LLaMA-Factory/tests/train/test_sft_trainer.py

# 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
from dataclasses import dataclass, field
from typing import Any, Dict, List

import pytest
from transformers import DataCollatorWithPadding

from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
from llamafactory.hparams import get_train_args
from llamafactory.model import load_model, load_tokenizer
from llamafactory.train.sft.trainer import CustomSeq2SeqTrainer


DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")

TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")

TRAIN_ARGS = {
    "model_name_or_path": TINY_LLAMA,
    "stage": "sft",
    "do_train": True,
    "finetuning_type": "lora",
    "dataset": "llamafactory/tiny-supervised-dataset",
    "dataset_dir": "ONLINE",
    "template": "llama3",
    "cutoff_len": 1024,
    "overwrite_cache": False,
    "overwrite_output_dir": True,
    "per_device_train_batch_size": 1,
    "max_steps": 1,
}


@dataclass
class DataCollatorWithVerbose(DataCollatorWithPadding):
    verbose_list: List[Dict[str, Any]] = field(default_factory=list)

    def __call__(self, features: List[Dict[str, Any]]) -> Dict[str, Any]:
        self.verbose_list.extend(features)
        batch = super().__call__(features)
        return {k: v[:, :1] for k, v in batch.items()}  # truncate input length


@pytest.mark.parametrize("disable_shuffling", [False, True])
def test_shuffle(disable_shuffling: bool):
    model_args, data_args, training_args, finetuning_args, _ = get_train_args(
        {
            "output_dir": os.path.join("output", f"shuffle{str(disable_shuffling).lower()}"),
            "disable_shuffling": disable_shuffling,
            **TRAIN_ARGS,
        }
    )
    tokenizer_module = load_tokenizer(model_args)
    tokenizer = tokenizer_module["tokenizer"]
    template = get_template_and_fix_tokenizer(tokenizer, data_args)
    dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
    model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
    data_collator = DataCollatorWithVerbose(tokenizer=tokenizer)
    trainer = CustomSeq2SeqTrainer(
        model=model,
        args=training_args,
        finetuning_args=finetuning_args,
        data_collator=data_collator,
        **dataset_module,
        **tokenizer_module,
    )
    trainer.train()
    if disable_shuffling:
        assert data_collator.verbose_list[0]["input_ids"] == dataset_module["train_dataset"][0]["input_ids"]
    else:
        assert data_collator.verbose_list[0]["input_ids"] != dataset_module["train_dataset"][0]["input_ids"]