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Feat(test): Add tests for alpaca chatml prompt tokenizer #1088

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Feat(test): Add tests for alpaca chatml prompt tokenizer #1088

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draft for adding test for tokenizer
JohanWork Jan 10, 2024
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clean up
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clean up
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fix pre commit
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fix pylint
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Revert "fix pylint"
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add pylint exception for pytest fixture
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update spelling and import promptstyle
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344 changes: 344 additions & 0 deletions tests/prompt_strategies/test_alpacha.py
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Original file line number Diff line number Diff line change
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"""
Test module for alpaca integration w chatml
"""
import pytest
from datasets import Dataset
from tokenizers import AddedToken
from transformers import AutoTokenizer

from axolotl.datasets import TokenizedPromptDataset
from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
from axolotl.prompters import AlpacaPrompter, PromptStyle


@pytest.fixture(name="alpaca_dataset")
def fixture_alpaca_dataset():
return Dataset.from_list(
[
{
"instruction": "Evaluate this sentence for spelling and grammar mistakes",
"input": "He finnished his meal and left the resturant",
"output": "He finished his meal and left the restaurant.",
}
]
)


@pytest.fixture(name="tokenizer")
def fixture_tokenizer():
# pylint: disable=all
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer.add_special_tokens(
{
"eos_token": AddedToken(
"<|im_end|>", rstrip=False, lstrip=False, normalized=False
)
}
)
tokenizer.add_tokens(
[
AddedToken("<|im_start|>", rstrip=False, lstrip=False, normalized=False),
]
)

return tokenizer


class TestAlpacaChatml:
"""
Test class for alpaca prompter
"""

def test_no_double_im_end(self, alpaca_dataset, tokenizer):
strategy = AlpacaPromptTokenizingStrategy(
AlpacaPrompter(prompt_style=PromptStyle.CHATML.value),
tokenizer,
False, # train_on_inputs
2048, # sequence_len
)

dataset_wrapper = TokenizedPromptDataset(
strategy, alpaca_dataset, process_count=1
)

input_ids = dataset_wrapper[0]["input_ids"]

assert input_ids == [
1, # Bos
32001,
1587,
13,
20548,
336,
349,
396,
13126,
369,
13966,
264,
3638,
28725,
5881,
1360,
395,
396,
2787,
369,
5312,
3629,
2758,
28723,
12018,
264,
2899,
369,
6582,
1999,
2691,
274,
272,
2159,
28723,
32000,
28705,
13, # instruction
32001,
2188,
13,
16627,
11931,
456,
12271,
354,
668,
3572,
304,
18756,
3479,
17179,
13,
2428,
854,
28711,
1497,
516,
11314,
304,
1749,
272,
1846,
324,
440,
32000,
28705,
13, # input
32001,
13892,
13,
650,
5967,
516,
11314,
304,
1749,
272,
9926,
28723,
32000, # output
]
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def test_no_train_on_input(self, alpaca_dataset, tokenizer):
strategy = AlpacaPromptTokenizingStrategy(
AlpacaPrompter(prompt_style=PromptStyle.CHATML.value),
tokenizer,
False, # train_on_inputs
2048, # sequence_len
)

dataset_wrapper = TokenizedPromptDataset(
strategy, alpaca_dataset, process_count=1
)

labels = dataset_wrapper[0]["labels"]

assert labels == [
-100, # bos
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100, # instruction
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100,
-100, # input
-100,
-100,
-100,
650,
5967,
516,
11314,
304,
1749,
272,
9926,
28723,
32000, # Output
]

def test_w_train_on_input(self, alpaca_dataset, tokenizer):
strategy = AlpacaPromptTokenizingStrategy(
AlpacaPrompter(prompt_style=PromptStyle.CHATML.value),
tokenizer,
True, # train_on_inputs
2048, # sequence_len
)

dataset_wrapper = TokenizedPromptDataset(
strategy, alpaca_dataset, process_count=1
)

labels = dataset_wrapper[0]["labels"]

assert labels == [
1, # Bos
32001,
1587,
13,
20548,
336,
349,
396,
13126,
369,
13966,
264,
3638,
28725,
5881,
1360,
395,
396,
2787,
369,
5312,
3629,
2758,
28723,
12018,
264,
2899,
369,
6582,
1999,
2691,
274,
272,
2159,
28723,
32000,
28705,
13, # instruction
32001,
2188,
13,
16627,
11931,
456,
12271,
354,
668,
3572,
304,
18756,
3479,
17179,
13,
2428,
854,
28711,
1497,
516,
11314,
304,
1749,
272,
1846,
324,
440,
32000,
28705,
13, # input
32001,
13892,
13,
650,
5967,
516,
11314,
304,
1749,
272,
9926,
28723,
32000, # output
]