How can I create a custom autograd function in PyTorch?

How can I create a custom autograd function in PyTorch?

To create a custom autograd function in PyTorch, you need to subclass torch.autograd.Function and implement two static methods: forward and backward. The forward method computes the output, while the backward method computes the gradients.

Can you show me a simple example of a custom autograd function?

Sure! Here's a simple example of a custom autograd function that squares its input:

import torch

class Square(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input):
        ctx.save_for_backward(input)
        return input * input

    @staticmethod
    def backward(ctx, grad_output):
        input, = ctx.saved_tensors
        grad_input = grad_output * 2 * input
        return grad_input

You can use this function like this:

input = torch.tensor([2.0], requires_grad=True)
output = Square.apply(input)
output.backward()
print(input.grad)  # Should print tensor([4.])

What if I want to add more complex operations in the forward method?

You can include any operations supported by PyTorch in the forward method. Just ensure that you save any necessary tensors for the backward method using ctx.save_for_backward(). This allows you to compute gradients based on those tensors.

Are there any performance considerations I should be aware of?

Yes, when creating custom autograd functions, be mindful of the operations you use in the forward and backward methods. Avoid unnecessary computations and ensure that you are using in-place operations where appropriate to optimize performance.