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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 unittest
import pytest
from datasets import Dataset
from alignment import DataArguments, ModelArguments, apply_chat_template, get_datasets, get_tokenizer
class GetDatasetsTest(unittest.TestCase):
"""Each of these test datasets has 100 examples"""
def test_loading_data_args(self):
dataset_mixer = {
"HuggingFaceH4/testing_alpaca_small": 0.5,
"HuggingFaceH4/testing_self_instruct_small": 0.3,
"HuggingFaceH4/testing_codealpaca_small": 0.2,
}
data_args = DataArguments(dataset_mixer=dataset_mixer)
datasets = get_datasets(data_args)
self.assertEqual(len(datasets["train"]), 100)
self.assertEqual(len(datasets["test"]), 300)
def test_loading_data_dict(self):
dataset_mixer = {
"HuggingFaceH4/testing_alpaca_small": 0.5,
"HuggingFaceH4/testing_self_instruct_small": 0.3,
"HuggingFaceH4/testing_codealpaca_small": 0.2,
}
datasets = get_datasets(dataset_mixer)
self.assertEqual(len(datasets["train"]), 100)
self.assertEqual(len(datasets["test"]), 300)
def test_loading_with_unit_fractions(self):
dataset_mixer = {
"HuggingFaceH4/testing_alpaca_small": 1.0,
"HuggingFaceH4/testing_self_instruct_small": 1.0,
"HuggingFaceH4/testing_codealpaca_small": 1.0,
}
datasets = get_datasets(dataset_mixer)
self.assertEqual(len(datasets["train"]), 300)
self.assertEqual(len(datasets["test"]), 300)
def test_loading_with_fractions_greater_than_unity(self):
dataset_mixer = {
"HuggingFaceH4/testing_alpaca_small": 0.7,
"HuggingFaceH4/testing_self_instruct_small": 0.4,
}
datasets = get_datasets(dataset_mixer)
self.assertEqual(len(datasets["train"]), 70 + 40)
self.assertEqual(len(datasets["test"]), 200)
def test_loading_fails_with_negative_fractions(self):
dataset_mixer = {
"HuggingFaceH4/testing_alpaca_small": 0.7,
"HuggingFaceH4/testing_self_instruct_small": -0.3,
}
with pytest.raises(ValueError, match=r"Dataset fractions cannot be negative."):
get_datasets(dataset_mixer)
def test_loading_single_split_with_unit_fractions(self):
dataset_mixer = {
"HuggingFaceH4/testing_alpaca_small": 1.0,
}
datasets = get_datasets(dataset_mixer, splits=["test"])
self.assertEqual(len(datasets["test"]), 100)
self.assertRaises(KeyError, lambda: datasets["train"])
class ApplyChatTemplateTest(unittest.TestCase):
def setUp(self):
model_args = ModelArguments(model_name_or_path="HuggingFaceH4/zephyr-7b-alpha")
data_args = DataArguments()
self.tokenizer = get_tokenizer(model_args, data_args)
self.dataset = Dataset.from_dict(
{
"prompt": ["Hello!"],
"messages": [[{"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Bonjour!"}]],
"chosen": [[{"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Bonjour!"}]],
"rejected": [[{"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Hola!"}]],
}
)
def test_sft(self):
dataset = self.dataset.map(
apply_chat_template,
fn_kwargs={"tokenizer": self.tokenizer, "task": "sft"},
remove_columns=self.dataset.column_names,
)
self.assertDictEqual(
dataset[0],
{"text": "<|system|>\n</s>\n<|user|>\nHello!</s>\n<|assistant|>\nBonjour!</s>\n"},
)
def test_generation(self):
# Remove last turn from messages
dataset = self.dataset.map(lambda x: {"messages": x["messages"][:-1]})
dataset = dataset.map(
apply_chat_template,
fn_kwargs={"tokenizer": self.tokenizer, "task": "generation"},
remove_columns=self.dataset.column_names,
)
self.assertDictEqual(
dataset[0],
{"text": "<|system|>\n</s>\n<|user|>\nHello!</s>\n<|assistant|>\n"},
)
def test_rm(self):
dataset = self.dataset.map(
apply_chat_template,
fn_kwargs={"tokenizer": self.tokenizer, "task": "rm"},
remove_columns=self.dataset.column_names,
)
self.assertDictEqual(
dataset[0],
{
"text_chosen": "<|system|>\n</s>\n<|user|>\nHello!</s>\n<|assistant|>\nBonjour!</s>\n",
"text_rejected": "<|system|>\n</s>\n<|user|>\nHello!</s>\n<|assistant|>\nHola!</s>\n",
},
)
def test_dpo(self):
dataset = self.dataset.map(
apply_chat_template,
fn_kwargs={"tokenizer": self.tokenizer, "task": "dpo"},
remove_columns=self.dataset.column_names,
)
self.assertDictEqual(
dataset[0],
{
"text_prompt": "<|system|>\n</s>\n<|user|>\nHello!</s>\n<|assistant|>\n",
"text_chosen": "Bonjour!</s>\n",
"text_rejected": "Hola!</s>\n",
},
)
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