wehub-resource-sync
271 lines
8.2 KiB
Python
271 lines
8.2 KiB
Python
import argparse
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import os
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import dgl
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import torch
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import torch.distributed as dist
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import torch.multiprocessing as mp
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import torch.nn as nn
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import torch.nn.functional as F
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from dgl.data.rdf import AIFBDataset, AMDataset, BGSDataset, MUTAGDataset
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from dgl.dataloading import DataLoader, MultiLayerNeighborSampler
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from dgl.nn.pytorch import RelGraphConv
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from torch.nn.parallel import DistributedDataParallel
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from torchmetrics.functional import accuracy
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class RGCN(nn.Module):
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def __init__(self, num_nodes, h_dim, out_dim, num_rels):
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super().__init__()
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self.emb = nn.Embedding(num_nodes, h_dim)
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# two-layer RGCN
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self.conv1 = RelGraphConv(
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h_dim,
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h_dim,
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num_rels,
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regularizer="basis",
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num_bases=num_rels,
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self_loop=False,
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)
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self.conv2 = RelGraphConv(
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h_dim,
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out_dim,
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num_rels,
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regularizer="basis",
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num_bases=num_rels,
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self_loop=False,
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)
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def forward(self, g):
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x = self.emb(g[0].srcdata[dgl.NID])
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h = F.relu(
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self.conv1(g[0], x, g[0].edata[dgl.ETYPE], g[0].edata["norm"])
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)
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h = self.conv2(g[1], h, g[1].edata[dgl.ETYPE], g[1].edata["norm"])
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return h
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def evaluate(model, labels, num_classes, dataloader, inv_target):
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model.eval()
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eval_logits = []
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eval_seeds = []
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with torch.no_grad():
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for input_nodes, output_nodes, blocks in dataloader:
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output_nodes = inv_target[output_nodes]
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for block in blocks:
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block.edata["norm"] = dgl.norm_by_dst(block).unsqueeze(1)
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logits = model(blocks)
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eval_logits.append(logits.cpu().detach())
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eval_seeds.append(output_nodes.cpu().detach())
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eval_logits = torch.cat(eval_logits)
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eval_seeds = torch.cat(eval_seeds)
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num_seeds = len(eval_seeds)
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loc_sum = accuracy(
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eval_logits.argmax(dim=1),
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labels[eval_seeds].cpu(),
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task="multiclass",
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num_classes=num_classes,
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) * float(num_seeds)
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return torch.tensor([loc_sum.item(), float(num_seeds)])
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def train(
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proc_id,
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device,
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g,
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target_idx,
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labels,
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num_classes,
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train_idx,
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inv_target,
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model,
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):
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# define loss function and optimizer
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loss_fcn = nn.CrossEntropyLoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
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# construct sampler and dataloader
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sampler = MultiLayerNeighborSampler([4, 4])
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train_loader = DataLoader(
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g,
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target_idx[train_idx],
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sampler,
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device=device,
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batch_size=100,
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shuffle=True,
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use_ddp=True,
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)
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# no separate validation subset, use train index instead for validation
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val_loader = DataLoader(
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g,
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target_idx[train_idx],
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sampler,
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device=device,
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batch_size=100,
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shuffle=False,
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use_ddp=True,
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)
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for epoch in range(50):
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model.train()
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total_loss = 0
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for it, (input_nodes, output_nodes, blocks) in enumerate(train_loader):
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output_nodes = inv_target[output_nodes]
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for block in blocks:
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block.edata["norm"] = dgl.norm_by_dst(block).unsqueeze(1)
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logits = model(blocks)
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loss = loss_fcn(logits, labels[output_nodes])
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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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# torchmetric accuracy defined as num_correct_labels / num_train_nodes
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# loc_acc_split = [loc_accuracy * loc_num_train_nodes, loc_num_train_nodes]
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loc_acc_split = evaluate(
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model, labels, num_classes, val_loader, inv_target
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).to(device)
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dist.reduce(loc_acc_split, 0)
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if proc_id == 0:
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acc = loc_acc_split[0] / loc_acc_split[1]
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print(
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"Epoch {:05d} | Loss {:.4f} | Val. 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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def run(proc_id, nprocs, devices, g, data):
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# find corresponding device for my rank
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device = devices[proc_id]
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torch.cuda.set_device(device)
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# initialize process group and unpack data for sub-processes
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dist.init_process_group(
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backend="nccl",
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init_method="tcp://127.0.0.1:12345",
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world_size=nprocs,
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rank=proc_id,
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)
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(
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num_rels,
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num_classes,
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labels,
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train_idx,
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test_idx,
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target_idx,
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inv_target,
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) = data
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labels = labels.to(device)
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inv_target = inv_target.to(device)
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# create RGCN model (distributed)
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in_size = g.num_nodes()
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model = RGCN(in_size, 16, num_classes, num_rels).to(device)
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model = DistributedDataParallel(
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model, device_ids=[device], output_device=device
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)
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# training + testing
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train(
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proc_id,
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device,
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g,
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target_idx,
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labels,
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num_classes,
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train_idx,
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inv_target,
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model,
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)
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test_sampler = MultiLayerNeighborSampler(
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[-1, -1]
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) # -1 for sampling all neighbors
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test_loader = DataLoader(
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g,
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target_idx[test_idx],
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test_sampler,
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device=device,
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batch_size=32,
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shuffle=False,
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use_ddp=True,
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)
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loc_acc_split = evaluate(
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model, labels, num_classes, test_loader, inv_target
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).to(device)
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dist.reduce(loc_acc_split, 0)
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if proc_id == 0:
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acc = loc_acc_split[0] / loc_acc_split[1]
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print("Test accuracy {:.4f}".format(acc))
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# cleanup process group
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dist.destroy_process_group()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="RGCN for entity classification with sampling (multi-gpu)"
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)
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parser.add_argument(
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"--dataset",
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type=str,
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default="aifb",
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help="Dataset name ('aifb', 'mutag', 'bgs', 'am').",
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)
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parser.add_argument(
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"--gpu",
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type=str,
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default="0",
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help="GPU(s) in use. Can be a list of gpu ids for multi-gpu training,"
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" e.g., 0,1,2,3.",
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)
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args = parser.parse_args()
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devices = list(map(int, args.gpu.split(",")))
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nprocs = len(devices)
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print(
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f"Training with DGL built-in RGCN module with sampling using",
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nprocs,
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f"GPU(s)",
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)
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# load and preprocess dataset at master(parent) process
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if args.dataset == "aifb":
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data = AIFBDataset()
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elif args.dataset == "mutag":
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data = MUTAGDataset()
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elif args.dataset == "bgs":
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data = BGSDataset()
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elif args.dataset == "am":
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data = AMDataset()
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else:
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raise ValueError("Unknown dataset: {}".format(args.dataset))
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g = data[0]
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num_rels = len(g.canonical_etypes)
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category = data.predict_category
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labels = g.nodes[category].data.pop("labels")
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train_mask = g.nodes[category].data.pop("train_mask")
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test_mask = g.nodes[category].data.pop("test_mask")
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# find target category and node id
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category_id = g.ntypes.index(category)
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g = dgl.to_homogeneous(g)
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node_ids = torch.arange(g.num_nodes())
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target_idx = node_ids[g.ndata[dgl.NTYPE] == category_id]
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# rename the fields as they can be changed by DataLoader
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g.ndata["ntype"] = g.ndata.pop(dgl.NTYPE)
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g.ndata["type_id"] = g.ndata.pop(dgl.NID)
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# find the mapping (inv_target) from global node IDs to type-specific node IDs
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inv_target = torch.empty((g.num_nodes(),), dtype=torch.int64)
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inv_target[target_idx] = torch.arange(
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0, target_idx.shape[0], dtype=inv_target.dtype
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)
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# avoid creating certain graph formats and train/test indexes in each sub-process to save momory
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g.create_formats_()
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train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze()
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test_idx = torch.nonzero(test_mask, as_tuple=False).squeeze()
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# thread limiting to avoid resource competition
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os.environ["OMP_NUM_THREADS"] = str(mp.cpu_count() // 2 // nprocs)
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data = (
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num_rels,
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data.num_classes,
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labels,
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train_idx,
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test_idx,
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target_idx,
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inv_target,
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)
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mp.spawn(run, args=(nprocs, devices, g, data), nprocs=nprocs)
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