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""" |
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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 HFRLTrainerBuilder |
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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 TestHFRLTrainerBuilder: |
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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 = HFRLTrainerBuilder(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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