Files
axolotl/tests/e2e/patched/test_fa_xentropy.py
Wing Lian 9871fa060b optim e2e tests to run a bit faster (#2069) [skip ci]
* optim e2e tests to run a bit faster

* run prequant w/o lora_modules_to_save

* use smollm2
2024-11-18 12:35:31 -05:00

90 lines
2.6 KiB
Python

"""
E2E tests for lora llama
"""
import logging
import os
import unittest
from importlib import reload
from pathlib import Path
import pytest
from transformers.utils import is_torch_bf16_gpu_available
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.fixture(autouse=True)
def reload_transformers():
import transformers.models.llama.modeling_llama
yield
reload(transformers.models.llama.modeling_llama)
class TestFAXentropyLlama(unittest.TestCase):
"""
Test case for Llama models using LoRA w multipack
"""
@with_temp_dir
def test_lora_packing_fa_cross_entropy(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "JackFram/llama-68m",
"tokenizer_type": "LlamaTokenizer",
"sequence_len": 1024,
"sample_packing": True,
"flash_attention": True,
"flash_attn_cross_entropy": True,
"load_in_8bit": True,
"adapter": "lora",
"lora_r": 32,
"lora_alpha": 64,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.2,
"special_tokens": {
"unk_token": "<unk>",
"bos_token": "<s>",
"eos_token": "</s>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 1,
"max_steps": 10,
"save_steps": 10,
"micro_batch_size": 8,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
}
)
if is_torch_bf16_gpu_available():
cfg.bf16 = True
else:
cfg.fp16 = True
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) / "adapter_model.bin").exists()