fix: use text_column even when not packing for pretraining (#2254)

* fix: use text_column even when not packing for pretraining

* feat: update test to check when not packing

* chore: lint

* Update src/axolotl/utils/data/pretraining.py

Co-authored-by: Wing Lian <wing.lian@gmail.com>

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>
Co-authored-by: Wing Lian <wing.lian@gmail.com>

src/axolotl/utils/data/pretraining.py

@@ -18,10 +18,13 @@ LOG = logging.getLogger("axolotl")
 
 
 def encode_pretraining(
-    tokenizer: PreTrainedTokenizerBase, max_tokens: int, examples: Dict[str, List]
+    tokenizer: PreTrainedTokenizerBase,
+    max_tokens: int,
+    examples: Dict[str, List],
+    text_column: str = "text",
 ) -> Dict[str, List]:
     res = tokenizer(
-        examples["text"],
+        examples[text_column],
         truncation=True,
         max_length=max_tokens - 2,
         add_special_tokens=True,
@@ -196,7 +199,12 @@ def wrap_pretraining_dataset(
         # set this to 1 so downstream data_loader doesn't try to increase the batch again
         cfg.micro_batch_size = 1
     else:
-        encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
+        encode = functools.partial(
+            encode_pretraining,
+            tokenizer,
+            max_tokens,
+            text_column=cfg.pretraining_dataset[0].text_column or "text",
+        )
 
     if cfg.shuffle_merged_datasets:
         dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)

tests/e2e/test_llama_pretrain.py

@@ -4,7 +4,8 @@ E2E tests for llama pretrain
 
 import logging
 import os
-import unittest
+
+import pytest
 
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
@@ -12,19 +13,22 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 
-from .utils import check_model_output_exists, with_temp_dir
+from .utils import check_model_output_exists
 
 LOG = logging.getLogger("axolotl.tests.e2e")
 os.environ["WANDB_DISABLED"] = "true"
 
 
-class TestPretrainLlama(unittest.TestCase):
+class TestPretrainLlama:
     """
     Test case for Llama models w pretraining
     """
 
-    @with_temp_dir
-    def test_pretrain_w_sample_packing(self, temp_dir):
+    @pytest.mark.parametrize(
+        "sample_packing",
+        [True, False],
+    )
+    def test_pretrain(self, temp_dir, sample_packing):
         # pylint: disable=duplicate-code
         cfg = DictDefault(
             {
@@ -32,7 +36,7 @@ class TestPretrainLlama(unittest.TestCase):
                 "tokenizer_type": "LlamaTokenizer",
                 "flash_attention": True,
                 "sequence_len": 1024,
-                "sample_packing": True,
+                "sample_packing": sample_packing,
                 "special_tokens": {
                     "unk_token": "<unk>",
                     "bos_token": "<s>",