File size: 3,898 Bytes
83ddd7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | # 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.extras.packages import is_transformers_version_greater_than
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
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [16])
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)
ref_chosen_prompt_ids = ref_tokenizer.apply_chat_template(chosen_messages[:-1], add_generation_prompt=True)
ref_rejected_input_ids = ref_tokenizer.apply_chat_template(rejected_messages)
ref_rejected_prompt_ids = ref_tokenizer.apply_chat_template(rejected_messages[:-1], add_generation_prompt=True)
if is_transformers_version_greater_than("5.0.0"):
ref_chosen_input_ids = ref_chosen_input_ids["input_ids"]
ref_rejected_input_ids = ref_rejected_input_ids["input_ids"]
ref_chosen_prompt_ids = ref_chosen_prompt_ids["input_ids"]
ref_rejected_prompt_ids = ref_rejected_prompt_ids["input_ids"]
chosen_prompt_len = len(ref_chosen_prompt_ids)
rejected_prompt_len = len(ref_rejected_prompt_ids)
ref_chosen_labels = [IGNORE_INDEX] * chosen_prompt_len + ref_chosen_input_ids[chosen_prompt_len:]
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
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