Autograd Internals

Autograd implements reverse-mode differentiation for tensor computations.

Concept

• Variables wrap tensors and record operations.
• A computation tape records the forward pass.
• Backpropagation traverses the graph from outputs to inputs.

Internal flow

Forward pass
   ├─ operations create nodes
   └─ tape stores references
Backward pass
   └─ gradients propagate through nodes

Memory behavior

• Gradients are stored as tensors in the autograd graph.
• The graph retains references to intermediate tensors until backward completes.
• Use detach() or explicit scope if intermediate reuse is not needed.

When to use

• Use autograd for custom differentiable layers.
• Use it only when the estimator requires gradients.

When not to use

• Do not use autograd for tree-based or non-differentiable models.
• Avoid building deep graphs for very large tensors without measuring memory usage.