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| # Copyright 2025 the LlamaFactory team. | |
| # | |
| # 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 os | |
| import random | |
| import pytest | |
| from datasets import load_dataset | |
| from transformers import AutoTokenizer | |
| from llamafactory.extras.constants import IGNORE_INDEX | |
| from llamafactory.train.test_utils import load_dataset_module | |
| DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data") | |
| TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3") | |
| TRAIN_ARGS = { | |
| "model_name_or_path": TINY_LLAMA3, | |
| "stage": "rm", | |
| "do_train": True, | |
| "finetuning_type": "full", | |
| "dataset": "dpo_en_demo", | |
| "dataset_dir": "REMOTE:" + DEMO_DATA, | |
| "template": "llama3", | |
| "cutoff_len": 8192, | |
| "output_dir": "dummy_dir", | |
| "overwrite_output_dir": True, | |
| "fp16": True, | |
| } | |
| def _convert_sharegpt_to_openai(messages: list[dict[str, str]]) -> list[dict[str, str]]: | |
| role_mapping = {"human": "user", "gpt": "assistant", "system": "system"} | |
| new_messages = [] | |
| for message in messages: | |
| new_messages.append({"role": role_mapping[message["from"]], "content": message["value"]}) | |
| return new_messages | |
| def test_pairwise_data(num_samples: int): | |
| train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"] | |
| ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3) | |
| original_data = load_dataset(DEMO_DATA, name="dpo_en_demo", split="train") | |
| indexes = random.choices(range(len(original_data)), k=num_samples) | |
| for index in indexes: | |
| chosen_messages = original_data["conversations"][index] + [original_data["chosen"][index]] | |
| rejected_messages = original_data["conversations"][index] + [original_data["rejected"][index]] | |
| chosen_messages = _convert_sharegpt_to_openai(chosen_messages) | |
| rejected_messages = _convert_sharegpt_to_openai(rejected_messages) | |
| ref_chosen_input_ids = ref_tokenizer.apply_chat_template(chosen_messages) | |
| chosen_prompt_len = len(ref_tokenizer.apply_chat_template(chosen_messages[:-1], add_generation_prompt=True)) | |
| ref_chosen_labels = [IGNORE_INDEX] * chosen_prompt_len + ref_chosen_input_ids[chosen_prompt_len:] | |
| ref_rejected_input_ids = ref_tokenizer.apply_chat_template(rejected_messages) | |
| rejected_prompt_len = len( | |
| ref_tokenizer.apply_chat_template(rejected_messages[:-1], add_generation_prompt=True) | |
| ) | |
| ref_rejected_labels = [IGNORE_INDEX] * rejected_prompt_len + ref_rejected_input_ids[rejected_prompt_len:] | |
| assert train_dataset["chosen_input_ids"][index] == ref_chosen_input_ids | |
| assert train_dataset["chosen_labels"][index] == ref_chosen_labels | |
| assert train_dataset["rejected_input_ids"][index] == ref_rejected_input_ids | |
| assert train_dataset["rejected_labels"][index] == ref_rejected_labels | |