1d70f24b50
* Add s2_attn to hijack flash code * Refactor code to account for s2_attn * Add test for models utils * Add ``s2_attention`` option to llama configs * Add ``s2_attention`` option to README config * Format code to appease linter * chore: lint * Remove xpos and llama-landmark [bad merge] * add e2e smoke tests for shifted sparse attention * remove stray patch from merge * update yml with link to paper for s2_attention/longlora * fix assertion check for full fine tune * increase sequence len for tests and PR feedback updates * reduce context len to 16k for tests * reduce context len to 16k for tests * reduce batch size for larger context len and udpate test to check message * fix test for message --------- Co-authored-by: joecummings <jrcummings@devvm050.nha0.facebook.com> Co-authored-by: Wing Lian <wing.lian@gmail.com>
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
"""
|
|
E2E tests for llama w/ S2 attn
|
|
"""
|
|
|
|
import logging
|
|
import os
|
|
import unittest
|
|
from pathlib import Path
|
|
|
|
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 TestLlamaShiftedSparseAttention(unittest.TestCase):
|
|
"""
|
|
Test case for Llama models using S2 Attn
|
|
"""
|
|
|
|
@with_temp_dir
|
|
def test_lora_s2_attn(self, temp_dir):
|
|
# pylint: disable=duplicate-code
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "JackFram/llama-68m",
|
|
"tokenizer_type": "LlamaTokenizer",
|
|
"sequence_len": 16384,
|
|
"sample_packing": False,
|
|
"flash_attention": True,
|
|
"s2_attention": True,
|
|
"load_in_8bit": True,
|
|
"adapter": "lora",
|
|
"lora_r": 32,
|
|
"lora_alpha": 16,
|
|
"lora_dropout": 0.05,
|
|
"lora_target_linear": True,
|
|
"val_set_size": 0.1,
|
|
"special_tokens": {},
|
|
"datasets": [
|
|
{
|
|
"path": "Yukang/LongAlpaca-12k",
|
|
"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",
|
|
"max_steps": 10,
|
|
"save_steps": 5,
|
|
"eval_steps": 5,
|
|
"bf16": "auto",
|
|
}
|
|
)
|
|
|
|
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) / "adapter_model.bin").exists()
|
|
|
|
@with_temp_dir
|
|
def test_fft_s2_attn(self, temp_dir):
|
|
# pylint: disable=duplicate-code
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": "JackFram/llama-68m",
|
|
"tokenizer_type": "LlamaTokenizer",
|
|
"sequence_len": 16384,
|
|
"sample_packing": False,
|
|
"flash_attention": True,
|
|
"s2_attention": True,
|
|
"val_set_size": 0.1,
|
|
"special_tokens": {},
|
|
"datasets": [
|
|
{
|
|
"path": "Yukang/LongAlpaca-12k",
|
|
"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",
|
|
"max_steps": 10,
|
|
"save_steps": 5,
|
|
"eval_steps": 5,
|
|
"bf16": "auto",
|
|
}
|
|
)
|
|
|
|
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()
|