simplify log_hyperparameters() and log best_checkpoint and checkpoint…

…_dir (#153)

requirements.txt

@@ -1,7 +1,7 @@
 # --------- pytorch-ie --------- #
 pytorch-ie>=0.28.0,<0.30.0
 pie-datasets>=0.8.1,<0.9.0
-pie-modules>=0.8.0,<0.9.0
+pie-modules>=0.9.0,<0.10.0
 
 # --------- hydra --------- #
 hydra-core>=1.3.0

src/evaluate.py

@@ -107,7 +107,7 @@ def evaluate(cfg: DictConfig) -> Tuple[dict, dict]:
 
     if logger:
         log.info("Logging hyperparameters!")
-        utils.log_hyperparameters(object_dict)
+        utils.log_hyperparameters(logger=logger, model=model, taskmodule=taskmodule, config=cfg)
 
     log.info("Starting testing!")
     trainer.test(model=model, datamodule=datamodule, ckpt_path=cfg.ckpt_path)

src/train.py

@@ -33,13 +33,14 @@
 # https://github.com/ashleve/pyrootutils
 # ------------------------------------------------------------------------------------ #
 
+import os
 from typing import Any, Dict, List, Optional, Tuple
 
 import hydra
 import pytorch_lightning as pl
-from hydra.utils import get_class
 from omegaconf import DictConfig
 from pie_datasets import DatasetDict
+from pie_modules.models.interface import RequiresTaskmoduleConfig
 from pytorch_ie.core import PyTorchIEModel, TaskModule
 from pytorch_ie.models import *  # noqa: F403
 from pytorch_ie.models.interface import RequiresModelNameOrPath, RequiresNumClasses
@@ -121,7 +122,7 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]:
     log.info(f"Instantiating model <{cfg.model._target_}>")
     # get additional model arguments
     additional_model_kwargs: Dict[str, Any] = {}
-    model_cls = get_class(cfg.model["_target_"])
+    model_cls = hydra.utils.get_class(cfg.model["_target_"])
     # NOTE: MODIFY THE additional_model_kwargs IF YOUR MODEL REQUIRES ANY MORE PARAMETERS FROM THE TASKMODULE!
     # SEE EXAMPLES BELOW.
     if issubclass(model_cls, RequiresNumClasses):
@@ -134,6 +135,9 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]:
     if isinstance(taskmodule, ChangesTokenizerVocabSize):
         additional_model_kwargs["tokenizer_vocab_size"] = len(taskmodule.tokenizer)
 
+    if issubclass(model_cls, RequiresTaskmoduleConfig):
+        additional_model_kwargs["taskmodule_config"] = taskmodule.config
+
     # initialize the model
     model: PyTorchIEModel = hydra.utils.instantiate(
         cfg.model, _convert_="partial", **additional_model_kwargs
@@ -160,7 +164,7 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]:
 
     if logger:
         log.info("Logging hyperparameters!")
-        utils.log_hyperparameters(object_dict)
+        utils.log_hyperparameters(logger=logger, model=model, taskmodule=taskmodule, config=cfg)
 
     if cfg.model_save_dir is not None:
         log.info(f"Save taskmodule to {cfg.model_save_dir} [push_to_hub={cfg.push_to_hub}]")
@@ -177,6 +181,12 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]:
     best_ckpt_path = trainer.checkpoint_callback.best_model_path
     if best_ckpt_path != "":
         log.info(f"Best ckpt path: {best_ckpt_path}")
+        best_checkpoint_file = os.path.basename(best_ckpt_path)
+        utils.log_hyperparameters(
+            logger=logger,
+            best_checkpoint=best_checkpoint_file,
+            checkpoint_dir=trainer.checkpoint_callback.dirpath,
+        )
 
     if not cfg.trainer.get("fast_dev_run"):
         if cfg.model_save_dir is not None:

src/utils/logging_utils.py

@@ -1,6 +1,11 @@
 import logging
 from importlib.util import find_spec
+from typing import List, Optional, Union
 
+from omegaconf import DictConfig, OmegaConf
+from pie_modules.models.interface import RequiresTaskmoduleConfig
+from pytorch_ie import PyTorchIEModel, TaskModule
+from pytorch_lightning.loggers import Logger
 from pytorch_lightning.utilities import rank_zero_only
 
 
@@ -22,7 +27,14 @@ def get_pylogger(name=__name__) -> logging.Logger:
 
 
 @rank_zero_only
-def log_hyperparameters(object_dict: dict) -> None:
+def log_hyperparameters(
+    logger: Optional[List[Logger]] = None,
+    config: Optional[Union[dict, DictConfig]] = None,
+    model: Optional[PyTorchIEModel] = None,
+    taskmodule: Optional[TaskModule] = None,
+    key_prefix: str = "_",
+    **kwargs,
+) -> None:
     """Controls which config parts are saved by lightning loggers.
 
     Additional saves:
@@ -31,48 +43,42 @@ def log_hyperparameters(object_dict: dict) -> None:
 
     hparams = {}
 
-    cfg = object_dict["cfg"]
-    model = object_dict["model"]
-    taskmodule = object_dict["taskmodule"]
-    trainer = object_dict["trainer"]
-
-    if not trainer.logger:
+    if not logger:
         log.warning("Logger not found! Skipping hyperparameter logging...")
         return
 
-    # choose which parts of hydra config will be saved to loggers
-    # here we use the taskmodule/model config how it is after preparation/initialization
-    hparams["taskmodule"] = taskmodule._config()
-    hparams["model"] = model._config()
-
-    # 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["dataset"] = cfg["dataset"]
-    hparams["trainer"] = cfg["trainer"]
-
-    hparams["callbacks"] = cfg.get("callbacks")
-    hparams["extras"] = cfg.get("extras")
-
-    hparams["pipeline_type"] = cfg.get("pipeline_type")
-    hparams["tags"] = cfg.get("tags")
-    hparams["ckpt_path"] = cfg.get("ckpt_path")
-    hparams["seed"] = cfg.get("seed")
-
-    hparams["monitor_metric"] = cfg.get("monitor_metric")
-    hparams["monitor_mode"] = cfg.get("monitor_mode")
-
-    hparams["model_save_dir"] = cfg.get("model_save_dir")
+    # this is just for backwards compatibility: usually, the taskmodule_config should be passed to
+    # the model and, thus, be logged there automatically
+    if model is not None and not isinstance(model, RequiresTaskmoduleConfig):
+        if taskmodule is None:
+            raise ValueError(
+                "If model is not an instance of RequiresTaskmoduleConfig, taskmodule must be passed!"
+            )
+        # here we use the taskmodule/model config how it is after preparation/initialization
+        hparams["taskmodule_config"] = taskmodule.config
+
+    if model is not None:
+        # save number of model parameters
+        hparams[f"{key_prefix}num_params/total"] = sum(p.numel() for p in model.parameters())
+        hparams[f"{key_prefix}num_params/trainable"] = sum(
+            p.numel() for p in model.parameters() if p.requires_grad
+        )
+        hparams[f"{key_prefix}num_params/non_trainable"] = sum(
+            p.numel() for p in model.parameters() if not p.requires_grad
+        )
+
+    if config is not None:
+        hparams[f"{key_prefix}config"] = (
+            OmegaConf.to_container(config, resolve=True) if OmegaConf.is_config(config) else config
+        )
+
+    # add additional hparams
+    for k, v in kwargs.items():
+        hparams[f"{key_prefix}{k}"] = v
 
     # send hparams to all loggers
-    for logger in trainer.loggers:
-        logger.log_hyperparams(hparams)
+    for current_logger in logger:
+        current_logger.log_hyperparams(hparams)
 
 
 def close_loggers() -> None: