20620771f1
* fix for pretrain with packing * fix model name and loss expected * make sure to check with micro batch size for pretraining * change loss threshholds based on parametrization * make tests smaller for CI * fix pretrain packing * fix pretrain packing test * address pr feedback
101 lines
3.0 KiB
Python
101 lines
3.0 KiB
Python
"""Module for testing streaming dataset sequence packing"""
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import functools
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import unittest
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import pytest
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import torch
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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from transformers import AutoTokenizer
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from axolotl.utils.data import get_dataset_wrapper, wrap_pretraining_dataset
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from axolotl.utils.dict import DictDefault
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class TestPretrainingPacking(unittest.TestCase):
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"""
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Test class for packing streaming dataset sequences
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"""
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def setUp(self) -> None:
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# pylint: disable=duplicate-code
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self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
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self.tokenizer.pad_token = "</s>"
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@pytest.mark.flaky(retries=3, delay=5)
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def test_packing_stream_dataset(self):
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# pylint: disable=duplicate-code
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dataset = load_dataset(
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"allenai/c4",
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"en",
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streaming=True,
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)["train"]
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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": "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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"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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self.tokenizer,
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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_pretraining_dataset(
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dataset,
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self.tokenizer,
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cfg,
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ds_wrapper_partial,
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max_tokens=cfg.sequence_len,
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batch_size=cfg.micro_batch_size,
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seed=cfg.seed or 42,
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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 > 10:
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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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if __name__ == "__main__":
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unittest.main()
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