60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import random
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import pytest
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from datasets import load_dataset
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from transformers import AutoTokenizer
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from llamafactory.extras.constants import IGNORE_INDEX
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from llamafactory.train.test_utils import load_train_dataset
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DEMO_DATA = os.environ.get("DEMO_DATA", "llamafactory/demo_data")
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TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA,
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"stage": "kto",
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"do_train": True,
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"finetuning_type": "full",
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"dataset": "kto_en_demo",
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"dataset_dir": "REMOTE:" + DEMO_DATA,
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"template": "llama3",
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"cutoff_len": 8192,
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"overwrite_cache": True,
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"output_dir": "dummy_dir",
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"overwrite_output_dir": True,
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"fp16": True,
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}
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@pytest.mark.parametrize("num_samples", [16])
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def test_feedback_data(num_samples: int):
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train_dataset = load_train_dataset(**TRAIN_ARGS)
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ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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original_data = load_dataset(DEMO_DATA, name="kto_en_demo", split="train")
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indexes = random.choices(range(len(original_data)), k=num_samples)
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for index in indexes:
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messages = original_data["messages"][index]
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ref_input_ids = ref_tokenizer.apply_chat_template(messages)
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prompt_len = len(ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True))
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ref_labels = [IGNORE_INDEX] * prompt_len + ref_input_ids[prompt_len:]
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assert train_dataset["input_ids"][index] == ref_input_ids
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assert train_dataset["labels"][index] == ref_labels
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assert train_dataset["kto_tags"][index] == original_data["label"][index]
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