wehub-resource-sync
257 lines
8.3 KiB
Python
257 lines
8.3 KiB
Python
"""
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This script demonstrates how to use dgl sparse library to sample on graph and
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train model. It trains and tests a LADIES model using the sparse power and
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sp_broadcast_v 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 LADIES 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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│ └───> LADIES.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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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.data import AsNodePredDataset
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from dgl.sparse import sp_broadcast_v
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from ogb.nodeproppred import DglNodePropPredDataset
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class SAGEConv(nn.Module):
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r"""LADIES layer from `Layer-Dependent Importance Sampling
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for Training Deep and Large Graph Convolutional Networks
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<https://arxiv.org/abs/1911.07323.pdf>`__"""
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def __init__(
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self,
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in_size,
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out_size,
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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_size, in_size
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self._out_size = out_size
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self.fc_neigh = nn.Linear(self._in_src_feats, out_size, bias=False)
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self.fc_self = nn.Linear(self._in_dst_feats, out_size, 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 LADIES(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 LADIES.
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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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def multilayer_sample(A, fanouts, seeds, ndata):
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sampled_matrices = []
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src = seeds
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#########################################################################
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# (HIGHLIGHT) Using the sparse sample operator to preform LADIES sampling
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# algorithm from the neighboring nodes of the seeds nodes.
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# The sparse sp_power operator is applied to compute sample probability,
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# and sp_broadcast_v is then employed to normalize weight by performing
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# division operations on column.
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#########################################################################
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for fanout in fanouts:
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# Sample neighbors.
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sub_A = A.index_select(1, src)
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# Compute probability weight.
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row_probs = (sub_A**2).sum(1)
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row_probs = row_probs / row_probs.sum(0)
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# Layer-wise sample nodes.
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row_ids = torch.multinomial(row_probs, fanout, replacement=False)
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# Add self-loop.
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row_ids = torch.cat((row_ids, src), 0).unique()
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sampled_matrix = sub_A.index_select(0, row_ids)
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# Normalize edge weights.
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div_matirx = sp_broadcast_v(
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sampled_matrix, row_probs[row_ids].reshape(-1, 1), "truediv"
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)
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div_matirx = sp_broadcast_v(div_matirx, div_matirx.sum(0), "truediv")
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# Save the sampled matrix.
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sampled_matrices.insert(0, div_matirx)
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src = row_ids
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x = ndata["feat"][src]
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y = ndata["label"][seeds]
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return sampled_matrices, x, y
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def evaluate(model, A, dataloader, ndata, num_classes):
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model.eval()
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ys = []
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y_hats = []
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fanouts = [4000, 4000, 4000]
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for seeds in dataloader:
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with torch.no_grad():
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sampled_matrices, x, y = multilayer_sample(A, fanouts, seeds, ndata)
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ys.append(y)
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y_hats.append(model(sampled_matrices, x))
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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, A, ndata, dataset, model, batch_size):
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inf_id = dataset.test_idx.to(device)
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inf_dataloader = torch.utils.data.DataLoader(inf_id, batch_size=batch_size)
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acc = evaluate(model, A, inf_dataloader, ndata, dataset.num_classes)
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return acc
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def train(device, A, ndata, dataset, model):
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# Create sampler & dataloader.
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train_idx = dataset.train_idx.to(device)
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val_idx = dataset.val_idx.to(device)
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train_dataloader = torch.utils.data.DataLoader(
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train_idx, batch_size=1024, shuffle=True
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)
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val_dataloader = torch.utils.data.DataLoader(val_idx, batch_size=1024)
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4)
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fanouts = [4000, 4000, 4000]
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for epoch in range(20):
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model.train()
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total_loss = 0
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for it, seeds in enumerate(train_dataloader):
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sampled_matrices, x, y = multilayer_sample(A, fanouts, seeds, ndata)
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y_hat = model(sampled_matrices, x)
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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, A, val_dataloader, ndata, dataset.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="LADIESConv")
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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 LADIES 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 layer-wise 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 = AsNodePredDataset(DglNodePropPredDataset("ogbn-products"))
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g = dataset[0]
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# Create LADIES model.
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in_size = g.ndata["feat"].shape[1]
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out_size = dataset.num_classes
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model = LADIES(in_size, 256, out_size).to(device)
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# Create sparse.
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indices = torch.stack(g.edges())
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N = g.num_nodes()
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A = dglsp.spmatrix(indices, shape=(N, N)).coalesce()
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I = dglsp.identity(A.shape)
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# Initialize laplacian matrix.
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A_hat = A + I
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D_hat = dglsp.diag(A_hat.sum(1)) ** -0.5
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A_norm = D_hat @ A_hat @ D_hat
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A_norm = A_norm.to(device)
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g = g.to(device)
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# Model training.
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print("Training...")
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train(device, A_norm, g.ndata, dataset, model)
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# Test the model.
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print("Testing...")
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acc = validate(device, A_norm, g.ndata, dataset, model, batch_size=2048)
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print(f"Test accuracy {acc:.4f}")
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