fix: num_items_in_batch wrong type in kd trainer loss

src/axolotl/integrations/kd/trainer.py

@@ -74,6 +74,9 @@ class AxolotlKDTrainer(AxolotlTrainer):
         target_token_ids_for_loss = target_token_ids[..., 1:, :].contiguous()
         target_mask_for_loss = target_mask[..., 1:, :].contiguous()
 
+        if num_items_in_batch is None:
+            num_items_in_batch = -1
+
         if self.args.kd_zscore_base_temp:
             loss_kd = topk_kd_loss_with_zscore(
                 shift_logits,

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
+from axolotl.utils.distributed import is_local_main_process, zero_first
 from axolotl.utils.trainer import (
     calculate_total_num_steps,
     process_datasets_for_packing,
@@ -66,31 +66,32 @@ LOG = logging.getLogger(__name__)
 def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
     prompters = []
     if not cfg.pretraining_dataset:
-        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,
-            )
+        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,
+                )
     else:
         # Load streaming dataset if pretraining_dataset is given
         path = cfg.pretraining_dataset
@@ -271,7 +272,7 @@ 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 use `axolotl preprocess` to prepare your dataset."
+                "Processing datasets during training can lead to VRAM instability. Please pre-process your dataset."
             )
 
         if cfg.seed: