File size: 2,991 Bytes
b3a61e8 788649f b3a61e8 788649f b3a61e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
"""
E2E tests for resuming training
"""
import logging
import os
import re
import subprocess
import unittest
from pathlib import Path
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 most_recent_subdir, with_temp_dir
LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true"
class TestResumeLlama(unittest.TestCase):
"""
Test case for resuming training of llama models
"""
@with_temp_dir
def test_resume_qlora_packed(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,
"load_in_4bit": True,
"adapter": "qlora",
"lora_r": 32,
"lora_alpha": 64,
"lora_dropout": 0.05,
"lora_target_linear": True,
"val_set_size": 0.1,
"special_tokens": {},
"datasets": [
{
"path": "vicgalle/alpaca-gpt4",
"type": "alpaca",
},
],
"num_epochs": 2,
"micro_batch_size": 1,
"gradient_accumulation_steps": 1,
"output_dir": temp_dir,
"learning_rate": 0.00001,
"optimizer": "adamw_torch",
"lr_scheduler": "cosine",
"save_steps": 10,
"save_total_limit": 5,
"max_steps": 40,
}
)
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)
resume_cfg = cfg | DictDefault(
{
"resume_from_checkpoint": f"{temp_dir}/checkpoint-30/",
}
)
normalize_config(resume_cfg)
cli_args = TrainerCliArgs()
train(cfg=resume_cfg, cli_args=cli_args, dataset_meta=dataset_meta)
assert (Path(temp_dir) / "adapter_model.bin").exists()
tb_log_path_1 = most_recent_subdir(temp_dir + "/runs")
cmd = f"tensorboard --inspect --logdir {tb_log_path_1}"
res = subprocess.run(
cmd, shell=True, text=True, capture_output=True, check=True
)
pattern = r"first_step\s+(\d+)"
first_steps = int(re.findall(pattern, res.stdout)[0])
assert first_steps == 31
|