chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import argparse
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import dgl
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import dgl.nn as dglnn
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchmetrics.functional as MF
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import tqdm
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from dgl.data import AsNodePredDataset
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from dgl.dataloading import (
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DataLoader,
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MultiLayerFullNeighborSampler,
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NeighborSampler,
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)
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from ogb.nodeproppred import DglNodePropPredDataset
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class SAGE(nn.Module):
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def __init__(self, in_size, hid_size, out_size):
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super().__init__()
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self.layers = nn.ModuleList()
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# three-layer GraphSAGE-mean
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self.layers.append(dglnn.SAGEConv(in_size, hid_size, "mean"))
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self.layers.append(dglnn.SAGEConv(hid_size, hid_size, "mean"))
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self.layers.append(dglnn.SAGEConv(hid_size, out_size, "mean"))
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self.dropout = nn.Dropout(0.5)
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self.hid_size = hid_size
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self.out_size = out_size
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def forward(self, blocks, x):
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h = x
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for l, (layer, block) in enumerate(zip(self.layers, blocks)):
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h = layer(block, h)
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if l != len(self.layers) - 1:
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h = F.relu(h)
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h = self.dropout(h)
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return h
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def inference(self, g, device, batch_size):
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"""Conduct layer-wise inference to get all the node embeddings."""
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feat = g.ndata["feat"]
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sampler = MultiLayerFullNeighborSampler(1, prefetch_node_feats=["feat"])
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dataloader = DataLoader(
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g,
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torch.arange(g.num_nodes()).to(g.device),
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sampler,
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device=device,
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batch_size=batch_size,
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shuffle=False,
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drop_last=False,
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num_workers=0,
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)
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buffer_device = torch.device("cpu")
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pin_memory = buffer_device != device
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for l, layer in enumerate(self.layers):
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y = torch.empty(
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g.num_nodes(),
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self.hid_size if l != len(self.layers) - 1 else self.out_size,
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dtype=feat.dtype,
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device=buffer_device,
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pin_memory=pin_memory,
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)
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feat = feat.to(device)
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for input_nodes, output_nodes, blocks in tqdm.tqdm(dataloader):
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x = feat[input_nodes]
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h = layer(blocks[0], x) # len(blocks) = 1
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if l != len(self.layers) - 1:
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h = F.relu(h)
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h = self.dropout(h)
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# by design, our output nodes are contiguous
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y[output_nodes[0] : output_nodes[-1] + 1] = h.to(buffer_device)
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feat = y
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return y
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def evaluate(model, graph, dataloader, num_classes):
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model.eval()
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ys = []
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y_hats = []
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for it, (input_nodes, output_nodes, blocks) in enumerate(dataloader):
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with torch.no_grad():
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x = blocks[0].srcdata["feat"]
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ys.append(blocks[-1].dstdata["label"])
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y_hats.append(model(blocks, x))
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return MF.accuracy(
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torch.cat(y_hats),
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torch.cat(ys),
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task="multiclass",
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num_classes=num_classes,
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)
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def layerwise_infer(device, graph, nid, model, num_classes, batch_size):
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model.eval()
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with torch.no_grad():
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pred = model.inference(
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graph, device, batch_size
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) # pred in buffer_device
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pred = pred[nid]
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label = graph.ndata["label"][nid].to(pred.device)
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return MF.accuracy(
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pred, label, task="multiclass", num_classes=num_classes
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)
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def train(args, device, g, dataset, model, num_classes):
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# create sampler & dataloader
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train_idx = dataset.train_idx.to(device)
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val_idx = dataset.val_idx.to(device)
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sampler = NeighborSampler(
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[10, 10, 10], # fanout for [layer-0, layer-1, layer-2]
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prefetch_node_feats=["feat"],
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prefetch_labels=["label"],
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)
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use_uva = args.mode == "mixed"
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train_dataloader = DataLoader(
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g,
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train_idx,
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sampler,
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device=device,
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batch_size=1024,
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shuffle=True,
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drop_last=False,
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num_workers=0,
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use_uva=use_uva,
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)
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val_dataloader = DataLoader(
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g,
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val_idx,
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sampler,
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device=device,
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batch_size=1024,
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shuffle=True,
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drop_last=False,
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num_workers=0,
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use_uva=use_uva,
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)
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opt = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4)
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for epoch in range(10):
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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(
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train_dataloader
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):
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x = blocks[0].srcdata["feat"]
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y = blocks[-1].dstdata["label"]
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y_hat = model(blocks, x)
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loss = F.cross_entropy(y_hat, y)
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opt.zero_grad()
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loss.backward()
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opt.step()
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total_loss += loss.item()
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acc = evaluate(model, g, val_dataloader, num_classes)
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print(
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"Epoch {:05d} | Loss {:.4f} | 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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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--mode",
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default="mixed",
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choices=["cpu", "mixed", "puregpu"],
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help="Training mode. 'cpu' for CPU training, 'mixed' for CPU-GPU mixed training, "
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"'puregpu' for pure-GPU training.",
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)
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parser.add_argument(
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"--dt",
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type=str,
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default="float",
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help="data type(float, bfloat16)",
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)
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args = parser.parse_args()
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if not torch.cuda.is_available():
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args.mode = "cpu"
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print(f"Training in {args.mode} mode.")
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# load and preprocess dataset
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print("Loading data")
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dataset = AsNodePredDataset(DglNodePropPredDataset("ogbn-products"))
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g = dataset[0]
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g = g.to("cuda" if args.mode == "puregpu" else "cpu")
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num_classes = dataset.num_classes
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device = torch.device("cpu" if args.mode == "cpu" else "cuda")
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# create GraphSAGE model
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in_size = g.ndata["feat"].shape[1]
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out_size = dataset.num_classes
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model = SAGE(in_size, 256, out_size).to(device)
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# convert model and graph to bfloat16 if needed
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if args.dt == "bfloat16":
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g = dgl.to_bfloat16(g)
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model = model.to(dtype=torch.bfloat16)
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# model training
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print("Training...")
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train(args, device, g, dataset, model, num_classes)
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# test the model
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print("Testing...")
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acc = layerwise_infer(
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device, g, dataset.test_idx, model, num_classes, batch_size=4096
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)
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print("Test Accuracy {:.4f}".format(acc.item()))
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