RL/DPO (#935)

* ipo-dpo trainer

* fix missing abstract method

* chatml template, grad checkpointing kwargs support

* fix steps calc for RL and add dataloader kwargs

* wip to fix dpo and start ppo

* more fixes

* refactor to generalize map fn

* fix dataset loop and handle argilla pref dataset

* set training args

* load reference model on seperate gpu if more than one device

* no auto upload to hub for dpo, don't add lora adapters to ref model for dpo

* fixes for rl training

* support for ipo from yaml

* set dpo training args from the config, add tests

* chore: lint

* set sequence_len for model in test

* add RLHF docs

tests/core/test_trainer_builder.py

@@ -0,0 +1,59 @@
+"""
+unit tests for axolotl.core.trainer_builder
+"""
+import pytest
+
+from axolotl.core.trainer_builder import HFDPOTrainerBuilder
+from axolotl.utils.dict import DictDefault
+from axolotl.utils.models import load_model, load_tokenizer
+
+
+@pytest.fixture(name="cfg")
+def fixture_cfg():
+    return DictDefault(
+        {
+            "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
+            "model_type": "AutoModelForCausalLM",
+            "tokenizer_type": "LlamaTokenizer",
+            "micro_batch_size": 1,
+            "gradient_accumulation_steps": 1,
+            "learning_rate": 0.00005,
+            "save_steps": 100,
+            "output_dir": "./model-out",
+            "warmup_steps": 10,
+            "gradient_checkpointing": False,
+            "optimizer": "adamw_torch",
+            "sequence_len": 2048,
+            "rl": True,
+            "adam_beta1": 0.998,
+            "adam_beta2": 0.9,
+            "adam_epsilon": 0.00001,
+            "dataloader_num_workers": 1,
+            "dataloader_pin_memory": True,
+        }
+    )
+
+
+@pytest.fixture(name="tokenizer")
+def fixture_tokenizer(cfg):
+    return load_tokenizer(cfg)
+
+
+@pytest.fixture(name="model")
+def fixture_model(cfg, tokenizer):
+    return load_model(cfg, tokenizer)
+
+
+class TestHFDPOTrainerBuilder:
+    """
+    TestCase class for DPO trainer builder
+    """
+
+    def test_build_training_arguments(self, cfg, model, tokenizer):
+        builder = HFDPOTrainerBuilder(cfg, model, tokenizer)
+        training_arguments = builder.build_training_arguments(100)
+        assert training_arguments.adam_beta1 == 0.998
+        assert training_arguments.adam_beta2 == 0.9
+        assert training_arguments.adam_epsilon == 0.00001
+        assert training_arguments.dataloader_num_workers == 1
+        assert training_arguments.dataloader_pin_memory is True