def compute_grad(objective_fn, args, kwargs, grad_fn=None):
r"""Compute gradient of the objective function at the given point and return it along with
the objective function forward pass (if available).
Args:
objective_fn (function): the objective function for optimization
args (tuple): tuple of NumPy arrays containing the current parameters for the
objection function
kwargs (dict): keyword arguments for the objective function
grad_fn (function): optional gradient function of the objective function with respect to
the variables ``args``. If ``None``, the gradient function is computed automatically.
Must return the same shape of tuple [array] as the autograd derivative.
Returns:
tuple (array): NumPy array containing the gradient :math:`\nabla f(x^{(t)})` and the
objective function output. If ``grad_fn`` is provided, the objective function
will not be evaluted and instead ``None`` will be returned.
"""
g = get_gradient(objective_fn) if grad_fn is None else grad_fn
grad = g(*args, **kwargs)
forward = getattr(g, "forward", None)
if len(args) == 1:
grad = (grad, )
return grad, forward
def compute_grad(self, objective_fn, x, grad_fn=None):
r"""Compute gradient of the objective_fn at at the shifted point :math:`(x -
m\times\text{accumulation})` and return it along with the objective function
forward pass (if available).
Args:
objective_fn (function): the objective function for optimization
x (array): NumPy array containing the current values of the variables to be updated
grad_fn (function): Optional gradient function of the objective function with respect to
the variables ``x``. If ``None``, the gradient function is computed automatically.
Returns:
tuple: The NumPy array containing the gradient :math:`\nabla f(x^{(t)})` and the
objective function output. If ``grad_fn`` is provided, the objective function
will not be evaluted and instead ``None`` will be returned.
"""
x_flat = _flatten(x)
if self.accumulation is None:
shifted_x_flat = list(x_flat)
else:
shifted_x_flat = [
e - self.momentum * a
for a, e in zip(self.accumulation, x_flat)
]
shifted_x = unflatten(shifted_x_flat, x)
g = get_gradient(objective_fn) if grad_fn is None else grad_fn
grad = g(shifted_x)
forward = getattr(g, "forward", None)
return grad, forward
def compute_grad(self, objective_fn, args, kwargs, grad_fn=None):
r"""Compute gradient of the objective function at at the shifted point :math:`(x -
m\times\text{accumulation})` and return it along with the objective function forward pass
(if available).
Args:
objective_fn (function): the objective function for optimization.
args (tuple): tuple of NumPy arrays containing the current values for the
objection function.
kwargs (dict): keyword arguments for the objective function.
grad_fn (function): optional gradient function of the objective function with respect to
the variables ``x``. If ``None``, the gradient function is computed automatically.
Must return the same shape of tuple [array] as the autograd derivative.
Returns:
tuple [array]: the NumPy array containing the gradient :math:`\nabla f(x^{(t)})` and the
objective function output. If ``grad_fn`` is provided, the objective function
will not be evaluted and instead ``None`` will be returned.
"""
shifted_args = list(args)
trainable_args = []
for arg in args:
if getattr(arg, "requires_grad", True):
trainable_args.append(arg)
if self.accumulation:
for index, arg in enumerate(trainable_args):
if self.accumulation[index]:
x_flat = _flatten(arg)
acc = _flatten(self.accumulation[index])
shifted_x_flat = [
e - self.momentum * a for a, e in zip(acc, x_flat)
]
shifted_args[index] = unflatten(shifted_x_flat, arg)
if isinstance(shifted_args[index], ndarray):
# Due to a bug in unflatten, input PennyLane tensors
# are being unwrapped. Here, we cast them back to PennyLane
# tensors.
# TODO: remove when the following is fixed:
# https://github.com/PennyLaneAI/pennylane/issues/966
shifted_args[index] = shifted_args[index].view(tensor)
shifted_args[index].requires_grad = True
g = get_gradient(objective_fn) if grad_fn is None else grad_fn
grad = g(*shifted_args, **kwargs)
forward = getattr(g, "forward", None)
if len(trainable_args) == 1:
grad = (grad, )
return grad, forward
def compute_grad(objective_fn, x, grad_fn=None):
r"""Compute gradient of the objective_fn at the point x and return it along with the
objective function forward pass (if available).
Args:
objective_fn (function): the objective function for optimization
x (array): NumPy array containing the current values of the variables to be updated
grad_fn (function): Optional gradient function of the objective function with respect to
the variables ``x``. If ``None``, the gradient function is computed automatically.
Returns:
tuple: The NumPy array containing the gradient :math:`\nabla f(x^{(t)})` and the
objective function output. If ``grad_fn`` is provided, the objective function
will not be evaluted and instead ``None`` will be returned.
"""
g = get_gradient(objective_fn) if grad_fn is None else grad_fn
grad = g(x)
forward = getattr(g, "forward", None)
return grad, forward
def _optimize(self, objective_fn, parameters: ndarray, maxiter, *args,
grad_fn, **kwargs) -> Tuple[ndarray, float, ndarray]:
shape = parameters.shape
shaped_fn = self._reshaping_objective_fn(objective_fn, shape, **kwargs)
approx_grad = False if grad_fn is not None else True
updated_parameters, cost, info = sciopt.fmin_l_bfgs_b(
shaped_fn,
parameters.flatten(),
fprime=get_gradient(shaped_fn) if grad_fn is None else grad_fn,
*args,
approx_grad=approx_grad,
bounds=self.bounds,
m=self.m,
factr=self.factr,
pgtol=self.pgtol,
epsilon=self.epsilon,
iprint=self.iprint,
maxfun=self.maxfun,
maxiter=maxiter,
disp=self.disp,
callback=self.callback,
maxls=self.maxls,
)
return updated_parameters.reshape(shape), cost, info["grad"]