976f85195a
* fixes to accelerator so that iterable pretraining datasets work * fix the pretraining test params * split batches, not dispatch batches needs to be set * update c4 datasets * set epochs in pretrain config test * need to set both split_batches and dispatch_batches to false for pretraining * fix bool val in comment
68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
"""
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E2E tests for llama pretrain
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"""
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import logging
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import os
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import unittest
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from pathlib import Path
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from axolotl.cli import load_datasets
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from axolotl.common.cli import TrainerCliArgs
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from axolotl.train import train
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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 .utils import with_temp_dir
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LOG = logging.getLogger("axolotl.tests.e2e")
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os.environ["WANDB_DISABLED"] = "true"
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class TestPretrainLlama(unittest.TestCase):
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"""
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Test case for Llama models w pretraining
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"""
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@with_temp_dir
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def test_pretrain_w_sample_packing(self, temp_dir):
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# pylint: disable=duplicate-code
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cfg = DictDefault(
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{
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"base_model": "JackFram/llama-68m",
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"tokenizer_type": "LlamaTokenizer",
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"flash_attention": True,
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"sequence_len": 1024,
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"sample_packing": True,
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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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"pretraining_dataset": [
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{
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"path": "allenai/c4",
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"name": "en",
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"type": "pretrain",
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}
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],
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"max_steps": 5,
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"num_epochs": 1,
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"micro_batch_size": 1,
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"gradient_accumulation_steps": 1,
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"val_set_size": 0.0,
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"output_dir": temp_dir,
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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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"save_safetensors": True,
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"bf16": "auto",
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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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assert (Path(temp_dir) / "model.safetensors").exists()
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