cd856b45b1
* feat:add support dataset_num_processes * chore * required changes * requested chnages * required chnages * required changes * required changes * elif get_default_process_count() * add:del data * Update cicd/Dockerfile.jinja Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> * Update cicd/single_gpu.py Co-authored-by: NanoCode012 <kevinvong@rocketmail.com> --------- Co-authored-by: salman <salman.mohammadi@outlook.com> Co-authored-by: NanoCode012 <kevinvong@rocketmail.com>
111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
"""Module for testing dataset sequence packing"""
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import unittest
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from transformers import AutoTokenizer
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from axolotl.cli.args import TrainerCliArgs
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from axolotl.common.datasets import load_datasets
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from axolotl.train import setup_model_and_trainer
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from axolotl.utils.config import normalize_config, validate_config
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from axolotl.utils.dict import DictDefault
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from tests.e2e.utils import with_temp_dir
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from tests.hf_offline_utils import enable_hf_offline
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class TestPacking(unittest.TestCase):
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"""
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Test class for packing dataset sequences
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"""
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@enable_hf_offline
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def setUp(self) -> None:
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self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
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self.tokenizer.add_special_tokens(
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{
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"bos_token": "<s>",
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"eos_token": "</s>",
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"unk_token": "<unk>",
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}
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)
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@with_temp_dir
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def test_lora_packing(self, temp_dir):
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cfg = DictDefault(
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{
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"base_model": "HuggingFaceTB/SmolLM2-135M",
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"tokenizer_type": "AutoTokenizer",
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"sequence_len": 1024,
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"sample_packing": True,
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"multipack_real_batches": False,
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"eval_sample_packing": 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": 0.2,
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"special_tokens": {
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"pad_token": "<|endoftext|>",
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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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"dataset_num_proc": 4,
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"num_epochs": 1,
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"max_steps": 20,
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"save_steps": 10,
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"micro_batch_size": 8,
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"gradient_accumulation_steps": 1,
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"output_dir": temp_dir,
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"learning_rate": 0.00001,
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"optimizer": "adamw_torch_fused",
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"lr_scheduler": "cosine",
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"fp16": False,
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"bf16": False,
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}
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)
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cfg = validate_config(cfg)
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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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(
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trainer,
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_,
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_,
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_,
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_,
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) = setup_model_and_trainer(cfg, dataset_meta)
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sampler = trainer._get_eval_sampler(trainer.eval_dataset)
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assert "MultipackBatchSampler" in sampler.__class__.__name__
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assert (
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"V2BatchSamplerDataCollatorForSeq2Seq"
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in trainer.eval_data_collator.__class__.__name__
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)
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dataloader = trainer.get_eval_dataloader(trainer.eval_dataset)
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dataloader_iter = iter(dataloader)
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batch = next(dataloader_iter)
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assert batch["input_ids"].shape == (1, 8192)
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sampler = trainer._get_train_sampler(trainer.train_dataset)
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assert "MultipackBatchSampler" in sampler.__class__.__name__
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assert (
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"V2BatchSamplerDataCollatorForSeq2Seq"
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in trainer.train_data_collator.__class__.__name__
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)
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dataloader = trainer.get_train_dataloader()
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dataloader_iter = iter(dataloader)
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batch = next(dataloader_iter)
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assert batch["input_ids"].shape == (1, 8192)
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if __name__ == "__main__":
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unittest.main()
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