Getting gradient of vectorized function in pytorch
I am brand new to PyTorch and want to do what I assume is a very simple thing but am having a lot of difficulty. 

I have the function sin(x) * cos(x) + x^2 and I want to get the derivative of that function at any point. 

If I do this with one point it works perfectly as 

x = torch.autograd.Variable(torch.Tensor([4]),requires_grad=True)
y = torch.sin(x)*torch.cos(x)+torch.pow(x,2)
y.backward()
print(x.grad) # outputs tensor([7.8545])


However, I want to be able to pass in a vector as x and for it to evaluate the derivative element-wise. For example:

Input: [4., 4., 4.,]
Output: tensor([7.8545, 7.8545, 7.8545])


But I can't seem to get this working. 

I tried simply doing

x = torch.tensor([4., 4., 4., 4.], requires_grad=True)
out = torch.sin(x)*torch.cos(x)+x.pow(2)
out.backward()
print(x.grad)


But I get the error "RuntimeError: grad can be implicitly created only for scalar outputs"

How do I adjust this code for vectors? 

Thanks in advance,

Here you can find relevant discussion about your error.

In essence, when you call backward() without arguments it is implicitly converted to backward(torch.Tensor([1])), where torch.Tensor([1]) is the output value with respect to which gradients are calculated.

If you pass 4 (or more) inputs, each needs a value with respect to which you calculate gradient. You can pass torch.ones_like explicitly to backward like this:

import torch

x = torch.tensor([4.0, 2.0, 1.5, 0.5], requires_grad=True)
out = torch.sin(x) * torch.cos(x) + x.pow(2)
# Pass tensor of ones, each for each item in x
out.backward(torch.ones_like(x))
print(x.grad)

