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"""
E2E tests for lora llama
"""
import logging
import os
import unittest
from pathlib import Path
import pytest
from axolotl.cli import load_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="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, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "pytorch_model.bin").exists()
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