File size: 2,227 Bytes
0b4cf5b 2f586d1 0b4cf5b |
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 |
"""
test module for the axolotl.utis.data module
"""
import unittest
from transformers import LlamaTokenizer
from axolotl.utils.data import encode_pretraining, md5
class TestEncodePretraining(unittest.TestCase):
"""
test class for encode pretraining and md5 helper
"""
def setUp(self):
self.tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b")
self.tokenizer.add_special_tokens(
{
"eos_token": "</s>",
"bos_token": "<s>",
"unk_token": "<unk>",
"pad_token": "<pad>",
}
)
self.max_tokens = 15 # set a small number for easy inspection
def test_encode_pretraining(self):
examples = {
"text": [
"Hello, world!",
"Nice to meet you.",
"lorem ipsum dolor sit amet.",
"Nice to meet you again!.",
"hello, hello",
]
}
result = encode_pretraining(self.tokenizer, self.max_tokens, examples["text"])
self.assertEqual(len(result["input_ids"]), 3)
# Assert the length of input_ids and attention_mask is correct
self.assertEqual(len(result["input_ids"][0]), self.max_tokens)
self.assertEqual(len(result["attention_mask"][0]), self.max_tokens)
# Assert EOS and PAD tokens are correctly added
# hello world! is 4 tokens
self.assertEqual(result["input_ids"][0][0], self.tokenizer.bos_token_id)
self.assertEqual(result["input_ids"][0][5], self.tokenizer.eos_token_id)
self.assertEqual(result["input_ids"][0][6], self.tokenizer.pad_token_id)
# second part, 5 tokens
self.assertEqual(result["input_ids"][0][7], self.tokenizer.bos_token_id)
self.assertEqual(result["input_ids"][0][13], self.tokenizer.eos_token_id)
self.assertEqual(result["input_ids"][0][14], self.tokenizer.pad_token_id)
def test_md5(self):
self.assertEqual(md5("hello world"), "5eb63bbbe01eeed093cb22bb8f5acdc3")
self.assertEqual(
md5("hello world", "utf-8"), "5eb63bbbe01eeed093cb22bb8f5acdc3"
)
if __name__ == "__main__":
unittest.main()
|