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"""
E2E tests for lora llama
"""
import logging
import os
import tempfile
import unittest
from axolotl.common.cli import TrainerCliArgs
from axolotl.train import TrainDatasetMeta, train
from axolotl.utils.config import normalize_config
from axolotl.utils.data import prepare_dataset
from axolotl.utils.dict import DictDefault
from axolotl.utils.models import load_tokenizer
LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true"
def load_datasets(
*,
cfg: DictDefault,
cli_args: TrainerCliArgs, # pylint:disable=unused-argument
) -> TrainDatasetMeta:
tokenizer = load_tokenizer(cfg)
train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
return TrainDatasetMeta(
train_dataset=train_dataset,
eval_dataset=eval_dataset,
total_num_steps=total_num_steps,
)
class TestLoraLlama(unittest.TestCase):
"""
Test case for Llama models using LoRA
"""
def test_lora(self):
cfg = DictDefault(
{
"base_model": "JackFram/llama-68m",
"base_model_config": "JackFram/llama-68m",
"tokenizer_type": "LlamaTokenizer",
"sequence_len": 1024,
"load_in_8bit": True,
"adapter": "lora",
"lora_r": 32,
"lora_alpha": 64,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 8,
"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_lora_packing(self):
cfg = DictDefault(
{
"base_model": "JackFram/llama-68m",
"base_model_config": "JackFram/llama-68m",
"tokenizer_type": "LlamaTokenizer",
"sequence_len": 1024,
"sample_packing": True,
"flash_attention": True,
"load_in_8bit": True,
"adapter": "lora",
"lora_r": 32,
"lora_alpha": 64,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.1,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 8,
"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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