reclassified an info-level message to debug-level, updated changelog,…

… reverted no-longer necessary FTS tutorial notebook CLI workaround

CHANGELOG.md

@@ -9,11 +9,17 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 - Support for Lightning and PyTorch ``2.4.0``
+- Support for Python ``3.12``
 
 ### Changed
 
 - Changed default value of the ``frozen_bn_track_running_stats`` option to the FTS callback constructor to ``True``.
 
+### Deprecated
+
+- removed support for PyTorch `2.0`
+- removed support for Python `3.8`
+
 ## [2.3.3] - 2024-07-09
 
 - Support for Lightning <= ``2.3.3`` (includes critical security fixes) and PyTorch <= ``2.3.1``

src/finetuning_scheduler/strategy_adapters/base.py

@@ -22,13 +22,12 @@
 
 import torch
 
-from lightning.fabric.utilities import rank_zero_info
+from lightning.fabric.utilities import rank_zero_info, rank_zero_debug
 from lightning.fabric.utilities.types import ReduceLROnPlateau
 from lightning.pytorch import LightningModule, Trainer
 from lightning.pytorch.callbacks import Callback
 from lightning.pytorch.callbacks import BaseFinetuning
 from lightning.pytorch.strategies.strategy import Strategy
-from lightning.pytorch.utilities.rank_zero import rank_zero_debug
 
 
 class StrategyAdapter:
@@ -327,7 +326,7 @@ def _module_specific_freezing(self, modules: torch.nn.Module) -> None:
             None
         """
         if self.fts_handle.frozen_bn_track_running_stats:
-            rank_zero_info("Since `frozen_bn_track_running_stats` is currently set to `True`, FinetuningScheduler"
+            rank_zero_debug("Since `frozen_bn_track_running_stats` is currently set to `True`, FinetuningScheduler"
                             " will set `track_running_stats` to `True` for all `BatchNorm` layers.")
             modules = BaseFinetuning.flatten_modules(modules)  # type: ignore[assignment]
             for mod in modules:

src/fts_examples/stable/ipynb_src/fts_superglue_nb.py

@@ -253,22 +253,13 @@ def __init__(
         super().__init__()
         task_name = task_name if task_name in TASK_NUM_LABELS.keys() else DEFAULT_TASK
         self.text_fields = self.TASK_TEXT_FIELD_MAP[task_name]
-        self.init_hparams = {
-            "model_name_or_path": model_name_or_path,
-            "task_name": task_name,
-            "max_seq_length": max_seq_length,
-            "train_batch_size": train_batch_size,
-            "eval_batch_size": eval_batch_size,
-            "dataloader_kwargs": dataloader_kwargs,
-            "tokenizers_parallelism": tokenizers_parallelism,
-        }
         # starting with HF Datasets v3.x, trust_remote_code must be `True` https://bit.ly/hf_datasets_trust_remote_req
         self.trust_remote_code = True
-        self.save_hyperparameters(self.init_hparams)
         self.dataloader_kwargs = {
             "num_workers": dataloader_kwargs.get("num_workers", 0),
             "pin_memory": dataloader_kwargs.get("pin_memory", False),
         }
+        self.save_hyperparameters()
         os.environ["TOKENIZERS_PARALLELISM"] = "true" if self.hparams.tokenizers_parallelism else "false"
         self.tokenizer = AutoTokenizer.from_pretrained(
             self.hparams.model_name_or_path, use_fast=True, local_files_only=False