chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""
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This script demonstrate how to use dgl sparse library to sample on graph and
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train model. It trains and tests a GraphSAGE model using the sparse sample and
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compact operators to sample submatrix from the whole matrix.
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This flowchart describes the main functional sequence of the provided example.
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main
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│
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├───> Load and preprocess full dataset
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│
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├───> Instantiate SAGE model
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│
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├───> train
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│ │
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│ └───> Training loop
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│ │
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│ ├───> Sample submatrix
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│ │
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│ └───> SAGE.forward
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└───> test
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│
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├───> Sample submatrix
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│
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└───> Evaluate the model
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"""
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import argparse
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from functools import partial
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import dgl.graphbolt as gb
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import dgl.sparse as dglsp
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchmetrics.functional as MF
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from dgl.graphbolt.subgraph_sampler import SubgraphSampler
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from torch.utils.data import functional_datapipe
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from tqdm import tqdm
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class SAGEConv(nn.Module):
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r"""GraphSAGE layer from `Inductive Representation Learning on
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Large Graphs <https://arxiv.org/pdf/1706.02216.pdf>`__
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"""
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def __init__(
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self,
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in_feats,
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out_feats,
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):
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super(SAGEConv, self).__init__()
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self._in_src_feats, self._in_dst_feats = in_feats, in_feats
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self._out_feats = out_feats
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self.fc_neigh = nn.Linear(self._in_src_feats, out_feats, bias=False)
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self.fc_self = nn.Linear(self._in_dst_feats, out_feats, bias=True)
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self.reset_parameters()
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def reset_parameters(self):
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gain = nn.init.calculate_gain("relu")
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nn.init.xavier_uniform_(self.fc_self.weight, gain=gain)
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nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain)
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def forward(self, A, feat):
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feat_src = feat
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feat_dst = feat[: A.shape[1]]
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# Aggregator type: mean.
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srcdata = self.fc_neigh(feat_src)
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# Divided by degree.
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D_hat = dglsp.diag(A.sum(0)) ** -1
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A_div = A @ D_hat
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# Conv neighbors.
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dstdata = A_div.T @ srcdata
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rst = self.fc_self(feat_dst) + dstdata
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return rst
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class SAGE(nn.Module):
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def __init__(self, in_size, hid_size, out_size):
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super().__init__()
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self.layers = nn.ModuleList()
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# Three-layer GraphSAGE-gcn.
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self.layers.append(SAGEConv(in_size, hid_size))
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self.layers.append(SAGEConv(hid_size, hid_size))
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self.layers.append(SAGEConv(hid_size, out_size))
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self.dropout = nn.Dropout(0.5)
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self.hid_size = hid_size
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self.out_size = out_size
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def forward(self, sampled_matrices, x):
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hidden_x = x
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for layer_idx, (layer, sampled_matrix) in enumerate(
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zip(self.layers, sampled_matrices)
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):
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hidden_x = layer(sampled_matrix, hidden_x)
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if layer_idx != len(self.layers) - 1:
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hidden_x = F.relu(hidden_x)
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hidden_x = self.dropout(hidden_x)
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return hidden_x
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@functional_datapipe("sample_sparse_neighbor")
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class SparseNeighborSampler(SubgraphSampler):
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def __init__(self, datapipe, matrix, fanouts):
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super().__init__(datapipe)
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self.matrix = matrix
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# Convert fanouts to a list of tensors.
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self.fanouts = []
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for fanout in fanouts:
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if not isinstance(fanout, torch.Tensor):
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fanout = torch.LongTensor([int(fanout)])
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self.fanouts.insert(0, fanout)
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def sample_subgraphs(self, seeds, seeds_timestamp=None):
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sampled_matrices = []
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src = seeds.long()
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#####################################################################
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# (HIGHLIGHT) Using the sparse sample operator to preform random
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# sampling on the neighboring nodes of the seeds nodes. The sparse
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# compact operator is then employed to compact and relabel the sampled
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# matrix, resulting in the sampled matrix and the relabel index.
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#####################################################################
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for fanout in self.fanouts:
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# Sample neighbors.
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sampled_matrix = self.matrix.sample(1, fanout, ids=src).coalesce()
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# Compact the sampled matrix.
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compacted_mat, row_ids = sampled_matrix.compact(0)
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sampled_matrices.insert(0, compacted_mat)
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src = row_ids
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return src, sampled_matrices
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############################################################################
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# (HIGHLIGHT) Create a multi-process dataloader with dgl graphbolt package.
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############################################################################
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def create_dataloader(A, fanouts, ids, features, device):
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datapipe = gb.ItemSampler(ids, batch_size=1024)
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# Customize graphbolt sampler by sparse.
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datapipe = datapipe.sample_sparse_neighbor(A, fanouts)
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# Use grapbolt to fetch features.
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datapipe = datapipe.fetch_feature(features, node_feature_keys=["feat"])
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datapipe = datapipe.copy_to(device)
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dataloader = gb.DataLoader(datapipe)
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return dataloader
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def evaluate(model, dataloader, num_classes):
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model.eval()
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ys = []
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y_hats = []
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for it, data in tqdm(enumerate(dataloader), "Evaluating"):
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with torch.no_grad():
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node_feature = data.node_features["feat"].float()
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blocks = data.sampled_subgraphs
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y = data.labels
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ys.append(y)
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y_hats.append(model(blocks, node_feature))
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return MF.accuracy(
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torch.cat(y_hats),
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torch.cat(ys),
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task="multiclass",
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num_classes=num_classes,
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)
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def validate(device, dataset, model, num_classes):
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test_set = dataset.tasks[0].test_set
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test_dataloader = create_dataloader(
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A, [10, 10, 10], test_set, features, device
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)
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acc = evaluate(model, test_dataloader, num_classes)
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return acc
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def train(device, A, features, dataset, num_classes, model):
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# Create sampler & dataloader.
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train_set = dataset.tasks[0].train_set
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train_dataloader = create_dataloader(
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A, [10, 10, 10], train_set, features, device
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)
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valid_set = dataset.tasks[0].validation_set
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val_dataloader = create_dataloader(
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A, [10, 10, 10], valid_set, features, device
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)
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4)
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for epoch in range(10):
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model.train()
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total_loss = 0
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for it, data in tqdm(enumerate(train_dataloader), "Training"):
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node_feature = data.node_features["feat"].float()
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blocks = data.sampled_subgraphs
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y = data.labels
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y_hat = model(blocks, node_feature)
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loss = F.cross_entropy(y_hat, y)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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acc = evaluate(model, val_dataloader, num_classes)
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print(
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"Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
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epoch, total_loss / (it + 1), acc.item()
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)
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)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="GraphSAGE")
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parser.add_argument(
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"--mode",
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default="gpu",
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choices=["cpu", "gpu"],
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help="Training mode. 'cpu' for CPU training, 'gpu' for GPU training.",
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)
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args = parser.parse_args()
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if not torch.cuda.is_available():
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args.mode = "cpu"
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print(f"Training in {args.mode} mode.")
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#####################################################################
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# (HIGHLIGHT) This example implements a graphSAGE algorithm by sparse
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# operators, which involves sampling a subgraph from a full graph and
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# conducting training.
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#
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# First, the whole graph is loaded onto the CPU or GPU and transformed
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# to sparse matrix. To obtain the training subgraph, it samples three
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# submatrices by seed nodes, which contains their randomly sampled
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# 1-hop, 2-hop, and 3-hop neighbors. Then, the features of the
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# subgraph are input to the network for training.
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#####################################################################
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# Load and preprocess dataset.
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print("Loading data")
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device = torch.device("cpu" if args.mode == "cpu" else "cuda")
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dataset = gb.BuiltinDataset("ogbn-products").load()
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g = dataset.graph
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features = dataset.feature
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# Create GraphSAGE model.
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in_size = features.size("node", None, "feat")[0]
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num_classes = dataset.tasks[0].metadata["num_classes"]
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out_size = num_classes
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model = SAGE(in_size, 256, out_size).to(device)
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# Create sparse.
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N = g.num_nodes
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A = dglsp.from_csc(g.csc_indptr.long(), g.indices.long(), shape=(N, N))
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# Model training.
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print("Training...")
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train(device, A, features, dataset, num_classes, model)
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# Test the model.
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print("Testing...")
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acc = validate(device, dataset, model, num_classes)
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print(f"Test accuracy {acc:.4f}")
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