68 lines
2.4 KiB
Python
68 lines
2.4 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.data import get_dataset
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from llamafactory.hparams import get_train_args
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from llamafactory.model import load_tokenizer
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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": "sft",
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"do_train": True,
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"finetuning_type": "full",
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"dataset": "llamafactory/tiny-supervised-dataset",
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"dataset_dir": "ONLINE",
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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_supervised(num_samples: int):
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model_args, data_args, training_args, _, _ = get_train_args(TRAIN_ARGS)
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tokenizer_module = load_tokenizer(model_args)
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tokenizer = tokenizer_module["tokenizer"]
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tokenized_data = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
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ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
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original_data = load_dataset(TRAIN_ARGS["dataset"], 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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prompt = original_data[index]["instruction"]
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if original_data[index]["input"]:
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prompt += "\n" + original_data[index]["input"]
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messages = [
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{"role": "user", "content": prompt},
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{"role": "assistant", "content": original_data[index]["output"]},
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]
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templated_result = ref_tokenizer.apply_chat_template(messages, tokenize=False)
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decoded_result = tokenizer.decode(tokenized_data["input_ids"][index])
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assert templated_result == decoded_result
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