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""" |
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E2E tests for resuming training |
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""" |
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import logging |
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import os |
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import re |
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import subprocess |
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import unittest |
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from pathlib import Path |
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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 most_recent_subdir, 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 TestResumeLlama(unittest.TestCase): |
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""" |
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Test case for resuming training of llama models |
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""" |
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@with_temp_dir |
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def test_resume_qlora(self, temp_dir): |
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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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"load_in_4bit": True, |
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"adapter": "qlora", |
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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": 0.1, |
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"special_tokens": {}, |
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"datasets": [ |
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{ |
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"path": "vicgalle/alpaca-gpt4", |
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"type": "alpaca", |
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}, |
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], |
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"num_epochs": 2, |
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"micro_batch_size": 1, |
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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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"save_steps": 10, |
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"save_total_limit": 5, |
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"max_steps": 40, |
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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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resume_cfg = cfg | DictDefault( |
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{ |
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"resume_from_checkpoint": f"{temp_dir}/checkpoint-30/", |
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} |
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) |
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normalize_config(resume_cfg) |
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cli_args = TrainerCliArgs() |
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train(cfg=resume_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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tb_log_path_1 = most_recent_subdir(temp_dir + "/runs") |
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cmd = f"tensorboard --inspect --logdir {tb_log_path_1}" |
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res = subprocess.run( |
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cmd, shell=True, text=True, capture_output=True, check=True |
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) |
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pattern = r"first_step\s+(\d+)" |
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first_steps = int(re.findall(pattern, res.stdout)[0]) |
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assert first_steps == 31 |
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