Problem with backward hook function #598
Description
Hi,
there is something strange in the backward step (or maybe something I don't understand). If I define a Module that takes 3 inputs, the grad_input has to be of size 3, right ? But this is not the case here (from the backward_hook point of view):
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
def bh(m,go,gi):
print("Grad Input")
print(go)
print("Grad Output")
print(gi)
class M(nn.Module):
def __init__(self):
super(M,self).__init__()
self.register_backward_hook(bh)
def forward(self,x,y,z):
return (x+y+z)
x=Variable(torch.randn(1,5),requires_grad=True)
y=Variable(torch.randn(1,5),requires_grad=True)
z=Variable(torch.randn(1,5),requires_grad=True)
criterion=nn.MSELoss()
mod=M()
out=mod(x,y,z)
loss=criterion(out,Variable(torch.randn(1,5)))
loss.backward()```
In that case, when I print grad_input throught the hook function, it is just composed of two elements... Could you tell me where am I wrong ? But `x.grad, y.grad and z.grad` seem correctly computed
cc @ezyang @gchanan @zou3519 @SsnL @albanD @gqchen