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import json
import os
import subprocess
import sys
from pathlib import Path
from unittest import mock
from unittest.mock import Mock, call, ANY
wd = (Path(__file__).parent.parent / "scripts").absolute()
import requests
def train_tokenizer(destination_path):
destination_path.mkdir(parents=True, exist_ok=True)
# download the tiny shakespeare dataset
input_file_path = destination_path / "input.txt"
if not input_file_path.exists():
data_url = "https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt"
with open(input_file_path, "w") as f:
f.write(requests.get(data_url).text)
from lit_llama import Tokenizer
Tokenizer.train(input=input_file_path, destination=destination_path, vocab_size=100)
return destination_path / "tokenizer.model"
def test_prepare_sample(tmp_path):
sys.path.append(str(wd))
tokenizer_path = train_tokenizer(tmp_path)
sample_path = tmp_path / "sample"
source_path = sample_path / "source"
dest_path = sample_path / "dest"
source_path.mkdir(parents=True, exist_ok=True)
sample = {
"meta": {"some": "info"},
"text": "some text"
}
jsonl_sample = "\n".join([json.dumps(el) for el in [sample] * 2])
import prepare_redpajama
for filename in prepare_redpajama.filenames_sample:
with open(source_path / filename, "w") as f:
f.write(jsonl_sample)
prepare_redpajama.prepare(source_path=source_path, tokenizer_path=tokenizer_path, destination_path=dest_path, sample=True)
bin_files = [el.replace(".jsonl", "_0000000000.bin") for el in prepare_redpajama.filenames_sample]
assert set(os.listdir(dest_path)) == set(bin_files)
from lit_llama import Tokenizer
from lit_llama.packed_dataset import PackedDataset
tokenizer = Tokenizer(tokenizer_path)
# artificially set block_size to fit the text
block_size = len(tokenizer.encode("some text"))
for filename in bin_files:
filenames = [os.path.join(dest_path, filename)]
dataset = PackedDataset(filenames=filenames, n_chunks=1, block_size=block_size, shuffle=False)
dataset_iter = iter(dataset)
assert tokenizer.decode(next(dataset_iter)) == "some text"
assert tokenizer.decode(next(dataset_iter)) == "some text"
def test_prepare_full(tmp_path):
sys.path.append(str(wd))
tokenizer_path = train_tokenizer(tmp_path)
full_path = tmp_path / "full"
source_path = full_path / "source"
dest_path = full_path / "dest"
source_path.mkdir(parents=True, exist_ok=True)
sample = {
"meta": {"some": "info"},
"text": "some text"
}
jsonl_sample = "\n".join([json.dumps(el) for el in [sample] * 2])
import prepare_redpajama
arxiv_file = source_path / "arxiv" / "arxiv_0.jsonl"
arxiv_file.parent.mkdir(parents=True, exist_ok=True)
with open(arxiv_file, "w") as f:
f.write(jsonl_sample)
import zstandard as zstd
cc_file = source_path / "common_crawl" / "cc_0.jsonl"
cc_file.parent.mkdir(parents=True, exist_ok=True)
with zstd.open(cc_file, "wt", encoding="utf-8") as f:
f.write(jsonl_sample)
filename_sets = {
"arxiv": "arxiv/arxiv*",
"common_crawl": "common_crawl/*",
}
with mock.patch("prepare_redpajama.filename_sets", filename_sets):
prepare_redpajama.prepare(source_path=source_path, tokenizer_path=tokenizer_path, destination_path=dest_path, sample=False)
all_names = prepare_redpajama.filename_sets.keys()
bin_files = [el + "_0000000000.bin" for el in all_names]
assert set(os.listdir(dest_path)) == set(bin_files)
from lit_llama import Tokenizer
from lit_llama.packed_dataset import PackedDataset
tokenizer = Tokenizer(tokenizer_path)
# artificially set block_size to fit the text
block_size = len(tokenizer.encode("some text"))
filenames = [os.path.join(dest_path, el) for el in bin_files]
for filename in filenames:
dataset = PackedDataset(filenames=[filename], n_chunks=1, block_size=block_size, shuffle=False)
dataset_iter = iter(dataset)
assert tokenizer.decode(next(dataset_iter)) == "some text"
assert tokenizer.decode(next(dataset_iter)) == "some text"
def test_cli():
cli_path = wd / "prepare_redpajama.py"
output = subprocess.check_output([sys.executable, cli_path, "-h"])
output = str(output.decode())
assert 'Prepare the "Red Pajama"' in output
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