""" e2e tests to make sure all the hooks are fired on the plugin """ import os from pathlib import Path from axolotl.common.datasets import load_datasets from axolotl.integrations.base import BasePlugin from axolotl.train import train from axolotl.utils.config import normalize_config, prepare_plugins, validate_config from axolotl.utils.dict import DictDefault from ..utils import check_model_output_exists class LogHooksPlugin(BasePlugin): """ fixture to capture in a log file each hook that was fired """ base_dir = Path("/tmp/axolotl-log-hooks") def __init__(self): self.base_dir.mkdir(parents=True, exist_ok=True) try: os.remove(self.base_dir.joinpath("plugin_hooks.log")) except FileNotFoundError: pass def post_trainer_create(self, cfg, trainer): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("post_trainer_create\n") def pre_model_load(self, cfg): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("pre_model_load\n") def post_model_build(self, cfg, model): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("post_model_build\n") def pre_lora_load(self, cfg, model): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("pre_lora_load\n") def post_lora_load(self, cfg, model): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("post_lora_load\n") def post_model_load(self, cfg, model): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("post_model_load\n") def create_optimizer(self, cfg, trainer): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("create_optimizer\n") def get_trainer_cls(self, cfg): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("get_trainer_cls\n") def create_lr_scheduler( self, cfg, trainer, optimizer, num_training_steps ): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("create_lr_scheduler\n") def add_callbacks_pre_trainer(self, cfg, model): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("add_callbacks_pre_trainer\n") return [] def add_callbacks_post_trainer( self, cfg, trainer ): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("add_callbacks_post_trainer\n") return [] def post_train(self, cfg, model): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("post_train\n") def post_train_unload(self, cfg): # pylint: disable=unused-argument with open( self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8" ) as f: f.write("post_train_unload\n") class TestPluginHooks: """ e2e tests to make sure all the hooks are fired during the training """ def test_plugin_hooks(self, temp_dir): # pylint: disable=duplicate-code cfg = DictDefault( { "base_model": "HuggingFaceTB/SmolLM2-135M", "plugins": [ "tests.e2e.integrations.test_hooks.LogHooksPlugin", ], "tokenizer_type": "AutoTokenizer", "sequence_len": 1024, "adapter": "lora", "lora_r": 8, "lora_alpha": 16, "lora_dropout": 0.05, "lora_target_linear": True, "val_set_size": 0.02, "special_tokens": { "pad_token": "<|endoftext|>", }, "datasets": [ { "path": "mhenrichsen/alpaca_2k_test", "type": "alpaca", }, ], "num_epochs": 1, "micro_batch_size": 2, "gradient_accumulation_steps": 1, "output_dir": temp_dir, "learning_rate": 0.00001, "optimizer": "adamw_torch_fused", "lr_scheduler": "cosine", "max_steps": 5, "flash_attention": True, "bf16": "auto", "save_first_step": False, } ) cfg = validate_config(cfg) prepare_plugins(cfg) normalize_config(cfg) dataset_meta = load_datasets(cfg=cfg) train(cfg=cfg, dataset_meta=dataset_meta) check_model_output_exists(temp_dir, cfg) with open( "/tmp/axolotl-log-hooks" + "/plugin_hooks.log", "r", encoding="utf-8" ) as f: file_contents = f.readlines() file_contents = "\n".join(file_contents) assert "post_trainer_create" in file_contents assert "pre_model_load" in file_contents assert "post_model_build" in file_contents assert "pre_lora_load" in file_contents assert "post_lora_load" in file_contents assert "post_model_load" in file_contents # assert "create_optimizer" in file_contents # not implemented yet assert "get_trainer_cls" in file_contents assert "create_lr_scheduler" in file_contents assert "add_callbacks_pre_trainer" in file_contents assert "add_callbacks_post_trainer" in file_contents assert "post_train" in file_contents # assert "post_train_unload" in file_contents # not called from test train call try: os.remove("/tmp/axolotl-log-hooks" + "/plugin_hooks.log") except FileNotFoundError: pass