70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
"""
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E2E tests for lora llama
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"""
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import os
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import unittest
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import pytest
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from axolotl.cli.args import TrainerCliArgs
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from axolotl.common.datasets import load_datasets
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.logging import get_logger
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from .utils import check_model_output_exists, with_temp_dir
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LOG = get_logger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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@pytest.mark.skip(reason="skipping until upstreamed into transformers")
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class TestMamba(unittest.TestCase):
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"""
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Test case for Mamba models
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"""
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@with_temp_dir
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def test_fft(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "state-spaces/mamba-130m",
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"model_type": "MambaLMHeadModel",
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"tokenizer_type": "AutoTokenizer",
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"tokenizer_config": "EleutherAI/gpt-neox-20b",
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"flash_attention": False,
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"sequence_len": 1024,
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"load_in_8bit": False,
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"val_set_size": 0.0,
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"gradient_checkpointing": False,
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"num_epochs": 2,
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"micro_batch_size": 2,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"max_steps": 20,
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"save_steps": 10,
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"eval_steps": None,
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"save_safetensors": False,
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}
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)
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cfg = validate_config(cfg)
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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