DPO cleanup (#1126)

* cleanup dpo to be a little more extensible, add zephyr/nectar strategy

* fix eos slash

* support for eval split

* fix kwargs

* handle empty evals

* don't load peft model for dpo

* ensure dpo traning args gets bf16 for peft if applicable

* fix duplicate kwargs for bf16

* make sure to respect the configured lr scheduler

* supprt trainer callback to push config to wandb

* set dataloader preload args

* ensure that we are loading the lora when merging

* Update src/axolotl/utils/data.py

Co-authored-by: Agus <[email protected]>

* support local datasets for dpo

Co-authored-by: Agus <[email protected]>

* chore: lint

* dpo/kto/ipo smoke tests w lora, simplify dpo dataset type names

* add split to dpo tests

* fix rebase/merging error

* handle edge case w logging

* use accelerator for dpo datasets so it doesn't break the logger

* missing args

* validate checkpoint is an adapter for now

* log warning when dataset strategy is not loadable

---------

Co-authored-by: Agus <[email protected]>

src/axolotl/cli/__init__.py

@@ -17,7 +17,6 @@ import yaml
 # add src to the pythonpath so we don't need to pip install this
 from accelerate.commands.config import config_args
 from art import text2art
-from datasets import concatenate_datasets, load_dataset
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
@@ -30,7 +29,7 @@ from axolotl.utils.config import (
     normalize_config,
     validate_config,
 )
-from axolotl.utils.data import prepare_dataset
+from axolotl.utils.data import load_prepare_dpo_datasets, prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
 from axolotl.utils.mlflow_ import setup_mlflow_env_vars
@@ -343,81 +342,7 @@ def load_rl_datasets(
     cfg: DictDefault,
     cli_args: TrainerCliArgs,  # pylint: disable=unused-argument
 ) -> TrainDatasetMeta:
-    train_datasets: List[Any] = []
-    for i, ds_cfg in enumerate(cfg.datasets):
-        train_datasets.insert(i, load_dataset(ds_cfg["path"], split=ds_cfg["split"]))
-    # eval_dataset = load_dataset(
-    #     cfg.test_datasets[0]["path"], split=cfg.test_datasets[0]["split"]
-    # )
-    eval_dataset = None
-
-    def argilla_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
-        if "system" in sample and sample["system"]:
-            sample["prompt"] = (
-                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
-            )
-        else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
-        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
-        return sample
-
-    def intel_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
-        if "system" in sample and sample["system"]:
-            sample["prompt"] = (
-                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
-            )
-        else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
-        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
-        return sample
-
-    def apply_chatml(sample):  # pylint: disable=possibly-unused-variable
-        if "system" in sample and sample["system"]:
-            sample["prompt"] = (
-                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-            )
-        else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
-        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
-        return sample
-
-    def ultra_apply_chatml(sample):  # pylint: disable=possibly-unused-variable
-        if "system" in sample and sample["system"]:
-            sample["prompt"] = (
-                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
-                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-            )
-        else:
-            sample[
-                "prompt"
-            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
-        sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
-        sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
-        return sample
-
-    for i, data_set in enumerate(train_datasets):
-        _type = cfg.datasets[i]["type"]
-        ds_type_fn = locals()[_type]
-        train_datasets[i] = data_set.map(
-            ds_type_fn,
-            desc="Mapping RL Dataset",
-        )
-    train_dataset = concatenate_datasets(train_datasets)
-
-    # eval_dataset = eval_dataset.map(intel_apply_chatml)
-
+    train_dataset, eval_dataset = load_prepare_dpo_datasets(cfg)
     total_num_steps = int(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )

src/axolotl/core/trainer_builder.py

@@ -12,14 +12,19 @@ from abc import abstractmethod
 from dataclasses import dataclass, field
 from functools import wraps
 from pathlib import Path
-from typing import Optional, Type, Union
+from typing import List, Optional, Type, Union
 
 import torch
 import transformers
 from datasets import Dataset
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
-from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
+from transformers import (
+    EarlyStoppingCallback,
+    Trainer,
+    TrainerCallback,
+    TrainingArguments,
+)
 from transformers.trainer_utils import seed_worker
 from trl import DPOTrainer
 
@@ -460,6 +465,7 @@ class TrainerBuilderBase(abc.ABC):
     _train_dataset = None
     _eval_dataset = None
     _model_ref = None
+    _peft_config = None
 
     def __init__(self, cfg, model, tokenizer):
         self.cfg = cfg
@@ -490,13 +496,26 @@ class TrainerBuilderBase(abc.ABC):
     def eval_dataset(self, dataset):
         self._eval_dataset = dataset
 
+    @property
+    def peft_config(self):
+        return self._peft_config
+
+    @peft_config.setter
+    def peft_config(self, peft_config):
+        self._peft_config = peft_config
+
     @abstractmethod
     def build(self, total_num_steps):
         pass
 
-    @abstractmethod
-    def get_callbacks(self):
-        pass
+    def get_callbacks(self) -> List[TrainerCallback]:
+        callbacks = []
+        if self.cfg.use_wandb:
+            callbacks.append(
+                SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
+            )
+
+        return callbacks
 
     @abstractmethod
     def get_post_trainer_create_callbacks(self, trainer):
@@ -504,12 +523,6 @@ class TrainerBuilderBase(abc.ABC):
         Callbacks added after the trainer is created, usually b/c these need access to the trainer
         """
 
-
-class HFCausalTrainerBuilder(TrainerBuilderBase):
-    """
-    Build the HuggingFace training args/trainer for Causal models
-    """
-
     def hook_pre_create_training_args(self, training_arguments_kwargs):
         # TODO
         return training_arguments_kwargs
@@ -526,10 +539,16 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         # TODO
         return trainer
 
+
+class HFCausalTrainerBuilder(TrainerBuilderBase):
+    """
+    Build the HuggingFace training args/trainer for Causal models
+    """
+
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         callbacks.append(GPUStatsCallback(self.cfg))
-        callbacks.append(EvalFirstStepCallback)
+        callbacks.append(EvalFirstStepCallback())
 
         if self.cfg.relora_steps:
             callbacks.append(ReLoRACallback(self.cfg))
@@ -538,7 +557,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             hasattr(self.model, "use_bettertransformer")
             and self.model.use_bettertransformer is True
         ):
-            callbacks.append(SaveBetterTransformerModelCallback)
+            callbacks.append(SaveBetterTransformerModelCallback())
 
         if self.cfg.use_wandb:
             callbacks.append(
@@ -931,7 +950,7 @@ class HFDPOTrainerBuilder(TrainerBuilderBase):
     """
 
     def get_callbacks(self):
-        callbacks = []
+        callbacks = super().get_callbacks()
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -949,21 +968,60 @@ class HFDPOTrainerBuilder(TrainerBuilderBase):
         ]:
             if hasattr(self.cfg, arg) and getattr(self.cfg, arg) is not None:
                 training_args_kwargs[arg] = getattr(self.cfg, arg)
+
+        if self.cfg.hub_model_id:
+            training_args_kwargs["hub_model_id"] = self.cfg.hub_model_id
+            training_args_kwargs["push_to_hub"] = True
+            training_args_kwargs["hub_private_repo"] = True
+            training_args_kwargs["hub_always_push"] = True
+
+            if self.cfg.hub_strategy:
+                training_args_kwargs["hub_strategy"] = self.cfg.hub_strategy
+
+        if self.cfg.save_safetensors is not None:
+            training_args_kwargs["save_safetensors"] = self.cfg.save_safetensors
+
+        if self.eval_dataset:
+            training_args_kwargs["evaluation_strategy"] = "steps"
+            training_args_kwargs["eval_steps"] = self.cfg.eval_steps
+        else:
+            training_args_kwargs["evaluation_strategy"] = "no"
+        if self.cfg.bf16 or self.cfg.bfloat16:
+            training_args_kwargs["bf16"] = True
+
+        training_args_kwargs["lr_scheduler_type"] = (
+            self.cfg.lr_scheduler if self.cfg.lr_scheduler else "cosine"
+        )
+        training_args_kwargs["lr_scheduler_kwargs"] = (
+            self.cfg.lr_scheduler_kwargs if self.cfg.lr_scheduler_kwargs else {}
+        )
+
+        if self.cfg.dataloader_pin_memory is not None:
+            training_args_kwargs[
+                "dataloader_pin_memory"
+            ] = self.cfg.dataloader_pin_memory
+        if self.cfg.dataloader_num_workers is not None:
+            training_args_kwargs[
+                "dataloader_num_workers"
+            ] = self.cfg.dataloader_num_workers
+        if self.cfg.dataloader_prefetch_factor is not None:
+            training_args_kwargs[
+                "dataloader_prefetch_factor"
+            ] = self.cfg.dataloader_prefetch_factor
+
         training_args = TrainingArguments(
             per_device_train_batch_size=self.cfg.micro_batch_size,
-            max_steps=total_num_steps,
+            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,
-            evaluation_strategy="no",
-            # eval_steps=self.cfg.eval_steps,
             save_strategy="steps",
             save_steps=self.cfg.save_steps,
             output_dir=self.cfg.output_dir,
             warmup_steps=self.cfg.warmup_steps,
-            bf16=True,
             gradient_checkpointing=self.cfg.gradient_checkpointing,
-            gradient_checkpointing_kwargs={"use_reentrant": False},
+            gradient_checkpointing_kwargs=self.cfg.gradient_checkpointing_kwargs
+            or {"use_reentrant": False},
             logging_first_step=True,
             logging_steps=1,
             optim=self.cfg.optimizer,
@@ -982,22 +1040,27 @@ class HFDPOTrainerBuilder(TrainerBuilderBase):
                 dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         elif self.cfg.rl == "kto_pair":
             dpo_trainer_kwargs["loss_type"] = "kto_pair"
-
+        if self.eval_dataset:
+            dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
+        if self.cfg.adapter and self.peft_config:
+            dpo_trainer_kwargs["peft_config"] = self.peft_config
         dpo_trainer = DPOTrainer(
             self.model,
             self.model_ref,
             args=training_args,
             beta=self.cfg.dpo_beta or 0.1,
             train_dataset=self.train_dataset,
-            # eval_dataset=self.eval_dataset,
-            eval_dataset=None,
             tokenizer=self.tokenizer,
             max_length=self.cfg.sequence_len,
             max_target_length=None,
             max_prompt_length=self.cfg.sequence_len,
             generate_during_eval=True,
+            callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
+        dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
+        for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
+            dpo_trainer.add_callback(callback)
 
         return dpo_trainer
 

src/axolotl/prompt_strategies/dpo/__init__.py

@@ -0,0 +1,21 @@
+"""
+module for DPO style dataset transform strategies
+"""
+
+import importlib
+import logging
+
+LOG = logging.getLogger("axolotl")
+
+
+def load(strategy, cfg):
+    try:
+        load_fn = strategy.split(".")[-1]
+        strategy = ".".join(strategy.split(".")[:-1])
+        mod = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies.dpo")
+        func = getattr(mod, load_fn)
+        load_kwargs = {}
+        return func(cfg, **load_kwargs)
+    except Exception:  # pylint: disable=broad-exception-caught
+        LOG.warning(f"unable to load strategy {strategy}")
+        return None

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -0,0 +1,85 @@
+"""
+DPO strategies for chatml
+"""
+
+
+def argilla(
+    cfg,
+):  # pylint: disable=possibly-unused-variable,unused-argument
+    def transform_fn(sample):
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['instruction']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen_response']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected_response']}<|im_end|>"
+        return sample
+
+    return transform_fn
+
+
+def intel(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+    """
+    For Intel Orca DPO Pairs
+    """
+
+    def transform_fn(sample):
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['question']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
+        return sample
+
+    return transform_fn
+
+
+def prompt_pairs(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+    def transform_fn(sample):
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected']}<|im_end|>"
+        return sample
+
+    return transform_fn
+
+
+def ultra(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+    """
+    for ultrafeedback binarized conversations
+    """
+
+    def transform_fn(sample):
+        if "system" in sample and sample["system"]:
+            sample["prompt"] = (
+                f"<|im_start|>system\n{sample['system']}<|im_end|>\n"
+                f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+            )
+        else:
+            sample[
+                "prompt"
+            ] = f"<|im_start|>user\n{sample['prompt']}<|im_end|>\n<|im_start|>assistant\n"
+        sample["chosen"] = f"{sample['chosen'][1]['content']}<|im_end|>"
+        sample["rejected"] = f"{sample['rejected'][1]['content']}<|im_end|>"
+        return sample
+
+    return transform_fn

src/axolotl/prompt_strategies/dpo/zephyr.py

@@ -0,0 +1,21 @@
+"""
+DPO strategies for zephyr
+"""
+
+
+def nectar(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+    def transform_fn(sample):
+        data = {}
+        data["prompt"] = (
+            "<|system|>\n</s>\n"
+            "<|user|>\n"
+            f"{sample['prompt']}</s>\n"
+            "<|assistant|>\n"
+        )
+        answers = sorted(sample["answers"], key=lambda x: x["rank"])
+        data["chosen"] = answers[-1]["answer"]
+        data["rejected"] = answers[-2]["answer"]
+
+        return data
+
+    return transform_fn

src/axolotl/train.py

@@ -96,7 +96,12 @@ def train(
         freeze_parameters_except(model, cfg.unfrozen_parameters)
 
     trainer = setup_trainer(
-        cfg, train_dataset, eval_dataset, (model, model_ref), tokenizer, total_num_steps
+        cfg,
+        train_dataset,
+        eval_dataset,
+        (model, model_ref, peft_config),
+        tokenizer,
+        total_num_steps,
     )
 
     if hasattr(model, "config"):

src/axolotl/utils/data.py

@@ -4,7 +4,7 @@ import hashlib
 import logging
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 from datasets import (
@@ -21,6 +21,7 @@ from transformers import PreTrainedTokenizerBase
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies import load
+from axolotl.prompt_strategies.dpo import load as load_dpo
 from axolotl.prompt_tokenizers import (
     AlpacaMultipleChoicePromptTokenizingStrategy,
     AlpacaPromptTokenizingStrategy,
@@ -850,3 +851,50 @@ def encode_packed_pretraining(
             chunked_data[feature].append(collated_features[feature].squeeze(0))
 
     return chunked_data
+
+
+def load_prepare_dpo_datasets(cfg):
+    def load_split(dataset_cfgs, _cfg):
+        split_datasets: List[Any] = []
+        for i, ds_cfg in enumerate(dataset_cfgs):
+            if ds_cfg["ds_type"] == "json":
+                for data_file in ds_cfg["data_files"]:
+                    data_files = {ds_cfg["split"]: data_file}
+                    ds = load_dataset(  # pylint: disable=invalid-name
+                        "json",
+                        data_files=data_files,
+                        split=ds_cfg["split"],
+                    )
+                    split_datasets.insert(i, ds)
+            else:
+                ds = load_dataset(  # pylint: disable=invalid-name
+                    ds_cfg["path"],
+                    split=ds_cfg["split"],
+                )
+                split_datasets.insert(i, ds)
+
+        for i, data_set in enumerate(split_datasets):
+            _type = dataset_cfgs[i]["type"]
+            if _type:
+                ds_transform_fn = load_dpo(_type, _cfg)
+                split_datasets[i] = data_set.map(
+                    ds_transform_fn,
+                    desc="Mapping RL Dataset",
+                )
+            else:
+                # If no `type` is provided, assume the dataset is already in the expected format with
+                # "prompt", "chosen" and "rejected" already preprocessed
+                split_datasets[i] = data_set
+
+        return concatenate_datasets(split_datasets)
+
+    with zero_first(is_main_process()):
+        train_dataset = load_split(cfg.datasets, cfg)
+
+        eval_dataset = None
+        if cfg.test_datasets:
+            eval_dataset = load_split(cfg.test_datasets, cfg)
+        if not eval_dataset:
+            eval_dataset = None
+
+    return train_dataset, eval_dataset

src/axolotl/utils/models.py

@@ -682,7 +682,12 @@ def load_model(
 
     lora_config = None
     if not reference_model or cfg.lora_model_dir:
-        model, lora_config = load_adapter(model, cfg, cfg.adapter)
+        # if we're not loading the reference model, then we're loading the model for training
+        # then the dpo trainer doesn't want the peft model loaded over it, it just wants the lora/peft config
+        if cfg.adapter and cfg.rl in ["dpo", "ipo", "kto_pair"] and not cfg.merge_lora:
+            _, lora_config = load_lora(model, cfg, inference=False, config_only=True)
+        else:
+            model, lora_config = load_adapter(model, cfg, cfg.adapter)
 
     if cfg.ddp and not load_in_8bit and not (cfg.rl and cfg.load_in_4bit):
         model.to(f"cuda:{cfg.local_rank}")
@@ -770,8 +775,8 @@ def find_all_linear_names(model):
     return list(lora_module_names)
 
 
-def load_lora(model, cfg, inference=False):
-    # type: (PreTrainedModel, DictDefault, bool) -> Tuple[PreTrainedModel, Optional[PeftConfig]]
+def load_lora(model, cfg, inference=False, config_only=False):
+    # type: (PreTrainedModel, DictDefault, bool, bool) -> Tuple[Optional[PreTrainedModel], Optional[PeftConfig]]
 
     from peft import LoraConfig, PeftModel, get_peft_model
 
@@ -794,6 +799,9 @@ def load_lora(model, cfg, inference=False):
         task_type="CAUSAL_LM",
     )
 
+    if config_only:
+        return None, lora_config
+
     if cfg.lora_model_dir:
         LOG.debug("Loading pretained PEFT - LoRA")
         model_kwargs: Any = {}

src/axolotl/utils/trainer.py

@@ -316,9 +316,10 @@ def prepare_optim_env(cfg):
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
-    if cfg.rl:
+    if cfg.rl in ["dpo", "ipo", "kto_pair"]:
         trainer_builder = HFDPOTrainerBuilder(cfg, model[0], tokenizer)
         trainer_builder.model_ref = model[1]
+        trainer_builder.peft_config = model[2]
     else:
         trainer_builder = HFCausalTrainerBuilder(cfg, model[0], tokenizer)
 

tests/e2e/test_dpo.py

@@ -0,0 +1,157 @@
+"""
+E2E tests for lora llama
+"""
+
+import logging
+import os
+import unittest
+from pathlib import Path
+
+from axolotl.cli import load_rl_datasets
+from axolotl.common.cli import TrainerCliArgs
+from axolotl.train import train
+from axolotl.utils.config import normalize_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import with_temp_dir
+
+LOG = logging.getLogger("axolotl.tests.e2e")
+os.environ["WANDB_DISABLED"] = "true"
+
+
+class TestDPOLlamaLora(unittest.TestCase):
+    """
+    Test case for DPO Llama models using LoRA
+    """
+
+    @with_temp_dir
+    def test_dpo_lora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.1,
+                "lora_target_linear": True,
+                "special_tokens": {},
+                "rl": "dpo",
+                "datasets": [
+                    {
+                        "path": "Intel/orca_dpo_pairs",
+                        "type": "chatml.intel",
+                        "split": "train",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "paged_adamw_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "warmup_steps": 5,
+                "gradient_checkpointing": True,
+                "gradient_checkpointing_kwargs": {"use_reentrant": True},
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
+
+    @with_temp_dir
+    def test_kto_pair_lora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.1,
+                "lora_target_linear": True,
+                "special_tokens": {},
+                "rl": "kto_pair",
+                "datasets": [
+                    {
+                        "path": "Intel/orca_dpo_pairs",
+                        "type": "chatml.intel",
+                        "split": "train",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "paged_adamw_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "warmup_steps": 5,
+                "gradient_checkpointing": True,
+                "gradient_checkpointing_kwargs": {"use_reentrant": True},
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
+
+    @with_temp_dir
+    def test_ipo_lora(self, temp_dir):
+        # pylint: disable=duplicate-code
+        cfg = DictDefault(
+            {
+                "base_model": "JackFram/llama-68m",
+                "tokenizer_type": "LlamaTokenizer",
+                "sequence_len": 1024,
+                "load_in_8bit": True,
+                "adapter": "lora",
+                "lora_r": 64,
+                "lora_alpha": 32,
+                "lora_dropout": 0.1,
+                "lora_target_linear": True,
+                "special_tokens": {},
+                "rl": "ipo",
+                "datasets": [
+                    {
+                        "path": "Intel/orca_dpo_pairs",
+                        "type": "chatml.intel",
+                        "split": "train",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "paged_adamw_8bit",
+                "lr_scheduler": "cosine",
+                "max_steps": 20,
+                "save_steps": 10,
+                "warmup_steps": 5,
+                "gradient_checkpointing": True,
+                "gradient_checkpointing_kwargs": {"use_reentrant": True},
+            }
+        )
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
+        assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()