx_fn(xt + dt * (k1 - k2 + k3)), t0 + dt)
return projx_fn(xt + (k1 + 3 * (k2 + k3) + k4) * dt * 0.125)
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