Update index.rst

docs/source/advanced/partition_parallel/index.rst

@@ -2,4 +2,113 @@ Distributed Partition Parallel
 ==============================
 
 .. note::
-    分布式分区并行训练部分
\ No newline at end of file
+    分布式分区并行训练部分
+
+
+
+The **distributed partition parallel** refers to the process of partitioning a large graph into
+multiple partitions and distributing them to different workers. Each worker is responsible for training
+a specific partition in parallel. The data is exchanged across different partitions.
+
+Starygl provide a very simple way to implement partition parallel with Route class. The Route class can effectively manage
+the data exchange between different partitions during training. It plays a crucial role in facilitating communication and synchronization
+between workers responsible for training specific graph partitions. The Route class provides a mechanism for efficiently routing and exchanging data,
+enabling seamless parallel training across distributed systems
+
+Here we provide an example to show how to use Route to implement partition parallel.
+Before you start with partition parallel. First you should decide how to partition your graph data. Starygl provide serveral
+partition algorithms
+
+- lgd
+- metis
+- multi-constraint metis
+
+In the following code, we first partition graph with specific algorithm.Then we save the node and edge feature with the
+correspond graph partition together.
+
+.. code-block:: python
+
+    def partition_graph(self,
+        root: str,
+        num_parts: int,
+        node_weight: Optional[str] = None,
+        edge_weight: Optional[str] = None,
+        algorithm: str = "metis",
+        partition_kwargs = None,):
+
+        assert not self.is_heterogeneous, "only support homomorphic graph"
+
+        num_nodes: int = self.node().num_nodes
+        edge_index: Tensor = self.edge_index()
+
+        logging.info(f"running partition aglorithm: {algorithm}")
+        partition_kwargs = partition_kwargs or {}
+
+        not_self_loop = (edge_index[0] != edge_index[1])
+
+        if node_weight is not None:
+            node_weight = self.node()[node_weight]
+
+        if edge_weight is not None:
+            edge_weight = self.edge()[edge_weight]
+            edge_weight = edge_weight[not_self_loop]
+
+        # partition graph
+        node_parts = metis_partition(
+            edge_index[:,not_self_loop],
+            num_nodes, num_parts,
+            node_weight=node_weight,
+            edge_weight=edge_weight,
+            **partition_kwargs,
+
+
+        root_path = Path(root).expanduser().resolve()
+        base_path = root_path / f"{algorithm}_{num_parts}"
+
+        # handle each partition
+        for i in range(num_parts):
+            npart_mask = node_parts == i
+            epart_mask = npart_mask[edge_index[1]]
+
+            raw_dst_ids: Tensor = torch.where(npart_mask)[0]
+            local_edges = edge_index[:, epart_mask]
+
+            raw_src_ids, local_edges = init_vc_edge_index(
+                raw_dst_ids, local_edges, bipartite=True,
+            )
+
+            # get GraphData obj
+            g = GraphData.from_bipartite(
+                local_edges,
+                raw_src_ids=raw_src_ids,
+                raw_dst_ids=raw_dst_ids,
+            )
+
+            # handle feature data
+            # ......
+
+            logging.info(f"saving partition data: {i+1}/{num_parts}")
+            # save each partition
+            torch.save(g, (base_path / f"{i:03d}").__str__())
+
+Next we will deal with our model.With Route, developers just need to change few lines of code to implement partition parallel
+In the example below, we just need to add one line code in the forward function. And the Route will help us manage the feature exchange.
+
+.. code-block:: python
+
+    class SimpleConv(pyg_nn.MessagePassing):
+        def __init__(self, in_feats: int, out_feats: int):
+            super().__init__(aggr="mean")
+            self.linear = nn.Linear(in_feats, out_feats)
+
+        def forward(self, x: Tensor, edge_index: Tensor, route: Route):
+            dst_len = x.size(0)
+            x = route.apply(x) # exchange features
+            return self.propagate(edge_index, x=x)[:dst_len]
+
+        def message(self, x_j: Tensor):
+            return x_j
+
+        def update(self, x: Tensor):
+            return F.relu(self.linear(x))
+