don't use zero first context for loading datasets

src/axolotl/utils/data/sft.py

@@ -53,7 +53,7 @@ from axolotl.utils.data.utils import (
     retry_on_request_exceptions,
 )
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.distributed import is_local_main_process, zero_first
+from axolotl.utils.distributed import is_local_main_process
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
@@ -66,32 +66,31 @@ LOG = logging.getLogger(__name__)
 def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
     prompters = []
     if not cfg.pretraining_dataset:
-        with zero_first(is_local_main_process()):
-            if cfg.test_datasets:
-                train_dataset, _, prompters = load_prepare_datasets(
-                    tokenizer,
-                    cfg,
-                    DEFAULT_DATASET_PREPARED_PATH,
-                    split="train",
-                    processor=processor,
-                    preprocess_iterable=preprocess_iterable,
-                )
-                _, eval_dataset, _ = load_prepare_datasets(
-                    tokenizer,
-                    cfg,
-                    DEFAULT_DATASET_PREPARED_PATH,
-                    split="test",
-                    processor=processor,
-                    preprocess_iterable=preprocess_iterable,
-                )
-            else:
-                train_dataset, eval_dataset, prompters = load_prepare_datasets(
-                    tokenizer,
-                    cfg,
-                    DEFAULT_DATASET_PREPARED_PATH,
-                    processor=processor,
-                    preprocess_iterable=preprocess_iterable,
-                )
+        if cfg.test_datasets:
+            train_dataset, _, prompters = load_prepare_datasets(
+                tokenizer,
+                cfg,
+                DEFAULT_DATASET_PREPARED_PATH,
+                split="train",
+                processor=processor,
+                preprocess_iterable=preprocess_iterable,
+            )
+            _, eval_dataset, _ = load_prepare_datasets(
+                tokenizer,
+                cfg,
+                DEFAULT_DATASET_PREPARED_PATH,
+                split="test",
+                processor=processor,
+                preprocess_iterable=preprocess_iterable,
+            )
+        else:
+            train_dataset, eval_dataset, prompters = load_prepare_datasets(
+                tokenizer,
+                cfg,
+                DEFAULT_DATASET_PREPARED_PATH,
+                processor=processor,
+                preprocess_iterable=preprocess_iterable,
+            )
     else:
         # Load streaming dataset if pretraining_dataset is given
         path = cfg.pretraining_dataset
@@ -272,7 +271,7 @@ def load_tokenized_prepared_datasets(
         LOG.info("Loading raw datasets...")
         if not cfg.is_preprocess:
             LOG.warning(
-                "Processing datasets during training can lead to VRAM instability. Please pre-process your dataset."
+                "Processing datasets during training can lead to VRAM instability. Please use `axolotl preprocess` to prepare your dataset."
             )
 
         if cfg.seed: