0ee98a0309
* fix token state json and mistral tokenizer issue * centralize constants * forgot to commit constants file * Fix weakref in pickling relora state dict * make curl a bit quieter so it doesn't log 2K lines * fix path traversal for olmoe test * more test fixes that weren't flagged previously * chore: lint * skip tests that fail b/c of OutOfResources * scattermoe as slow tests * update fbgemm-genai for torch 2.10
121 lines
4.1 KiB
Python
121 lines
4.1 KiB
Python
"""
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E2E tests for resuming training
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"""
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import os
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import re
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import subprocess
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from transformers.utils import is_torch_bf16_gpu_available
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from axolotl.common.datasets import load_datasets
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from axolotl.core.trainers.constants import TOKENS_STATE_FILE
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from ..utils import check_model_output_exists, most_recent_subdir, require_torch_2_6_0
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class TestResumeLlama:
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"""
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Test case for resuming training of llama models
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"""
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@require_torch_2_6_0
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def test_resume_lora_packed(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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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_8bit": True,
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"adapter": "lora",
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.001,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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"datasets": [
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{
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"path": "tatsu-lab/alpaca",
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"type": "alpaca",
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"split": "train[:10%]",
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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_8bit",
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"lr_scheduler": "cosine",
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"save_steps": 3,
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"save_total_limit": 5,
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"max_steps": 15,
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"use_tensorboard": True,
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"save_first_step": False,
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"include_tkps": True,
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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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cfg = validate_config(cfg)
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normalize_config(cfg)
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dataset_meta = load_datasets(cfg=cfg)
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initial_total_num_tokens = cfg.total_num_tokens
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assert initial_total_num_tokens is not None, (
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"total_num_tokens should be calculated during load_datasets"
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)
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train(cfg=cfg, dataset_meta=dataset_meta)
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checkpoint_path = f"{temp_dir}/checkpoint-9"
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tokens_state_path = os.path.join(checkpoint_path, TOKENS_STATE_FILE)
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assert os.path.isfile(tokens_state_path), (
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f"{TOKENS_STATE_FILE} should exist in checkpoint at {tokens_state_path}"
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)
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resume_cfg = cfg | DictDefault(
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{
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"resume_from_checkpoint": f"{temp_dir}/checkpoint-9/",
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}
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)
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normalize_config(resume_cfg)
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assert resume_cfg.total_num_tokens == initial_total_num_tokens, (
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f"total_num_tokens should be preserved on resume. "
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f"Expected {initial_total_num_tokens}, got {resume_cfg.total_num_tokens}"
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)
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resume_dataset_meta = load_datasets(cfg=resume_cfg)
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assert resume_cfg.total_num_tokens == initial_total_num_tokens, (
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f"total_num_tokens should not be recalculated when resuming. "
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f"Expected {initial_total_num_tokens}, got {resume_cfg.total_num_tokens}"
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)
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train(cfg=resume_cfg, dataset_meta=resume_dataset_meta)
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assert resume_cfg.total_num_tokens == initial_total_num_tokens, (
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f"total_num_tokens should remain unchanged after resume training. "
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f"Expected {initial_total_num_tokens}, got {resume_cfg.total_num_tokens}"
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)
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check_model_output_exists(temp_dir, cfg)
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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 == 10
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