cd84325253
* allow plugins to return their own dataset * add post_trainer_create and wire up * add hook check * address PR feedback: * remove annotation causing circular import
192 lines
6.7 KiB
Python
192 lines
6.7 KiB
Python
"""
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e2e tests to make sure all the hooks are fired on the plugin
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"""
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import os
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from pathlib import Path
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from axolotl.cli.args import TrainerCliArgs
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from axolotl.common.datasets import load_datasets
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from axolotl.integrations.base import BasePlugin
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from axolotl.train import train
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from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
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from axolotl.utils.dict import DictDefault
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from ..utils import check_model_output_exists
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class LogHooksPlugin(BasePlugin):
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"""
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fixture to capture in a log file each hook that was fired
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"""
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base_dir = Path("/tmp/axolotl-log-hooks")
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def __init__(self):
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self.base_dir.mkdir(parents=True, exist_ok=True)
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try:
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os.remove(self.base_dir.joinpath("plugin_hooks.log"))
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except FileNotFoundError:
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pass
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def post_trainer_create(self, cfg, trainer): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("post_trainer_create\n")
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def pre_model_load(self, cfg): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("pre_model_load\n")
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def post_model_build(self, cfg, model): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("post_model_build\n")
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def pre_lora_load(self, cfg, model): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("pre_lora_load\n")
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def post_lora_load(self, cfg, model): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("post_lora_load\n")
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def post_model_load(self, cfg, model): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("post_model_load\n")
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def create_optimizer(self, cfg, trainer): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("create_optimizer\n")
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def get_trainer_cls(self, cfg): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("get_trainer_cls\n")
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def create_lr_scheduler(
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self, cfg, trainer, optimizer, num_training_steps
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): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("create_lr_scheduler\n")
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def add_callbacks_pre_trainer(self, cfg, model): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("add_callbacks_pre_trainer\n")
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return []
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def add_callbacks_post_trainer(
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self, cfg, trainer
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): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("add_callbacks_post_trainer\n")
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return []
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def post_train(self, cfg, model): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("post_train\n")
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def post_train_unload(self, cfg): # pylint: disable=unused-argument
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with open(
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self.base_dir.joinpath("plugin_hooks.log"), "a", encoding="utf-8"
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) as f:
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f.write("post_train_unload\n")
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class TestPluginHooks:
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"""
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e2e tests to make sure all the hooks are fired during the training
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"""
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def test_plugin_hooks(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": "HuggingFaceTB/SmolLM2-135M",
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"plugins": [
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"tests.e2e.integrations.test_hooks.LogHooksPlugin",
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],
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"tokenizer_type": "AutoTokenizer",
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"sequence_len": 1024,
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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": 0.02,
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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": "mhenrichsen/alpaca_2k_test",
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"type": "alpaca",
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},
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],
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"num_epochs": 1,
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"micro_batch_size": 2,
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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_fused",
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"lr_scheduler": "cosine",
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"max_steps": 5,
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"flash_attention": True,
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"bf16": "auto",
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}
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)
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cfg = validate_config(cfg)
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prepare_plugins(cfg)
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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, dataset_meta=dataset_meta)
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check_model_output_exists(temp_dir, cfg)
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with open(
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"/tmp/axolotl-log-hooks" + "/plugin_hooks.log", "r", encoding="utf-8"
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) as f:
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file_contents = f.readlines()
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file_contents = "\n".join(file_contents)
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assert "post_trainer_create" in file_contents
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assert "pre_model_load" in file_contents
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assert "post_model_build" in file_contents
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assert "pre_lora_load" in file_contents
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assert "post_lora_load" in file_contents
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assert "post_model_load" in file_contents
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# assert "create_optimizer" in file_contents # not implemented yet
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assert "get_trainer_cls" in file_contents
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assert "create_lr_scheduler" in file_contents
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assert "add_callbacks_pre_trainer" in file_contents
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assert "add_callbacks_post_trainer" in file_contents
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assert "post_train" in file_contents
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# assert "post_train_unload" in file_contents # not called from test train call
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try:
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os.remove("/tmp/axolotl-log-hooks" + "/plugin_hooks.log")
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except FileNotFoundError:
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pass
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