import dgl import numpy as np import torch as th import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import torch.multiprocessing as mp import dgl.nn.pytorch as dglnn import time import math import argparse from torch.nn.parallel import DistributedDataParallel import tqdm from utils import thread_wrapped_func from load_graph import load_reddit, inductive_split class SAGE(nn.Module): def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation, dropout): super().__init__() self.n_layers = n_layers self.n_hidden = n_hidden self.n_classes = n_classes self.layers = nn.ModuleList() self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean')) for i in range(1, n_layers - 1): self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean')) self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean')) self.dropout = nn.Dropout(dropout) self.activation = activation 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 = self.activation(h) h = self.dropout(h) return h def inference(self, g, x, device): """ Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling). g : the entire graph. x : the input of entire node set. The inference code is written in a fashion that it could handle any number of nodes and layers. """ # During inference with sampling, multi-layer blocks are very inefficient because # lots of computations in the first few layers are repeated. # Therefore, we compute the representation of all nodes layer by layer. The nodes # on each layer are of course splitted in batches. # TODO: can we standardize this? for l, layer in enumerate(self.layers): y = th.zeros(g.num_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes) sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1) dataloader = dgl.dataloading.NodeDataLoader( g, th.arange(g.num_nodes()), sampler, batch_size=args.batch_size, shuffle=True, drop_last=False, num_workers=args.num_workers) for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader): block = blocks[0] block = block.int().to(device) h = x[input_nodes].to(device) h = layer(block, h) if l != len(self.layers) - 1: h = self.activation(h) h = self.dropout(h) y[output_nodes] = h.cpu() x = y return y def compute_acc(pred, labels): """ Compute the accuracy of prediction given the labels. """ return (th.argmax(pred, dim=1) == labels).float().sum() / len(pred) def evaluate(model, g, nfeat, labels, val_nid, device): """ Evaluate the model on the validation set specified by ``val_nid``. g : The entire graph. inputs : The features of all the nodes. labels : The labels of all the nodes. val_nid : A node ID tensor indicating which nodes do we actually compute the accuracy for. device : The GPU device to evaluate on. """ model.eval() with th.no_grad(): pred = model.inference(g, nfeat, device) model.train() return compute_acc(pred[val_nid], labels[val_nid]) def load_subtensor(nfeat, labels, seeds, input_nodes, dev_id): """ Extracts features and labels for a subset of nodes. """ batch_inputs = nfeat[input_nodes].to(dev_id) batch_labels = labels[seeds].to(dev_id) return batch_inputs, batch_labels #### Entry point def run(proc_id, n_gpus, args, devices, data): # Start up distributed training, if enabled. dev_id = devices[proc_id] if n_gpus > 1: dist_init_method = 'tcp://{master_ip}:{master_port}'.format( master_ip='127.0.0.1', master_port='12345') world_size = n_gpus th.distributed.init_process_group(backend="nccl", init_method=dist_init_method, world_size=world_size, rank=proc_id) th.cuda.set_device(dev_id) # Unpack data n_classes, train_g, val_g, test_g = data if args.inductive: train_nfeat = train_g.ndata.pop('features') val_nfeat = val_g.ndata.pop('features') test_nfeat = test_g.ndata.pop('features') train_labels = train_g.ndata.pop('labels') val_labels = val_g.ndata.pop('labels') test_labels = test_g.ndata.pop('labels') else: train_nfeat = val_nfeat = test_nfeat = g.ndata.pop('features') train_labels = val_labels = test_labels = g.ndata.pop('labels') if not args.data_cpu: train_nfeat = train_nfeat.to(dev_id) train_labels = train_labels.to(dev_id) in_feats = train_nfeat.shape[1] train_mask = train_g.ndata['train_mask'] val_mask = val_g.ndata['val_mask'] test_mask = ~(test_g.ndata['train_mask'] | test_g.ndata['val_mask']) train_nid = train_mask.nonzero().squeeze() val_nid = val_mask.nonzero().squeeze() test_nid = test_mask.nonzero().squeeze() # Split train_nid train_nid = th.split(train_nid, math.ceil(len(train_nid) / n_gpus))[proc_id] # Create PyTorch DataLoader for constructing blocks sampler = dgl.dataloading.MultiLayerNeighborSampler( [int(fanout) for fanout in args.fan_out.split(',')]) dataloader = dgl.dataloading.NodeDataLoader( train_g, train_nid, sampler, batch_size=args.batch_size, shuffle=True, drop_last=False, num_workers=args.num_workers) # Define model and optimizer model = SAGE(in_feats, args.num_hidden, n_classes, args.num_layers, F.relu, args.dropout) model = model.to(dev_id) if n_gpus > 1: model = DistributedDataParallel(model, device_ids=[dev_id], output_device=dev_id) loss_fcn = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=args.lr) # Training loop avg = 0 iter_tput = [] for epoch in range(args.num_epochs): tic = time.time() # Loop over the dataloader to sample the computation dependency graph as a list of # blocks. for step, (input_nodes, seeds, blocks) in enumerate(dataloader): if proc_id == 0: tic_step = time.time() # Load the input features as well as output labels batch_inputs, batch_labels = load_subtensor(train_nfeat, train_labels, seeds, input_nodes, dev_id) blocks = [block.int().to(dev_id) for block in blocks] # Compute loss and prediction batch_pred = model(blocks, batch_inputs) loss = loss_fcn(batch_pred, batch_labels) optimizer.zero_grad() loss.backward() optimizer.step() if proc_id == 0: iter_tput.append(len(seeds) * n_gpus / (time.time() - tic_step)) if step % args.log_every == 0 and proc_id == 0: acc = compute_acc(batch_pred, batch_labels) print('Epoch {:05d} | Step {:05d} | Loss {:.4f} | Train Acc {:.4f} | Speed (samples/sec) {:.4f} | GPU {:.1f} MB'.format( epoch, step, loss.item(), acc.item(), np.mean(iter_tput[3:]), th.cuda.max_memory_allocated() / 1000000)) if n_gpus > 1: th.distributed.barrier() toc = time.time() if proc_id == 0: print('Epoch Time(s): {:.4f}'.format(toc - tic)) if epoch >= 5: avg += toc - tic if epoch % args.eval_every == 0 and epoch != 0: if n_gpus == 1: eval_acc = evaluate( model, val_g, val_nfeat, val_labels, val_nid, devices[0]) test_acc = evaluate( model, test_g, test_nfeat, test_labels, test_nid, devices[0]) else: eval_acc = evaluate( model.module, val_g, val_nfeat, val_labels, val_nid, devices[0]) test_acc = evaluate( model.module, test_g, test_nfeat, test_labels, test_nid, devices[0]) print('Eval Acc {:.4f}'.format(eval_acc)) print('Test Acc: {:.4f}'.format(test_acc)) if n_gpus > 1: th.distributed.barrier() if proc_id == 0: print('Avg epoch time: {}'.format(avg / (epoch - 4))) if __name__ == '__main__': argparser = argparse.ArgumentParser("multi-gpu training") argparser.add_argument('--gpu', type=str, default='0', help="Comma separated list of GPU device IDs.") argparser.add_argument('--num-epochs', type=int, default=20) argparser.add_argument('--num-hidden', type=int, default=16) argparser.add_argument('--num-layers', type=int, default=2) argparser.add_argument('--fan-out', type=str, default='10,25') argparser.add_argument('--batch-size', type=int, default=1000) argparser.add_argument('--log-every', type=int, default=20) argparser.add_argument('--eval-every', type=int, default=5) argparser.add_argument('--lr', type=float, default=0.003) argparser.add_argument('--dropout', type=float, default=0.5) argparser.add_argument('--num-workers', type=int, default=0, help="Number of sampling processes. Use 0 for no extra process.") argparser.add_argument('--inductive', action='store_true', help="Inductive learning setting") argparser.add_argument('--data-cpu', action='store_true', help="By default the script puts all node features and labels " "on GPU when using it to save time for data copy. This may " "be undesired if they cannot fit in GPU memory at once. " "This flag disables that.") args = argparser.parse_args() devices = list(map(int, args.gpu.split(','))) n_gpus = len(devices) g, n_classes = load_reddit() # Construct graph g = dgl.as_heterograph(g) if args.inductive: train_g, val_g, test_g = inductive_split(g) else: train_g = val_g = test_g = g # Create csr/coo/csc formats before launching training processes with multi-gpu. # This avoids creating certain formats in each sub-process, which saves momory and CPU. train_g.create_formats_() val_g.create_formats_() test_g.create_formats_() # Pack data data = n_classes, train_g, val_g, test_g if n_gpus == 1: run(0, n_gpus, args, devices, data) else: procs = [] for proc_id in range(n_gpus): p = mp.Process(target=thread_wrapped_func(run), args=(proc_id, n_gpus, args, devices, data)) p.start() procs.append(p) for p in procs: p.join()