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Manually reject step from ODE function with adaptive step solvers #210

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Manually reject step from ODE function with adaptive step solvers #210

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Add ability for ODE function to manually reject an adaptive step
slishak Sep 1, 2022
9f6cf71
Min step can be used to prevent infinite loop of timestep getting sma…
slishak Sep 1, 2022
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1 change: 1 addition & 0 deletions torchdiffeq/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
from ._impl import odeint
from ._impl import odeint_adjoint
from ._impl import odeint_event
from ._impl import RejectStepError
__version__ = "0.2.3"
1 change: 1 addition & 0 deletions torchdiffeq/_impl/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,2 +1,3 @@
from .odeint import odeint, odeint_event
from .adjoint import odeint_adjoint
from .rk_common import RejectStepError
53 changes: 32 additions & 21 deletions torchdiffeq/_impl/rk_common.py
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Expand Up @@ -38,6 +38,10 @@ def backward(ctx, grad_scratch):
return grad_scratch, grad_scratch[ctx.index], None


class RejectStepError(Exception):
pass


def _runge_kutta_step(func, y0, f0, t0, dt, t1, tableau):
"""Take an arbitrary Runge-Kutta step and estimate error.
Args:
Expand Down Expand Up @@ -262,28 +266,35 @@ def _adaptive_step(self, rk_state):

# Must be arranged as doing all the step_t handling, then all the jump_t handling, in case we
# trigger both. (i.e. interleaving them would be wrong.)

y1, f1, y1_error, k = _runge_kutta_step(self.func, y0, f0, t0, dt, t1, tableau=self.tableau)
# dtypes:
# y1.dtype == self.y0.dtype
# f1.dtype == self.y0.dtype
# y1_error.dtype == self.dtype
# k.dtype == self.y0.dtype

########################################################
# Error Ratio #
########################################################
error_ratio = _compute_error_ratio(y1_error, self.rtol, self.atol, y0, y1, self.norm)
accept_step = error_ratio <= 1

# Handle min max stepping
if dt > self.max_step:
try:
y1, f1, y1_error, k = _runge_kutta_step(self.func, y0, f0, t0, dt, t1, tableau=self.tableau)
# dtypes:
# y1.dtype == self.y0.dtype
# f1.dtype == self.y0.dtype
# y1_error.dtype == self.dtype
# k.dtype == self.y0.dtype
except RejectStepError as ex:
# self.func requested the step be rejected
# If already at minimum step size, stop integration as can't proceed
if dt <= self.min_step:
raise(ex)
error_ratio = torch.tensor(10.0, dtype=self.dtype, device=self.y0.device)
accept_step = False
if dt <= self.min_step:
accept_step = True

# dtypes:
# error_ratio.dtype == self.dtype
else:
########################################################
# Error Ratio #
########################################################
error_ratio = _compute_error_ratio(y1_error, self.rtol, self.atol, y0, y1, self.norm)
accept_step = error_ratio <= 1

# Handle min max stepping
if dt > self.max_step:
accept_step = False
if dt <= self.min_step:
accept_step = True

# dtypes:
# error_ratio.dtype == self.dtype

########################################################
# Update RK State #
Expand Down