1ed4de73b6
* CLI init refactor * fix * cleanup and (partial) docs * Adding documentation and continuing cleanup (in progress) * remove finetune.py script * continued cleanup and documentation * pytest fixes * review comments * fix * Fix * typing fixes * make sure the batch dataset patcher for multipack is always loaded when handling datasets * review comments * fix --------- Co-authored-by: Dan Saunders <dan@axolotl.ai> Co-authored-by: Wing Lian <wing@axolotl.ai>
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
"""
|
|
E2E tests for lora llama
|
|
"""
|
|
|
|
import logging
|
|
import os
|
|
import unittest
|
|
|
|
import pytest
|
|
|
|
from axolotl.cli.args import TrainerCliArgs
|
|
from axolotl.common.datasets import load_datasets
|
|
from axolotl.train import train
|
|
from axolotl.utils.config import normalize_config
|
|
from axolotl.utils.dict import DictDefault
|
|
|
|
from .utils import check_model_output_exists, with_temp_dir
|
|
|
|
LOG = logging.getLogger("axolotl.tests.e2e")
|
|
os.environ["WANDB_DISABLED"] = "true"
|
|
|
|
|
|
@pytest.mark.skip(reason="skipping until upstreamed into transformers")
|
|
class TestMamba(unittest.TestCase):
|
|
"""
|
|
Test case for Mamba models
|
|
"""
|
|
|
|
@with_temp_dir
|
|
def test_fft(self, temp_dir):
|
|
# pylint: disable=duplicate-code
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "state-spaces/mamba-130m",
|
|
"model_type": "MambaLMHeadModel",
|
|
"tokenizer_type": "AutoTokenizer",
|
|
"tokenizer_config": "EleutherAI/gpt-neox-20b",
|
|
"flash_attention": False,
|
|
"sequence_len": 1024,
|
|
"load_in_8bit": False,
|
|
"val_set_size": 0.0,
|
|
"datasets": [
|
|
{
|
|
"path": "mhenrichsen/alpaca_2k_test",
|
|
"type": "alpaca",
|
|
},
|
|
],
|
|
"gradient_checkpointing": False,
|
|
"num_epochs": 2,
|
|
"micro_batch_size": 2,
|
|
"gradient_accumulation_steps": 1,
|
|
"output_dir": temp_dir,
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch",
|
|
"lr_scheduler": "cosine",
|
|
"max_steps": 20,
|
|
"save_steps": 10,
|
|
"eval_steps": None,
|
|
"save_safetensors": False,
|
|
}
|
|
)
|
|
normalize_config(cfg)
|
|
cli_args = TrainerCliArgs()
|
|
dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
|
|
|
|
train(cfg=cfg, dataset_meta=dataset_meta)
|
|
check_model_output_exists(temp_dir, cfg)
|