PyTorch state_dict Bridge

This module is the general weight-interchange path for PyTorch users.

The important split is:

• Weights move through PyTorch state_dicts. PyTorch’s own documentation recommends saving a module’s learned parameters with torch.save(model.state_dict(), path) because that is the most flexible restoration format.
• Graphs move through graph capture (torch.export, FX, ONNX, or TorchLean NN.IR.Graph). A state_dict alone does not describe the model architecture; it only names tensors.

Lean should not try to parse PyTorch pickle/zip checkpoints directly. Instead, we emit a small Python adapter that loads a checkpoint with PyTorch, normalizes common wrappers such as {"state_dict": ...}, and writes shape-checkable JSON:

{
  "params": { "layer.weight": [[...]], "layer.bias": [...] },
  "meta": { "layer.weight": { "shape": [out, in], "dtype": "torch.float32" } }
}

NN.Runtime.PyTorch.Import.Core then parses the "params" object into typed TorchLean tensors. Architecture-specific loaders are still useful, but only for mapping names and shapes. The transport format itself is model-agnostic.

References:

• PyTorch documentation, "Saving and Loading Models": https://docs.pytorch.org/tutorials/beginner/saving_loading_models.html
• PyTorch torch.export user guide: https://docs.pytorch.org/docs/stable/user_guide/torch_compiler/export.html
• PyTorch FX overview: https://docs.pytorch.org/docs/stable/fx.html