[Model] Add dgl.nn.CuGraphSAGEConv model

docs/source/api/python/nn-pytorch.rst

@@ -14,7 +14,6 @@ Conv Layers
     ~dgl.nn.pytorch.conv.GraphConv
     ~dgl.nn.pytorch.conv.EdgeWeightNorm
     ~dgl.nn.pytorch.conv.RelGraphConv
-    ~dgl.nn.pytorch.conv.CuGraphRelGraphConv
     ~dgl.nn.pytorch.conv.TAGConv
     ~dgl.nn.pytorch.conv.GATConv
     ~dgl.nn.pytorch.conv.GATv2Conv
@@ -42,6 +41,17 @@ Conv Layers
     ~dgl.nn.pytorch.conv.PNAConv
     ~dgl.nn.pytorch.conv.DGNConv
 
+CuGraph Conv Layers
+----------------------------------------
+
+.. autosummary::
+    :toctree: ../../generated/
+    :nosignatures:
+    :template: classtemplate.rst
+
+    ~dgl.nn.pytorch.conv.CuGraphRelGraphConv
+    ~dgl.nn.pytorch.conv.CuGraphSAGEConv
+
 Dense Conv Layers
 ----------------------------------------
 

examples/advanced/cugraph/graphsage.py

@@ -0,0 +1,200 @@
+import argparse
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchmetrics.functional as MF
+import tqdm
+from dgl.data import AsNodePredDataset
+from dgl.dataloading import (
+    DataLoader,
+    MultiLayerFullNeighborSampler,
+    NeighborSampler,
+)
+from dgl.nn import CuGraphSAGEConv
+from ogb.nodeproppred import DglNodePropPredDataset
+
+
+class SAGE(nn.Module):
+    def __init__(self, in_size, hid_size, out_size):
+        super().__init__()
+        self.layers = nn.ModuleList()
+        # three-layer GraphSAGE-mean
+        self.layers.append(CuGraphSAGEConv(in_size, hid_size, "mean"))
+        self.layers.append(CuGraphSAGEConv(hid_size, hid_size, "mean"))
+        self.layers.append(CuGraphSAGEConv(hid_size, out_size, "mean"))
+        self.dropout = nn.Dropout(0.5)
+        self.hid_size = hid_size
+        self.out_size = out_size
+
+    def forward(self, blocks, x):
+        h = x
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
+            h = layer(block, h)
+            if l != len(self.layers) - 1:
+                h = F.relu(h)
+                h = self.dropout(h)
+        return h
+
+    def inference(self, g, device, batch_size):
+        """Conduct layer-wise inference to get all the node embeddings."""
+        feat = g.ndata["feat"]
+        sampler = MultiLayerFullNeighborSampler(1, prefetch_node_feats=["feat"])
+        dataloader = DataLoader(
+            g,
+            torch.arange(g.num_nodes()).to(g.device),
+            sampler,
+            device=device,
+            batch_size=batch_size,
+            shuffle=False,
+            drop_last=False,
+            num_workers=0,
+        )
+        buffer_device = torch.device("cpu")
+        pin_memory = buffer_device != device
+
+        for l, layer in enumerate(self.layers):
+            y = torch.empty(
+                g.num_nodes(),
+                self.hid_size if l != len(self.layers) - 1 else self.out_size,
+                device=buffer_device,
+                pin_memory=pin_memory,
+            )
+            feat = feat.to(device)
+            for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
+                x = feat[input_nodes]
+                h = layer(blocks[0], x)  # len(blocks) = 1
+                if l != len(self.layers) - 1:
+                    h = F.relu(h)
+                    h = self.dropout(h)
+                # by design, our output nodes are contiguous
+                y[output_nodes[0] : output_nodes[-1] + 1] = h.to(buffer_device)
+            feat = y
+        return y
+
+
+def evaluate(model, graph, dataloader):
+    model.eval()
+    ys = []
+    y_hats = []
+    for it, (input_nodes, output_nodes, blocks) in enumerate(dataloader):
+        with torch.no_grad():
+            x = blocks[0].srcdata["feat"]
+            ys.append(blocks[-1].dstdata["label"])
+            y_hats.append(model(blocks, x))
+    num_classes = y_hats[0].shape[1]
+    return MF.accuracy(
+        torch.cat(y_hats),
+        torch.cat(ys),
+        task="multiclass",
+        num_classes=num_classes,
+    )
+
+
+def layerwise_infer(device, graph, nid, model, batch_size):
+    model.eval()
+    with torch.no_grad():
+        pred = model.inference(
+            graph, device, batch_size
+        )  # pred in buffer_device
+        pred = pred[nid]
+        label = graph.ndata["label"][nid].to(pred.device)
+        num_classes = pred.shape[1]
+        return MF.accuracy(
+            pred, label, task="multiclass", num_classes=num_classes
+        )
+
+
+def train(args, device, g, dataset, model):
+    # create sampler & dataloader
+    train_idx = dataset.train_idx.to(device)
+    val_idx = dataset.val_idx.to(device)
+    sampler = NeighborSampler(
+        [10, 10, 10],  # fanout for [layer-0, layer-1, layer-2]
+        prefetch_node_feats=["feat"],
+        prefetch_labels=["label"],
+    )
+    use_uva = args.mode == "mixed"
+    train_dataloader = DataLoader(
+        g,
+        train_idx,
+        sampler,
+        device=device,
+        batch_size=1024,
+        shuffle=True,
+        drop_last=False,
+        num_workers=0,
+        use_uva=use_uva,
+    )
+
+    val_dataloader = DataLoader(
+        g,
+        val_idx,
+        sampler,
+        device=device,
+        batch_size=1024,
+        shuffle=True,
+        drop_last=False,
+        num_workers=0,
+        use_uva=use_uva,
+    )
+
+    opt = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4)
+
+    for epoch in range(10):
+        model.train()
+        total_loss = 0
+        for it, (input_nodes, output_nodes, blocks) in enumerate(
+            train_dataloader
+        ):
+            x = blocks[0].srcdata["feat"]
+            y = blocks[-1].dstdata["label"]
+            y_hat = model(blocks, x)
+            loss = F.cross_entropy(y_hat, y)
+            opt.zero_grad()
+            loss.backward()
+            opt.step()
+
+            total_loss += loss.item()
+        acc = evaluate(model, g, val_dataloader)
+        print(
+            "Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
+                epoch, total_loss / (it + 1), acc.item()
+            )
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--mode",
+        default="mixed",
+        choices=["mixed", "puregpu"],
+        help="Training mode. 'mixed' for CPU-GPU mixed training, "
+        "'puregpu' for pure-GPU training.",
+    )
+    args = parser.parse_args()
+    if not torch.cuda.is_available():
+        args.mode = "cpu"
+    print(f"Training in {args.mode} mode.")
+
+    # load and preprocess dataset
+    print("Loading data")
+    dataset = AsNodePredDataset(DglNodePropPredDataset("ogbn-products"))
+    g = dataset[0]
+    g = g.to("cuda" if args.mode == "puregpu" else "cpu")
+    device = torch.device("cpu" if args.mode == "cpu" else "cuda")
+
+    # create GraphSAGE model
+    in_size = g.ndata["feat"].shape[1]
+    out_size = dataset.num_classes
+    model = SAGE(in_size, 256, out_size).to(device)
+
+    # model training
+    print("Training...")
+    train(args, device, g, dataset, model)
+
+    # test the model
+    print("Testing...")
+    acc = layerwise_infer(device, g, dataset.test_idx, model, batch_size=4096)
+    print("Test Accuracy {:.4f}".format(acc.item()))

python/dgl/nn/pytorch/conv/__init__.py

@@ -7,6 +7,7 @@
 from .cfconv import CFConv
 from .chebconv import ChebConv
 from .cugraph_relgraphconv import CuGraphRelGraphConv
+from .cugraph_sageconv import CuGraphSAGEConv
 from .densechebconv import DenseChebConv
 from .densegraphconv import DenseGraphConv
 from .densesageconv import DenseSAGEConv
@@ -67,4 +68,5 @@
     "PNAConv",
     "DGNConv",
     "CuGraphRelGraphConv",
+    "CuGraphSAGEConv",
 ]

python/dgl/nn/pytorch/conv/cugraph_sageconv.py

@@ -0,0 +1,153 @@
+"""Torch Module for GraphSAGE layer using the aggregation primitives in
+cugraph-ops"""
+# pylint: disable=no-member, arguments-differ, invalid-name, too-many-arguments
+
+import torch
+from torch import nn
+
+try:
+    from pylibcugraphops import make_fg_csr, make_mfg_csr
+    from pylibcugraphops.torch.autograd import agg_concat_n2n as SAGEConvAgg
+except ImportError:
+    has_pylibcugraphops = False
+else:
+    has_pylibcugraphops = True
+
+
+class CuGraphSAGEConv(nn.Module):
+    r"""An accelerated GraphSAGE layer from `Inductive Representation Learning
+    on Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__ that leverages the
+    highly-optimized aggregation primitives in cugraph-ops:
+
+    .. math::
+        h_{\mathcal{N}(i)}^{(l+1)} &= \mathrm{aggregate}
+        \left(\{h_{j}^{l}, \forall j \in \mathcal{N}(i) \}\right)
+
+        h_{i}^{(l+1)} &= W \cdot \mathrm{concat}
+        (h_{i}^{l}, h_{\mathcal{N}(i)}^{(l+1)})
+
+    This module depends on :code:`pylibcugraphops` package, which can be
+    installed via :code:`conda install -c nvidia pylibcugraphops>=23.02`.
+
+    .. note::
+        This is an **experimental** feature.
+
+    Parameters
+    ----------
+    in_feats : int
+        Input feature size.
+    out_feats : int
+        Output feature size.
+    aggregator_type : str
+        Aggregator type to use (``mean``, ``sum``, ``min``, ``max``).
+    feat_drop : float
+        Dropout rate on features, default: ``0``.
+    bias : bool
+        If True, adds a learnable bias to the output. Default: ``True``.
+
+    Examples
+    --------
+    >>> import dgl
+    >>> import torch
+    >>> from dgl.nn import CuGraphSAGEConv
+    >>> device = 'cuda'
+    >>> g = dgl.graph(([0,1,2,3,2,5], [1,2,3,4,0,3])).to(device)
+    >>> g = dgl.add_self_loop(g)
+    >>> feat = torch.ones(6, 10).to(device)
+    >>> conv = CuGraphSAGEConv(10, 2, 'mean').to(device)
+    >>> res = conv(g, feat)
+    >>> res
+    tensor([[-1.1690,  0.1952],
+            [-1.1690,  0.1952],
+            [-1.1690,  0.1952],
+            [-1.1690,  0.1952],
+            [-1.1690,  0.1952],
+            [-1.1690,  0.1952]], device='cuda:0', grad_fn=<AddmmBackward0>)
+    """
+    MAX_IN_DEGREE_MFG = 500
+
+    def __init__(
+        self,
+        in_feats,
+        out_feats,
+        aggregator_type="mean",
+        feat_drop=0.0,
+        bias=True,
+    ):
+        if has_pylibcugraphops is False:
+            raise ModuleNotFoundError(
+                f"{self.__class__.__name__} requires pylibcugraphops >= 23.02. "
+                f"Install via `conda install -c nvidia 'pylibcugraphops>=23.02'`."
+            )
+
+        valid_aggr_types = {"max", "min", "mean", "sum"}
+        if aggregator_type not in valid_aggr_types:
+            raise ValueError(
+                f"Invalid aggregator_type. Must be one of {valid_aggr_types}. "
+                f"But got '{aggregator_type}' instead."
+            )
+
+        super().__init__()
+        self.in_feats = in_feats
+        self.out_feats = out_feats
+        self.aggr = aggregator_type
+        self.feat_drop = nn.Dropout(feat_drop)
+        self.linear = nn.Linear(2 * in_feats, out_feats, bias=bias)
+
+    def reset_parameters(self):
+        r"""Reinitialize learnable parameters."""
+        self.linear.reset_parameters()
+
+    def forward(self, g, feat, max_in_degree=None):
+        r"""Forward computation.
+
+        Parameters
+        ----------
+        g : DGLGraph
+            The graph.
+        feat : torch.Tensor
+            Node features. Shape: :math:`(N, D_{in})`.
+        max_in_degree : int
+            Maximum in-degree of destination nodes. It is only effective when
+            :attr:`g` is a :class:`DGLBlock`, i.e., bipartite graph. When
+            :attr:`g` is generated from a neighbor sampler, the value should be
+            set to the corresponding :attr:`fanout`. If not given,
+            :attr:`max_in_degree` will be calculated on-the-fly.
+
+        Returns
+        -------
+        torch.Tensor
+            Output node features. Shape: :math:`(N, D_{out})`.
+        """
+        offsets, indices, _ = g.adj_sparse("csc")
+
+        if g.is_block:
+            if max_in_degree is None:
+                max_in_degree = g.in_degrees().max().item()
+
+            if max_in_degree < self.MAX_IN_DEGREE_MFG:
+                _graph = make_mfg_csr(
+                    g.dstnodes(),
+                    offsets,
+                    indices,
+                    max_in_degree,
+                    g.num_src_nodes(),
+                )
+            else:
+                offsets_fg = torch.empty(
+                    g.num_src_nodes() + 1,
+                    dtype=offsets.dtype,
+                    device=offsets.device,
+                )
+                offsets_fg[: offsets.numel()] = offsets
+                offsets_fg[offsets.numel() :] = offsets[-1]
+
+                _graph = make_fg_csr(offsets_fg, indices)
+        else:
+            _graph = make_fg_csr(offsets, indices)
+
+        feat = self.feat_drop(feat)
+        h = SAGEConvAgg(feat, _graph, self.aggr)[: g.num_dst_nodes()]
+        h = self.linear(h)
+
+        return h