231a67e70b
* working * fixes * deprecate --iterable; cleanup * pretrain_multipack_buffer_size -> streaming_multipack_buffer_size * improvements * tests * remove unused * docs, examples * nit * nit * add val_set_size validation * val * nit * min * coderabbito * cleanup * nit * add depr warning, cleanup * nit * fix test, fix quarto * fix * review comments * review comments * fix
112 lines
3.4 KiB
Python
112 lines
3.4 KiB
Python
"""Module for testing streaming dataset sequence packing"""
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import functools
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import random
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import string
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import pytest
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import torch
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from datasets import IterableDataset
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from torch.utils.data import DataLoader
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from axolotl.utils.data import get_dataset_wrapper, wrap_streaming_dataset
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from axolotl.utils.dict import DictDefault
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class TestPretrainingPacking:
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"""
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Test class for packing streaming dataset sequences
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"""
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@pytest.fixture
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def random_text(self):
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# seed with random.seed(0) for reproducibility
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random.seed(0)
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# generate row of random text with "words" of between 2 and 10 characters and
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# between 400 to 1200 characters per line
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def rand_txt():
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return " ".join(
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[
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"".join(
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random.choices(string.ascii_lowercase, k=random.randint(2, 10))
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)
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for _ in range(random.randint(50, 200))
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]
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)
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# Create a list of 2000 random texts rather than just using it within the
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# generator so the test runs faster
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data = [rand_txt() for _ in range(500)]
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# Create an IterableDataset
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def generator():
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for row in data:
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yield {"text": row}
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return IterableDataset.from_generator(generator)
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@pytest.mark.flaky(retries=1, delay=5)
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def test_packing_stream_dataset(self, tokenizer_huggyllama, random_text):
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dataset = random_text
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cfg = DictDefault(
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{
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"pretraining_dataset": [
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{
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"path": "winglian/tiny-shakespeare",
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"type": "pretrain",
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}
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],
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"sample_packing": True,
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"pretrain_multipack_attn": True,
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"pad_to_sequence_len": True,
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"sequence_len": 2048,
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"micro_batch_size": 2,
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"sample_packing_group_size": 100000,
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"sample_packing_bin_size": 200,
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}
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)
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ds_wrapper_partial = functools.partial(
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get_dataset_wrapper,
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cfg.pretraining_dataset[0],
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tokenizer_huggyllama,
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cfg,
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cfg.pretraining_dataset[0]["type"] or "pretrain",
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)
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original_bsz = cfg.micro_batch_size
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train_dataset = wrap_streaming_dataset(
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dataset,
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tokenizer_huggyllama,
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cfg,
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ds_wrapper_partial,
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)
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trainer_loader = DataLoader(
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train_dataset,
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batch_size=1,
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collate_fn=None,
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drop_last=True,
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)
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idx = 0
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for data in trainer_loader:
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if idx > 3:
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break
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assert data["input_ids"].shape == torch.Size(
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[1, original_bsz * cfg.sequence_len]
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)
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assert data["position_ids"].shape == torch.Size(
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[1, original_bsz * cfg.sequence_len]
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)
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assert data["labels"].shape == torch.Size(
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[1, original_bsz * cfg.sequence_len]
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)
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assert "attention_mask" not in data
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# FIXME add back once we fix packing unpad/pad with attention mask
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# assert data["attention_mask"].shape == torch.Size(
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# [1, original_bsz * cfg.sequence_len]
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# )
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idx += 1
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