8c2e05ade3
* magnitude pruning of the optimizer * add alpaca chat template and fix relora patch * fix handling of lora adapter for relora * fix merge and save call * fixes for 8-bit lora merge * save intermediate checkpoint adapters * auto merge * fix eval check * handle relora annealing * fix anneal step logic * chore: lint * misx fix * fix types * Update tests/e2e/test_relora_llama.py * check for safetensors saved from relora
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
"""
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E2E tests for relora 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 pathlib import Path
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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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class TestReLoraLlama(unittest.TestCase):
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"""
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Test case for Llama models using LoRA
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"""
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@with_temp_dir
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def test_relora(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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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_modules": ["q_proj", "v_proj"],
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"relora_steps": 25,
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"relora_warmup_steps": 5,
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"relora_anneal_steps": 5,
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"relora_cpu_offload": True,
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"val_set_size": 0.0,
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"special_tokens": {},
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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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"warmup_steps": 15,
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"num_epochs": 2,
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"micro_batch_size": 4,
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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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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) / "model.safetensors").exists()
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