I have some problem when I implement my loss function.
(In actually, I want to implement the Maximum likelihood estimation. Thus I have to maintain the forward graph and add the new forward data every time.)
There is a RuntimeError about the inplace operation which may be the assign operation.
Does anyone encounter the same problem and has solved it?
The following code can reproduce the same error with torch version 0.6.1.
And there is a RuntimeError in the second optimizer.step().
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim

class Network(nn.Module):
    def __init__(self, dim, hidden_size):
        super(Network, self).__init__()
        self.fc1 = nn.Linear(dim, hidden_size)
        self.activate = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, 1)
    def forward(self, x):
        return self.fc2(self.activate(self.fc1(x)))
func = Network(3, 10).cuda()
optimizer = optim.SGD(func.parameters(), lr=1e-2)

context_1 = np.array([0.1,0.1,0.1])
context_2 = np.array([0.2,0.2,0.2])
tensor_1 = torch.from_numpy(context_1).float().cuda()
tensor_2 = torch.from_numpy(context_2).float().cuda()
a = func(tensor_1)
b = func(tensor_2)
loss = a*a
func.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()
loss = loss + b*b
func.zero_grad()
loss.backward(retain_graph=True)
optimizer.step()

Thanks!