81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
"""
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E2E tests for lora llama
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"""
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import logging
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import os
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import tempfile
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import unittest
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import TrainDatasetMeta, train
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from axolotl.utils.config import normalize_config
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from axolotl.utils.data import prepare_dataset
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.models import load_tokenizer
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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def load_datasets(
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*,
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cfg: DictDefault,
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cli_args: TrainerCliArgs, # pylint:disable=unused-argument
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) -> TrainDatasetMeta:
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tokenizer = load_tokenizer(cfg)
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train_dataset, eval_dataset, total_num_steps = prepare_dataset(cfg, tokenizer)
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return TrainDatasetMeta(
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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total_num_steps=total_num_steps,
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)
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class TestLoraLlama(unittest.TestCase):
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"""
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Test case for Llama models using LoRA
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"""
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def test_lora(self):
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cfg = DictDefault(
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{
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"base_model": "JackFram/llama-68m",
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"base_model_config": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"sequence_len": 1024,
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"load_in_8bit": True,
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"adapter": "lora",
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"lora_r": 32,
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"lora_target_linear": True,
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"val_set_size": 0.1,
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"special_tokens": {
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"unk_token": "<unk>",
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"bos_token": "<s>",
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"eos_token": "</s>",
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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": 2,
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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"output_dir": tempfile.mkdtemp(),
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch",
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"lr_scheduler": "cosine",
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}
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)
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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, cli_args=cli_args, dataset_meta=dataset_meta)
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