00cda8cc70
* data loading refactor (wip) * updates * progress * pytest * pytest fix * lint * zero_first -> filelock, more simplifications * small simplification * import change * nit * lint * simplify dedup * couldnt resist * review comments WIP * continued wip * minor changes * fix; remove contrived test * further refactor * set default seed in pydantic config * lint * continued simplication * lint * renaming and nits * filelock tests * fix * fix * lint * remove nullable arg * remove unnecessary code * moving dataset save fn to shared module * remove debug print * matching var naming * fn name change * coderabbit comments * naming nit * fix test
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
"""
|
|
E2E tests for relora llama
|
|
"""
|
|
|
|
import unittest
|
|
from pathlib import Path
|
|
|
|
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 TestReLoraLlama(unittest.TestCase):
|
|
"""
|
|
Test case for Llama models using LoRA
|
|
"""
|
|
|
|
@with_temp_dir
|
|
def test_relora(self, temp_dir):
|
|
# pylint: disable=duplicate-code
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"sequence_len": 2048,
|
|
"sample_packing": True,
|
|
"pad_to_sequence_len": True,
|
|
"flash_attention": True,
|
|
"load_in_8bit": True,
|
|
"adapter": "lora",
|
|
"lora_r": 8,
|
|
"lora_alpha": 16,
|
|
"lora_dropout": 0.05,
|
|
"lora_target_modules": ["q_proj", "v_proj"],
|
|
"relora_steps": 50,
|
|
"relora_warmup_steps": 10,
|
|
"relora_anneal_steps": 10,
|
|
"relora_prune_ratio": 0.9,
|
|
"relora_cpu_offload": True,
|
|
"val_set_size": 0.0,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"chat_template": "chatml",
|
|
"datasets": [
|
|
{
|
|
"path": "mlabonne/FineTome-100k",
|
|
"type": "chat_template",
|
|
"split": "train[:10%]",
|
|
"field_messages": "conversations",
|
|
"message_field_role": "from",
|
|
"message_field_content": "value",
|
|
},
|
|
],
|
|
"warmup_steps": 10,
|
|
"num_epochs": 2,
|
|
"max_steps": 105, # at least 2x relora_steps
|
|
"micro_batch_size": 2,
|
|
"gradient_accumulation_steps": 1,
|
|
"output_dir": temp_dir,
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_8bit",
|
|
"lr_scheduler": "cosine",
|
|
"save_safetensors": True,
|
|
"use_tensorboard": True,
|
|
}
|
|
)
|
|
|
|
cfg = validate_config(cfg)
|
|
normalize_config(cfg)
|
|
dataset_meta = load_datasets(cfg=cfg)
|
|
|
|
train(cfg=cfg, dataset_meta=dataset_meta)
|
|
check_model_output_exists(Path(temp_dir) / "checkpoint-100/adapter", cfg)
|
|
assert (
|
|
Path(temp_dir) / "checkpoint-100/relora/model.safetensors"
|
|
).exists(), "Relora model checkpoint not found"
|
|
|
|
check_tensorboard(
|
|
temp_dir + "/runs", "train/grad_norm", 0.2, "grad_norm is too high"
|
|
)
|