[FEA] Heterogeneous Distributed Sampling (rapidsai#4795)

Adds support for heterogeneous distributed sampling to the cuGraph distributed sampler.  Prerequisite for exposing this functionality to cuGraph-PyG.  Has been initially tested with cuGraph-PyG.

Updates the distributed sampler to use the new sampling API.

Merge after rapidsai#4775, rapidsai#4827, rapidsai#4820

Closes rapidsai#4773 
Closes rapidsai#4401

Authors:
  - Alex Barghi (https://github.com/alexbarghi-nv)
  - Joseph Nke (https://github.com/jnke2016)
  - Ralph Liu (https://github.com/nv-rliu)

Approvers:
  - Rick Ratzel (https://github.com/rlratzel)

URL: rapidsai#4795

Author: alexbarghi-nv

python/cugraph/cugraph/gnn/data_loading/dist_io/writer.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2024, NVIDIA CORPORATION.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION.
 # 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
@@ -79,9 +79,15 @@ def get_reader(
         return DistSampleReader(self._directory, format=self._format, rank=rank)
 
     def __write_minibatches_coo(self, minibatch_dict):
-        has_edge_ids = minibatch_dict["edge_id"] is not None
-        has_edge_types = minibatch_dict["edge_type"] is not None
-        has_weights = minibatch_dict["weight"] is not None
+        has_edge_ids = (
+            "edge_id" in minibatch_dict and minibatch_dict["edge_id"] is not None
+        )
+        has_edge_types = (
+            "edge_type" in minibatch_dict and minibatch_dict["edge_type"] is not None
+        )
+        has_weights = (
+            "weight" in minibatch_dict and minibatch_dict["weight"] is not None
+        )
 
         if minibatch_dict["renumber_map"] is None:
             raise ValueError(
@@ -92,22 +98,22 @@ def __write_minibatches_coo(self, minibatch_dict):
         if len(minibatch_dict["batch_id"]) == 0:
             return
 
-        fanout_length = (len(minibatch_dict["label_hop_offsets"]) - 1) // len(
-            minibatch_dict["batch_id"]
-        )
+        fanout_length = len(minibatch_dict["fanout"])
+        total_num_batches = (
+            len(minibatch_dict["label_hop_offsets"]) - 1
+        ) / fanout_length
 
-        for p in range(
-            0, int(ceil(len(minibatch_dict["batch_id"]) / self.__batches_per_partition))
-        ):
+        for p in range(0, int(ceil(total_num_batches / self.__batches_per_partition))):
             partition_start = p * (self.__batches_per_partition)
             partition_end = (p + 1) * (self.__batches_per_partition)
 
             label_hop_offsets_array_p = minibatch_dict["label_hop_offsets"][
                 partition_start * fanout_length : partition_end * fanout_length + 1
             ]
 
-            batch_id_array_p = minibatch_dict["batch_id"][partition_start:partition_end]
-            start_batch_id = batch_id_array_p[0]
+            num_batches_p = len(label_hop_offsets_array_p) - 1
+
+            start_batch_id = minibatch_dict["batch_start"]
 
             input_offsets_p = minibatch_dict["input_offsets"][
                 partition_start : (partition_end + 1)
@@ -171,7 +177,7 @@ def __write_minibatches_coo(self, minibatch_dict):
                 }
             )
 
-            end_batch_id = start_batch_id + len(batch_id_array_p) - 1
+            end_batch_id = start_batch_id + num_batches_p - 1
             rank = minibatch_dict["rank"] if "rank" in minibatch_dict else 0
 
             full_output_path = os.path.join(
@@ -188,9 +194,15 @@ def __write_minibatches_coo(self, minibatch_dict):
             )
 
     def __write_minibatches_csr(self, minibatch_dict):
-        has_edge_ids = minibatch_dict["edge_id"] is not None
-        has_edge_types = minibatch_dict["edge_type"] is not None
-        has_weights = minibatch_dict["weight"] is not None
+        has_edge_ids = (
+            "edge_id" in minibatch_dict and minibatch_dict["edge_id"] is not None
+        )
+        has_edge_types = (
+            "edge_type" in minibatch_dict and minibatch_dict["edge_type"] is not None
+        )
+        has_weights = (
+            "weight" in minibatch_dict and minibatch_dict["weight"] is not None
+        )
 
         if minibatch_dict["renumber_map"] is None:
             raise ValueError(
@@ -201,22 +213,22 @@ def __write_minibatches_csr(self, minibatch_dict):
         if len(minibatch_dict["batch_id"]) == 0:
             return
 
-        fanout_length = (len(minibatch_dict["label_hop_offsets"]) - 1) // len(
-            minibatch_dict["batch_id"]
-        )
+        fanout_length = len(minibatch_dict["fanout"])
+        total_num_batches = (
+            len(minibatch_dict["label_hop_offsets"]) - 1
+        ) / fanout_length
 
-        for p in range(
-            0, int(ceil(len(minibatch_dict["batch_id"]) / self.__batches_per_partition))
-        ):
+        for p in range(0, int(ceil(total_num_batches / self.__batches_per_partition))):
             partition_start = p * (self.__batches_per_partition)
             partition_end = (p + 1) * (self.__batches_per_partition)
 
             label_hop_offsets_array_p = minibatch_dict["label_hop_offsets"][
                 partition_start * fanout_length : partition_end * fanout_length + 1
             ]
 
-            batch_id_array_p = minibatch_dict["batch_id"][partition_start:partition_end]
-            start_batch_id = batch_id_array_p[0]
+            num_batches_p = len(label_hop_offsets_array_p) - 1
+
+            start_batch_id = minibatch_dict["batch_start"]
 
             input_offsets_p = minibatch_dict["input_offsets"][
                 partition_start : (partition_end + 1)
@@ -292,7 +304,7 @@ def __write_minibatches_csr(self, minibatch_dict):
                 }
             )
 
-            end_batch_id = start_batch_id + len(batch_id_array_p) - 1
+            end_batch_id = start_batch_id + num_batches_p - 1
             rank = minibatch_dict["rank"] if "rank" in minibatch_dict else 0
 
             full_output_path = os.path.join(
@@ -309,12 +321,19 @@ def __write_minibatches_csr(self, minibatch_dict):
             )
 
     def write_minibatches(self, minibatch_dict):
-        if (minibatch_dict["majors"] is not None) and (
-            minibatch_dict["minors"] is not None
-        ):
+        if "minors" not in minibatch_dict:
+            raise ValueError("invalid columns")
+
+        # PLC API specifies this behavior for empty input
+        # This needs to be handled here to avoid causing a hang
+        if len(minibatch_dict["minors"]) == 0:
+            return
+
+        if "majors" in minibatch_dict and minibatch_dict["majors"] is not None:
             self.__write_minibatches_coo(minibatch_dict)
-        elif (minibatch_dict["major_offsets"] is not None) and (
-            minibatch_dict["minors"] is not None
+        elif (
+            "major_offsets" in minibatch_dict
+            and minibatch_dict["major_offsets"] is not None
         ):
             self.__write_minibatches_csr(minibatch_dict)
         else: