Home/ Questions/Recommendation for the grad can be implicitly created only for scalar outputs error
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Leon Roy
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Asked: 2022-05-18T19:21:33+00:00 In:

Recommendation for the grad can be implicitly created only for scalar outputs error

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I get the error: grad can be implicitly created only for scalar outputs when I try to run the program below:

def basic_fun(x_cloned):
  res = []
  for i in range(len(x)):
  res.append(x_cloned[i] * x_cloned[i])
  print(res)
  return Variable(torch.FloatTensor(res))
 
 
 def get_grad(inp, grad_var):
  A = basic_fun(inp)
  A.backward()
  return grad_var.grad
 
 
 x = Variable(torch.FloatTensor([1, 2, 3, 4, 5]), requires_grad=True)
 x_cloned = x.clone()
 print(get_grad(x_cloned, x))

The error appears the system notifies as follows:

[tensor(1., grad_fn=<ThMulBackward>), tensor(4., grad_fn=<ThMulBackward>), tensor(9., grad_fn=<ThMulBackward>), tensor(16., grad_fn=<ThMulBackward>), tensor(25., grad_fn=<ThMulBackward>)]
 Traceback (most recent call last):
  File "/home/mhy/projects/pytorch-optim/predict.py", line 74, in <module>
  print(get_grad(x_cloned, x))
  File "/home/mhy/projects/pytorch-optim/predict.py", line 68, in get_grad
  A.backward()
  File "/home/mhy/.local/lib/python3.5/site-packages/torch/tensor.py", line 93, in backward
  torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/mhy/.local/lib/python3.5/site-packages/torch/autograd/__init__.py", line 90, in backward
  allow_unreachable=True) # allow_unreachable flag
 RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
Traceback (most recent call last):
  File "<project_path>/playground.py", line 22, in <module>
  print(get_grad(x_cloned, x))
  File "<project_path>/playground.py", line 16, in get_grad
  A.backward()
  File "/home/mhy/.local/lib/python3.5/site-packages/torch/tensor.py", line 93, in backward
  torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/mhy/.local/lib/python3.5/site-packages/torch/autograd/__init__.py", line 84, in backward
  grad_tensors = _make_grads(tensors, grad_tensors)
  File "/home/mhy/.local/lib/python3.5/site-packages/torch/autograd/__init__.py", line 28, in _make_grads
  raise RuntimeError("grad can be implicitly created only for scalar outputs")
 RuntimeError: grad can be implicitly created only for scalar outputs

I tried to solve it with another sample. I got the reference in the community forum, but it still returned an invalid result. If someone knows the solution, please give me the support. Thanks!

pytorch
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2 Answers

  1. Best Answer
    Expert

    The cause: I think this code is wrong:

    The solution: To fix this error, this code should be changed into:

    def basic_fun(x_cloned):
    res = torch.FloatTensor([0])
    for i in range(len(x)):
        res += x_cloned[i] * x_cloned[i]
    return res

    A scalar value is returned in this version.

    • 0

  2. in the basic_fun function, the res variable already is a torch-autograd-Variable you don’t need to convert it again. IMHO 

    def basic_fun(x_cloned):
 res = []
 for i in range(len(x)):
 res.append(x_cloned[i] * x_cloned[i])
 print(res)
 #return Variable(torch.FloatTensor(res))
 return res[0]

def get_grad(inp, grad_var):
 A = basic_fun(inp)
 A.backward()
 return grad_var.grad


x = Variable(torch.FloatTensor([1, 2, 3, 4, 5]), requires_grad=True)
x_cloned = x.clone()
print(get_grad(x_cloned, x))
    • 23

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