Episode 10 - PyTorch Module named attributes

Описание: Unlock the inner mechanisms of PyTorch’s module attribute handling. We start with how a module’s named_children() generator walks the direct submodule graph, yielding each child only once with its fully‑qualified name . Then we compare it to the simpler children() variant that returns just the modules themselves.

Next, we explore named_parameters(), built atop the generic named_members() helper that recursively collects every named parameter—including custom ones defined as attributes—so you can see exactly how weights, biases, and user‑added buffers surface during introspection.

We walk through get_parameter(), which parses a dotted path, safely retrieves the target submodule, and raises an informative AttributeError if the name is invalid or the value isn’t a parameter, keeping your debugging loop clean and predictable.

Finally, we demonstrate a lightweight yet powerful apply_to_module() utility that recursively walks the module tree, applying a user‑supplied function to each leaf and composite node alike, letting you inject logic or transformations across the entire model hierarchy .

Whether you’re auditing a complex network or building custom wrappers, this episode gives you the low‑level view of how PyTorch makes module attributes both discoverable and manipulable.