dmlc--dgl
Quan (Andy) Gan
70695ff8f8
* pytorch lightning initial examples * revert most changes in dataloader to favor #2886. * address comments
208 行
8.2 KiB
Python
208 行
8.2 KiB
Python
import dgl
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import numpy as np
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import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import dgl.nn.pytorch as dglnn
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import dgl.function as fn
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import time
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import argparse
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import tqdm
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import glob
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import os
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from negative_sampler import NegativeSampler
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from pytorch_lightning.metrics import Accuracy
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from pytorch_lightning.callbacks import ModelCheckpoint, Callback
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from pytorch_lightning import LightningDataModule, LightningModule, Trainer
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from model import SAGE, compute_acc_unsupervised as compute_acc
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from load_graph import load_reddit, inductive_split, load_ogb
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class CrossEntropyLoss(nn.Module):
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def forward(self, block_outputs, pos_graph, neg_graph):
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with pos_graph.local_scope():
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pos_graph.ndata['h'] = block_outputs
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pos_graph.apply_edges(fn.u_dot_v('h', 'h', 'score'))
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pos_score = pos_graph.edata['score']
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with neg_graph.local_scope():
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neg_graph.ndata['h'] = block_outputs
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neg_graph.apply_edges(fn.u_dot_v('h', 'h', 'score'))
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neg_score = neg_graph.edata['score']
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score = th.cat([pos_score, neg_score])
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label = th.cat([th.ones_like(pos_score), th.zeros_like(neg_score)]).long()
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loss = F.binary_cross_entropy_with_logits(score, label.float())
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return loss
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class SAGELightning(LightningModule):
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def __init__(self,
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in_feats,
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n_hidden,
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n_classes,
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n_layers,
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activation,
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dropout,
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lr):
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super().__init__()
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self.save_hyperparameters()
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self.module = SAGE(in_feats, n_hidden, n_classes, n_layers, activation, dropout)
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self.lr = lr
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self.loss_fcn = CrossEntropyLoss()
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def training_step(self, batch, batch_idx):
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input_nodes, pos_graph, neg_graph, mfgs = batch
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mfgs = [mfg.int().to(device) for mfg in mfgs]
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pos_graph = pos_graph.to(device)
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neg_graph = neg_graph.to(device)
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batch_inputs = mfgs[0].srcdata['features']
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batch_labels = mfgs[-1].dstdata['labels']
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batch_pred = self.module(mfgs, batch_inputs)
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loss = self.loss_fcn(batch_pred, pos_graph, neg_graph)
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self.log('train_loss', loss, prog_bar=True, on_step=False, on_epoch=True)
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return loss
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def validation_step(self, batch, batch_idx):
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input_nodes, output_nodes, mfgs = batch
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mfgs = [mfg.int().to(device) for mfg in mfgs]
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batch_inputs = mfgs[0].srcdata['features']
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batch_labels = mfgs[-1].dstdata['labels']
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batch_pred = self.module(mfgs, batch_inputs)
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return batch_pred
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def configure_optimizers(self):
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optimizer = th.optim.Adam(self.parameters(), lr=self.lr)
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return optimizer
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class DataModule(LightningDataModule):
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def __init__(self, dataset_name, data_cpu=False, fan_out=[10, 25],
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device=th.device('cpu'), batch_size=1000, num_workers=4):
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super().__init__()
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if dataset_name == 'reddit':
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g, n_classes = load_reddit()
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n_edges = g.num_edges()
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reverse_eids = th.cat([
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th.arange(n_edges // 2, n_edges),
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th.arange(0, n_edges // 2)])
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elif dataset_name == 'ogbn-products':
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g, n_classes = load_ogb('ogbn-products')
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n_edges = g.num_edges()
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# The reverse edge of edge 0 in OGB products dataset is 1.
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# The reverse edge of edge 2 is 3. So on so forth.
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reverse_eids = th.arange(n_edges) ^ 1
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else:
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raise ValueError('unknown dataset')
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train_nid = th.nonzero(g.ndata['train_mask'], as_tuple=True)[0]
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val_nid = th.nonzero(g.ndata['val_mask'], as_tuple=True)[0]
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test_nid = th.nonzero(~(g.ndata['train_mask'] | g.ndata['val_mask']), as_tuple=True)[0]
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sampler = dgl.dataloading.MultiLayerNeighborSampler([int(_) for _ in fan_out])
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dataloader_device = th.device('cpu')
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if not data_cpu:
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train_nid = train_nid.to(device)
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val_nid = val_nid.to(device)
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test_nid = test_nid.to(device)
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g = g.formats(['csc'])
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g = g.to(device)
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dataloader_device = device
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self.g = g
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self.train_nid, self.val_nid, self.test_nid = train_nid, val_nid, test_nid
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self.sampler = sampler
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self.device = dataloader_device
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self.batch_size = batch_size
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self.num_workers = num_workers
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self.in_feats = g.ndata['features'].shape[1]
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self.n_classes = n_classes
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self.reverse_eids = reverse_eids
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def train_dataloader(self):
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return dgl.dataloading.EdgeDataLoader(
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self.g,
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np.arange(self.g.num_edges()),
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self.sampler,
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exclude='reverse_id',
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reverse_eids=self.reverse_eids,
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negative_sampler=NegativeSampler(self.g, args.num_negs, args.neg_share),
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device=self.device,
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batch_size=self.batch_size,
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shuffle=True,
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drop_last=False,
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num_workers=self.num_workers)
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def val_dataloader(self):
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# Note that the validation data loader is a NodeDataLoader
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# as we want to evaluate all the node embeddings.
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return dgl.dataloading.NodeDataLoader(
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self.g,
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np.arange(self.g.num_nodes()),
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self.sampler,
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device=self.device,
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batch_size=self.batch_size,
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shuffle=False,
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drop_last=False,
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num_workers=self.num_workers)
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class UnsupervisedClassification(Callback):
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def on_validation_epoch_start(self, trainer, pl_module):
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self.val_outputs = []
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def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
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self.val_outputs.append(outputs)
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def on_validation_epoch_end(self, trainer, pl_module):
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node_emb = th.cat(self.val_outputs, 0)
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g = trainer.datamodule.g
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labels = g.ndata['labels']
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f1_micro, f1_macro = compute_acc(
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node_emb, labels, trainer.datamodule.train_nid,
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trainer.datamodule.val_nid, trainer.datamodule.test_nid)
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pl_module.log('val_f1_micro', f1_micro)
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if __name__ == '__main__':
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argparser = argparse.ArgumentParser("multi-gpu training")
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argparser.add_argument("--gpu", type=int, default=0)
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argparser.add_argument('--dataset', type=str, default='reddit')
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argparser.add_argument('--num-epochs', type=int, default=20)
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argparser.add_argument('--num-hidden', type=int, default=16)
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argparser.add_argument('--num-layers', type=int, default=2)
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argparser.add_argument('--num-negs', type=int, default=1)
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argparser.add_argument('--neg-share', default=False, action='store_true',
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help="sharing neg nodes for positive nodes")
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argparser.add_argument('--fan-out', type=str, default='10,25')
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argparser.add_argument('--batch-size', type=int, default=10000)
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argparser.add_argument('--log-every', type=int, default=20)
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argparser.add_argument('--eval-every', type=int, default=1000)
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argparser.add_argument('--lr', type=float, default=0.003)
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argparser.add_argument('--dropout', type=float, default=0.5)
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argparser.add_argument('--num-workers', type=int, default=0,
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help="Number of sampling processes. Use 0 for no extra process.")
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args = argparser.parse_args()
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if args.gpu >= 0:
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device = th.device('cuda:%d' % args.gpu)
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else:
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device = th.device('cpu')
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datamodule = DataModule(
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args.dataset, True, [int(_) for _ in args.fan_out.split(',')],
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device, args.batch_size, args.num_workers)
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model = SAGELightning(
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datamodule.in_feats, args.num_hidden, datamodule.n_classes, args.num_layers,
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F.relu, args.dropout, args.lr)
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# Train
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unsupervised_callback = UnsupervisedClassification()
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checkpoint_callback = ModelCheckpoint(monitor='val_f1_micro', save_top_k=1)
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trainer = Trainer(gpus=[args.gpu] if args.gpu != -1 else None,
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max_epochs=args.num_epochs,
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val_check_interval=1000,
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callbacks=[checkpoint_callback, unsupervised_callback],
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num_sanity_val_steps=0)
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trainer.fit(model, datamodule=datamodule)
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