Source code for pyro.ops.integrator

# Copyright (c) 2017-2019 Uber Technologies, Inc.
# SPDX-License-Identifier: Apache-2.0

import warnings
from typing import Callable, Dict

from torch.autograd import grad

# Registry for exception handlers that can be used to catch certain failures
# during computation of `potential_fn` within `potential_grad`.
_EXCEPTION_HANDLERS: Dict[str, Callable[[Exception], bool]] = {}

[docs]def velocity_verlet( z, r, potential_fn, kinetic_grad, step_size, num_steps=1, z_grads=None ): r""" Second order symplectic integrator that uses the velocity verlet algorithm. :param dict z: dictionary of sample site names and their current values (type :class:`~torch.Tensor`). :param dict r: dictionary of sample site names and corresponding momenta (type :class:`~torch.Tensor`). :param callable potential_fn: function that returns potential energy given z for each sample site. The negative gradient of the function with respect to ``z`` determines the rate of change of the corresponding sites' momenta ``r``. :param callable kinetic_grad: a function calculating gradient of kinetic energy w.r.t. momentum variable. :param float step_size: step size for each time step iteration. :param int num_steps: number of discrete time steps over which to integrate. :param torch.Tensor z_grads: optional gradients of potential energy at current ``z``. :return tuple (z_next, r_next, z_grads, potential_energy): next position and momenta, together with the potential energy and its gradient w.r.t. ``z_next``. """ z_next = z.copy() r_next = r.copy() for _ in range(num_steps): z_next, r_next, z_grads, potential_energy = _single_step_verlet( z_next, r_next, potential_fn, kinetic_grad, step_size, z_grads ) return z_next, r_next, z_grads, potential_energy
def _single_step_verlet(z, r, potential_fn, kinetic_grad, step_size, z_grads=None): r""" Single step velocity verlet that modifies the `z`, `r` dicts in place. """ z_grads = potential_grad(potential_fn, z)[0] if z_grads is None else z_grads for site_name in r: r[site_name] = r[site_name] + 0.5 * step_size * ( -z_grads[site_name] ) # r(n+1/2) r_grads = kinetic_grad(r) for site_name in z: z[site_name] = z[site_name] + step_size * r_grads[site_name] # z(n+1) z_grads, potential_energy = potential_grad(potential_fn, z) for site_name in r: r[site_name] = r[site_name] + 0.5 * step_size * (-z_grads[site_name]) # r(n+1) return z, r, z_grads, potential_energy
[docs]def potential_grad(potential_fn, z): """ Gradient of `potential_fn` w.r.t. parameters z. :param potential_fn: python callable that takes in a dictionary of parameters and returns the potential energy. :param dict z: dictionary of parameter values keyed by site name. :return: tuple of `(z_grads, potential_energy)`, where `z_grads` is a dictionary with the same keys as `z` containing gradients and potential_energy is a torch scalar. """ z_keys, z_nodes = zip(*z.items()) for node in z_nodes: node.requires_grad_(True) try: potential_energy = potential_fn(z) # handle exceptions as defined in the exception registry except Exception as e: if any(h(e) for h in _EXCEPTION_HANDLERS.values()): grads = {k: v.new_zeros(v.shape) for k, v in z.items()} return grads, z_nodes[0].new_tensor(float("nan")) else: raise e grads = grad(potential_energy, z_nodes) for node in z_nodes: node.requires_grad_(False) return dict(zip(z_keys, grads)), potential_energy.detach()
[docs]def register_exception_handler( name: str, handler: Callable[[Exception], bool], warn_on_overwrite: bool = True ) -> None: """ Register an exception handler for handling (primarily numerical) errors when evaluating the potential function. :param name: name of the handler (must be unique). :param handler: A callable mapping an Exception to a boolean. Exceptions that evaluate to true in any of the handlers are handled in the computation of the potential energy. :param warn_on_overwrite: If True, warns when overwriting a handler already registered under the provided name. """ if name in _EXCEPTION_HANDLERS and warn_on_overwrite: warnings.warn( f"Overwriting Exception handler already registered under key {name}.", RuntimeWarning, ) _EXCEPTION_HANDLERS[name] = handler
def _handle_torch_singular(exception: Exception) -> bool: """Exception handler for errors thrown on (numerically) singular matrices.""" # the actual type of the exception thrown is torch._C._LinAlgError if isinstance(exception, RuntimeError): msg = str(exception) return "singular" in msg or "input is not positive-definite" in msg return False # Register default exception handler register_exception_handler("torch_singular", _handle_torch_singular)