[data] fix loader (#7207)

* fix dataloader

* add test case

* fix type

* fix ci

* fix ci

* fix ci

* disable overwrite cache in ci

Former-commit-id: e84af0e140b1aafd1a6d6fe185a8e41c8fc5f831

tests/data/processor/test_feedback.py

@@ -20,7 +20,7 @@ from datasets import load_dataset
 from transformers import AutoTokenizer
 
 from llamafactory.extras.constants import IGNORE_INDEX
-from llamafactory.train.test_utils import load_train_dataset
+from llamafactory.train.test_utils import load_dataset_module
 
 
 DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
@@ -36,7 +36,6 @@ TRAIN_ARGS = {
     "dataset_dir": "REMOTE:" + DEMO_DATA,
     "template": "llama3",
     "cutoff_len": 8192,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,
@@ -45,7 +44,7 @@ TRAIN_ARGS = {
 
 @pytest.mark.parametrize("num_samples", [16])
 def test_feedback_data(num_samples: int):
-    train_dataset = load_train_dataset(**TRAIN_ARGS)
+    train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(DEMO_DATA, name="kto_en_demo", split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)

tests/data/processor/test_pairwise.py

@@ -21,7 +21,7 @@ from datasets import load_dataset
 from transformers import AutoTokenizer
 
 from llamafactory.extras.constants import IGNORE_INDEX
-from llamafactory.train.test_utils import load_train_dataset
+from llamafactory.train.test_utils import load_dataset_module
 
 
 DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
@@ -37,7 +37,6 @@ TRAIN_ARGS = {
     "dataset_dir": "REMOTE:" + DEMO_DATA,
     "template": "llama3",
     "cutoff_len": 8192,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,
@@ -55,7 +54,7 @@ def _convert_sharegpt_to_openai(messages: List[Dict[str, str]]) -> List[Dict[str
 
 @pytest.mark.parametrize("num_samples", [16])
 def test_pairwise_data(num_samples: int):
-    train_dataset = load_train_dataset(**TRAIN_ARGS)
+    train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(DEMO_DATA, name="dpo_en_demo", split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)

tests/data/processor/test_supervised.py

@@ -20,7 +20,7 @@ from datasets import load_dataset
 from transformers import AutoTokenizer
 
 from llamafactory.extras.constants import IGNORE_INDEX
-from llamafactory.train.test_utils import load_train_dataset
+from llamafactory.train.test_utils import load_dataset_module
 
 
 DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
@@ -36,7 +36,6 @@ TRAIN_ARGS = {
     "finetuning_type": "full",
     "template": "llama3",
     "cutoff_len": 8192,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,
@@ -45,7 +44,7 @@ TRAIN_ARGS = {
 
 @pytest.mark.parametrize("num_samples", [16])
 def test_supervised_single_turn(num_samples: int):
-    train_dataset = load_train_dataset(dataset_dir="ONLINE", dataset=TINY_DATA, **TRAIN_ARGS)
+    train_dataset = load_dataset_module(dataset_dir="ONLINE", dataset=TINY_DATA, **TRAIN_ARGS)["train_dataset"]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(TINY_DATA, split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)
@@ -64,7 +63,9 @@ def test_supervised_single_turn(num_samples: int):
 
 @pytest.mark.parametrize("num_samples", [8])
 def test_supervised_multi_turn(num_samples: int):
-    train_dataset = load_train_dataset(dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", **TRAIN_ARGS)
+    train_dataset = load_dataset_module(dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", **TRAIN_ARGS)[
+        "train_dataset"
+    ]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)
@@ -75,9 +76,9 @@ def test_supervised_multi_turn(num_samples: int):
 
 @pytest.mark.parametrize("num_samples", [4])
 def test_supervised_train_on_prompt(num_samples: int):
-    train_dataset = load_train_dataset(
+    train_dataset = load_dataset_module(
         dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", train_on_prompt=True, **TRAIN_ARGS
-    )
+    )["train_dataset"]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)
@@ -89,9 +90,9 @@ def test_supervised_train_on_prompt(num_samples: int):
 
 @pytest.mark.parametrize("num_samples", [4])
 def test_supervised_mask_history(num_samples: int):
-    train_dataset = load_train_dataset(
+    train_dataset = load_dataset_module(
         dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", mask_history=True, **TRAIN_ARGS
-    )
+    )["train_dataset"]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)

tests/data/processor/test_unsupervised.py

@@ -19,7 +19,7 @@ import pytest
 from datasets import load_dataset
 from transformers import AutoTokenizer
 
-from llamafactory.train.test_utils import load_train_dataset
+from llamafactory.train.test_utils import load_dataset_module
 
 
 DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
@@ -39,7 +39,6 @@ TRAIN_ARGS = {
     "dataset_dir": "REMOTE:" + DEMO_DATA,
     "template": "llama3",
     "cutoff_len": 8192,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,
@@ -48,7 +47,7 @@ TRAIN_ARGS = {
 
 @pytest.mark.parametrize("num_samples", [16])
 def test_unsupervised_data(num_samples: int):
-    train_dataset = load_train_dataset(**TRAIN_ARGS)
+    train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
     ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA)
     original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
     indexes = random.choices(range(len(original_data)), k=num_samples)

tests/data/test_converter.py

@@ -1,3 +1,17 @@
+# 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.
+
 from llamafactory.data import Role
 from llamafactory.data.converter import get_dataset_converter
 from llamafactory.data.parser import DatasetAttr

tests/data/test_loader.py

@@ -0,0 +1,56 @@
+# 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 llamafactory.train.test_utils import load_dataset_module
+
+
+DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
+
+TINY_LLAMA = os.getenv("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
+
+TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")
+
+TRAIN_ARGS = {
+    "model_name_or_path": TINY_LLAMA,
+    "stage": "sft",
+    "do_train": True,
+    "finetuning_type": "full",
+    "template": "llama3",
+    "dataset": TINY_DATA,
+    "dataset_dir": "ONLINE",
+    "cutoff_len": 8192,
+    "output_dir": "dummy_dir",
+    "overwrite_output_dir": True,
+    "fp16": True,
+}
+
+
+def test_load_train_only():
+    dataset_module = load_dataset_module(**TRAIN_ARGS)
+    assert dataset_module.get("train_dataset") is not None
+    assert dataset_module.get("eval_dataset") is None
+
+
+def test_load_val_size():
+    dataset_module = load_dataset_module(val_size=0.1, **TRAIN_ARGS)
+    assert dataset_module.get("train_dataset") is not None
+    assert dataset_module.get("eval_dataset") is not None
+
+
+def test_load_eval_data():
+    dataset_module = load_dataset_module(eval_dataset=TINY_DATA, **TRAIN_ARGS)
+    assert dataset_module.get("train_dataset") is not None
+    assert dataset_module.get("eval_dataset") is not None

tests/e2e/test_train.py

@@ -32,7 +32,6 @@ TRAIN_ARGS = {
     "dataset_dir": "REMOTE:" + DEMO_DATA,
     "template": "llama3",
     "cutoff_len": 1,
-    "overwrite_cache": False,
     "overwrite_output_dir": True,
     "per_device_train_batch_size": 1,
     "max_steps": 1,

tests/model/model_utils/test_checkpointing.py

@@ -33,7 +33,6 @@ TRAIN_ARGS = {
     "dataset_dir": "ONLINE",
     "template": "llama3",
     "cutoff_len": 1024,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,

tests/model/test_freeze.py

@@ -30,7 +30,6 @@ TRAIN_ARGS = {
     "dataset_dir": "ONLINE",
     "template": "llama3",
     "cutoff_len": 1024,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,

tests/model/test_full.py

@@ -30,7 +30,6 @@ TRAIN_ARGS = {
     "dataset_dir": "ONLINE",
     "template": "llama3",
     "cutoff_len": 1024,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,

tests/model/test_lora.py

@@ -42,7 +42,6 @@ TRAIN_ARGS = {
     "dataset_dir": "ONLINE",
     "template": "llama3",
     "cutoff_len": 1024,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,

tests/model/test_pissa.py

@@ -34,7 +34,6 @@ TRAIN_ARGS = {
     "dataset_dir": "ONLINE",
     "template": "llama3",
     "cutoff_len": 1024,
-    "overwrite_cache": True,
     "output_dir": "dummy_dir",
     "overwrite_output_dir": True,
     "fp16": True,

tests/train/test_sft_trainer.py

@@ -38,7 +38,6 @@ TRAIN_ARGS = {
     "dataset_dir": "ONLINE",
     "template": "llama3",
     "cutoff_len": 1024,
-    "overwrite_cache": False,
     "overwrite_output_dir": True,
     "per_device_train_batch_size": 1,
     "max_steps": 1,