support disable shuffling

Former-commit-id: 9d8c35fd6b838ede0bd6827c6c6121f2cba2b11b

tests/train/test_sft_trainer.py

@@ -0,0 +1,81 @@
+# 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
+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": f"dummy_dir/{disable_shuffling}", "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"]