ff939d8a64
* fix(dataset): normalize tokenizer config and change hash from tokenizer class to tokenizer path * fix: normalize config
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
"""
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unit tests for axolotl.core.trainer_builder
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"""
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import pytest
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from axolotl.core.trainer_builder import HFDPOTrainerBuilder
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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 axolotl.utils.models import load_model, load_tokenizer
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@pytest.fixture(name="cfg")
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def fixture_cfg():
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cfg = DictDefault(
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{
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"base_model": "TinyLlama/TinyLlama-1.1B-Chat-v0.6",
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"model_type": "AutoModelForCausalLM",
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"tokenizer_type": "LlamaTokenizer",
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"learning_rate": 0.00005,
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"save_steps": 100,
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"output_dir": "./model-out",
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"warmup_steps": 10,
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"gradient_checkpointing": False,
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"optimizer": "adamw_torch",
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"sequence_len": 2048,
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"rl": True,
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"adam_beta1": 0.998,
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"adam_beta2": 0.9,
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"adam_epsilon": 0.00001,
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"dataloader_num_workers": 1,
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"dataloader_pin_memory": True,
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"model_config_type": "llama",
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}
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)
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normalize_config(cfg)
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return cfg
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@pytest.fixture(name="tokenizer")
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def fixture_tokenizer(cfg):
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return load_tokenizer(cfg)
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@pytest.fixture(name="model")
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def fixture_model(cfg, tokenizer):
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return load_model(cfg, tokenizer)
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class TestHFDPOTrainerBuilder:
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"""
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TestCase class for DPO trainer builder
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"""
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def test_build_training_arguments(self, cfg, model, tokenizer):
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builder = HFDPOTrainerBuilder(cfg, model, tokenizer)
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training_arguments = builder.build_training_arguments(100)
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assert training_arguments.adam_beta1 == 0.998
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assert training_arguments.adam_beta2 == 0.9
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assert training_arguments.adam_epsilon == 0.00001
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assert training_arguments.dataloader_num_workers == 1
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assert training_arguments.dataloader_pin_memory is True
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