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

granth_jain: <p>Hi,</p>
<p>I am getting above error when trying a second backward.<br>
As I understand the solution to this is use a clone operation.</p>
<p>I have tried to clone everything in my code but still the issue comes.</p>
<p>can someone please help to let know what is the issue here.</p>
<p>def learn(self, state, action, reward, state_, done):</p>
<pre><code>    s = state.reshape(1,-1)
    s_ = state_.reshape(1,-1)
    action = action.reshape(1,-1)
    value, (hvo, cvo) = self.value((s.clone(), (self.actor_hx.clone(), self.actor_cx.clone())))
    value_ , (hvto, cvto) = self.target_value((s_.clone(), (self.actor_Hx.clone(), self.actor_Cx.clone())))
    
    if(done == True):
       value_ = 0
       
    actions, log_probs, (_, _) = self.actor.sample_normal(s.clone(), (self.actor_hx.clone(), self.actor_cx.clone()),reparameterize=False)
    
    q1_new_policy, (_, _) = self.critic_1.forward((s.clone(), actions.clone() , (self.actor_hx.clone(), self.actor_cx.clone())))
    q2_new_policy, (_, _) = self.critic_2.forward((s.clone(), actions.clone() , (self.actor_hx.clone(), self.actor_cx.clone())))
    critic_value = T.min(q1_new_policy, q2_new_policy)
    critic_value = critic_value
    
    self.value.optimizer.zero_grad()
    value_target = critic_value - log_probs
    value_target = value_target.reshape(value.shape)
    value_loss = 0.5 * (F.mse_loss(value, value_target))
    value_loss.backward(retain_graph=True)
    self.value.optimizer.step()
    
    actions, log_probs, (hao, cao) = self.actor.sample_normal(s.clone(), (self.actor_hx.clone(), self.actor_cx.clone()), reparameterize=True)
    #actions, log_probs = self.actor.sample_mvnormal(s, reparameterize=False)
    
    q1_new_policy, (_, _) = self.critic_1.forward((s.clone(), actions.clone(), (self.actor_hx.clone(), self.actor_cx.clone())))
    q2_new_policy, (_, _) = self.critic_2.forward((s.clone(), actions.clone(), (self.actor_hx.clone(), self.actor_cx.clone())))
    critic_value = T.min(q1_new_policy, q2_new_policy)
    critic_value = critic_value
    
    actor_loss = log_probs - critic_value
    self.actor.optimizer.zero_grad()
    actor_loss.backward(retain_graph=True)
    self.actor.optimizer.step()
    
    self.critic_1.optimizer.zero_grad()
    self.critic_2.optimizer.zero_grad()
    q_hat = self.scale*reward + self.gamma*value_
    q1_old_policy, (_, _) = self.critic_1.forward((s.clone(), action.clone(), (self.actor_hx.clone(), self.actor_cx.clone())))
    q2_old_policy, (_, _) = self.critic_2.forward((s.clone(), action.clone(), (self.actor_hx.clone(), self.actor_cx.clone())))
    critic_1_loss = 0.5*F.mse_loss(q1_old_policy, q_hat)
    critic_2_loss = 0.5*F.mse_loss(q2_old_policy, q_hat)
    
    critic_loss = critic_1_loss + critic_2_loss
    critic_loss.backward()
    self.critic_1.optimizer.step()
    self.critic_2.optimizer.step()
    self.update_network_parameters()
    
    self.actor_hx = self.actor_Hx.clone()
    self.actor_cx = self.actor_Cx.clone()
</code></pre>
<p>I am getting error at the following line.</p>
<p>actor_loss.backward(retain_graph=True)</p>
<p>Can some one please help to let me know what I am I missing.</p>

albanD: <p>Hi,</p>
<p>Can you give the stack trace of where the error comes from? And what does the anomaly mode gives you as the origin for the error? (if you use nightly version of pytorch, anomaly mode should point you to all the steps in double backward).</p>

granth_jain: <p>Hi,</p>
<p>I found the solution…after enable the autodetect variable…it gave the stacktrace where the problem was occurring.</p>
<p>there a variable was being updated inplace.</p>
<p>Thanks for the reply.</p>