65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
# Copyright 2024 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 llamafactory.data import get_dataset
|
|
from llamafactory.hparams import get_train_args
|
|
from llamafactory.model import load_tokenizer
|
|
|
|
|
|
TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
|
|
|
|
TRAIN_ARGS = {
|
|
"model_name_or_path": TINY_LLAMA,
|
|
"stage": "sft",
|
|
"do_train": True,
|
|
"finetuning_type": "full",
|
|
"dataset": "llamafactory/tiny-supervised-dataset",
|
|
"dataset_dir": "ONLINE",
|
|
"template": "llama3",
|
|
"cutoff_len": 8192,
|
|
"overwrite_cache": True,
|
|
"output_dir": "dummy_dir",
|
|
"overwrite_output_dir": True,
|
|
"fp16": True,
|
|
}
|
|
|
|
|
|
@pytest.mark.parametrize("num_samples", [16])
|
|
def test_supervised(num_samples: int):
|
|
model_args, data_args, training_args, _, _ = get_train_args(TRAIN_ARGS)
|
|
tokenizer_module = load_tokenizer(model_args)
|
|
tokenizer = tokenizer_module["tokenizer"]
|
|
tokenized_data = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
|
|
|
|
original_data = load_dataset(TRAIN_ARGS["dataset"], split="train")
|
|
indexes = random.choices(range(len(original_data)), k=num_samples)
|
|
for index in indexes:
|
|
decoded_result = tokenizer.decode(tokenized_data["input_ids"][index])
|
|
prompt = original_data[index]["instruction"]
|
|
if original_data[index]["input"]:
|
|
prompt += "\n" + original_data[index]["input"]
|
|
|
|
messages = [
|
|
{"role": "user", "content": prompt},
|
|
{"role": "assistant", "content": original_data[index]["output"]},
|
|
]
|
|
templated_result = tokenizer.apply_chat_template(messages, tokenize=False)
|
|
assert decoded_result == templated_result
|