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

src/axolotl/core/trainer_builder.py

@@ -1011,7 +1011,7 @@ def build_training_arguments(self, total_num_steps):
 
         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,

src/axolotl/prompt_strategies/dpo/chatml.py

@@ -3,7 +3,7 @@
 """
 
 
-def argilla_apply_chatml(
+def argilla(
     cfg,
 ):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
@@ -23,7 +23,11 @@ def transform_fn(sample):
     return transform_fn
 
 
-def intel_apply_chatml(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+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"] = (
@@ -41,7 +45,7 @@ def transform_fn(sample):
     return transform_fn
 
 
-def apply_chatml(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+def prompt_pairs(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
     def transform_fn(sample):
         if "system" in sample and sample["system"]:
             sample["prompt"] = (
@@ -59,7 +63,11 @@ def transform_fn(sample):
     return transform_fn
 
 
-def ultra_apply_chatml(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
+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"] = (

tests/e2e/test_dpo.py

@@ -0,0 +1,145 @@
+"""
+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",
+                    },
+                ],
+                "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,
+            }
+        )
+        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) / "adapter_model.bin").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",
+                    },
+                ],
+                "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,
+            }
+        )
+        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) / "adapter_model.bin").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",
+                    },
+                ],
+                "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,
+            }
+        )
+        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) / "adapter_model.bin").exists()