Pytorch 当我使用clone（）时，仍然存在“就地”操作错误

我尝试在任何我认为必要的地方添加

.clone（）

但我仍然得到了

就地操作

错误

这个错误发生在第二个纪元，而不是第一个纪元，当我得到这个错误的时候

class Model(torch.nn.Module):
    def __init__(self, user_M, item_M, feature_M, embedding_M):
        super(Model, self).__init__()
        self.dimension = embedding_M + feature_M + 1
        
        # Memories
        self.user_memory = torch.randn(user_M, 2, 1, self.dimension)
        self.item_memory = torch.randn(item_M, 2, 1, self.dimension)
        
        # Writer
        self.user_LSTM = nn.LSTMCell(self.dimension, self.dimension)
        self.item_LSTM = nn.LSTMCell(self.dimension, self.dimension)

    def score(self, user_id, item_id):
        user_em = torch.index_select(self.user_memory[:, 1, :].clone(), 0, user_id[:,0].clone())
        item_em = torch.index_select(self.item_memory[:, 1, :].clone(), 0, item_id[:,0].clone())
        return torch.sigmoid(torch.bmm(user_em, item_em.permute(0,2,1)))
    
    def forward(self, train_data, user_id, item_id):
        ui_train = train_data[:, 0:1].type(torch.LongTensor).clone()
        ii_train = train_data[:, 1:2].type(torch.LongTensor).clone()
        
        for row in range(len(train_data)):
            memory = self.user_memory[ui_train[row][0]].clone()
            h, c = self.user_LSTM(train_data[row:row+1].clone(), (memory[0], memory[1]))
            self.user_memory[ui_train[row]] = torch.stack((h, c))
        
        for row in range(len(train_data)):
            memory = self.item_memory[ii_train[row][0]].clone()
            h, c = self.item_LSTM(train_data[row:row+1].clone(), (memory[0], memory[1]))
            self.item_memory[ii_train[row]] = torch.stack((h, c))
        
        # Scoring
        y = self.score(user_id, item_id)
        return y.reshape(-1, 1)

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尝试使用-

torch.autograd.set\u detect\u normal（True）

-对我来说，最可能的罪魁祸首似乎是就地分配

self.user\u memory[ui\u train[row]=…

和

self.user\u memory[ii\u train[row]=…

。尝试使用-

torch.autograd.set\u detect\u normal（True）查找原因

-在我看来，最有可能的罪魁祸首似乎是原位分配

self.user\u memory[ui\u train[row]]=…

和

self.user\u memory[ii\u train[row]=…

。

model = Model(user_M, item_M, feature_M, 3)
criterion = torch.nn.BCELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)

for epoch in range(10):
    print(epoch)
    output = model(train_data, user_id, item_id)
    
    loss = criterion(output, label)
    
    optimizer.zero_grad()
    loss.backward(retain_graph=True)
    optimizer.step()

`RuntimeError`: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [176, 704]], which is output 0 of TBackward, is at version 2; expected version 1 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).