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"""Module for testing streaming dataset sequence packing"""
import functools
import unittest
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from axolotl.utils.data import get_dataset_wrapper, wrap_pretraining_dataset
from axolotl.utils.dict import DictDefault
class TestPretrainingPacking(unittest.TestCase):
"""
Test class for packing streaming dataset sequences
"""
def setUp(self) -> None:
# pylint: disable=duplicate-code
self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
self.tokenizer.pad_token = "</s>"
def test_packing_stream_dataset(self):
# pylint: disable=duplicate-code
dataset = load_dataset(
"c4",
"en",
streaming=True,
)["train"]
cfg = DictDefault(
{
"pretraining_dataset": [
{
"path": "c4",
"name": "en",
"type": "pretrain",
}
],
"sample_packing": True,
"pad_to_sequence_len": True,
"sequence_len": 2048,
"micro_batch_size": 2,
}
)
ds_wrapper_partial = functools.partial(
get_dataset_wrapper,
cfg.pretraining_dataset[0],
self.tokenizer,
cfg,
cfg.pretraining_dataset[0]["type"] or "pretrain",
)
original_bsz = cfg.micro_batch_size
train_dataset = wrap_pretraining_dataset(
dataset,
self.tokenizer,
cfg,
ds_wrapper_partial,
max_tokens=cfg.sequence_len,
batch_size=cfg.micro_batch_size,
seed=cfg.seed or 42,
)
trainer_loader = DataLoader(
train_dataset,
batch_size=1,
collate_fn=None,
drop_last=True,
)
idx = 0
for data in trainer_loader:
if idx > 10:
break
assert data["input_ids"].shape == torch.Size(
[1, original_bsz * cfg.sequence_len]
)
assert data["position_ids"].shape == torch.Size(
[1, original_bsz * cfg.sequence_len]
)
assert data["labels"].shape == torch.Size(
[1, original_bsz * cfg.sequence_len]
)
assert data["attention_mask"].shape == torch.Size(
[1, original_bsz * cfg.sequence_len]
)
idx += 1
if __name__ == "__main__":
unittest.main()
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