UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed

import torch
from torch.autograd import Variable

x = Variable(torch.FloatTensor([11.2]), requires_grad=True)
y = 2 * x

print(x)
print(y)

print(x.data)
print(y.data)

print(x.grad_fn)
print(y.grad_fn)

y.backward() # Calculates the gradients

print(x.grad)
print(y.grad)

Error

C:\Users\donhu\AppData\Local\Temp\ipykernel_9572\106071707.py:2: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at aten\src\ATen/core/TensorBody.h:485.)
  print(y.grad)

Source code https://github.com/donhuvy/Deep-learning-with-PyTorch-video/blob/master/1.5.variables.ipynb

How to fix?

>Solution :

Call y.retain_grad() before calling y.backward().

The reason is because by default PyTorch only populate .grad for leaf variables (variables that aren’t results of operations), which is x in your example. To ensure .grad is also populated for non-leaf variables like y, you need to call their .retain_grad() method.

Also worth noting that it’s a warning rather than an error.