798b5f5cfd
* fix: plugin rl overwrite trainer_cls * feat(test): add test to catch trainer_cls is not None
93 lines
2.7 KiB
Python
93 lines
2.7 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 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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from axolotl.utils.schemas.enums import RLType
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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": "HuggingFaceTB/SmolLM2-135M",
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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_fused",
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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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"special_tokens": {
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"pad_token": "<|endoftext|>",
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},
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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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class TestTrainerClsPlugin:
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"""
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TestCase class for trainer builder with plugin
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"""
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def test_trainer_cls_is_not_none_with_plugin(self, cfg, model, tokenizer):
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"""
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Test that the trainer cls is not none with plugin
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Fixes #2693
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"""
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cfg.plugins = ["axolotl.integrations.liger.LigerPlugin"]
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cfg.rl = RLType.KTO
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# Expected AttributeError as we don't pass regular model configs to RL trainer builder
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# If it throws `TypeError: None is not a callable object`, trainer_cls could be None
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with pytest.raises(
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AttributeError, match=r".*'tuple' object has no attribute 'config'.*"
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):
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builder = HFRLTrainerBuilder(cfg, model, tokenizer)
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builder.build(100)
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