64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
"""
|
|
E2E tests for process reward model w/ lora llama
|
|
"""
|
|
|
|
import unittest
|
|
|
|
from axolotl.common.datasets import load_datasets
|
|
from axolotl.train import train
|
|
from axolotl.utils.config import normalize_config, validate_config
|
|
from axolotl.utils.dict import DictDefault
|
|
|
|
from .utils import check_model_output_exists, check_tensorboard, with_temp_dir
|
|
|
|
|
|
class TestProcessRewardSmolLM2(unittest.TestCase):
|
|
"""
|
|
Test case for Llama process reward models using LoRA
|
|
"""
|
|
|
|
@with_temp_dir
|
|
def test_prm(self, temp_dir):
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"model_type": "AutoModelForTokenClassification",
|
|
"num_labels": 2,
|
|
"process_reward_model": True,
|
|
"sequence_len": 512,
|
|
"val_set_size": 0.0,
|
|
"datasets": [
|
|
{
|
|
"path": "trl-lib/math_shepherd",
|
|
"type": "stepwise_supervised",
|
|
"step_separator": "\n",
|
|
"split": "train[:10%]",
|
|
},
|
|
],
|
|
"max_steps": 100,
|
|
"num_epochs": 1,
|
|
"micro_batch_size": 4,
|
|
"gradient_accumulation_steps": 1,
|
|
"output_dir": temp_dir,
|
|
"learning_rate": 0.0005,
|
|
"optimizer": "adamw_torch",
|
|
"lr_scheduler": "cosine",
|
|
"gradient_checkpointing": True,
|
|
"warmup_ratio": 0.1,
|
|
"use_tensorboard": True,
|
|
"special_tokens": {"pad_token": "<|endoftext|>"},
|
|
"seed": 42,
|
|
"save_first_step": False,
|
|
}
|
|
)
|
|
cfg = validate_config(cfg)
|
|
normalize_config(cfg)
|
|
dataset_meta = load_datasets(cfg=cfg)
|
|
|
|
train(cfg=cfg, dataset_meta=dataset_meta)
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/train_loss", 2.7, "Train Loss (%s) is too high"
|
|
)
|
|
|
|
check_model_output_exists(temp_dir, cfg)
|