Merge branch 'master' into relgraphconv

tests/tools/test_dist_part.py

@@ -3,6 +3,7 @@
 import tempfile
 
 import dgl
+import dgl.backend as F
 
 import numpy as np
 import pyarrow.parquet as pq
@@ -19,7 +20,8 @@
 
 from distpartitioning import array_readwriter
 from distpartitioning.utils import generate_read_list
-from pytest_utils import create_chunked_dataset
+from pytest_utils import chunk_graph, create_chunked_dataset
+from scipy import sparse as spsp
 
 from tools.verification_utils import (
     verify_graph_feats,
@@ -202,6 +204,103 @@ def test_chunk_graph_arbitrary_chunks(
     )
 
 
+def create_mini_chunked_dataset(
+    root_dir,
+    num_chunks,
+    data_fmt,
+    edges_fmt,
+    vector_rows,
+    few_entity="node",
+    **kwargs,
+):
+    num_nodes = {"n1": 1000, "n2": 1010, "n3": 1020}
+    etypes = [
+        ("n1", "r1", "n2"),
+        ("n2", "r1", "n1"),
+        ("n1", "r2", "n3"),
+        ("n2", "r3", "n3"),
+    ]
+    node_items = ["n1", "n2", "n3"]
+    edges_coo = {}
+    for etype in etypes:
+        src_ntype, _, dst_ntype = etype
+        arr = spsp.random(
+            num_nodes[src_ntype],
+            num_nodes[dst_ntype],
+            density=0.001,
+            format="coo",
+            random_state=100,
+        )
+        edges_coo[etype] = (arr.row, arr.col)
+    edge_items = []
+    if few_entity == "edge":
+        edges_coo[("n1", "a0", "n2")] = (
+            torch.tensor([0, 1]),
+            torch.tensor([1, 0]),
+        )
+        edges_coo[("n1", "a1", "n3")] = (
+            torch.tensor([0, 1]),
+            torch.tensor([1, 0]),
+        )
+        edge_items.append(("n1", "a0", "n2"))
+        edge_items.append(("n1", "a1", "n3"))
+    elif few_entity == "node":
+        edges_coo[("n1", "r_few", "n_few")] = (
+            torch.tensor([0, 1]),
+            torch.tensor([1, 0]),
+        )
+        edges_coo[("a0", "a01", "n_1")] = (
+            torch.tensor([0, 1]),
+            torch.tensor([1, 0]),
+        )
+        edge_items.append(("n1", "r_few", "n_few"))
+        edge_items.append(("a0", "a01", "n_1"))
+        node_items.append("n_few")
+        node_items.append("n_1")
+        num_nodes["n_few"] = 2
+        num_nodes["n_1"] = 2
+    g = dgl.heterograph(edges_coo)
+
+    node_data = {}
+    edge_data = {}
+    # save feature
+    input_dir = os.path.join(root_dir, "data_test")
+
+    for ntype in node_items:
+        os.makedirs(os.path.join(input_dir, ntype))
+        feat = np.random.randn(num_nodes[ntype], 3)
+        feat_path = os.path.join(input_dir, f"{ntype}/feat.npy")
+        with open(feat_path, "wb") as f:
+            np.save(f, feat)
+        g.nodes[ntype].data["feat"] = torch.from_numpy(feat)
+        node_data[ntype] = {"feat": feat_path}
+
+    for etype in set(edge_items):
+        os.makedirs(os.path.join(input_dir, etype[1]))
+        num_edge = len(edges_coo[etype][0])
+        feat = np.random.randn(num_edge, 4)
+        feat_path = os.path.join(input_dir, f"{etype[1]}/feat.npy")
+        with open(feat_path, "wb") as f:
+            np.save(f, feat)
+        g.edges[etype].data["feat"] = torch.from_numpy(feat)
+        edge_data[etype] = {"feat": feat_path}
+
+    output_dir = os.path.join(root_dir, "chunked-data")
+    chunk_graph(
+        g,
+        "mag240m",
+        node_data,
+        edge_data,
+        num_chunks=num_chunks,
+        output_path=output_dir,
+        data_fmt=data_fmt,
+        edges_fmt=edges_fmt,
+        vector_rows=vector_rows,
+        **kwargs,
+    )
+    return g
+
+
 def _test_pipeline(
     num_chunks,
     num_parts,
@@ -373,6 +472,98 @@ def test_pipeline_feature_format(data_fmt):
     _test_pipeline(4, 4, 4, data_fmt=data_fmt)
 
 
+@pytest.mark.parametrize(
+    "num_chunks, num_parts, world_size",
+    [[4, 4, 4], [8, 4, 2], [8, 4, 4], [9, 6, 3], [11, 11, 1], [11, 4, 1]],
+)
+@pytest.mark.parametrize("few_entity", ["node", "edge"])
+def test_partition_hetero_few_entity(
+    num_chunks,
+    num_parts,
+    world_size,
+    few_entity,
+    graph_formats=None,
+    data_fmt="numpy",
+    edges_fmt="csv",
+    vector_rows=False,
+    num_chunks_nodes=None,
+    num_chunks_edges=None,
+    num_chunks_node_data=None,
+    num_chunks_edge_data=None,
+):
+    with tempfile.TemporaryDirectory() as root_dir:
+        g = create_mini_chunked_dataset(
+            root_dir,
+            num_chunks,
+            few_entity=few_entity,
+            data_fmt=data_fmt,
+            edges_fmt=edges_fmt,
+            vector_rows=vector_rows,
+            num_chunks_nodes=num_chunks_nodes,
+            num_chunks_edges=num_chunks_edges,
+            num_chunks_node_data=num_chunks_node_data,
+            num_chunks_edge_data=num_chunks_edge_data,
+        )
+
+        # Step1: graph partition
+        in_dir = os.path.join(root_dir, "chunked-data")
+        output_dir = os.path.join(root_dir, "parted_data")
+        os.system(
+            "python3 tools/partition_algo/random_partition.py "
+            "--in_dir {} --out_dir {} --num_partitions {}".format(
+                in_dir, output_dir, num_parts
+            )
+        )
+
+        # Step2: data dispatch
+        partition_dir = os.path.join(root_dir, "parted_data")
+        out_dir = os.path.join(root_dir, "partitioned")
+        ip_config = os.path.join(root_dir, "ip_config.txt")
+        with open(ip_config, "w") as f:
+            for i in range(world_size):
+                f.write(f"127.0.0.{i + 1}\n")
+
+        cmd = "python3 tools/dispatch_data.py"
+        cmd += f" --in-dir {in_dir}"
+        cmd += f" --partitions-dir {partition_dir}"
+        cmd += f" --out-dir {out_dir}"
+        cmd += f" --ip-config {ip_config}"
+        cmd += " --ssh-port 22"
+        cmd += " --process-group-timeout 60"
+        cmd += " --save-orig-nids"
+        cmd += " --save-orig-eids"
+        cmd += f" --graph-formats {graph_formats}" if graph_formats else ""
+        os.system(cmd)
+
+        # read original node/edge IDs
+        def read_orig_ids(fname):
+            orig_ids = {}
+            for i in range(num_parts):
+                ids_path = os.path.join(out_dir, f"part{i}", fname)
+                part_ids = load_tensors(ids_path)
+                for type, data in part_ids.items():
+                    if type not in orig_ids:
+                        orig_ids[type] = data
+                    else:
+                        orig_ids[type] = torch.cat((orig_ids[type], data))
+            return orig_ids
+
+        orig_nids = read_orig_ids("orig_nids.dgl")
+        orig_eids = read_orig_ids("orig_eids.dgl")
+
+        # load partitions and verify
+        part_config = os.path.join(out_dir, "metadata.json")
+        for i in range(num_parts):
+            part_g, node_feats, edge_feats, gpb, _, _, _ = load_partition(
+                part_config, i
+            )
+            verify_partition_data_types(part_g)
+            verify_partition_formats(part_g, graph_formats)
+            verify_graph_feats(
+                g, gpb, part_g, node_feats, edge_feats, orig_nids, orig_eids
+            )
+
+
 def test_utils_generate_read_list():
     read_list = generate_read_list(10, 4)
     assert np.array_equal(read_list[0], np.array([0, 1, 2]))

tools/distpartitioning/convert_partition.py

@@ -9,6 +9,7 @@
 import dgl.graphbolt as gb
 import numpy as np
 import torch as th
+import torch.distributed as dist
 from dgl import EID, ETYPE, NID, NTYPE
 
 from dgl.distributed.constants import DGL2GB_EID, GB_DST_ID
@@ -355,6 +356,34 @@ def _process_partition_gb(
     return indptr, indices[sorted_idx], edge_ids[sorted_idx]
 
 
+def _update_node_map(node_map_val, end_ids_per_rank, id_ntypes, prev_last_id):
+    """this function is modified from the function '_update_node_edge_map' in dgl.distributed.partition"""
+    # Update the node_map_val to be contiguous.
+    rank = dist.get_rank()
+    prev_end_id = (
+        end_ids_per_rank[rank - 1].item() if rank > 0 else prev_last_id
+    )
+    ntype_ids = {ntype: ntype_id for ntype_id, ntype in enumerate(id_ntypes)}
+    for ntype_id in list(ntype_ids.values()):
+        ntype = id_ntypes[ntype_id]
+        start_id = node_map_val[ntype][0][0]
+        end_id = node_map_val[ntype][0][1]
+        if not (start_id == -1 and end_id == -1):
+            continue
+        prev_ntype_id = (
+            ntype_ids[ntype] - 1
+            if ntype_ids[ntype] > 0
+            else max(ntype_ids.values())
+        )
+        prev_ntype = id_ntypes[prev_ntype_id]
+        if ntype_ids[ntype] == 0:
+            node_map_val[ntype][0][0] = prev_end_id
+        else:
+            node_map_val[ntype][0][0] = node_map_val[prev_ntype][0][1]
+        node_map_val[ntype][0][1] = node_map_val[ntype][0][0]
+    return node_map_val[ntype][0][-1]
+
+
 def create_graph_object(
     tot_node_count,
     tot_edge_count,
@@ -368,6 +397,7 @@ def create_graph_object(
     edgeid_offset,
     node_typecounts,
     edge_typecounts,
+    last_ids={},
     return_orig_nids=False,
     return_orig_eids=False,
     use_graphbolt=False,
@@ -512,12 +542,30 @@ def create_graph_object(
     shuffle_global_nid_range = (shuffle_global_nids[0], shuffle_global_nids[-1])
 
     # Determine the node ID ranges of different node types.
+    prev_last_id = last_ids.get(part_id - 1, 0)
     for ntype_name in global_nid_ranges:
         ntype_id = ntypes_map[ntype_name]
         type_nids = shuffle_global_nids[ntype_ids == ntype_id]
-        node_map_val[ntype_name].append(
-            [int(type_nids[0]), int(type_nids[-1]) + 1]
-        )
+        if len(type_nids) == 0:
+            node_map_val[ntype_name].append([-1, -1])
+        else:
+            node_map_val[ntype_name].append(
+                [int(type_nids[0]), int(type_nids[-1]) + 1]
+            )
+            last_id = th.tensor(
+                [max(prev_last_id, int(type_nids[-1]) + 1)], dtype=th.int64
+            )
+    id_ntypes = list(global_nid_ranges.keys())
+
+    gather_last_ids = [
+        th.zeros(1, dtype=th.int64) for _ in range(dist.get_world_size())
+    ]
+
+    dist.all_gather(gather_last_ids, last_id)
+    prev_last_id = _update_node_map(
+        node_map_val, gather_last_ids, id_ntypes, prev_last_id
+    )
+    last_ids[part_id] = prev_last_id
 
     # process edges
     memory_snapshot("CreateDGLObj_AssignEdgeData: ", part_id)

tools/distpartitioning/data_shuffle.py

@@ -489,6 +489,10 @@ def exchange_feature(
         feat_dims_dtype.append(DATA_TYPE_ID[torch.float32])
         feature_dimension = 0
 
+    feature_dimension_tensor = torch.tensor([feature_dimension])
+    dist.all_reduce(feature_dimension_tensor, op=dist.ReduceOp.MAX)
+    feature_dimension = feature_dimension_tensor.item()
+
     logging.debug(f"Sending the feature shape information - {feat_dims_dtype}")
     all_dims_dtype = allgather_sizes(
         feat_dims_dtype, world_size, num_parts, return_sizes=True
@@ -553,7 +557,11 @@ def exchange_feature(
         else:
             cur_features[local_feat_key] = output_feat_list
             cur_global_ids[local_feat_key] = output_id_list
-
+    else:
+        cur_features[local_feat_key] = torch.empty(
+            (0, feature_dimension), dtype=torch.float32
+        )
+        cur_global_ids[local_feat_key] = torch.empty((0,), dtype=torch.int64)
     return cur_features, cur_global_ids
 
 
@@ -1301,6 +1309,7 @@ def prepare_local_data(src_data, local_part_id):
     if params.graph_formats:
         graph_formats = params.graph_formats.split(",")
 
+    prev_last_ids = {}
     for local_part_id in range(params.num_parts // world_size):
         # Synchronize for each local partition of the graph object.
         dist.barrier()
@@ -1340,6 +1349,7 @@ def prepare_local_data(src_data, local_part_id):
                 schema_map[constants.STR_NUM_NODES_PER_TYPE],
             ),
             edge_typecounts,
+            prev_last_ids,
             return_orig_nids=params.save_orig_nids,
             return_orig_eids=params.save_orig_eids,
             use_graphbolt=params.use_graphbolt,
@@ -1390,6 +1400,19 @@ def prepare_local_data(src_data, local_part_id):
         ] = json_metadata
         memory_snapshot("MetadataCreateComplete: ", rank)
 
+        last_id_tensor = torch.tensor(
+            [prev_last_ids[rank + (local_part_id * world_size)]],
+            dtype=torch.int64,
+        )
+        gather_list = [
+            torch.zeros(1, dtype=torch.int64) for _ in range(world_size)
+        ]
+        dist.all_gather(gather_list, last_id_tensor)
+        for rank_id, last_id in enumerate(gather_list):
+            prev_last_ids[
+                rank_id + (local_part_id * world_size)
+            ] = last_id.item()
+
     if rank == 0:
         # get meta-data from all partitions and merge them on rank-0
         metadata_list = gather_metadata_json(output_meta_json, rank, world_size)

tools/distpartitioning/dataset_utils.py

@@ -547,6 +547,11 @@ def get_dataset(
                     autogenerate_column_names=True,
                 )
                 parse_options = pyarrow.csv.ParseOptions(delimiter=" ")
+
+                if os.path.getsize(edge_file) == 0:
+                    # if getsize() == 0, the file is empty, indicating that the partition doesn't have this attribute.
+                    # The src_ids and dst_ids should remain empty.
+                    continue
                 with pyarrow.csv.open_csv(
                     edge_file,
                     read_options=read_options,