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"""
E2E tests for lora llama
"""
import logging
import os
import tempfile
import unittest
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
LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true"
class TestPhi(unittest.TestCase):
"""
Test case for Llama models using LoRA
"""
def test_ft(self):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "microsoft/phi-1_5",
"base_model_config": "microsoft/phi-1_5",
"trust_remote_code": True,
"model_type": "MixFormerSequentialForCausalLM",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 2048,
"sample_packing": False,
"load_in_8bit": True,
"adapter": None,
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<|endoftext|>",
"bos_token": "<|endoftext|>",
"eos_token": "<|endoftext|>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"dataset_shard_num": 10,
"dataset_shard_idx": 0,
"num_epochs": 1,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"output_dir": tempfile.mkdtemp(),
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
}
)
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)
def test_ft_packed(self):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "microsoft/phi-1_5",
"base_model_config": "microsoft/phi-1_5",
"trust_remote_code": True,
"model_type": "MixFormerSequentialForCausalLM",
"tokenizer_type": "AutoTokenizer",
"sequence_len": 2048,
"sample_packing": True,
"load_in_8bit": True,
"adapter": None,
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<|endoftext|>",
"bos_token": "<|endoftext|>",
"eos_token": "<|endoftext|>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"dataset_shard_num": 10,
"dataset_shard_idx": 0,
"num_epochs": 1,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"output_dir": tempfile.mkdtemp(),
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
}
)
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)
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