[GraphBolt] Add `force_reload` to OnDiskDataset. (#6930)

Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-133.us-west-2.compute.internal>

python/dgl/graphbolt/impl/ondisk_dataset.py

 """GraphBolt OnDiskDataset."""
 
 import os
+import shutil
 from copy import deepcopy
 from typing import Dict, List, Union
 
@@ -34,7 +35,9 @@ __all__ = ["OnDiskDataset", "preprocess_ondisk_dataset", "BuiltinDataset"]
 
 
 def preprocess_ondisk_dataset(
-    dataset_dir: str, include_original_edge_id: bool = False
+    dataset_dir: str,
+    include_original_edge_id: bool = False,
+    force_preprocess: bool = False,
 ) -> str:
     """Preprocess the on-disk dataset. Parse the input config file,
     load the data, and save the data in the format that GraphBolt supports.
@@ -45,6 +48,8 @@ def preprocess_ondisk_dataset(
         The path to the dataset directory.
     include_original_edge_id : bool, optional
         Whether to include the original edge id in the FusedCSCSamplingGraph.
+    force_preprocess: bool, optional
+        Whether to force reload the ondisk dataset.
 
     Returns
     -------
@@ -62,13 +67,22 @@ def preprocess_ondisk_dataset(
         )
 
     # 0. Check if the dataset is already preprocessed.
-    preprocess_metadata_path = os.path.join("preprocessed", "metadata.yaml")
+    processed_dir_prefix = "preprocessed"
+    preprocess_metadata_path = os.path.join(
+        processed_dir_prefix, "metadata.yaml"
+    )
     if os.path.exists(os.path.join(dataset_dir, preprocess_metadata_path)):
-        print("The dataset is already preprocessed.")
-        return os.path.join(dataset_dir, preprocess_metadata_path)
+        if force_preprocess:
+            shutil.rmtree(os.path.join(dataset_dir, processed_dir_prefix))
+            print(
+                "The on-disk dataset is re-preprocessing, so the existing "
+                + "preprocessed dataset has been removed."
+            )
+        else:
+            print("The dataset is already preprocessed.")
+            return os.path.join(dataset_dir, preprocess_metadata_path)
 
     print("Start to preprocess the on-disk dataset.")
-    processed_dir_prefix = "preprocessed"
 
     # Check if the metadata.yaml exists.
     metadata_file_path = os.path.join(dataset_dir, "metadata.yaml")
@@ -376,15 +390,22 @@ class OnDiskDataset(Dataset):
         The YAML file path.
     include_original_edge_id: bool, optional
         Whether to include the original edge id in the FusedCSCSamplingGraph.
+    force_preprocess: bool, optional
+        Whether to force reload the ondisk dataset.
     """
 
     def __init__(
-        self, path: str, include_original_edge_id: bool = False
+        self,
+        path: str,
+        include_original_edge_id: bool = False,
+        force_preprocess: bool = False,
     ) -> None:
         # Always call the preprocess function first. If already preprocessed,
         # the function will return the original path directly.
         self._dataset_dir = path
-        yaml_path = preprocess_ondisk_dataset(path, include_original_edge_id)
+        yaml_path = preprocess_ondisk_dataset(
+            path, include_original_edge_id, force_preprocess
+        )
         with open(yaml_path) as f:
             self._yaml_data = yaml.load(f, Loader=yaml.loader.SafeLoader)
         self._loaded = False

tests/python/pytorch/graphbolt/impl/test_ondisk_dataset.py

@@ -1531,6 +1531,79 @@ def test_OnDiskDataset_preprocess_yaml_content_windows():
             )
 
 
+def test_OnDiskDataset_preprocess_force_preprocess(capsys):
+    """Test force preprocess of OnDiskDataset."""
+    with tempfile.TemporaryDirectory() as test_dir:
+        # All metadata fields are specified.
+        dataset_name = "graphbolt_test"
+        num_nodes = 4000
+        num_edges = 20000
+        num_classes = 10
+
+        # Generate random graph.
+        yaml_content = gbt.random_homo_graphbolt_graph(
+            test_dir,
+            dataset_name,
+            num_nodes,
+            num_edges,
+            num_classes,
+        )
+        yaml_file = os.path.join(test_dir, "metadata.yaml")
+        with open(yaml_file, "w") as f:
+            f.write(yaml_content)
+
+        # First preprocess on-disk dataset.
+        preprocessed_metadata_path = (
+            gb.ondisk_dataset.preprocess_ondisk_dataset(
+                test_dir, include_original_edge_id=False, force_preprocess=False
+            )
+        )
+        captured = capsys.readouterr().out.split("\n")
+        assert captured == [
+            "Start to preprocess the on-disk dataset.",
+            "Finish preprocessing the on-disk dataset.",
+            "",
+        ]
+        with open(preprocessed_metadata_path, "r") as f:
+            target_yaml_data = yaml.safe_load(f)
+        assert target_yaml_data["tasks"][0]["name"] == "link_prediction"
+
+        # Change yaml_data, but do not force preprocess on-disk dataset.
+        with open(yaml_file, "r") as f:
+            yaml_data = yaml.safe_load(f)
+        yaml_data["tasks"][0]["name"] = "fake_name"
+        with open(yaml_file, "w") as f:
+            yaml.dump(yaml_data, f)
+        preprocessed_metadata_path = (
+            gb.ondisk_dataset.preprocess_ondisk_dataset(
+                test_dir, include_original_edge_id=False, force_preprocess=False
+            )
+        )
+        captured = capsys.readouterr().out.split("\n")
+        assert captured == ["The dataset is already preprocessed.", ""]
+        with open(preprocessed_metadata_path, "r") as f:
+            target_yaml_data = yaml.safe_load(f)
+        assert target_yaml_data["tasks"][0]["name"] == "link_prediction"
+
+        # Force preprocess on-disk dataset.
+        preprocessed_metadata_path = (
+            gb.ondisk_dataset.preprocess_ondisk_dataset(
+                test_dir, include_original_edge_id=False, force_preprocess=True
+            )
+        )
+        captured = capsys.readouterr().out.split("\n")
+        assert captured == [
+            "The on-disk dataset is re-preprocessing, so the existing "
+            + "preprocessed dataset has been removed.",
+            "Start to preprocess the on-disk dataset.",
+            "Finish preprocessing the on-disk dataset.",
+            "",
+        ]
+        with open(preprocessed_metadata_path, "r") as f:
+            target_yaml_data = yaml.safe_load(f)
+        assert target_yaml_data["tasks"][0]["name"] == "fake_name"
+
+
 @pytest.mark.parametrize("edge_fmt", ["csv", "numpy"])
 def test_OnDiskDataset_load_name(edge_fmt):
     """Test preprocess of OnDiskDataset."""
@@ -2182,6 +2255,73 @@ def test_OnDiskDataset_heterogeneous(include_original_edge_id, edge_fmt):
         dataset = None
 
 
+def test_OnDiskDataset_force_preprocess(capsys):
+    """Test force preprocess of OnDiskDataset."""
+    with tempfile.TemporaryDirectory() as test_dir:
+        # All metadata fields are specified.
+        dataset_name = "graphbolt_test"
+        num_nodes = 4000
+        num_edges = 20000
+        num_classes = 10
+
+        # Generate random graph.
+        yaml_content = gbt.random_homo_graphbolt_graph(
+            test_dir,
+            dataset_name,
+            num_nodes,
+            num_edges,
+            num_classes,
+        )
+        yaml_file = os.path.join(test_dir, "metadata.yaml")
+        with open(yaml_file, "w") as f:
+            f.write(yaml_content)
+
+        # First preprocess on-disk dataset.
+        dataset = gb.OnDiskDataset(
+            test_dir, include_original_edge_id=False, force_preprocess=False
+        ).load()
+        captured = capsys.readouterr().out.split("\n")
+        assert captured == [
+            "Start to preprocess the on-disk dataset.",
+            "Finish preprocessing the on-disk dataset.",
+            "",
+        ]
+        tasks = dataset.tasks
+        assert tasks[0].metadata["name"] == "link_prediction"
+
+        # Change yaml_data, but do not force preprocess on-disk dataset.
+        with open(yaml_file, "r") as f:
+            yaml_data = yaml.safe_load(f)
+        yaml_data["tasks"][0]["name"] = "fake_name"
+        with open(yaml_file, "w") as f:
+            yaml.dump(yaml_data, f)
+        dataset = gb.OnDiskDataset(
+            test_dir, include_original_edge_id=False, force_preprocess=False
+        ).load()
+        captured = capsys.readouterr().out.split("\n")
+        assert captured == ["The dataset is already preprocessed.", ""]
+        tasks = dataset.tasks
+        assert tasks[0].metadata["name"] == "link_prediction"
+
+        # Force preprocess on-disk dataset.
+        dataset = gb.OnDiskDataset(
+            test_dir, include_original_edge_id=False, force_preprocess=True
+        ).load()
+        captured = capsys.readouterr().out.split("\n")
+        assert captured == [
+            "The on-disk dataset is re-preprocessing, so the existing "
+            + "preprocessed dataset has been removed.",
+            "Start to preprocess the on-disk dataset.",
+            "Finish preprocessing the on-disk dataset.",
+            "",
+        ]
+        tasks = dataset.tasks
+        assert tasks[0].metadata["name"] == "fake_name"
+
+        tasks = None
+        dataset = None
+
+
 def test_OnDiskTask_repr_homogeneous():
     item_set = gb.ItemSet(
         (torch.arange(0, 5), torch.arange(5, 10)),