File size: 2,961 Bytes
53e0dae | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import json
import pytest
import torch
from litdata import optimize
from litdata.streaming import StreamingDataset, TokensLoader
from torch.utils._pytree import tree_map
def tokenize(data):
for story in data:
yield torch.tensor(story)
def fake_chunk(path, data):
optimize(
fn=tokenize,
inputs=[data] * len(data),
output_dir=str(path),
num_workers=1,
chunk_bytes="200MB",
item_loader=TokensLoader(),
)
@pytest.mark.parametrize(
("max_seq_len", "expected"),
[
(2, [[0, 23, 15], [63, 0, 73], [5, 0, 1], [1999, 0, 13]]),
(5, [[0, 23, 15, 63, 0, 73], [5, 0, 1, 1999, 0, 13]]),
(6, [[0, 23, 15, 63, 0, 73, 5]]),
(7, [[0, 23, 15, 63, 0, 73, 5, 0]]),
],
)
def test_pretok_dataset(tmp_path, max_seq_len, expected):
fake_data = [0, 23, 15, 63, 0, 73, 5, 0, 1, 1999, 0, 13]
assert len(fake_data) == 12
fake_chunk(tmp_path, [fake_data])
dataset = StreamingDataset(
input_dir=str(tmp_path), item_loader=TokensLoader(block_size=max_seq_len + 1), shuffle=False, drop_last=False
)
actual = tree_map(torch.Tensor.tolist, list(dataset))
assert actual == expected
def test_tokenize(tmp_path, monkeypatch):
from litgpt.data.tinystories import tokenize
story1, story2 = "foo bar", " fun "
data = [{"story": story1}, {"story": story2}]
shard_path = tmp_path / "data.json"
with open(shard_path, "w", encoding="utf-8") as f:
json.dump(data, f)
class Tokenizer:
bos_id = 0
def encode(self, text, bos, eos):
assert bos
assert not eos
return [self.bos_id] + [ord(c) for c in text]
monkeypatch.setenv("DATA_OPTIMIZER_GLOBAL_RANK", "0")
monkeypatch.setenv("DATA_OPTIMIZER_NUM_WORKERS", "1")
data = tokenize(str(shard_path), Tokenizer())
assert list(data) == [[0, 102, 111, 111, 32, 98, 97, 114], [0, 102, 117, 110]]
def test_tinystories_datamodule(tmp_path):
from litgpt.data.tinystories import TinyStories
data_dir = tmp_path / "tinystories"
datamodule = TinyStories(data_dir, seed=42, num_workers=1)
datamodule.connect(max_seq_length=2)
# simulate `datamodule.prepare_data`
train_data_dir = data_dir / "train"
train_data_dir.mkdir(parents=True)
fake_chunk(train_data_dir, [[12], [0, 23, 15, 63, 0], [73, 5, 0, 1, 1999, 0, 13]])
datamodule.setup()
tr_dataloader = datamodule.train_dataloader()
tr_dataloader.shuffle = False
actual = tree_map(torch.Tensor.tolist, list(tr_dataloader))
# there is 1 sample per index in the data (13)
assert actual == [
[[73, 5, 0]],
[[12, 0, 23]],
[[5, 0, 1]],
[[0, 73, 5]],
[[1999, 0, 13]],
[[0, 1, 1999]],
[[1, 1999, 0]],
[[0, 23, 15]],
[[13, 12, 0]],
[[63, 0, 73]],
[[23, 15, 63]],
[[15, 63, 0]],
[[0, 13, 12]],
]
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