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dpo/kto/ipo smoke tests w lora, simplify dpo dataset type names
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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() |