more dpo fixes for dataset loading and docs

docs/rlhf.md

@@ -34,6 +34,16 @@ datasets:
 rl: ipo
 ```
 
+#### Using local dataset files
+```yaml
+datasets:
+  - ds_type: json
+    data_files:
+      - orca_rlhf.jsonl
+    split: train
+    type: chatml.intel
+```
+
 #### Trl autounwrap for peft
 
 Trl supports autounwrapping peft models, so that a ref model does not need to be additionally loaded, leading to less VRAM needed. This is on by default. To turn it off, pass the following config.

src/axolotl/cli/preprocess.py

@@ -13,6 +13,7 @@
     check_user_token,
     load_cfg,
     load_datasets,
+    load_rl_datasets,
     print_axolotl_text_art,
 )
 from axolotl.common.cli import PreprocessCliArgs
@@ -43,7 +44,11 @@ def do_cli(config: Path = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    _ = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    if parsed_cfg.rl:
+        load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+    else:
+        load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
+
     LOG.info(
         Fore.GREEN
         + f"Success! Preprocessed data path: `dataset_prepared_path: {parsed_cfg.dataset_prepared_path}`"

src/axolotl/core/trainer_builder.py

@@ -996,6 +996,12 @@ def build_training_arguments(self, total_num_steps):
         training_args_kwargs["lr_scheduler_kwargs"] = (
             self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
         )
+        if self.cfg.remove_unused_columns is not None:
+            training_args_kwargs[
+                "remove_unused_columns"
+            ] = self.cfg.remove_unused_columns
+        else:
+            training_args_kwargs["remove_unused_columns"] = False
 
         if self.cfg.dataloader_pin_memory is not None:
             training_args_kwargs[
@@ -1013,7 +1019,6 @@ def build_training_arguments(self, total_num_steps):
         training_args = TrainingArguments(
             per_device_train_batch_size=self.cfg.micro_batch_size,
             max_steps=self.cfg.max_steps or total_num_steps,
-            remove_unused_columns=False,
             gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
             learning_rate=self.cfg.learning_rate,
             save_strategy="steps",

src/axolotl/utils/data.py

@@ -7,6 +7,7 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
+import yaml
 from datasets import (
     Dataset,
     DatasetDict,
@@ -853,6 +854,41 @@ def encode_packed_pretraining(
     return chunked_data
 
 
+def _get_path(ds_hash, cfg):
+    prepared_ds_path = (
+        Path(cfg.dataset_prepared_path) / ds_hash
+        if cfg.dataset_prepared_path
+        else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
+    )
+
+    return prepared_ds_path
+
+
+def _load_preprocessed_ds(cfg, sub_cfg):
+    ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
+    prepared_ds_path = _get_path(ds_hash, cfg)
+    dataset = None
+
+    if (
+        cfg.dataset_prepared_path
+        and any(prepared_ds_path.glob("*"))
+        and not cfg.is_preprocess
+    ):
+        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        dataset = load_from_disk(str(prepared_ds_path))
+
+    return dataset
+
+
+def _save_preprocessed_ds(cfg, sub_cfg, dataset):
+    ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
+    prepared_ds_path = _get_path(ds_hash, cfg)
+
+    if cfg.is_preprocess and is_main_process():
+        LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
+        dataset.save_to_disk(str(prepared_ds_path))
+
+
 def load_prepare_dpo_datasets(cfg):
     def load_split(dataset_cfgs, _cfg):
         split_datasets: List[Any] = []
@@ -889,12 +925,25 @@ def load_split(dataset_cfgs, _cfg):
         return concatenate_datasets(split_datasets)
 
     with zero_first(is_main_process()):
-        train_dataset = load_split(cfg.datasets, cfg)
+        train_is_preprocessed = False
+        eval_is_preprocessed = False
+        if train_dataset := _load_preprocessed_ds(cfg, cfg.datasets):
+            train_is_preprocessed = True
+        else:
+            train_dataset = load_split(cfg.datasets, cfg)
 
         eval_dataset = None
         if cfg.test_datasets:
-            eval_dataset = load_split(cfg.test_datasets, cfg)
+            if eval_dataset := _load_preprocessed_ds(cfg, cfg.test_datasets):
+                eval_is_preprocessed = True
+            else:
+                eval_dataset = load_split(cfg.test_datasets, cfg)
         if not eval_dataset:
             eval_dataset = None
 
+        if not train_is_preprocessed:
+            _save_preprocessed_ds(cfg, cfg.datasets, train_dataset)
+        if eval_dataset and not eval_is_preprocessed:
+            _save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
+
     return train_dataset, eval_dataset