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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import unittest
from datasets import Dataset, DatasetDict
from parameterized import parameterized
from transformers import AutoTokenizer
from trl.data_utils import (
apply_chat_template,
extract_prompt,
is_conversational,
maybe_apply_chat_template,
maybe_extract_prompt,
maybe_unpair_preference_dataset,
unpair_preference_dataset,
)
class IsConversationalTester(unittest.TestCase):
conversational_examples = [
{ # Language modeling
"messages": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is blue."},
],
},
{ # Prompt only
"prompt": [{"role": "user", "content": "What color is the sky?"}],
},
{ # Pompt-completion
"prompt": [{"role": "user", "content": "What color is the sky?"}],
"completion": [{"role": "assistant", "content": "It is blue."}],
},
{ # Preference
"prompt": [{"role": "user", "content": "What color is the sky?"}],
"chosen": [{"role": "assistant", "content": "It is blue."}],
"rejected": [{"role": "assistant", "content": "It is green."}],
},
{ # Preference with implicit prompt
"chosen": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is blue."},
],
"rejected": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is green."},
],
},
{ # Unpaired preference
"prompt": [{"role": "user", "content": "What color is the sky?"}],
"completion": [{"role": "assistant", "content": "It is blue."}],
"label": True,
},
]
non_conversational_examples = [
{"prompt": "The sky is", "completion": " blue."},
{"text": "The sky is blue."},
{"prompt": "The sky is"},
{"prompt": "The sky is", "chosen": " blue.", "rejected": " green."},
{"prompt": "The sky is", "completion": " blue.", "label": True},
]
@parameterized.expand(itertools.product(conversational_examples))
def test_conversational(self, example):
self.assertTrue(is_conversational(example))
@parameterized.expand(itertools.product(non_conversational_examples))
def test_non_conversational(self, example):
self.assertFalse(is_conversational(example))
class ApplyChatTemplateTester(unittest.TestCase):
tokenizers = [
"trl-internal-testing/tiny-random-Qwen2-7B-Instruct",
"trl-internal-testing/tiny-random-Meta-Llama-3.1-8B-Instruct",
"trl-internal-testing/tiny-random-Meta-Llama-3-8B-Instruct",
"trl-internal-testing/tiny-random-DeepSeek-Coder-V2-Instruct",
"trl-internal-testing/tiny-random-Phi-3-mini-128k-instruct",
"trl-internal-testing/tiny-random-gemma-2-9b-it",
"trl-internal-testing/tiny-random-Mistral-7B-Instruct-v0.1",
"trl-internal-testing/tiny-random-Mistral-7B-Instruct-v0.2",
]
conversational_examples = [
{ # Language modeling
"messages": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is blue."},
],
},
{ # Prompt only
"prompt": [{"role": "user", "content": "What color is the sky?"}],
},
{ # Pompt-completion
"prompt": [{"role": "user", "content": "What color is the sky?"}],
"completion": [{"role": "assistant", "content": "It is blue."}],
},
{ # Preference
"prompt": [{"role": "user", "content": "What color is the sky?"}],
"chosen": [{"role": "assistant", "content": "It is blue."}],
"rejected": [{"role": "assistant", "content": "It is green."}],
},
{ # Preference with implicit prompt
"chosen": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is blue."},
],
"rejected": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is green."},
],
},
{ # Unpaired preference
"prompt": [{"role": "user", "content": "What color is the sky?"}],
"completion": [{"role": "assistant", "content": "It is blue."}],
"label": True,
},
]
non_conversational_examples = [
{"prompt": "The sky is", "completion": " blue."},
{"text": "The sky is blue."},
{"prompt": "The sky is"},
{"prompt": "The sky is", "chosen": " blue.", "rejected": " green."},
{"chosen": "The sky is blue.", "rejected": "The sky is green."},
{"prompt": "The sky is", "completion": " blue.", "label": True},
]
@parameterized.expand(itertools.product(tokenizers, conversational_examples))
def test_apply_chat_template(self, tokenizer_id, example):
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
result = apply_chat_template(example, tokenizer)
# Checking if the result is a dictionary
self.assertIsInstance(result, dict)
# The chat template should be applied to the the following keys
for key in ["prompt", "chosen", "rejected", "completion"]:
if key in example:
self.assertIn(key, result)
self.assertIsInstance(result[key], str)
# Exception for messages, the key is "text" once the chat template is applied
if "messages" in example:
self.assertIn("text", result)
self.assertIsInstance(result["text"], str)
# The label should be kept
if "label" in example:
self.assertIn("label", result)
self.assertIsInstance(result["label"], bool)
self.assertEqual(result["label"], example["label"])
# both conversational and non-conversational examples
@parameterized.expand(itertools.product(tokenizers, conversational_examples + non_conversational_examples))
def test_maybe_apply_chat_template(self, tokenizer_id, example):
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
result = maybe_apply_chat_template(example, tokenizer)
# Checking if the result is a dictionary
self.assertIsInstance(result, dict)
# The chat template should be applied to the the following keys
for key in ["prompt", "chosen", "rejected", "completion"]:
if key in example:
self.assertIn(key, result)
self.assertIsInstance(result[key], str)
# Exception for messages, the key is "text" once the chat template is applied
if "messages" in example:
self.assertIn("text", result)
self.assertIsInstance(result["text"], str)
# The label should be kept
if "label" in example:
self.assertIn("label", result)
self.assertIsInstance(result["label"], bool)
self.assertEqual(result["label"], example["label"])
class UnpairPreferenceDatasetTester(unittest.TestCase):
paired_dataset = Dataset.from_dict(
{
"prompt": ["The sky is", "The sun is"],
"chosen": [" blue.", " in the sky."],
"rejected": [" green.", " in the sea."],
}
)
unpaired_dataset = Dataset.from_dict(
{
"prompt": ["The sky is", "The sun is", "The sky is", "The sun is"],
"completion": [" blue.", " in the sky.", " green.", " in the sea."],
"label": [True, True, False, False],
}
)
def test_unpair_preference_dataset(self):
# Test that a paired dataset is correctly converted to unpaired
unpaired_dataset = unpair_preference_dataset(self.paired_dataset)
self.assertEqual(
unpaired_dataset.to_dict(),
self.unpaired_dataset.to_dict(),
"The paired dataset should be converted to unpaired.",
)
def test_unpair_preference_dataset_dict(self):
# Test that a paired dataset dict is correctly converted to unpaired
paired_dataset_dict = DatasetDict({"abc": self.paired_dataset})
unpaired_dataset_dict = unpair_preference_dataset(paired_dataset_dict)
self.assertEqual(
unpaired_dataset_dict["abc"].to_dict(),
self.unpaired_dataset.to_dict(),
"The paired dataset should be converted to unpaired.",
)
def test_maybe_unpair_preference_dataset(self):
# Test that a paired dataset is correctly converted to unpaired with maybe_unpair_preference_dataset
unpaired_dataset = maybe_unpair_preference_dataset(self.paired_dataset)
self.assertEqual(
unpaired_dataset.to_dict(),
self.unpaired_dataset.to_dict(),
"The paired dataset should be converted to unpaired.",
)
def test_maybe_unpair_preference_dataset_dict(self):
# Test that a paired dataset dict is correctly converted to unpaired with maybe_unpair_preference_dataset
paired_dataset_dict = DatasetDict({"abc": self.paired_dataset})
unpaired_dataset_dict = maybe_unpair_preference_dataset(paired_dataset_dict)
self.assertEqual(
unpaired_dataset_dict["abc"].to_dict(),
self.unpaired_dataset.to_dict(),
"The paired dataset should be converted to unpaired.",
)
def test_maybe_unpair_preference_dataset_already_paired(self):
# Test that a paired dataset remains unchanged with maybe_unpair_preference_dataset
unpaired_dataset = maybe_unpair_preference_dataset(self.unpaired_dataset)
self.assertEqual(
unpaired_dataset.to_dict(),
self.unpaired_dataset.to_dict(),
"The unpaired dataset should remain unchanged.",
)
def test_maybe_unpair_preference_dataset_dict_already_paired(self):
# Test that a paired dataset dict remains unchanged with maybe_unpair_preference_dataset
unpaired_dataset_dict = maybe_unpair_preference_dataset(DatasetDict({"abc": self.unpaired_dataset}))
self.assertEqual(
unpaired_dataset_dict["abc"].to_dict(),
self.unpaired_dataset.to_dict(),
"The unpaired dataset should remain unchanged.",
)
class ExtractPromptTester(unittest.TestCase):
example_implicit_prompt_conversational = {
"chosen": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is blue."},
],
"rejected": [
{"role": "user", "content": "What color is the sky?"},
{"role": "assistant", "content": "It is green."},
],
}
example_explicit_prompt_conversational = {
"prompt": [
{"role": "user", "content": "What color is the sky?"},
],
"chosen": [
{"role": "assistant", "content": "It is blue."},
],
"rejected": [
{"role": "assistant", "content": "It is green."},
],
}
example_implicit_prompt_standard = {
"chosen": "The sky is blue.",
"rejected": "The sky is green.",
}
example_explicit_prompt_standard = {
"prompt": "The sky is",
"chosen": " blue.",
"rejected": " green.",
}
def test_extract_prompt_conversational(self):
# Test that the prompt is correctly extracted from the dataset
example_extracted_prompt = extract_prompt(self.example_implicit_prompt_conversational)
self.assertEqual(
example_extracted_prompt,
self.example_explicit_prompt_conversational,
"The prompt is not correctly extracted from the dataset.",
)
def test_maybe_extract_prompt_conversational(self):
# Test that the prompt is correctly extracted from the dataset with maybe_extract_prompt
example_extracted_prompt = maybe_extract_prompt(self.example_implicit_prompt_conversational)
self.assertEqual(
example_extracted_prompt,
self.example_explicit_prompt_conversational,
"The prompt is not correctly extracted from the dataset.",
)
def test_maybe_extract_prompt_conversational_already_explicit(self):
# Test that the prompt remains unchanged with maybe_extract_prompt
example_extracted_prompt = maybe_extract_prompt(self.example_explicit_prompt_conversational)
self.assertEqual(
example_extracted_prompt,
self.example_explicit_prompt_conversational,
"The prompt should remain unchanged.",
)
def test_extract_prompt_standard(self):
# Test that the prompt is correctly extracted from the dataset
example_extracted_prompt = extract_prompt(self.example_implicit_prompt_standard)
self.assertEqual(
example_extracted_prompt,
self.example_explicit_prompt_standard,
"The prompt is not correctly extracted from the dataset.",
)
def test_maybe_extract_prompt_standard(self):
# Test that the prompt is correctly extracted from the dataset with maybe_extract_prompt
example_extracted_prompt = maybe_extract_prompt(self.example_implicit_prompt_standard)
self.assertEqual(
example_extracted_prompt,
self.example_explicit_prompt_standard,
"The prompt is not correctly extracted from the dataset.",
)
def test_maybe_extract_prompt_standard_already_explicit(self):
# Test that the prompt remains unchanged with maybe_extract_prompt
example_extracted_prompt = maybe_extract_prompt(self.example_explicit_prompt_standard)
self.assertEqual(
example_extracted_prompt,
self.example_explicit_prompt_standard,
"The prompt should remain unchanged.",
)
# Run the tests
if __name__ == "__main__":
unittest.main()
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