mirror of
https://github.com/hiyouga/LlamaFactory.git
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bf04ca6af8
Co-authored-by: frozenleaves <frozen@Mac.local> Co-authored-by: hiyouga <hiyouga@buaa.edu.cn>
69 lines
2.5 KiB
Python
69 lines
2.5 KiB
Python
# Copyright 2025 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.packages import is_transformers_version_greater_than
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from llamafactory.train.test_utils import load_dataset_module
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DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
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TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
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TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")
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TRAIN_ARGS = {
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"model_name_or_path": TINY_LLAMA3,
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"stage": "ppo",
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"do_train": True,
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"finetuning_type": "full",
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"reward_model": "",
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"reward_model_type": "full",
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"dataset": "system_chat",
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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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"output_dir": "dummy_dir",
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"overwrite_output_dir": True,
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"fp16": True,
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"report_to": "none", # transfromers compatibility
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}
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@pytest.mark.runs_on(["cpu", "mps"])
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@pytest.mark.parametrize("num_samples", [16])
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def test_unsupervised_data(num_samples: int):
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train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
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ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
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original_data = load_dataset(DEMO_DATA, name="system_chat", 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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ref_prompt_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
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if is_transformers_version_greater_than("5.0.0"):
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ref_input_ids = ref_input_ids["input_ids"]
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ref_prompt_ids = ref_prompt_ids["input_ids"]
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ref_labels = ref_input_ids[len(ref_prompt_ids) :]
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assert train_dataset["input_ids"][index] == ref_prompt_ids
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assert train_dataset["labels"][index] == ref_labels
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