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from typing import Any, Dict
from lightning.pytorch.utilities import rank_zero_only
from omegaconf import OmegaConf
from pflow.utils import pylogger
log = pylogger.get_pylogger(__name__)
@rank_zero_only
def log_hyperparameters(object_dict: Dict[str, Any]) -> None:
"""Controls which config parts are saved by Lightning loggers.
Additionally saves:
- Number of model parameters
:param object_dict: A dictionary containing the following objects:
- `"cfg"`: A DictConfig object containing the main config.
- `"model"`: The Lightning model.
- `"trainer"`: The Lightning trainer.
"""
hparams = {}
cfg = OmegaConf.to_container(object_dict["cfg"])
model = object_dict["model"]
trainer = object_dict["trainer"]
if not trainer.logger:
log.warning("Logger not found! Skipping hyperparameter logging...")
return
hparams["model"] = cfg["model"]
# save number of model parameters
hparams["model/params/total"] = sum(p.numel() for p in model.parameters())
hparams["model/params/trainable"] = sum(p.numel() for p in model.parameters() if p.requires_grad)
hparams["model/params/non_trainable"] = sum(p.numel() for p in model.parameters() if not p.requires_grad)
hparams["data"] = cfg["data"]
hparams["trainer"] = cfg["trainer"]
hparams["callbacks"] = cfg.get("callbacks")
hparams["extras"] = cfg.get("extras")
hparams["task_name"] = cfg.get("task_name")
hparams["tags"] = cfg.get("tags")
hparams["ckpt_path"] = cfg.get("ckpt_path")
hparams["seed"] = cfg.get("seed")
# send hparams to all loggers
for logger in trainer.loggers:
logger.log_hyperparams(hparams)
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