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"""
E2E tests for falcon
"""
import logging
import os
import unittest
from pathlib import Path
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"
class TestFalconPatched(unittest.TestCase):
"""
Test case for Falcon models
"""
@with_temp_dir
def test_qlora(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "illuin/tiny-random-FalconForCausalLM",
"flash_attention": True,
"sample_packing": True,
"sequence_len": 2048,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.1,
"lora_target_linear": True,
"lora_modules_to_save": ["word_embeddings", "lm_head"],
"val_set_size": 0.1,
"special_tokens": {
"bos_token": "<|endoftext|>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"bf16": "auto",
}
)
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()
@with_temp_dir
def test_ft(self, temp_dir):
# pylint: disable=duplicate-code
cfg = DictDefault(
{
"base_model": "illuin/tiny-random-FalconForCausalLM",
"flash_attention": True,
"sample_packing": True,
"sequence_len": 2048,
"val_set_size": 0.1,
"special_tokens": {
"bos_token": "<|endoftext|>",
"pad_token": "<|endoftext|>",
},
"datasets": [
{
"path": "mhenrichsen/alpaca_2k_test",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_bnb_8bit",
"lr_scheduler": "cosine",
"max_steps": 20,
"save_steps": 10,
"eval_steps": 10,
"bf16": "auto",
}
)
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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