CODEWITHSUNDEEP
pytorch
golmschenk
golmschenk

Reputation: 12404

PyTorch - Element-wise multiplication between a variable and a tensor?

As of PyTorch 0.4 this question is no longer valid. In 0.4 Tensors and Variables were merged.

How can I perform element-wise multiplication with a variable and a tensor in PyTorch? With two tensors works fine. With a variable and a scalar works fine. But when attempting to perform element-wise multiplication with a variable and tensor I get:

XXXXXXXXXXX in mul
    assert not torch.is_tensor(other)
AssertionError

For example, when running the following:

import torch

x_tensor = torch.Tensor([[1, 2], [3, 4]])
y_tensor = torch.Tensor([[5, 6], [7, 8]])

x_variable = torch.autograd.Variable(x_tensor)

print(x_tensor * y_tensor)
print(x_variable * 2)
print(x_variable * y_tensor)

I would expect the first and last print statements to show similar results. The first two multiplications work as expected, with the error coming up in the third. I have attempted the aliases of * in PyTorch (i.e. x_variable.mul(y_tensor), torch.mul(y_tensor, x_variable), etc.).

It seems that element-wise multiplication between a tensor and a variable is not supported given the error and the code which produces it. Is this correct? Or is there something I'm missing? Thank you!

Upvotes: 8

Views: 16772

Answers (1)

mbpaulus
mbpaulus

Reputation: 7691

Yes, you are correct. Elementwise multiplication (like most other operations) is only supported for Tensor * Tensor or Variable * Variable, but not for Tensor * Variable.

To perform your multiplication above, wrap your Tensor as a Variable which doesn't require gradients. The additional overhead is insignificant. 

y_variable = torch.autograd.Variable(y_tensor, requires_grad=False)
x_variable * y_variable # returns Variable

But obviously, only use Variables though, if you actually require automatic differentiation through a graph. Else you can just perform the operation on the Tensors directly as you did in your question.

Upvotes: 13

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