[Misc] Black auto fix. (#4651)

Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

examples/pytorch/graphsage/advanced/negative_sampler.py

 import torch as th
+
 import dgl
 
+
 class NegativeSampler(object):
     def __init__(self, g, k, neg_share=False, device=None):
         if device is None:
@@ -16,6 +18,6 @@ class NegativeSampler(object):
             dst = self.weights.multinomial(n, replacement=True)
             dst = dst.view(-1, 1, self.k).expand(-1, self.k, -1).flatten()
         else:
-            dst = self.weights.multinomial(n*self.k, replacement=True)
+            dst = self.weights.multinomial(n * self.k, replacement=True)
         src = src.repeat_interleave(self.k)
         return src, dst

examples/pytorch/graphsage/advanced/pure_gpu_node_classification.py

+import argparse
+import time
+
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchmetrics.functional as MF
+import tqdm
+from ogb.nodeproppred import DglNodePropPredDataset
+
 import dgl
 import dgl.nn as dglnn
-import time
-import numpy as np
-from ogb.nodeproppred import DglNodePropPredDataset
-import tqdm
-import argparse
+
 
 class SAGE(nn.Module):
     def __init__(self, in_feats, n_hidden, n_classes):
         super().__init__()
         self.layers = nn.ModuleList()
-        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean'))
-        self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean'))
-        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean'))
+        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, "mean"))
+        self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, "mean"))
+        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, "mean"))
         self.dropout = nn.Dropout(0.5)
         self.n_hidden = n_hidden
         self.n_classes = n_classes
@@ -33,20 +36,31 @@ class SAGE(nn.Module):
     def inference(self, g, device, batch_size, num_workers, buffer_device=None):
         # The difference between this inference function and the one in the official
         # example is that the intermediate results can also benefit from prefetching.
-        feat = g.ndata['feat']
-        sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1, prefetch_node_feats=['feat'])
+        feat = g.ndata["feat"]
+        sampler = dgl.dataloading.MultiLayerFullNeighborSampler(
+            1, prefetch_node_feats=["feat"]
+        )
         dataloader = dgl.dataloading.DataLoader(
-                g, torch.arange(g.num_nodes()).to(g.device), sampler, device=device,
-                batch_size=batch_size, shuffle=False, drop_last=False,
-                num_workers=num_workers)
+            g,
+            torch.arange(g.num_nodes()).to(g.device),
+            sampler,
+            device=device,
+            batch_size=batch_size,
+            shuffle=False,
+            drop_last=False,
+            num_workers=num_workers,
+        )
 
         if buffer_device is None:
             buffer_device = device
 
         for l, layer in enumerate(self.layers):
             y = torch.empty(
-                g.num_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes,
-                device=buffer_device, pin_memory=True)
+                g.num_nodes(),
+                self.n_hidden if l != len(self.layers) - 1 else self.n_classes,
+                device=buffer_device,
+                pin_memory=True,
+            )
             feat = feat.to(device)
             for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
                 # use an explicitly contuous slice
@@ -57,44 +71,64 @@ class SAGE(nn.Module):
                     h = self.dropout(h)
                 # be design, our output nodes are contiguous so we can take
                 # advantage of that here
-                y[output_nodes[0]:output_nodes[-1]+1] = h.to(buffer_device)
+                y[output_nodes[0] : output_nodes[-1] + 1] = h.to(buffer_device)
             feat = y
         return y
 
 
-
-dataset = DglNodePropPredDataset('ogbn-products')
+dataset = DglNodePropPredDataset("ogbn-products")
 graph, labels = dataset[0]
-graph.ndata['label'] = labels.squeeze()
+graph.ndata["label"] = labels.squeeze()
 split_idx = dataset.get_idx_split()
-train_idx, valid_idx, test_idx = split_idx['train'], split_idx['valid'], split_idx['test']
+train_idx, valid_idx, test_idx = (
+    split_idx["train"],
+    split_idx["valid"],
+    split_idx["test"],
+)
 
-device = 'cuda'
+device = "cuda"
 train_idx = train_idx.to(device)
 valid_idx = valid_idx.to(device)
 test_idx = test_idx.to(device)
 
 graph = graph.to(device)
 
-model = SAGE(graph.ndata['feat'].shape[1], 256, dataset.num_classes).to(device)
+model = SAGE(graph.ndata["feat"].shape[1], 256, dataset.num_classes).to(device)
 opt = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=5e-4)
 
 sampler = dgl.dataloading.NeighborSampler(
-        [15, 10, 5], prefetch_node_feats=['feat'], prefetch_labels=['label'])
+    [15, 10, 5], prefetch_node_feats=["feat"], prefetch_labels=["label"]
+)
 train_dataloader = dgl.dataloading.DataLoader(
-        graph, train_idx, sampler, device=device, batch_size=1024, shuffle=True,
-        drop_last=False, num_workers=0, use_uva=False)
+    graph,
+    train_idx,
+    sampler,
+    device=device,
+    batch_size=1024,
+    shuffle=True,
+    drop_last=False,
+    num_workers=0,
+    use_uva=False,
+)
 valid_dataloader = dgl.dataloading.DataLoader(
-        graph, valid_idx, sampler, device=device, batch_size=1024, shuffle=True,
-        drop_last=False, num_workers=0, use_uva=False)
+    graph,
+    valid_idx,
+    sampler,
+    device=device,
+    batch_size=1024,
+    shuffle=True,
+    drop_last=False,
+    num_workers=0,
+    use_uva=False,
+)
 
 durations = []
 for _ in range(10):
     model.train()
     t0 = time.time()
     for it, (input_nodes, output_nodes, blocks) in enumerate(train_dataloader):
-        x = blocks[0].srcdata['feat']
-        y = blocks[-1].dstdata['label']
+        x = blocks[0].srcdata["feat"]
+        y = blocks[-1].dstdata["label"]
         y_hat = model(blocks, x)
         loss = F.cross_entropy(y_hat, y)
         opt.zero_grad()
@@ -103,7 +137,7 @@ for _ in range(10):
         if it % 20 == 0:
             acc = MF.accuracy(torch.argmax(y_hat, dim=1), y)
             mem = torch.cuda.max_memory_allocated() / 1000000
-            print('Loss', loss.item(), 'Acc', acc.item(), 'GPU Mem', mem, 'MB')
+            print("Loss", loss.item(), "Acc", acc.item(), "GPU Mem", mem, "MB")
     tt = time.time()
     print(tt - t0)
     durations.append(tt - t0)
@@ -113,19 +147,19 @@ for _ in range(10):
     y_hats = []
     for it, (input_nodes, output_nodes, blocks) in enumerate(valid_dataloader):
         with torch.no_grad():
-            x = blocks[0].srcdata['feat']
-            ys.append(blocks[-1].dstdata['label'])
+            x = blocks[0].srcdata["feat"]
+            ys.append(blocks[-1].dstdata["label"])
             y_hats.append(torch.argmax(model(blocks, x), dim=1))
     acc = MF.accuracy(torch.cat(y_hats), torch.cat(ys))
-    print('Validation acc:', acc.item())
+    print("Validation acc:", acc.item())
 
 print(np.mean(durations[4:]), np.std(durations[4:]))
 
 # Test accuracy and offline inference of all nodes
 model.eval()
 with torch.no_grad():
-    pred = model.inference(graph, device, 4096, 0, 'cpu')
+    pred = model.inference(graph, device, 4096, 0, "cpu")
     pred = pred[test_idx].to(device)
-    label = graph.ndata['label'][test_idx]
+    label = graph.ndata["label"][test_idx]
     acc = MF.accuracy(torch.argmax(pred, dim=1), label)
-    print('Test acc:', acc.item())
+    print("Test acc:", acc.item())

examples/pytorch/graphsage/dist/partition_graph.py

-import dgl
-import numpy as np
-import torch as th
 import argparse
-import time
-import sys
 import os
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
-from load_graph import load_reddit, load_ogb
+import sys
+import time
+
+import numpy as np
+import torch as th
+
+import dgl
+
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+from load_graph import load_ogb, load_reddit
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     argparser = argparse.ArgumentParser("Partition builtin graphs")
-    argparser.add_argument('--dataset', type=str, default='reddit',
-                           help='datasets: reddit, ogb-product, ogb-paper100M')
-    argparser.add_argument('--num_parts', type=int, default=4,
-                           help='number of partitions')
-    argparser.add_argument('--part_method', type=str, default='metis',
-                           help='the partition method')
-    argparser.add_argument('--balance_train', action='store_true',
-                           help='balance the training size in each partition.')
-    argparser.add_argument('--undirected', action='store_true',
-                           help='turn the graph into an undirected graph.')
-    argparser.add_argument('--balance_edges', action='store_true',
-                           help='balance the number of edges in each partition.')
-    argparser.add_argument('--num_trainers_per_machine', type=int, default=1,
-                           help='the number of trainers per machine. The trainer ids are stored\
-                                in the node feature \'trainer_id\'')
-    argparser.add_argument('--output', type=str, default='data',
-                           help='Output path of partitioned graph.')
+    argparser.add_argument(
+        "--dataset",
+        type=str,
+        default="reddit",
+        help="datasets: reddit, ogb-product, ogb-paper100M",
+    )
+    argparser.add_argument(
+        "--num_parts", type=int, default=4, help="number of partitions"
+    )
+    argparser.add_argument(
+        "--part_method", type=str, default="metis", help="the partition method"
+    )
+    argparser.add_argument(
+        "--balance_train",
+        action="store_true",
+        help="balance the training size in each partition.",
+    )
+    argparser.add_argument(
+        "--undirected",
+        action="store_true",
+        help="turn the graph into an undirected graph.",
+    )
+    argparser.add_argument(
+        "--balance_edges",
+        action="store_true",
+        help="balance the number of edges in each partition.",
+    )
+    argparser.add_argument(
+        "--num_trainers_per_machine",
+        type=int,
+        default=1,
+        help="the number of trainers per machine. The trainer ids are stored\
+                                in the node feature 'trainer_id'",
+    )
+    argparser.add_argument(
+        "--output",
+        type=str,
+        default="data",
+        help="Output path of partitioned graph.",
+    )
     args = argparser.parse_args()
 
     start = time.time()
-    if args.dataset == 'reddit':
+    if args.dataset == "reddit":
         g, _ = load_reddit()
-    elif args.dataset == 'ogb-product':
-        g, _ = load_ogb('ogbn-products')
-    elif args.dataset == 'ogb-paper100M':
-        g, _ = load_ogb('ogbn-papers100M')
-    print('load {} takes {:.3f} seconds'.format(args.dataset, time.time() - start))
-    print('|V|={}, |E|={}'.format(g.number_of_nodes(), g.number_of_edges()))
-    print('train: {}, valid: {}, test: {}'.format(th.sum(g.ndata['train_mask']),
-                                                  th.sum(g.ndata['val_mask']),
-                                                  th.sum(g.ndata['test_mask'])))
+    elif args.dataset == "ogb-product":
+        g, _ = load_ogb("ogbn-products")
+    elif args.dataset == "ogb-paper100M":
+        g, _ = load_ogb("ogbn-papers100M")
+    print(
+        "load {} takes {:.3f} seconds".format(args.dataset, time.time() - start)
+    )
+    print("|V|={}, |E|={}".format(g.number_of_nodes(), g.number_of_edges()))
+    print(
+        "train: {}, valid: {}, test: {}".format(
+            th.sum(g.ndata["train_mask"]),
+            th.sum(g.ndata["val_mask"]),
+            th.sum(g.ndata["test_mask"]),
+        )
+    )
     if args.balance_train:
-        balance_ntypes = g.ndata['train_mask']
+        balance_ntypes = g.ndata["train_mask"]
     else:
         balance_ntypes = None
 
@@ -52,8 +84,13 @@ if __name__ == '__main__':
             sym_g.ndata[key] = g.ndata[key]
         g = sym_g
 
-    dgl.distributed.partition_graph(g, args.dataset, args.num_parts, args.output,
-                                    part_method=args.part_method,
-                                    balance_ntypes=balance_ntypes,
-                                    balance_edges=args.balance_edges,
-                                    num_trainers_per_machine=args.num_trainers_per_machine)
+    dgl.distributed.partition_graph(
+        g,
+        args.dataset,
+        args.num_parts,
+        args.output,
+        part_method=args.part_method,
+        balance_ntypes=balance_ntypes,
+        balance_edges=args.balance_edges,
+        num_trainers_per_machine=args.num_trainers_per_machine,
+    )

examples/pytorch/graphsage/distgnn/partition_graph.py

@@ -11,47 +11,49 @@ Copyright (c) 2021 Intel Corporation
 """
 
 
-import os
-import sys
-import numpy as np
+import argparse
 import csv
-from statistics import mean
+import os
 import random
+import sys
 import time
-import argparse
-sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+from statistics import mean
+
+import numpy as np
+
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
 from load_graph import load_ogb
+
 import dgl
+from dgl.base import DGLError
 from dgl.data import load_data
 from dgl.distgnn.partition import partition_graph
 from dgl.distgnn.tools import load_proteins
-from dgl.base import DGLError
-
 
 if __name__ == "__main__":
     argparser = argparse.ArgumentParser()
-    argparser.add_argument('--dataset', type=str, default='cora')
-    argparser.add_argument('--num-parts', type=int, default=2)
-    argparser.add_argument('--out-dir', type=str, default='./')
+    argparser.add_argument("--dataset", type=str, default="cora")
+    argparser.add_argument("--num-parts", type=int, default=2)
+    argparser.add_argument("--out-dir", type=str, default="./")
     args = argparser.parse_args()
 
     dataset = args.dataset
     num_community = args.num_parts
-    out_dir = 'Libra_result_' + dataset   ## "Libra_result_" prefix is mandatory
+    out_dir = "Libra_result_" + dataset  ## "Libra_result_" prefix is mandatory
     resultdir = os.path.join(args.out_dir, out_dir)
 
     print("Input dataset for partitioning: ", dataset)
-    if args.dataset == 'ogbn-products':
+    if args.dataset == "ogbn-products":
         print("Loading ogbn-products")
-        G, _ = load_ogb('ogbn-products')
-    elif args.dataset == 'ogbn-papers100M':
+        G, _ = load_ogb("ogbn-products")
+    elif args.dataset == "ogbn-papers100M":
         print("Loading ogbn-papers100M")
-        G, _ = load_ogb('ogbn-papers100M')
-    elif args.dataset == 'proteins':
-        G = load_proteins('proteins')
-    elif args.dataset == 'ogbn-arxiv':
+        G, _ = load_ogb("ogbn-papers100M")
+    elif args.dataset == "proteins":
+        G = load_proteins("proteins")
+    elif args.dataset == "ogbn-arxiv":
         print("Loading ogbn-arxiv")
-        G, _ = load_ogb('ogbn-arxiv')
+        G, _ = load_ogb("ogbn-arxiv")
     else:
         try:
             G = load_data(args)[0]

examples/pytorch/graphsage/load_graph.py

-import dgl
 import torch as th
 
+import dgl
+
+
 def load_reddit(self_loop=True):
     from dgl.data import RedditDataset
 
     # load reddit data
     data = RedditDataset(self_loop=self_loop)
     g = data[0]
-    g.ndata['features'] = g.ndata.pop('feat')
-    g.ndata['labels'] = g.ndata.pop('label')
+    g.ndata["features"] = g.ndata.pop("feat")
+    g.ndata["labels"] = g.ndata.pop("label")
     return g, data.num_classes
 
-def load_ogb(name, root='dataset'):
+
+def load_ogb(name, root="dataset"):
     from ogb.nodeproppred import DglNodePropPredDataset
 
-    print('load', name)
+    print("load", name)
     data = DglNodePropPredDataset(name=name, root=root)
-    print('finish loading', name)
+    print("finish loading", name)
     splitted_idx = data.get_idx_split()
     graph, labels = data[0]
     labels = labels[:, 0]
 
-    graph.ndata['features'] = graph.ndata.pop('feat')
-    graph.ndata['labels'] = labels
-    in_feats = graph.ndata['features'].shape[1]
+    graph.ndata["features"] = graph.ndata.pop("feat")
+    graph.ndata["labels"] = labels
+    in_feats = graph.ndata["features"].shape[1]
     num_labels = len(th.unique(labels[th.logical_not(th.isnan(labels))]))
 
     # Find the node IDs in the training, validation, and test set.
-    train_nid, val_nid, test_nid = splitted_idx['train'], splitted_idx['valid'], splitted_idx['test']
+    train_nid, val_nid, test_nid = (
+        splitted_idx["train"],
+        splitted_idx["valid"],
+        splitted_idx["test"],
+    )
     train_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
     train_mask[train_nid] = True
     val_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
     val_mask[val_nid] = True
     test_mask = th.zeros((graph.number_of_nodes(),), dtype=th.bool)
     test_mask[test_nid] = True
-    graph.ndata['train_mask'] = train_mask
-    graph.ndata['val_mask'] = val_mask
-    graph.ndata['test_mask'] = test_mask
-    print('finish constructing', name)
+    graph.ndata["train_mask"] = train_mask
+    graph.ndata["val_mask"] = val_mask
+    graph.ndata["test_mask"] = test_mask
+    print("finish constructing", name)
     return graph, num_labels
 
+
 def inductive_split(g):
     """Split the graph into training graph, validation graph, and test graph by training
     and validation masks.  Suitable for inductive models."""
-    train_g = g.subgraph(g.ndata['train_mask'])
-    val_g = g.subgraph(g.ndata['train_mask'] | g.ndata['val_mask'])
+    train_g = g.subgraph(g.ndata["train_mask"])
+    val_g = g.subgraph(g.ndata["train_mask"] | g.ndata["val_mask"])
     test_g = g
     return train_g, val_g, test_g