# adapted https://github.com/snap-stanford/ogb/blob/master/examples/graphproppred/code2/main_pyg.py import argparse import random import numpy as np import torch.optim as optim import pandas as pd import torch.multiprocessing from torchvision import transforms from tqdm import tqdm from ogb.graphproppred import Evaluator from src.utils import augment_edge, ASTNodeEncoder from src.model.gnn import GNN from src.data.dataset import PygGraphPropPredDataset from src.data.dataloader import DataLoader from src.data.data_parallel import DataParallel # make sure summary_report is imported after src.utils (also from dependencies) from src.utils_file import * torch.multiprocessing.set_sharing_strategy('file_system') multicls_criterion = torch.nn.CrossEntropyLoss() path = os.path.abspath(__file__) path = path[:path.rindex("/")] + "/../" PATH = os.path.abspath(path) def train(model, device, loader, optimizer, args, evaluator): model.train() y_true = [] y_pred = [] loss_accum = 0 for step, batch in enumerate(tqdm(loader, desc="Iteration")): # one b(atch) per device batch = [b for b in batch if not b.x.shape[0] == 1 and not b.batch[-1] == 0] if batch: pred = model(batch) optimizer.zero_grad() trg = torch.cat([b.y.to(device) for b in batch], dim=0) loss = multicls_criterion(pred, trg.to(torch.long).view(-1,)) loss.backward() if args.clip > 0: torch.nn.utils.clip_grad_norm(model.parameters(), args.clip) optimizer.step() loss_accum += loss.item() y_true.append(trg.view(-1,1).detach().cpu()) y_pred.append(torch.argmax(pred.detach(), dim=1).view(-1, 1).cpu()) y_true = torch.cat(y_true, dim=0).numpy() y_pred = torch.cat(y_pred, dim=0).numpy() # print(y_true) # print(y_pred) input_dict = {"y_true": y_true, "y_pred": y_pred} return loss_accum / (step + 1), evaluator.eval(input_dict) def eval(model, device, loader, evaluator): model.eval() y_true = [] y_pred = [] for step, batch in enumerate(tqdm(loader, desc="Iteration")): # one b(atch) per device batch = [b for b in batch if not b.x.shape[0] == 1] if batch: with torch.no_grad(): pred = model(batch) y_true += [b.y.view(-1, 1).detach().cpu() for b in batch] y_pred.append(torch.argmax(pred.detach(), dim=1).view(-1, 1).cpu()) y_pred = torch.cat(y_pred, dim=0) y_true = torch.cat(y_true, dim=0).view(y_pred.shape) y_true = y_true.numpy() y_pred = y_pred.numpy() # print(y_true) # print(y_pred) input_dict = {"y_true": y_true, "y_pred": y_pred} return evaluator.eval(input_dict) def main(): parser = argparse.ArgumentParser(description='GNN baselines on Project Codenet data with Pytorch Geometrics') parser.add_argument('--device', type=int, default=0, help='which gpu to use if any (default: 0)') parser.add_argument('--gnn', type=str, default="gcn", help='GNN gin, gin-virtual, or gcn, or gcn-virtual (default: gcn), ...') parser.add_argument('--drop_ratio', type=float, default=0, help='dropout ratio (default: 0)') parser.add_argument('--num_layer', type=int, default=5, help='number of GNN message passing layers (default: 5)') parser.add_argument('--emb_dim', type=int, default=300, help='dimensionality of hidden units in GNNs (default: 300)') parser.add_argument('--feat_nums', type=str, default="", help='comma separated string containing number of categories per feature ' '(default: "", meaning "let it be computed")') parser.add_argument('--batch_size', type=int, default=80, help='input batch size for training (default: 128)') parser.add_argument('--epochs', type=int, default=1000, help='number of epochs to train (default: 1000)') parser.add_argument('--num_workers', type=int, default=0, help='number of workers (default: 0)') parser.add_argument('--dataset', type=str, default="small", choices={"small","Java250", "Python800", "C++1000", "C++1400"}, help='dataset name (default: python1k)') parser.add_argument('--filename', type=str, default="test", help='filename to output result (default: test)') parser.add_argument('--dir_data', type=str, default=os.path.join(PATH, 'data'), help='directory where data should be stored (default: REPOSITORY/data)') parser.add_argument('--dir_results', type=str, default=os.path.join(PATH, 'results'), help='results directory (default: REPOSITORY/results)') parser.add_argument('--dir_save', default=os.path.join(PATH, 'saved_models'), help='directory to save checkpoints in (default: REPOSITORY/saved_models)') parser.add_argument('--checkpointing', default=0, type=int, choices=[0, 1], help='if you want to use checkpointing (1) or not (0) (default: 0)') parser.add_argument('--checkpoint', default="", help='path of checkpoint if any') parser.add_argument('--runs', default=10, type=int, help='number of runs (default: 10)') parser.add_argument('--clip', default=0, type=float, help='clipping value if gradient clipping should be uses (default: 0) ') parser.add_argument('--lr', default=1e-3, type=float, help='learning rate (default: 1e-3)') parser.add_argument('--patience', default=20, type=float, help='patience (default: 20)') ### args = parser.parse_args() device = torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu") os.makedirs(args.dir_results, exist_ok=True) os.makedirs(args.dir_save, exist_ok=True) save_args(args, os.path.join(args.dir_results, "a_" + args.filename + '.csv')) train_file = os.path.join(args.dir_results, 'd_' + args.filename + '.csv') if not os.path.exists(train_file): with open(train_file, 'w') as f: f.write("run,e,loss,tr_acc,va_acc,te_acc\n") res_file = os.path.join(args.dir_results, args.filename + '.csv') if not os.path.exists(res_file): with open(res_file, 'w') as f: f.write("r,e,tr_acc,va_acc,te_acc\n") # run, epoch, best results according to validation ### dataloading and splitting dataset = PygGraphPropPredDataset(name=args.dataset, root=os.path.abspath("../data") if args.dir_data is None else args.dir_data) print("Data loading done!") split_idx = dataset.get_idx_split() assert dataset[0].num_node_features <= args.emb_dim ### set the transform function # augment_edge: add next-token edge as well as inverse edges. add edge attributes. net = max(dataset.data.edge_type).item()+1 if hasattr(dataset.data, "edge_type") else 0 dataset.transform = transforms.Compose([lambda data: augment_edge(data, net)]) ### automatic acc evaluator. takes some dataset name as input evaluator = Evaluator("ogbg-ppa") edim = args.emb_dim dims = [] dataset.data.x.abs_() # ensure no negative -- should not be in there anyway if not args.feat_nums: # need to find out for d in range(dataset.data.num_node_features): dims += [max(dataset.data.x[:, d]).item() + 1] else: dims = [int(s) for s in args.feat_nums.split(",")] ### Encode node features into emb_dim vectors. node_encoder = ASTNodeEncoder(edim, max_depth=20, enc_dims=dims) start_run = 1 checkpoint_fn = "" train_results, valid_results, test_results = [], [], [] # on run level if args.checkpointing and args.checkpoint: s = args.checkpoint[:-3].split("_") start_run = int(s[-2]) start_epoch = int(s[-1]) + 1 checkpoint_fn = os.path.join(args.dir_save, args.checkpoint) # need to remove it in any case if start_epoch > args.epochs: # DISCARD checkpoint's model (ie not results), need a new model! args.checkpoint = "" start_run += 1 results = load_checkpoint_results(checkpoint_fn) train_results, valid_results, test_results, train_curve, valid_curve, test_curve = results # start for run in range(start_run, args.runs + 1): # run-specific settings & data splits torch.manual_seed(run) random.seed(run) np.random.seed(run) if torch.cuda.is_available(): torch.cuda.manual_seed(run) torch.backends.cudnn.benchmark = True # torch.backends.cudnn.deterministic = True # torch.backends.cudnn.benchmark = False n_devices = torch.cuda.device_count() if torch.cuda.device_count() > 0 else 1 train_loader = DataLoader(dataset[split_idx["train"]], batch_size=args.batch_size, shuffle=True, num_workers = args.num_workers, n_devices=n_devices) valid_loader = DataLoader(dataset[split_idx["valid"]], batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, n_devices=n_devices) test_loader = DataLoader(dataset[split_idx["test"]], batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers, n_devices=n_devices) start_epoch = 1 # model etc. model = init_model(args, node_encoder, numclass=dataset.num_classes, edge_attr_dim=net+2) print("Let's use", torch.cuda.device_count(), "GPUs! -- DataParallel running also on CPU only") device_ids = list(range(torch.cuda.device_count())) if torch.cuda.device_count() > 0 else None model = DataParallel(model, device_ids) model.to(device) optimizer = optim.Adam(model.parameters(), lr=args.lr) # overwrite some settings if args.checkpointing and args.checkpoint: # signal that it has been used args.checkpoint = "" results, start_epoch, model, optimizer = load_checkpoint(checkpoint_fn, model, optimizer) train_results, valid_results, test_results, train_curve, valid_curve, test_curve = results start_epoch += 1 else: valid_curve, test_curve, train_curve = [], [], [] # start new epoch for epoch in range(start_epoch, args.epochs + 1): old_checkpoint_fn = checkpoint_fn checkpoint_fn = '%s.pt' % os.path.join(args.dir_save, args.filename + "_" + str(run) + "_" + str(epoch)) print("=====Run {}, Epoch {}".format(run, epoch)) loss, train_perf = train(model, device, train_loader, optimizer, args, evaluator) valid_perf = eval(model, device, valid_loader, evaluator) test_perf = eval(model, device, test_loader, evaluator) print({'Train': train_perf, 'Validation': valid_perf, 'Test': test_perf}) with open(train_file, 'a') as f: f.write("{},{},{:.4f},{:.4f},{:.4f},{:.4f}\n".format(run, epoch, loss, train_perf[dataset.eval_metric], valid_perf[dataset.eval_metric], test_perf[dataset.eval_metric])) train_curve.append(train_perf[dataset.eval_metric]) valid_curve.append(valid_perf[dataset.eval_metric]) test_curve.append(test_perf[dataset.eval_metric]) if args.checkpointing: create_checkpoint(checkpoint_fn, epoch, model, optimizer, (train_results, valid_results, test_results, train_curve, valid_curve, test_curve)) if run > 1 or epoch > 1: remove_checkpoint(old_checkpoint_fn) best_val_epoch = np.argmax(np.array(valid_curve)) if args.patience > 0 and best_val_epoch + 1 + args.patience < epoch: print("Early stopping!") break print('Finished training for run {} !'.format(run)+"*"*20) print('Best validation score: {}'.format(valid_curve[best_val_epoch])) print('Test score: {}'.format(test_curve[best_val_epoch])) with open(res_file, 'a') as f: results = [run, best_val_epoch, train_curve[best_val_epoch], valid_curve[best_val_epoch],test_curve[best_val_epoch]] f.writelines(",".join([str(v) for v in results]) + "\n") train_results += [train_curve[best_val_epoch]] valid_results += [valid_curve[best_val_epoch]] test_results += [test_curve[best_val_epoch]] results = list(summary_report(train_results)) + list(summary_report(valid_results)) + list(summary_report(test_results)) # with open(res_file, 'a') as f: # f.writelines(str(run)+ ",_," + ",".join([str(v) for v in results]) + "\n") print(",".join([str(v) for v in results])) results = list(summary_report(train_results)) + list(summary_report(valid_results)) + list(summary_report(test_results)) with open(res_file, 'a') as f: f.writelines(str(run) + ",_," + ",".join([str(v) for v in results]) + "\n") # print(",".join([str(v) for v in results])) # we might want to add runs later # if args.checkpointing: # remove_checkpoint(checkpoint_fn) def init_model(args, node_encoder, numclass=275, edge_attr_dim=2): # this was only relevant for regression version n, m = 1, 1 if args.gnn == 'gin': model = GNN(num_vocab=n, max_seq_len=m, node_encoder=node_encoder, num_layer=args.num_layer, gnn_type='gin', emb_dim=args.emb_dim, drop_ratio=args.drop_ratio, virtual_node=False, num_class=numclass, edge_attr_dim=edge_attr_dim) elif args.gnn == 'gin-virtual': model = GNN(num_vocab=n, max_seq_len=m, node_encoder=node_encoder, num_layer=args.num_layer, gnn_type='gin', emb_dim=args.emb_dim, drop_ratio=args.drop_ratio, virtual_node=True, num_class=numclass, edge_attr_dim=edge_attr_dim) elif args.gnn == 'gcn': model = GNN(num_vocab=n, max_seq_len=m, node_encoder=node_encoder, num_layer=args.num_layer, gnn_type='gcn', emb_dim=args.emb_dim, drop_ratio=args.drop_ratio, virtual_node=False, num_class=numclass, edge_attr_dim=edge_attr_dim) elif args.gnn == 'gcn-virtual': model = GNN(num_vocab=n, max_seq_len=m, node_encoder=node_encoder, num_layer=args.num_layer, gnn_type='gcn', emb_dim=args.emb_dim, drop_ratio=args.drop_ratio, virtual_node=True, num_class=numclass, edge_attr_dim=edge_attr_dim) else: raise ValueError('Invalid GNN type') return model if __name__ == "__main__": main()