98af5388ba
* bump flash attention 2.5.8 -> 2.6.1 * use triton implementation of cross entropy from flash attn * add smoke test for flash attn cross entropy patch * fix args to xentropy.apply * handle tuple from triton loss fn * ensure the patch tests run independently * use the wrapper already built into flash attn for cross entropy * mark pytest as forked for patches * use pytest xdist instead of forked, since cuda doesn't like forking * limit to 1 process and use dist loadfile for pytest * change up pytest for fixture to reload transformers w monkeypathc
88 lines
2.5 KiB
Python
88 lines
2.5 KiB
Python
"""
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E2E tests for lora llama
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"""
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import logging
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import os
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import unittest
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from importlib import reload
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from pathlib import Path
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import pytest
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from transformers.utils import is_torch_bf16_gpu_available
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from axolotl.cli import load_datasets
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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from axolotl.utils.config import normalize_config
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from axolotl.utils.dict import DictDefault
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from ..utils import with_temp_dir
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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@pytest.fixture(autouse=True)
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def reload_transformers():
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import transformers.models.llama.modeling_llama
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yield
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reload(transformers.models.llama.modeling_llama)
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class TestFAXentropyLlama(unittest.TestCase):
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"""
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Test case for Llama models using LoRA w multipack
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"""
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@with_temp_dir
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def test_lora_packing_fa_cross_entropy(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"sample_packing": True,
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"flash_attention": True,
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"flash_attn_cross_entropy": True,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.2,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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},
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"datasets": [
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{
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"path": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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}
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)
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if is_torch_bf16_gpu_available():
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cfg.bf16 = True
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else:
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cfg.fp16 = True
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normalize_config(cfg)
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cli_args = TrainerCliArgs()
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dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
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train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
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assert (Path(temp_dir) / "adapter_model.bin").exists()
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