def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation, dropout, lr): super().__init__() self.save_hyperparameters() self.module = SAGE(in_feats, n_hidden, n_classes, n_layers, activation, dropout) self.lr = lr self.loss_fcn = CrossEntropyLoss()
def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation, dropout, lr): super().__init__() self.save_hyperparameters() self.module = SAGE(in_feats, n_hidden, n_classes, n_layers, activation, dropout) self.lr = lr # The usage of `train_acc` and `val_acc` is the recommended practice from now on as per # https://torchmetrics.readthedocs.io/en/latest/pages/lightning.html self.train_acc = Accuracy() self.val_acc = Accuracy()
def run(args, device, data): # Unpack data n_classes, train_g, val_g, test_g, train_nfeat, train_labels, \ val_nfeat, val_labels, test_nfeat, test_labels = data in_feats = train_nfeat.shape[1] train_nid = th.nonzero(train_g.ndata['train_mask'], as_tuple=True)[0] val_nid = th.nonzero(val_g.ndata['val_mask'], as_tuple=True)[0] test_nid = th.nonzero( ~(test_g.ndata['train_mask'] | test_g.ndata['val_mask']), as_tuple=True)[0] dataloader_device = th.device('cpu') if args.sample_gpu: train_nid = train_nid.to(device) # copy only the csc to the GPU train_g = train_g.formats(['csc']) train_g = train_g.to(device) dataloader_device = device # 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, device=dataloader_device, batch_size=args.batch_size, shuffle=True, drop_last=False, num_workers=args.num_workers) if args.data_cpu: # Convert input feature tensor to unified tensor train_nfeat = dgl.contrib.UnifiedTensor(train_nfeat, device=device) # Define model and optimizer model = SAGE(in_feats, args.num_hidden, n_classes, args.num_layers, F.relu, args.dropout) model = model.to(device) 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. tic_step = time.time() for step, (input_nodes, seeds, blocks) in enumerate(dataloader): # Load the input features as well as output labels batch_inputs, batch_labels = load_subtensor( train_nfeat, train_labels, seeds, input_nodes, device) blocks = [block.int().to(device) 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() iter_tput.append(len(seeds) / (time.time() - tic_step)) if step % args.log_every == 0: acc = compute_acc(batch_pred, batch_labels) gpu_mem_alloc = th.cuda.max_memory_allocated( ) / 1000000 if th.cuda.is_available() else 0 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:]), gpu_mem_alloc)) tic_step = time.time() toc = time.time() print('Epoch Time(s): {:.4f}'.format(toc - tic)) if epoch >= 5: avg += toc - tic if epoch % args.eval_every == 0 and epoch != 0: eval_acc = evaluate(model, val_g, val_nfeat, val_labels, val_nid, device) print('Eval Acc {:.4f}'.format(eval_acc)) test_acc = evaluate(model, test_g, test_nfeat, test_labels, test_nid, device) print('Test Acc: {:.4f}'.format(test_acc)) print('Avg epoch time: {}'.format(avg / (epoch - 4)))
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() # 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, use_ddp=n_gpus > 1, device=dev_id if args.num_workers == 0 else None, 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): if n_gpus > 1: dataloader.set_epoch(epoch) 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)))
def run(proc_id, n_gpus, args, devices, data): # Unpack data device = 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) train_mask, val_mask, test_mask, n_classes, g = data nfeat = g.ndata.pop('feat') labels = g.ndata.pop('label') in_feats = nfeat.shape[1] train_nid = th.LongTensor(np.nonzero(train_mask)).squeeze() val_nid = th.LongTensor(np.nonzero(val_mask)).squeeze() test_nid = th.LongTensor(np.nonzero(test_mask)).squeeze() # Create PyTorch DataLoader for constructing blocks n_edges = g.num_edges() train_seeds = th.arange(n_edges) # Create sampler sampler = dgl.dataloading.MultiLayerNeighborSampler( [int(fanout) for fanout in args.fan_out.split(',')]) dataloader = dgl.dataloading.EdgeDataLoader( g, train_seeds, sampler, exclude='reverse_id', # For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2. reverse_eids=th.cat( [th.arange(n_edges // 2, n_edges), th.arange(0, n_edges // 2)]).to(train_seeds), negative_sampler=NegativeSampler(g, args.num_negs, args.neg_share), device=device, use_ddp=n_gpus > 1, 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, args.num_hidden, args.num_layers, F.relu, args.dropout) model = model.to(device) if n_gpus > 1: model = DistributedDataParallel(model, device_ids=[device], output_device=device) loss_fcn = CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=args.lr) # Training loop avg = 0 iter_pos = [] iter_neg = [] iter_d = [] iter_t = [] best_eval_acc = 0 best_test_acc = 0 for epoch in range(args.num_epochs): if n_gpus > 1: dataloader.set_epoch(epoch) tic = time.time() # Loop over the dataloader to sample the computation dependency graph as a list of # blocks. tic_step = time.time() for step, (input_nodes, pos_graph, neg_graph, blocks) in enumerate(dataloader): batch_inputs = nfeat[input_nodes].to(device) d_step = time.time() pos_graph = pos_graph.to(device) neg_graph = neg_graph.to(device) blocks = [block.int().to(device) for block in blocks] # Compute loss and prediction batch_pred = model(blocks, batch_inputs) loss = loss_fcn(batch_pred, pos_graph, neg_graph) optimizer.zero_grad() loss.backward() optimizer.step() t = time.time() pos_edges = pos_graph.num_edges() neg_edges = neg_graph.num_edges() iter_pos.append(pos_edges / (t - tic_step)) iter_neg.append(neg_edges / (t - tic_step)) iter_d.append(d_step - tic_step) iter_t.append(t - d_step) if step % args.log_every == 0: gpu_mem_alloc = th.cuda.max_memory_allocated( ) / 1000000 if th.cuda.is_available() else 0 print( '[{}]Epoch {:05d} | Step {:05d} | Loss {:.4f} | Speed (samples/sec) {:.4f}|{:.4f} | Load {:.4f}| train {:.4f} | GPU {:.1f} MB' .format(proc_id, epoch, step, loss.item(), np.mean(iter_pos[3:]), np.mean(iter_neg[3:]), np.mean(iter_d[3:]), np.mean(iter_t[3:]), gpu_mem_alloc)) tic_step = time.time() if step % args.eval_every == 0 and proc_id == 0: eval_acc, test_acc = evaluate(model, g, nfeat, labels, train_nid, val_nid, test_nid, device) print('Eval Acc {:.4f} Test Acc {:.4f}'.format( eval_acc, test_acc)) if eval_acc > best_eval_acc: best_eval_acc = eval_acc best_test_acc = test_acc print('Best Eval Acc {:.4f} Test Acc {:.4f}'.format( best_eval_acc, best_test_acc)) toc = time.time() if proc_id == 0: print('Epoch Time(s): {:.4f}'.format(toc - tic)) if epoch >= 5: avg += toc - tic if n_gpus > 1: th.distributed.barrier() if proc_id == 0: print('Avg epoch time: {}'.format(avg / (epoch - 4)))
def run(args, device, data): # Unpack data # the reading data part need to be changed n_classes, train_g, val_g, test_g, train_nfeat, train_labels, \ val_nfeat, val_labels, test_nfeat, test_labels, idx_train, \ idx_val, idx_test = data in_feats = train_nfeat.shape[1] dataloader_device = device train_nid = th.tensor(idx_train, device=dataloader_device) test_nid = th.tensor(idx_test, device=dataloader_device) val_nid = th.tensor(idx_val, device=dataloader_device) # Create PyTorch DataLoader for constructing blocks sampler = dgl.dataloading.MultiLayerNeighborSampler( [int(fanout) for fanout in args.fan_out.split(',')]) # their new dataloader will automatically call our graphsage. # no need to change this part dataloader = dgl.dataloading.DataLoader(train_g, train_nid, sampler, device=dataloader_device, 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(device) 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. tic_step = time.time() for step, (input_nodes, seeds, blocks) in enumerate(dataloader): # Load the input features as well as output labels batch_inputs, batch_labels = load_subtensor( train_nfeat, train_labels, seeds, input_nodes, device) blocks = [block.int().to(device) 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() iter_tput.append(len(seeds) / (time.time() - tic_step)) if step % args.log_every == 0: acc = compute_acc(batch_pred, batch_labels) # gpu_mem_alloc = th.cuda.max_memory_allocated() / 1000000 \ # if th.cuda.is_available() else 0 print( 'Epoch {: 05d} | Step {: 05d} | Loss {: .4f} | Train Acc {: .4f} | Speed (samples/sec) {: .4f}' .format(epoch, step, loss.item(), acc.item(), np.mean(iter_tput[3:]))) tic_step = time.time() toc = time.time() print('Epoch Time(s): {:.4f}'.format(toc - tic)) if epoch >= 5: avg += toc - tic if epoch % args.eval_every == 0 and epoch != 0: eval_acc = evaluate(model, val_g, val_nfeat, val_labels, val_nid, device) print('Eval Acc {:.4f}'.format(eval_acc)) test_acc = evaluate(model, test_g, test_nfeat, test_labels, test_nid, device) print('Test Acc: {:.4f}'.format(test_acc)) print('Avg epoch time: {}'.format(avg / (epoch - 4)))