adding more log messages in training_utils

src/refiners/training_utils/clock.py

@@ -170,12 +170,18 @@ class TrainingClock(Callback["Trainer[BaseConfig, Any]"]):
         self.log(f"Epoch {trainer.clock.epoch} started.")
 
     def on_epoch_end(self, trainer: "Trainer[BaseConfig, Any]") -> None:
+        self.log(f"Epoch {trainer.clock.epoch} ended.")
         trainer.clock.epoch += 1
         trainer.clock.num_batches_processed = 0
 
     def on_batch_begin(self, trainer: "Trainer[BaseConfig, Any]") -> None:
+        if self.num_minibatches_processed == 0:
+            self.log(f"Iteration {trainer.clock.iteration} started.")
         self.log(f"Step {trainer.clock.step} started.")
 
+    def on_batch_end(self, trainer: "Trainer[BaseConfig, Any]") -> None:
+        self.log(f"Step {trainer.clock.step} ended.")
+
     def on_backward_end(self, trainer: "Trainer[BaseConfig, Any]") -> None:
         trainer.clock.step += 1
         trainer.clock.num_batches_processed += 1

src/refiners/training_utils/common.py

@@ -67,6 +67,7 @@ def scoped_seed(seed: int | Callable[..., int] | None = None) -> Callable[..., C
             actual_seed = seed(*args) if callable(seed) else seed
             seed_everything(seed=actual_seed)
             result = func(*args, **kwargs)
+            logger.debug(f"Restoring previous seed state")
             random.setstate(random_state)
             np.random.set_state(numpy_state)
             torch.set_rng_state(torch_state)

src/refiners/training_utils/trainer.py

@@ -103,8 +103,11 @@ def register_model():
             model = func(self, config)
             model = model.to(self.device, dtype=self.dtype)
             if config.requires_grad is not None:
+                logger.info(f"Setting requires_grad to {config.requires_grad} for model: {name}")
                 model.requires_grad_(requires_grad=config.requires_grad)
             learnable_parameters = [param for param in model.parameters() if param.requires_grad]
+            numel = sum(param.numel() for param in learnable_parameters)
+            logger.info(f"Number of learnable parameters in {name}: {human_readable_number(numel)}")
             self.models[name] = ModelItem(
                 name=name, config=config, model=model, learnable_parameters=learnable_parameters
             )