Add example employing both PVC tiles (#8032)

Distributed GAT training, targeting XPU devices. PVC has 2 tiles, each reports itself as a separate device. DDP approach allows us to employ both tiles.

Additional requirements:
- IPEX (`intel_extension_for_pytorch`)
- oneCCL (`oneccl_bindings_for_pytorch`)

We need to import both these modules, as they extend torch module with XPU/oneCCL related functionality.

Run with:
`mpirun -np 2 python distributed_sampling_xpu.py`

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

CHANGELOG.md

@@ -6,6 +6,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ## [2.5.0] - 2023-MM-DD
 
 ### Added
+- Added distributed `GAT + ogbn-products` example targeting XPU device ([#8032](https://github.com/pyg-team/pytorch_geometric/pull/8032))
 
 ### Changed
 

examples/multi_gpu/distributed_sampling_xpu.py

@@ -0,0 +1,203 @@
+"""
+Distributed GAT training, targeting XPU devices.
+PVC has 2 tiles, each reports itself as a separate
+device. DDP approach allows us to employ both tiles.
+
+Additional requirements:
+    IPEX (intel_extension_for_pytorch)
+    oneCCL (oneccl_bindings_for_pytorch)
+
+    We need to import both these modules, as they extend
+    torch module with XPU/oneCCL related functionality.
+
+Run with:
+    mpirun -np 2 python distributed_sampling_xpu.py
+"""
+
+import copy
+import os
+import os.path as osp
+from typing import Tuple, Union
+
+import intel_extension_for_pytorch  # noqa
+import oneccl_bindings_for_pytorch  # noqa
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
+from torch import Tensor
+from torch.nn import Linear as Lin
+from torch.nn.parallel import DistributedDataParallel as DDP
+from tqdm import tqdm
+
+from torch_geometric.loader import NeighborLoader
+from torch_geometric.nn import GATConv
+
+
+class GAT(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        hidden_channels: int,
+        out_channels: int,
+        num_layers: int,
+        heads: int,
+    ):
+        super().__init__()
+
+        self.num_layers = num_layers
+
+        self.convs = torch.nn.ModuleList()
+        self.convs.append(GATConv(dataset.num_features, hidden_channels,
+                                  heads))
+        for _ in range(num_layers - 2):
+            self.convs.append(
+                GATConv(heads * hidden_channels, hidden_channels, heads))
+        self.convs.append(
+            GATConv(heads * hidden_channels, out_channels, heads,
+                    concat=False))
+
+        self.skips = torch.nn.ModuleList()
+        self.skips.append(Lin(dataset.num_features, hidden_channels * heads))
+        for _ in range(num_layers - 2):
+            self.skips.append(
+                Lin(hidden_channels * heads, hidden_channels * heads))
+        self.skips.append(Lin(hidden_channels * heads, out_channels))
+
+    def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
+        for i, (conv, skip) in enumerate(zip(self.convs, self.skips)):
+            x = conv(x, edge_index) + skip(x)
+            if i != self.num_layers - 1:
+                x = F.elu(x)
+                x = F.dropout(x, p=0.5, training=self.training)
+        return x
+
+    def inference(
+        self,
+        x_all: Tensor,
+        device: Union[str, torch.device],
+        subgraph_loader: NeighborLoader,
+    ) -> Tensor:
+        pbar = tqdm(total=x_all.size(0) * self.num_layers)
+        pbar.set_description("Evaluating")
+
+        # Compute representations of nodes layer by layer, using *all*
+        # available edges. This leads to faster computation in contrast to
+        # immediately computing the final representations of each batch.
+        for i in range(self.num_layers):
+            xs = []
+            for batch in subgraph_loader:
+                x = x_all[batch.n_id].to(device)
+                edge_index = batch.edge_index.to(device)
+                x = self.convs[i](x, edge_index) + self.skips[i](x)
+                x = x[:batch.batch_size]
+                if i != self.num_layers - 1:
+                    x = F.elu(x)
+                xs.append(x.cpu())
+
+                pbar.update(batch.batch_size)
+
+            x_all = torch.cat(xs, dim=0)
+
+        pbar.close()
+
+        return x_all
+
+
+def run(rank: int, world_size: int, dataset: PygNodePropPredDataset):
+    device = f"xpu:{rank}"
+
+    split_idx = dataset.get_idx_split()
+    split_idx["train"] = (split_idx["train"].split(
+        split_idx["train"].size(0) // world_size, dim=0)[rank].clone())
+    data = dataset[0].to(device, "x", "y")
+
+    kwargs = dict(batch_size=1024, num_workers=0, pin_memory=True)
+    train_loader = NeighborLoader(data, input_nodes=split_idx["train"],
+                                  num_neighbors=[10, 10, 5], **kwargs)
+
+    if rank == 0:
+        subgraph_loader = NeighborLoader(copy.copy(data), num_neighbors=[-1],
+                                         **kwargs)
+        evaluator = Evaluator(name="ogbn-products")
+
+    torch.manual_seed(12345)
+    model = GAT(dataset.num_features, 128, dataset.num_classes, num_layers=3,
+                heads=4).to(device)
+    model = DDP(model, device_ids=[device])
+    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+    for epoch in range(1, 21):
+        model.train()
+        for batch in train_loader:
+            optimizer.zero_grad()
+            out = model(batch.x,
+                        batch.edge_index.to(device))[:batch.batch_size]
+            loss = F.cross_entropy(out, batch.y[:batch.batch_size].squeeze())
+            loss.backward()
+            optimizer.step()
+
+        dist.barrier()
+
+        if rank == 0:
+            print(f"Epoch: {epoch:02d}, Loss: {loss:.4f}")
+
+        if rank == 0 and epoch % 5 == 0:  # Evaluation on a single GPU
+            model.eval()
+            with torch.no_grad():
+                out = model.module.inference(data.x, device, subgraph_loader)
+
+            y_true = data.y.to(out.device)
+            y_pred = out.argmax(dim=-1, keepdim=True)
+
+            train_acc = evaluator.eval({
+                "y_true": y_true[split_idx["train"]],
+                "y_pred": y_pred[split_idx["train"]],
+            })["acc"]
+            val_acc = evaluator.eval({
+                "y_true": y_true[split_idx["valid"]],
+                "y_pred": y_pred[split_idx["valid"]],
+            })["acc"]
+            test_acc = evaluator.eval({
+                "y_true": y_true[split_idx["test"]],
+                "y_pred": y_pred[split_idx["test"]],
+            })["acc"]
+
+            print(f"Train: {train_acc:.4f}, Val: {val_acc:.4f}, "
+                  f"Test: {test_acc:.4f}")
+
+        dist.barrier()
+
+    dist.destroy_process_group()
+
+
+def get_dist_params() -> Tuple[int, int, str]:
+    master_addr = "127.0.0.1"
+    master_port = "29500"
+    os.environ["MASTER_ADDR"] = master_addr
+    os.environ["MASTER_PORT"] = master_port
+
+    mpi_rank = int(os.environ.get("PMI_RANK", -1))
+    mpi_world_size = int(os.environ.get("PMI_SIZE", -1))
+    rank = mpi_rank if mpi_world_size > 0 else os.environ.get("RANK", 0)
+    world_size = (mpi_world_size if mpi_world_size > 0 else os.environ.get(
+        "WORLD_SIZE", 1))
+
+    os.environ["RANK"] = str(rank)
+    os.environ["WORLD_SIZE"] = str(world_size)
+
+    init_method = f"tcp://{master_addr}:{master_port}"
+
+    return rank, world_size, init_method
+
+
+if __name__ == "__main__":
+    rank, world_size, init_method = get_dist_params()
+    dist.init_process_group(backend="ccl", init_method=init_method,
+                            world_size=world_size, rank=rank)
+
+    path = osp.join(osp.dirname(osp.realpath(__file__)), "../../data",
+                    "ogbn-products")
+    dataset = PygNodePropPredDataset("ogbn-products", path)
+
+    run(rank, world_size, dataset)