# Understanding Autograd + ReLU(inplace = True)

How do I compute/check/understand gradients of inplace ReLU? I did `clone()` to bypass “a leaf Variable that requires grad has been used in an in-place operation”. Gradient should obviously be 0, but I get 1.

``````import torch
import torch.nn.functional as F

# not in place
# tensor([0.])

# inplace
# tensor([1.])
``````

My final usecase is testing some ResNet-like code like following:

``````def residual_activation_inplace(x, residual, activation, inplace):
for r in residual:
x += r
return activation(x, inplace = inplace)

x = torch.rand(3, 4)
residual = [torch.rand(3, 4), torch.rand(3, 4)]
y = residual_activation_inplace(x, residual, F.relu, inplace = True)
``````

Hi,

Since you apply the relu inplace in the second case, `x` now points to the output of the relu. And so you actually do `dx/dx = 1`.

If you do the following to have access to the gradient of the original x (before the inplace), it will work.

``````x = torch.tensor([-1.0]).requires_grad_()
x2 = x.clone()
``````

In spirit, you can replace `F.relu_(x)` by `y = F.relu(x); x = y`. Where `x` is the actual python variable and thus will affect everything that uses x.

`d(x)/dx = 1` - makes sense

Hi, guys,

I am confused by the gradient of inplace version ReLU until I found this thread. However, it is still unclear how the backward performs in inplace ReLU.

name: relu_(Tensor(a!) self) → Tensor(a!)

we can see the inplace ReLU (relu_) replies on the output tensor (result) for its back-propogation, however, this tensor has already been modified to non-negative range. It is confusing how to passing back gradient as we lost the sign of the tensor.

See example below, for inplace ReLU, the saved_tensors in the backward function is non-negative

“”"
We can implement our own custom autograd Functions by subclassing
torch.autograd.Function and implementing the forward and backward passes
which operate on Tensors.
“”"
@staticmethod
def forward(ctx, input):
“”"
In the forward pass we receive a Tensor containing the input and return
a Tensor containing the output. ctx is a context object that can be used
to stash information for backward computation. You can cache arbitrary
objects for use in the backward pass using the ctx.save_for_backward method.
“”"
ctx.save_for_backward(input)
return input.clamp(min=0)
@staticmethod
“”"
In the backward pass we receive a Tensor containing the gradient of the loss
with respect to the output, and we need to compute the gradient of the loss
with respect to the input.
“”"
input, = ctx.saved_tensors
grad_input[input < 0] = 0 # for inplace version, grad_input = grad_output, as input is modified into non-negative range?

Thus, the only way for correct inplace ReLU backward propogration is to save another tensor which indicate the sign of the input (flag = input < 0)?

In other word, for inplace ReLU, a flag (e. g. flag = input < 0) tensor is saved for backward, rather than the input/output tensor?

Hi,

The gradient we want to compute here is indeed: `1 if input > 0` and `0 if inputs <= 0`.
The nice thing is that `inputs <= 0` <=> `relu(inputs) = 0`.
So we can actually compute the gradient based on the result with `grad_input[result == 0] = 0` (or with <=, that would give the same result as result >=0).

1 Like

Thank you for the explain. With grad_input[result == 0] = 0, no necessary for extra flag stored. Clear.