b3a61e8ce2
* use tensorboard to see if resume from checkpoint works * make sure e2e test is either fp16 or bf16 * set max_steps and save limit so we have the checkpoint when testing resuming * fix test parameters
96 lines
2.9 KiB
Python
96 lines
2.9 KiB
Python
"""
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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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# 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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"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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