`RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation`

[matte-esse]

the following code generates an error in some of the most recent versions of py-torch:

oac-explore/trainer/trainer.py

Lines 146 to 159 in cbc0333

	"""
	Update networks
	"""
	self.qf1_optimizer.zero_grad()
	qf1_loss.backward()
	self.qf1_optimizer.step()
	self.qf2_optimizer.zero_grad()
	qf2_loss.backward()
	self.qf2_optimizer.step()
	self.policy_optimizer.zero_grad()
	policy_loss.backward()
	self.policy_optimizer.step()

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation

In order to solve it is necessary to move these lines

oac-explore/trainer/trainer.py

Lines 120 to 124 in cbc0333

	q_new_actions = torch.min(
	self.qf1(obs, new_obs_actions),
	self.qf2(obs, new_obs_actions),
	)
	policy_loss = (alpha * log_pi - q_new_actions).mean()

between the q networks gradient steps and the steps on the policy network as so:

"""
Update networks
"""
self.qf1_optimizer.zero_grad()
qf1_loss.backward(retain_graph=True)
self.qf1_optimizer.step()

self.qf2_optimizer.zero_grad()
qf2_loss.backward(retain_graph=True)
self.qf2_optimizer.step()

q_new_actions = torch.min(
    self.qf1(obs, new_obs_actions),
    self.qf2(obs, new_obs_actions),
)
policy_loss = (alpha * log_pi - q_new_actions).mean()

self.policy_optimizer.zero_grad()
policy_loss.backward(retain_graph=True)
self.policy_optimizer.step()

Be aware that if you simply use an old version of pytorch to solve this problem the behaviour might not be what you expect since the policy_loss was computed based on a network which no longer exists

[quanvuong]

Hi,

Thank you for your comment! I have indeed encountered a similar issue. I think another option is to move the policy update operation before the q update operation.