| """ |
| E2E tests for lora llama |
| """ |
|
|
| import logging |
| import os |
| import unittest |
| from pathlib import Path |
|
|
| import pytest |
|
|
| 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" |
|
|
|
|
| @pytest.mark.skip(reason="doesn't seem to work on modal") |
| class TestDPOLlamaLora(unittest.TestCase): |
| """ |
| Test case for DPO Llama models using LoRA |
| """ |
|
|
| @with_temp_dir |
| def test_dpo_lora(self, temp_dir): |
| |
| 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): |
| |
| 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): |
| |
| 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() |
|
|
| @with_temp_dir |
| def test_orpo_lora(self, temp_dir): |
| |
| 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": "orpo", |
| "orpo_alpha": 0.1, |
| "remove_unused_columns": False, |
| "chat_template": "chatml", |
| "datasets": [ |
| { |
| "path": "argilla/ultrafeedback-binarized-preferences-cleaned", |
| "type": "chat_template.argilla", |
| "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() |
|
|
