I am getting these errors when using the torch.legacy.optim L-BFGS and Adam optimizers:
Running optimization with ADAM:
/usr/local/lib/python2.7/dist-packages/torch/legacy/optim/adam.py:65: UserWarning: tensor1 is not broadcastable to self, but they have the same number of elements.  Falling back to deprecated pointwise behavior.
  x.addcdiv_(-stepSize, state['m'], state['denom'])

Running optimization with L-BFGS:
('<optim.lbfgs>', 'creating recyclable direction/step/history buffers')
/usr/local/lib/python2.7/dist-packages/torch/legacy/optim/lbfgs.py:197: UserWarning: other is not broadcastable to self, but they have the same number of elements.  Falling back to deprecated pointwise behavior.
  x.add_(t, d)

Are these warnings indicative of a problem with either optimzer? A problem with the inputs I am feeding them? Or are these warning safe to ignore?
I am trying to diagnose a problem with my code, and would like to know if either of these warning messages may be causing my issues, or even related to my issues?
I was using the latest pip version when I got these errors.
Edit:
This is my feval function:
num_calls = [0]
def feval(x):
  num_calls[0] += 1
  net.updateOutput(x.cuda())
  grad = net.updateGradInput(x.cuda(), dy.cuda())
  loss = 0
  for mod in content_losses:
    loss = loss + mod.loss
  for mod in style_losses:
    loss = loss + mod.loss
  return loss, grad.view(grad.nelement())

And this is how I run it:
optim_state = None
if params.optimizer == 'lbfgs':
  optim_state = {
    "maxIter": params.num_iterations,
    "verbose": True,
    "tolX": -1,
    "tolFun": -1,
  }
  if params.lbfgs_num_correction > 0:
    optim_state.nCorrection = params.lbfgs_num_correction
elif params.optimizer == 'adam':
    optim_state = {
      "learningRate": params.learning_rate,
    }

# Run optimization.
if params.optimizer == 'lbfgs':
  print("Running optimization with L-BFGS")
  x, losses = optim.lbfgs(feval, img, optim_state)
elif params.optimizer == 'adam':
  print("Running optimization with ADAM")
  for t in xrange(params.num_iterations):
    x, losses = optim.adam(feval, img, optim_state)