No, I don’t think so, as the frameworks would have to use a common Autograd implementation.
E.g. while PyTorch uses its Autograd backend, creates the computation graph during the forward pass, and stored the .grad_fns as attributes of activation tensors, the second framework would need to be able to automatically combine the PyTorch output to it’s “backend engine” (however it’s implemented).
That being said, you might be able to manually combine different frameworks together.