python - 无法在pytorch代码中找到就地操作？

我在PyTorch中具有以下实现，可以使用LSTM学习：

https://gist.github.com/rahulbhadani/f1d64042cc5a80280755cac262aa48aa

但是，该代码遇到就地操作错误

我的错误输出是：

/home/ivory/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  # Remove the CWD from sys.path while we load stuff.
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-86-560ec78f2b64> in <module>
     27 linear = torch.nn.Linear(hidden_nums, output_dim)
     28
---> 29 global_loss_list = global_training(lstm2)

<ipython-input-84-152890a3028c> in global_training(optimizee)
      3     adam_global_optimizer = torch.optim.Adam([{'params': optimizee.parameters()},
      4                                      {'params':linear.parameters()}], lr = 0.0001)
----> 5     _, global_loss_1 = learn2(LSTM_Optimizee, training_steps, retain_graph_flag=True, reset_theta=True)
      6
      7     print(global_loss_1)

<ipython-input-83-0357a528b94d> in learn2(optimizee, unroll_train_steps, retain_graph_flag, reset_theta)
     43             # requires_grad=True. These are accumulated into x.grad for every
     44             # parameter x. In pseudo-code: x.grad += dloss/dx
---> 45             loss.backward(retain_graph = retain_graph_flag) #The default is False, when the optimized LSTM is set to True
     46
     47             print('x.grad: {}'.format(x.grad))

~/anaconda3/lib/python3.7/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)
    116                 products. Defaults to ``False``.
    117         """
--> 118         torch.autograd.backward(self, gradient, retain_graph, create_graph)
    119
    120     def register_hook(self, hook):

~/anaconda3/lib/python3.7/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
     91     Variable._execution_engine.run_backward(
     92         tensors, grad_tensors, retain_graph, create_graph,
---> 93         allow_unreachable=True)  # allow_unreachable flag
     94
     95

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [1, 10]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

我试图跟踪错误，但没有成功。在这方面的任何帮助将不胜感激。

谢谢。

最佳答案

我认为问题在于以下几行：

global_loss_list.append(global_loss.detach_())

PyTorch中就地操作的约定是在函数名称的末尾使用_（与detach_相同）。我相信您不应该就地分离。换句话说，将detach_更改为detach

关于python - 无法在pytorch代码中找到就地操作？，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/57500582/