Add a config not to shuffle merged dataset (#1394) [skip ci]

* Add a config not to shuffle merged dataset

* Update README.md

* Update src/axolotl/utils/config/models/input/v0_4_1/__init__.py

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

* invert the condition name

* update README

* info -> debug

---------

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

README.md

@@ -678,6 +678,10 @@ datasets:
       # For `completion` datsets only, uses the provided field instead of `text` column
       field:
 
+# If false, the datasets will not be shuffled and will keep their original order in `datasets`.
+# The same applies to the `test_datasets` option and the `pretraining_dataset` option. Default is true.
+shuffle_merged_datasets: true
+
 # A list of one or more datasets to eval the model with.
 # You can use either test_datasets, or val_set_size, but not both.
 test_datasets:

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -416,6 +416,7 @@ class AxolotlInputConfig(
 
     datasets: Optional[conlist(Union[SFTDataset, DPODataset], min_length=1)] = None  # type: ignore
     test_datasets: Optional[conlist(Union[SFTDataset, DPODataset], min_length=1)] = None  # type: ignore
+    shuffle_merged_datasets: Optional[bool] = True
     dataset_prepared_path: Optional[str] = None
     dataset_shard_num: Optional[int] = None
     dataset_shard_idx: Optional[int] = None

src/axolotl/utils/data.py

@@ -415,8 +415,11 @@ def load_tokenized_prepared_datasets(
         dataset = concatenate_datasets(datasets)
 
         if len(datasets) > 1:
-            LOG.info("shuffle merged datasets")
+            if cfg.shuffle_merged_datasets:
-            dataset = dataset.shuffle(seed=seed)
+                LOG.debug("shuffle merged datasets")
+                dataset = dataset.shuffle(seed=seed)
+            else:
+                LOG.debug("NOT shuffling merged datasets")
 
         dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
@@ -819,7 +822,11 @@ def wrap_pretraining_dataset(
     else:
         encode = functools.partial(encode_pretraining, tokenizer, max_tokens)
 
-    dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)
+    if cfg.shuffle_merged_datasets:
+        dataset = dataset.shuffle(seed=seed, buffer_size=buffer_size)
+    else:
+        LOG.debug("NOT shuffling merged pretraining datasets")
+
     dataset = dataset.map(
         encode,
         batched=True,