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
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
"""E2E tests for llama pretrain"""
|
|
|
|
import pytest
|
|
|
|
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
|
|
|
|
|
|
class TestPretrainLlama:
|
|
"""Test case for Llama models w pretraining"""
|
|
|
|
@pytest.mark.parametrize(
|
|
"sample_packing",
|
|
[True, False],
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"pretrain_multipack_attn",
|
|
[True, False],
|
|
)
|
|
def test_pretrain(self, temp_dir, sample_packing, pretrain_multipack_attn):
|
|
if not sample_packing and pretrain_multipack_attn:
|
|
return
|
|
|
|
# pylint: disable=duplicate-code
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "HuggingFaceTB/SmolLM2-135M",
|
|
"flash_attention": True,
|
|
"sequence_len": 1024,
|
|
"sample_packing": sample_packing,
|
|
"pretrain_multipack_attn": pretrain_multipack_attn,
|
|
"dataset_processes": 1,
|
|
"special_tokens": {
|
|
"pad_token": "<|endoftext|>",
|
|
},
|
|
"pretraining_dataset": [
|
|
{
|
|
"path": "allenai/c4",
|
|
"name": "en",
|
|
"type": "pretrain",
|
|
}
|
|
],
|
|
"max_steps": 5,
|
|
"num_epochs": 1,
|
|
"micro_batch_size": 2,
|
|
"gradient_accumulation_steps": 1,
|
|
"val_set_size": 0.0,
|
|
"output_dir": temp_dir,
|
|
"learning_rate": 0.00001,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"save_safetensors": True,
|
|
"bf16": "auto",
|
|
"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(temp_dir, cfg)
|
|
loss_threshold = 3.5
|
|
if sample_packing and not pretrain_multipack_attn:
|
|
loss_threshold = 6.5
|
|
check_tensorboard(
|
|
temp_dir + "/runs",
|
|
"train/train_loss",
|
|
loss_threshold,
|
|
"Train Loss is too high",
|
|
)
|