evomo.utils.state

Helpers for optional algorithm state whose shape is known after evaluation.

Functions

register_lazy_buffer(→ None)

Register optional persistent state, materializing it before checkpoint loading.

Module Contents

evomo.utils.state.register_lazy_buffer(module: torch.nn.Module, name: str, *, device_like: str) → None

Register optional persistent state, materializing it before checkpoint loading.

The buffer starts as None and accepts normal tensor assignments after evaluation. A fresh instance can restore its shape and dtype from a checkpoint. Already initialized buffers retain PyTorch’s normal shape validation.

device_like names an existing tensor attribute on the same module, such as "pop". Its device is read at load time, so use a parameter or buffer that follows module device transfers. The tensor itself is not captured by the hook. Standard load_state_dict(assign=True) semantics still take precedence: assigned tensors retain the checkpoint’s device and dtype.

As with ordinary None buffers, absent checkpoint keys leave fresh state as None. This helper does not infer or validate a problem’s number of constraints.

Example:

self.pop = Mutable(population)
register_lazy_buffer(self, "cv", device_like="pop")