Can anyone explain what is going on here?
import torch
from torch.autograd import Variable
import numpy as np
x = Variable(torch.Tensor([3.0]),requires_grad=True)
b = np.float32(3)
b*x
Returns
Out[11]:
array([[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[Variable containing:
9
[torch.FloatTensor of size 1]
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]], dtype=object)
Casting b to a normal float on the other hand gives the expected result.
It is a problem on how numpy multiplication is done unfortunately, because you do b*x, it is numpy implementation that is used. You can try doing x*b and it should fail with a nice error message from pytorch.
Actually, x*b works fine. Quite a trap though.
Unfortunately there is nothing we can do because due to the way python handle the * operator, if the left element is a numpy object, the numpy functions are going to be used and they are not aware of pytorch tensors 
Fair enough, it may be worth a mention somewhere in the docs perhaps. And many thanks for clarifying!
The problem also only occurs when you multiply a numpy float with a Variable, tensors are ok actually.