| """ |
| unit tests for axolotl.core.trainer_builder |
| """ |
|
|
| import pytest |
|
|
| from axolotl.core.trainer_builder import HFRLTrainerBuilder |
| from axolotl.utils.config import normalize_config |
| from axolotl.utils.dict import DictDefault |
| from axolotl.utils.models import load_model, load_tokenizer |
|
|
|
|
| @pytest.fixture(name="cfg") |
| def fixture_cfg(): |
| cfg = DictDefault( |
| { |
| "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6", |
| "model_type": "AutoModelForCausalLM", |
| "tokenizer_type": "LlamaTokenizer", |
| "micro_batch_size": 1, |
| "gradient_accumulation_steps": 1, |
| "learning_rate": 0.00005, |
| "save_steps": 100, |
| "output_dir": "./model-out", |
| "warmup_steps": 10, |
| "gradient_checkpointing": False, |
| "optimizer": "adamw_torch", |
| "sequence_len": 2048, |
| "rl": True, |
| "adam_beta1": 0.998, |
| "adam_beta2": 0.9, |
| "adam_epsilon": 0.00001, |
| "dataloader_num_workers": 1, |
| "dataloader_pin_memory": True, |
| "model_config_type": "llama", |
| } |
| ) |
|
|
| normalize_config(cfg) |
|
|
| return cfg |
|
|
|
|
| @pytest.fixture(name="tokenizer") |
| def fixture_tokenizer(cfg): |
| return load_tokenizer(cfg) |
|
|
|
|
| @pytest.fixture(name="model") |
| def fixture_model(cfg, tokenizer): |
| return load_model(cfg, tokenizer) |
|
|
|
|
| class TestHFRLTrainerBuilder: |
| """ |
| TestCase class for DPO trainer builder |
| """ |
|
|
| def test_build_training_arguments(self, cfg, model, tokenizer): |
| builder = HFRLTrainerBuilder(cfg, model, tokenizer) |
| training_arguments = builder.build_training_arguments(100) |
| assert training_arguments.adam_beta1 == 0.998 |
| assert training_arguments.adam_beta2 == 0.9 |
| assert training_arguments.adam_epsilon == 0.00001 |
| assert training_arguments.dataloader_num_workers == 1 |
| assert training_arguments.dataloader_pin_memory is True |
|
|
