00568c1539
* support for true batches with multipack * patch the map dataset fetcher to handle batches with packed indexes * patch 4d mask creation for sdp attention * better handling for BetterTransformer * patch general case for 4d mask * setup forward patch. WIP * fix patch file * support for multipack w/o flash attention for llama * cleanup * add warning about bf16 vs fp16 for multipack with sdpa * bugfixes * add 4d multipack tests, refactor patches * update tests and add warnings * fix e2e file check * skip sdpa test if not at least torch 2.1.1, update docs
75 lines
2.2 KiB
Python
75 lines
2.2 KiB
Python
"""
|
|
E2E tests for lora llama
|
|
"""
|
|
|
|
import logging
|
|
import os
|
|
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 with_temp_dir
|
|
|
|
LOG = logging.getLogger("axolotl.tests.e2e")
|
|
os.environ["WANDB_DISABLED"] = "true"
|
|
|
|
|
|
class TestFusedLlama(unittest.TestCase):
|
|
"""
|
|
Test case for Llama models using Fused layers
|
|
"""
|
|
|
|
@with_temp_dir
|
|
def test_fft_packing(self, temp_dir):
|
|
# pylint: disable=duplicate-code
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "JackFram/llama-68m",
|
|
"flash_attention": True,
|
|
"pad_to_sequence_len": True,
|
|
"flash_attn_fuse_qkv": True,
|
|
"flash_attn_fuse_mlp": True,
|
|
"sample_packing": True,
|
|
"sequence_len": 1024,
|
|
"val_set_size": 0.1,
|
|
"special_tokens": {
|
|
"unk_token": "<unk>",
|
|
"bos_token": "<s>",
|
|
"eos_token": "</s>",
|
|
},
|
|
"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_torch",
|
|
"lr_scheduler": "cosine",
|
|
"max_steps": 20,
|
|
"save_steps": 10,
|
|
"eval_steps": 10,
|
|
}
|
|
)
|
|
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)
|
|
assert (Path(temp_dir) / "pytorch_model.bin").exists()
|