What is PyTorch leaf node? | B. Nikolic Software and Computing Blog
Understanding what a PyTorch leaf node is intuitive when you remember that PyTorch automatic differentiation is in the backward direction compared to the way the program is executed to compute its value – i.e., it is a backward-mode automatic differentiation.
Understanding what a PyTorch leaf node is intuitive when you remember that PyTorch automatic differentiation is in the backward direction compared to the way the program is executed to compute its value – i.e., it is a backward-mode automatic differentiation. Below are a couple of slides I’ve used before to illustrate backward mode autodiff: In PyTorch leaf nodes are therefore the values from which the computation begins . Here a simple program illustrating this: # The following two values are the leaf nodes x = T . ones ( 10 , requires_grad = True ) y = T . ones ( 10 , requires_grad = True )
related reading
- torch.Tensor.backwardpytorch.org
- Calculus on Computational Graphs: Backpropagation -- colah's blogcolah.github.io
- Yes you should understand backprop | by Andrej Karpathy | Mediumkarpathy.medium.com
- Learning PyTorch with Examples — PyTorch Tutorials 2.12.0+cu130 documentationpytorch.org
- Making Deep Learning go Brrrr From First Principleshorace.io
- Differentiable Programming from Scratchthenumb.at
- PyTorch internals : ezyang's blogblog.ezyang.com
- Transformers from scratch | peterbloem.nlpeterbloem.nl
- PyTorch in One Hour: From Tensors to Training Neural Networks on Multiple GPUssebastianraschka.com
- Reverse-mode automatic differentiation from scratch, in Python | sidsitesidsite.com
- The Annotated Transformernlp.seas.harvard.edu
- Taking PyTorch for Granted | whnrehiew.github.io