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"""Module for testing prompt tokenizers."""
import json
import logging
import unittest
from pathlib import Path
from typing import Optional
import pytest
from transformers import AutoTokenizer, LlamaTokenizer
from axolotl.prompt_strategies.alpaca_chat import NoSystemPrompter
from axolotl.prompt_strategies.alpaca_w_system import (
InstructionWSystemPromptTokenizingStrategy,
SystemDataPrompter,
)
from axolotl.prompt_strategies.llama2_chat import (
Llama2ChatPrompter,
LLama2ChatTokenizingStrategy,
)
from axolotl.prompt_tokenizers import (
AlpacaPromptTokenizingStrategy,
ShareGPTPromptTokenizingStrategy,
)
from axolotl.prompters import AlpacaPrompter, PromptStyle, ShareGPTPrompterV2
LOG = logging.getLogger("axolotl")
class TestPromptTokenizationStrategies(unittest.TestCase):
"""
Test class for prompt tokenization strategies.
"""
_caplog: Optional[pytest.LogCaptureFixture] = None
@pytest.fixture(autouse=True)
def inject_fixtures(self, caplog):
self._caplog = caplog
def setUp(self) -> None:
# pylint: disable=duplicate-code
self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
self.tokenizer.add_special_tokens(
{
"bos_token": "<s>",
"eos_token": "</s>",
"unk_token": "<unk>",
}
)
def test_sharegpt_integration(self):
with open(
Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
) as fin:
data = fin.read()
conversation = json.loads(data)
with open(
Path(__file__).parent / "fixtures/conversation.tokenized.json",
encoding="utf-8",
) as fin:
data = fin.read()
tokenized_conversation = json.loads(data)
prompter = ShareGPTPrompterV2()
strat = ShareGPTPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
example = strat.tokenize_prompt(conversation)
for fields in ["input_ids", "attention_mask", "labels"]:
self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
self.assertEqual(example[fields], tokenized_conversation[fields])
def test_sharegpt_warnings_integration(self):
with open(
Path(__file__).parent / "fixtures/conversation.missingturns.json",
encoding="utf-8",
) as fin:
data = fin.read()
conversation = json.loads(data)
prompter = ShareGPTPrompterV2()
strat = ShareGPTPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
with self._caplog.at_level(logging.WARNING):
strat.tokenize_prompt(conversation)
assert "assistant turn has empty text" in self._caplog.records[1].message
def test_sharegpt_warnings_turns(self):
conversation = {
"conversations": [
{"from": "system", "value": "lorem"},
{"from": "gpt", "value": "ipsum"},
{"from": "human", "value": "dolor"},
{"from": "human", "value": "dolor"},
{"from": "gpt", "value": "sit"},
]
}
prompter = ShareGPTPrompterV2()
strat = ShareGPTPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
with self._caplog.at_level(logging.WARNING):
strat.tokenize_prompt(conversation)
assert (
"Role did not alternate between turns (gpt and human)"
in self._caplog.records[0].message
)
def test_sharegpt_changes_roles(self):
conversation = {
"roles": ["USER", "CHARACTER"],
"conversations": [
{"from": "system", "value": "lorem"},
{"from": "gpt", "value": "ipsum"},
{"from": "human", "value": "dolor"},
{"from": "gpt", "value": "sit"},
],
}
prompter = ShareGPTPrompterV2()
strat = ShareGPTPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
with self._caplog.at_level(logging.WARNING):
res = strat.tokenize_prompt(conversation)
assert "CHARACTER" in self.tokenizer.decode(res["input_ids"])
def test_sharegpt_assistant_label_ignore(self):
conversation = {
"roles": ["user", "assistant"],
"conversations": [
{"from": "system", "value": "lorem"},
{"from": "gpt", "value": "ipsum"},
{"from": "human", "value": "dolor"},
{"from": "gpt", "value": "sit"},
],
}
prompter = ShareGPTPrompterV2()
strat = ShareGPTPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
with self._caplog.at_level(logging.WARNING):
res = strat.tokenize_prompt(conversation)
idx = res["input_ids"].index(20255) # assistant token
assert res["labels"][idx] == -100
def test_no_sys_prompt(self):
"""
tests the interface between the user and assistant parts
"""
prompter = NoSystemPrompter()
# pylint: disable=duplicate-code
strat = AlpacaPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
sample = {
"instruction": "hello cruel. lorem ipsum dolor sit amet.",
"output": "world!",
}
example = strat.tokenize_prompt(sample)
world_idx = example["input_ids"].index(3186)
assert example["labels"][world_idx] == 3186
assert example["labels"][world_idx - 1] == -100
def test_alpaca(self):
"""
tests the interface between the user and assistant parts
"""
# pylint: disable=duplicate-code
prompter = AlpacaPrompter()
strat = AlpacaPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
sample = {"instruction": "hello!", "output": "Hi! How can I help?"}
example = strat.tokenize_prompt(sample)
world_idx = example["input_ids"].index(6324)
assert example["labels"][world_idx] == 6324
assert example["labels"][world_idx - 1] == -100
class InstructionWSystemPromptTokenizingStrategyTest(unittest.TestCase):
"""
Test class for prompt tokenization strategies with sys prompt from the dataset
"""
def setUp(self) -> None:
# pylint: disable=duplicate-code
self.tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
self.tokenizer.add_special_tokens(
{
"bos_token": "<s>",
"eos_token": "</s>",
"unk_token": "<unk>",
}
)
def test_system_alpaca(self):
prompter = SystemDataPrompter(PromptStyle.CHAT.value)
strat = InstructionWSystemPromptTokenizingStrategy(
prompter,
self.tokenizer,
False,
2048,
)
sample = {
"system": "use cot",
"instruction": "hello!",
"output": "Hi! How can I help?",
}
example = strat.tokenize_prompt(sample)
assert example["input_ids"][0:5] == [
1,
28962,
1254,
12665,
29901,
] # "<s>SYSTEM:"
assert example["input_ids"][5:7] == [671, 20118] # " use cot"
assert example["input_ids"][8] == 11889 # USER
class Llama2ChatTokenizationTest(unittest.TestCase):
"""
Test class for prompt tokenization strategies with sys prompt from the dataset
"""
def setUp(self) -> None:
# pylint: disable=duplicate-code
self.tokenizer = LlamaTokenizer.from_pretrained("NousResearch/Llama-2-7b-hf")
# woraround because official Meta repos are not open
def test_llama2_chat_integration(self):
with open(
Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
) as fin:
data = fin.read()
conversation = json.loads(data)
with open(
Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
encoding="utf-8",
) as fin:
data = fin.read()
tokenized_conversation = json.loads(data)
prompter = Llama2ChatPrompter()
strat = LLama2ChatTokenizingStrategy(
prompter,
self.tokenizer,
False,
4096,
)
example = strat.tokenize_prompt(conversation)
for fields in ["input_ids", "attention_mask", "labels"]:
self.assertEqual(len(example[fields]), len(tokenized_conversation[fields]))
self.assertEqual(example[fields], tokenized_conversation[fields])
def compare_with_transformers_integration(self):
# this needs transformers >= v4.31.0
from transformers.models.llama.tokenization_llama import B_SYS, E_SYS
from transformers.pipelines.conversational import Conversation
# from transformers.models.llama.tokenization_llama import DEFAULT_SYSTEM_PROMPT
# broken as of 23/7/20
# see https://github.com/huggingface/transformers/pull/24935
# pylint: disable=C0103
DEFAULT_SYSTEM_PROMPT = """\
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
with open(
Path(__file__).parent / "fixtures/conversation.json", encoding="utf-8"
) as fin:
data = fin.read()
conversation = json.loads(data)
with open(
Path(__file__).parent / "fixtures/conversation.tokenized_llama2chat.json",
encoding="utf-8",
) as fin:
data = fin.read()
tokenized_conversation = json.loads(data)
user_input = []
answers = []
for msg in conversation["conversations"]:
if msg["from"] == "human":
user_input.append(msg["value"])
else:
answers.append(msg["value"])
hf_conf = Conversation(
text=user_input[-1],
past_user_inputs=[B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + user_input[0]]
+ user_input[1:-1],
generated_responses=answers,
)
# pylint: disable=W0212
hf_tokens = self.tokenizer._build_conversation_input_ids(hf_conf)
self.assertEqual(
hf_tokens, tokenized_conversation["input_ids"][: len(hf_tokens)]
)
if __name__ == "__main__":
unittest.main()
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