 # Maxpool from scratch

Hi, I am trying to implement maxpool fonction from scatch (for fun) and use backward() on it.
In my implementation below, the output Y of maxpool is correct (I verified it).
But the gradient of the input at a zero tensor, which is wrong.

code :

``````import torch
def maxpool_fp(X):
pool_dim = 2
pool_stripe = 2
bs, cx, dx, _ = list(X.size())  # batch size ; nb of channel of X ;  dimension of X
dy = int(X.shape / pool_stripe)  # dimension of Y

Y = X[:, :, :dy, :dy] * 1  # *1 to avoid: RuntimeError: leaf variable has been moved into the graph interior
for yn in range(bs):
for yc, x in enumerate(X[yn]):
for yh, h in enumerate(range(0, dx, pool_stripe)):
for yw, w in enumerate(range(0, dx, pool_stripe)):
Y[yn, yc, yh, yw] = torch.max(x[h:h + pool_dim, w:w + pool_dim]).item()
return Y

X = torch.randn(2, 2, 8, 8, requires_grad=True)

Y = maxpool_fp(X)
S = torch.sum(Y)
S.backward()

print("S =", S) # ==> Correct
``````

Hi,

The `.item()` that you use converts the result of your max into a python number. And we can’t track gradient for it. So it is ignored.
You will need to remove this `.item()` to get proper gradients.

1 Like

Great. Working now. Thanks a lot

But in order to keep track of the gradient I did this :

``````dy = int(X.shape / pool_stripe)  # dimension of Y
Y = X[:, :, :dy, :dy] * 1
``````

Can you see a more elegant way to do so? Or does it look fine for you ?

Why do you need the `*1` here?
Can you share the full code sample maybe that will be clearer Before removing item() the *1 was there avoid the Error:

RuntimeError: leaf variable has been moved into the graph interior

When executing the first :

``````
Y[yn, yc, yh, yw] = torch.max(x[h:h + pool_dim, w:w + pool_dim]).item()

``````

X is_leaf attribute changed from True to False. I put the *1 to set the is_leaf attribute to False in the first place in order to avoid the change.

After removing item(), the *1 is still needed to avoid another Error:

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [2, 2]], which is output 0 of SliceBackward, is at version 64; expected version 63 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).

which I can’t really explain

You can copy/past the code below and remove the *1 if you want to reproduce the RuntimeError. But please note that my problem was fixed by removing the .item(). I put this maxpool() function along with a hardcoded conv2d in a CNN model, and it works on MNIST.

``````
import torch

def maxpool_fp(X):
pool_dim = 2
pool_stripe = 2
bs, cx, dx, _ = list(X.size())  # batch size ; nb of channel of X ;  dimension of X
dy = int(X.shape / pool_stripe)  # dimension of Y

Y = X[:, :, :dy, :dy] * 1  # <=============
for yn in range(bs):
for yc, x in enumerate(X[yn]):
for yh, h in enumerate(range(0, dx, pool_stripe)):
for yw, w in enumerate(range(0, dx, pool_stripe)):
Y[yn, yc, yh, yw] = torch.max(x[h:h + pool_dim, w:w + pool_dim])
return Y

X = torch.randn(2, 2, 8, 8, requires_grad=True)

Y = maxpool_fp(X)
S = torch.sum(Y)
S.backward()

print("S =", S)
Yes, you can’t modify `X` inplace because it’s a leaf. And when you do `Y = X[:, :, :dy, :dy]`, `Y` is actually a view into `X`. And so when you modify `Y` inplace by doing `Y[yn, yc, yh, yw] = foo`, you also modify X leading to the error.
The right way to fix that is to use `.clone()` to ensure you get new memory that is not a view of X: `Y = X[:, :, :dy, :dy].clone()` (should be slightly faster than the *1 you use).