|
""" |
|
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() |
|
|
