[Model][Perf] Improve sage sampling performance (#1364)

* improve speed

* fix bugs

* upd reg test

examples/pytorch/graphsage/README.md

@@ -19,9 +19,17 @@ Results
 
 Run with following (available dataset: "cora", "citeseer", "pubmed")
 ```bash
-python3 graphsage.py --dataset cora --gpu 0
+python3 train_full.py --dataset cora --gpu 0
 ```
 
 * cora: ~0.8330 
 * citeseer: ~0.7110
 * pubmed: ~0.7830
+
+
+Train w/ mini-batch sampling (on the Reddit dataset)
+```bash
+python3 train_sampling.py
+```
+
+Accuracy: 0.9504

examples/pytorch/graphsage/train_sampling.py

+import dgl
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torch.multiprocessing as mp
+from torch.utils.data import DataLoader
+import dgl.function as fn
+import dgl.nn.pytorch as dglnn
+import time
+import argparse
+from _thread import start_new_thread
+from functools import wraps
+from dgl.data import RedditDataset
+import tqdm
+import traceback
+
+#### Neighbor sampler
+
+class NeighborSampler(object):
+    def __init__(self, g, fanouts):
+        self.g = g
+        self.fanouts = fanouts
+
+    def sample_blocks(self, seeds):
+        seeds = th.LongTensor(np.asarray(seeds))
+        blocks = []
+        for fanout in self.fanouts:
+            # For each seed node, sample ``fanout`` neighbors.
+            frontier = dgl.sampling.sample_neighbors(g, seeds, fanout, replace=True)
+            # Then we compact the frontier into a bipartite graph for message passing.
+            block = dgl.to_block(frontier, seeds)
+            # Obtain the seed nodes for next layer.
+            seeds = block.srcdata[dgl.NID]
+
+            blocks.insert(0, block)
+        return blocks
+
+class SAGE(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super().__init__()
+        self.n_layers = n_layers
+        self.n_hidden = n_hidden
+        self.n_classes = n_classes
+        self.layers = nn.ModuleList()
+        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean'))
+        for i in range(1, n_layers - 1):
+            self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean'))
+        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean'))
+        self.dropout = nn.Dropout(dropout)
+        self.activation = activation
+
+    def forward(self, blocks, x):
+        h = x
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
+            # We need to first copy the representation of nodes on the RHS from the
+            # appropriate nodes on the LHS.
+            # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
+            # would be (num_nodes_RHS, D)
+            h_dst = h[:block.number_of_nodes(block.dsttype)]
+            # Then we compute the updated representation on the RHS.
+            # The shape of h now becomes (num_nodes_RHS, D)
+            h = layer(block, (h, h_dst))
+            if l != len(self.layers) - 1:
+                h = self.activation(h)
+                h = self.dropout(h)
+        return h
+
+    def inference(self, g, x, batch_size, device):
+        """
+        Inference with the GraphSAGE model on full neighbors (i.e. without neighbor sampling).
+        g : the entire graph.
+        x : the input of entire node set.
+
+        The inference code is written in a fashion that it could handle any number of nodes and
+        layers.
+        """
+        # During inference with sampling, multi-layer blocks are very inefficient because
+        # lots of computations in the first few layers are repeated.
+        # Therefore, we compute the representation of all nodes layer by layer.  The nodes
+        # on each layer are of course splitted in batches.
+        # TODO: can we standardize this?
+        nodes = th.arange(g.number_of_nodes())
+        for l, layer in enumerate(self.layers):
+            y = th.zeros(g.number_of_nodes(), self.n_hidden if l != len(self.layers) - 1 else self.n_classes)
+
+            for start in tqdm.trange(0, len(nodes), batch_size):
+                end = start + batch_size
+                batch_nodes = nodes[start:end]
+                block = dgl.to_block(dgl.in_subgraph(g, batch_nodes), batch_nodes)
+                input_nodes = block.srcdata[dgl.NID]
+
+                h = x[input_nodes].to(device)
+                h_dst = h[:block.number_of_nodes(block.dsttype)]
+                h = layer(block, (h, h_dst))
+                if l != len(self.layers) - 1:
+                    h = self.activation(h)
+                    h = self.dropout(h)
+
+                y[start:end] = h.cpu()
+
+            x = y
+        return y
+
+def prepare_mp(g):
+    """
+    Explicitly materialize the CSR, CSC and COO representation of the given graph
+    so that they could be shared via copy-on-write to sampler workers and GPU
+    trainers.
+
+    This is a workaround before full shared memory support on heterogeneous graphs.
+    """
+    g.in_degree(0)
+    g.out_degree(0)
+    g.find_edges([0])
+
+def compute_acc(pred, labels):
+    """
+    Compute the accuracy of prediction given the labels.
+    """
+    return (th.argmax(pred, dim=1) == labels).float().sum() / len(pred)
+
+def evaluate(model, g, inputs, labels, val_mask, batch_size, device):
+    """
+    Evaluate the model on the validation set specified by ``val_mask``.
+    g : The entire graph.
+    inputs : The features of all the nodes.
+    labels : The labels of all the nodes.
+    val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
+    batch_size : Number of nodes to compute at the same time.
+    device : The GPU device to evaluate on.
+    """
+    model.eval()
+    with th.no_grad():
+        pred = model.inference(g, inputs, batch_size, device)
+    model.train()
+    return compute_acc(pred[val_mask], labels[val_mask])
+
+def load_subtensor(g, labels, seeds, input_nodes, device):
+    """
+    Copys features and labels of a set of nodes onto GPU.
+    """
+    batch_inputs = g.ndata['features'][input_nodes].to(device)
+    batch_labels = labels[seeds].to(device)
+    return batch_inputs, batch_labels
+
+#### Entry point
+def run(args, device, data):
+    # Unpack data
+    train_mask, val_mask, in_feats, labels, n_classes, g = data
+    train_nid = th.LongTensor(np.nonzero(train_mask)[0])
+    val_nid = th.LongTensor(np.nonzero(val_mask)[0])
+    train_mask = th.BoolTensor(train_mask)
+    val_mask = th.BoolTensor(val_mask)
+
+    # Create sampler
+    sampler = NeighborSampler(g, [int(fanout) for fanout in args.fan_out.split(',')])
+
+    # Create PyTorch DataLoader for constructing blocks
+    dataloader = DataLoader(
+        dataset=train_nid.numpy(),
+        batch_size=args.batch_size,
+        collate_fn=sampler.sample_blocks,
+        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()
+    loss_fcn = loss_fcn.to(device)
+    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.
+        for step, blocks in enumerate(dataloader):
+            tic_step = time.time()
+
+            # The nodes for input lies at the LHS side of the first block.
+            # The nodes for output lies at the RHS side of the last block.
+            input_nodes = blocks[0].srcdata[dgl.NID]
+            seeds = blocks[-1].dstdata[dgl.NID]
+
+            # Load the input features as well as output labels
+            batch_inputs, batch_labels = load_subtensor(g, labels, seeds, input_nodes, device)
+
+            # 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} MiB'.format(
+                    epoch, step, loss.item(), acc.item(), np.mean(iter_tput[3:]), gpu_mem_alloc))
+
+        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, g, g.ndata['features'], labels, val_mask, args.batch_size, device)
+            print('Eval Acc {:.4f}'.format(eval_acc))
+
+    print('Avg epoch time: {}'.format(avg / (epoch - 4)))
+
+if __name__ == '__main__':
+    argparser = argparse.ArgumentParser("multi-gpu training")
+    argparser.add_argument('--gpu', type=int, default=0,
+        help="GPU device ID. Use -1 for CPU training")
+    argparser.add_argument('--num-epochs', type=int, default=20)
+    argparser.add_argument('--num-hidden', type=int, default=16)
+    argparser.add_argument('--num-layers', type=int, default=2)
+    argparser.add_argument('--fan-out', type=str, default='10,25')
+    argparser.add_argument('--batch-size', type=int, default=1000)
+    argparser.add_argument('--log-every', type=int, default=20)
+    argparser.add_argument('--eval-every', type=int, default=5)
+    argparser.add_argument('--lr', type=float, default=0.003)
+    argparser.add_argument('--dropout', type=float, default=0.5)
+    argparser.add_argument('--num-workers', type=int, default=0,
+        help="Number of sampling processes. Use 0 for no extra process.")
+    args = argparser.parse_args()
+    
+    if args.gpu >= 0:
+        device = th.device('cuda:%d' % args.gpu)
+    else:
+        device = th.device('cpu')
+
+    # load reddit data
+    data = RedditDataset(self_loop=True)
+    train_mask = data.train_mask
+    val_mask = data.val_mask
+    features = th.Tensor(data.features)
+    in_feats = features.shape[1]
+    labels = th.LongTensor(data.labels)
+    n_classes = data.num_labels
+    # Construct graph
+    g = dgl.graph(data.graph.all_edges())
+    g.ndata['features'] = features
+    prepare_mp(g)
+    # Pack data
+    data = train_mask, val_mask, in_feats, labels, n_classes, g
+
+    run(args, device, data)

examples/pytorch/graphsage/graphsage_sampling.py → examples/pytorch/graphsage/train_sampling_multi_gpu.py

@@ -15,6 +15,7 @@ from functools import wraps
 from dgl.data import RedditDataset
 from torch.nn.parallel import DistributedDataParallel
 import tqdm
+import traceback
 
 #### Neighbor sampler
 
@@ -50,17 +51,16 @@ class SAGE(nn.Module):
         self.n_hidden = n_hidden
         self.n_classes = n_classes
         self.layers = nn.ModuleList()
-        self.layers.append(dglnn.SAGEConv(
-            in_feats, n_hidden, 'mean', feat_drop=dropout, activation=activation))
+        self.layers.append(dglnn.SAGEConv(in_feats, n_hidden, 'mean'))
         for i in range(1, n_layers - 1):
-            self.layers.append(dglnn.SAGEConv(
-                n_hidden, n_hidden, 'mean', feat_drop=dropout, activation=activation))
-        self.layers.append(dglnn.SAGEConv(
-            n_hidden, n_classes, 'mean', feat_drop=dropout))
+            self.layers.append(dglnn.SAGEConv(n_hidden, n_hidden, 'mean'))
+        self.layers.append(dglnn.SAGEConv(n_hidden, n_classes, 'mean'))
+        self.dropout = nn.Dropout(dropout)
+        self.activation = activation
 
     def forward(self, blocks, x):
         h = x
-        for layer, block in zip(self.layers, blocks):
+        for l, (layer, block) in enumerate(zip(self.layers, blocks)):
             # We need to first copy the representation of nodes on the RHS from the
             # appropriate nodes on the LHS.
             # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
@@ -69,6 +69,9 @@ class SAGE(nn.Module):
             # Then we compute the updated representation on the RHS.
             # The shape of h now becomes (num_nodes_RHS, D)
             h = layer(block, (h, h_dst))
+            if l != len(self.layers) - 1:
+                h = self.activation(h)
+                h = self.dropout(h)
         return h
 
     def inference(self, g, x, batch_size, device):
@@ -98,6 +101,9 @@ class SAGE(nn.Module):
                 h = x[input_nodes].to(device)
                 h_dst = h[:block.number_of_nodes(block.dsttype)]
                 h = layer(block, (h, h_dst))
+                if l != len(self.layers) - 1:
+                    h = self.activation(h)
+                    h = self.dropout(h)
 
                 y[start:end] = h.cpu()
 
@@ -181,10 +187,7 @@ def load_subtensor(g, labels, seeds, input_nodes, dev_id):
 
 #### Entry point
 
-@thread_wrapped_func
 def run(proc_id, n_gpus, args, devices, data):
-    dropout = 0.2
-
     # Start up distributed training, if enabled.
     dev_id = devices[proc_id]
     if n_gpus > 1:
@@ -217,10 +220,10 @@ def run(proc_id, n_gpus, args, devices, data):
         collate_fn=sampler.sample_blocks,
         shuffle=True,
         drop_last=False,
-        num_workers=args.num_workers_per_gpu)
+        num_workers=args.num_workers)
 
     # Define model and optimizer
-    model = SAGE(in_feats, args.num_hidden, n_classes, args.num_layers, F.relu, dropout)
+    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)
@@ -284,6 +287,7 @@ def run(proc_id, n_gpus, args, devices, data):
                     eval_acc = evaluate(model.module, g, g.ndata['features'], labels, val_mask, args.batch_size, 0)
                 print('Eval Acc {:.4f}'.format(eval_acc))
 
+
     if n_gpus > 1:
         th.distributed.barrier()
     if proc_id == 0:
@@ -291,7 +295,8 @@ def run(proc_id, n_gpus, args, devices, data):
 
 if __name__ == '__main__':
     argparser = argparse.ArgumentParser("multi-gpu training")
-    argparser.add_argument('--gpu', type=str, default='0')
+    argparser.add_argument('--gpu', type=str, default='0',
+        help="Comma separated list of GPU device IDs.")
     argparser.add_argument('--num-epochs', type=int, default=20)
     argparser.add_argument('--num-hidden', type=int, default=16)
     argparser.add_argument('--num-layers', type=int, default=2)
@@ -300,7 +305,9 @@ if __name__ == '__main__':
     argparser.add_argument('--log-every', type=int, default=20)
     argparser.add_argument('--eval-every', type=int, default=5)
     argparser.add_argument('--lr', type=float, default=0.003)
-    argparser.add_argument('--num-workers-per-gpu', type=int, default=0)
+    argparser.add_argument('--dropout', type=float, default=0.5)
+    argparser.add_argument('--num-workers', type=int, default=0,
+        help="Number of sampling processes. Use 0 for no extra process.")
     args = argparser.parse_args()
     
     devices = list(map(int, args.gpu.split(',')))
@@ -326,7 +333,8 @@ if __name__ == '__main__':
     else:
         procs = []
         for proc_id in range(n_gpus):
-            p = mp.Process(target=run, args=(proc_id, n_gpus, args, devices, data))
+            p = mp.Process(target=thread_wrapped_func(run),
+                           args=(proc_id, n_gpus, args, devices, data))
             p.start()
             procs.append(p)
         for p in procs:

src/array/cpu/rowwise_pick.h

@@ -76,7 +76,7 @@ COOMatrix CSRRowWisePick(CSRMatrix mat, IdArray rows,
   if (replace) {
     all_has_fanout = true;
   } else {
-    // #pragma omp parallel for reduction(&&:all_has_fanout)
+#pragma omp parallel for reduction(&&:all_has_fanout)
     for (int64_t i = 0; i < num_rows; ++i) {
       const IdxType rid = rows_data[i];
       const IdxType len = indptr[rid + 1] - indptr[rid];
@@ -84,7 +84,7 @@ COOMatrix CSRRowWisePick(CSRMatrix mat, IdArray rows,
     }
   }
 
-  // #pragma omp parallel for
+#pragma omp parallel for
   for (int64_t i = 0; i < num_rows; ++i) {
     const IdxType rid = rows_data[i];
     CHECK_LT(rid, mat.num_rows);

tests/regression/README.md

@@ -20,6 +20,7 @@ The basic use is execute a script, and get the needed results out of the printed
 
 ```bash
 docker run --name dgl-reg --rm --hostname=reg-machine --runtime=nvidia -dit dgllib/dgl-ci-gpu:conda /bin/bash
+docker cp /home/ubuntu/asv_data dgl-reg:/root/asv_data/
 docker exec dgl-reg bash /root/asv_data/run.sh
 docker cp dgl-reg:/root/regression/dgl/asv/. /home/ubuntu/asv_data/  # Change /home/ubuntu/asv to the path you want to put the result
 docker stop dgl-reg

tests/regression/bench_gcn.py

@@ -20,7 +20,7 @@ class GCNBenchmark:
     #     self.tmp_dir = Path(tempfile.mkdtemp())
 
     def setup(self, backend, dataset, gpu_id):
-        log_filename = Path("{}_{}_{}.log".format(backend, dataset, gpu_id))
+        log_filename = Path("gcn_{}_{}_{}.log".format(backend, dataset, gpu_id))
         if log_filename.exists():
             return
         gcn_path = base_path / "examples/{}/gcn/train.py".format(backend)

tests/regression/bench_sage.py

+# Write the benchmarking functions here.
+# See "Writing benchmarks" in the asv docs for more information.
+
+import subprocess
+import os
+from pathlib import Path
+import numpy as np
+import tempfile
+
+base_path = Path("~/regression/dgl/")
+
+
+class SAGEBenchmark:
+
+    params = [['pytorch'], ['0']]
+    param_names = ['backend', 'gpu']
+    timeout = 1800
+
+    # def setup_cache(self):
+    #     self.tmp_dir = Path(tempfile.mkdtemp())
+
+    def setup(self, backend, gpu):
+        log_filename = Path("sage_sampling_{}_{}.log".format(backend, gpu))
+        if log_filename.exists():
+            return
+        run_path = base_path / "examples/{}/graphsage/train_sampling.py".format(backend)
+        bashCommand = "/opt/conda/envs/{}-ci/bin/python {} --num-workers=4 --num-epochs=16 --gpu={}".format(
+            backend, run_path.expanduser(), gpu)
+        process = subprocess.Popen(bashCommand.split(), stdout=subprocess.PIPE,env=dict(os.environ, DGLBACKEND=backend))
+        output, error = process.communicate()
+        print(str(error))
+        log_filename.write_text(str(output))
+
+
+    def track_sage_time(self, backend):
+        log_filename = Path("sage_sampling_{}_{}.log".format(backend, gpu))
+        lines = log_filename.read_text().split("\\n")
+        time_list = []
+        for line in lines:
+            if line.startswith('Epoch Time'):
+                time_str = line.strip()[15:]
+                time_list.append(float(time_str))
+        return np.array(time_list).mean()
+
+    def track_sage_accuracy(self, backend):
+        log_filename = Path("sage_sampling_{}_{}.log".format(backend, gpu))
+        lines = log_filename.read_text().split("\\n")
+        test_acc = 0.
+        for line in lines:
+            if line.startswith('Eval Acc'):
+                acc_str = line.strip()[9:]
+                test_acc = float(acc_str)
+        return test_acc * 100
+
+
+SAGEBenchmark.track_sage_time.unit = 's'
+SAGEBenchmark.track_sage_accuracy.unit = '%'

tests/regression/publish.sh

+#!/bin/bash
+
+set -x
+
+if [ $# -ne 2 ]; then
+    REPO=dmlc
+    BRANCH=master
+else
+    REPO=$1
+    BRANCH=$2
+fi
+
 docker run --name dgl-reg --rm --hostname=reg-machine --runtime=nvidia -dit dgllib/dgl-ci-gpu:conda /bin/bash
 docker cp /home/ubuntu/asv_data dgl-reg:/root/asv_data/
-docker exec dgl-reg bash /root/asv_data/run.sh
+docker exec dgl-reg bash /root/asv_data/run.sh $REPO $BRANCH
 docker cp dgl-reg:/root/regression/dgl/asv/. /home/ubuntu/asv_data/
-docker stop dgl-reg
+docker stop dgl-reg
\ No newline at end of file

tests/regression/run.sh

+#!/bin/bash
+set -e
+
+if [ $# -ne 2 ]; then
+    echo "run.sh <repo> <branch>"
+    exit 1
+fi
+
+REPO=$1
+BRANCH=$2
+
 . /opt/conda/etc/profile.d/conda.sh
 
 cd ~
 mkdir regression
 cd regression
 # git config core.filemode false
-git clone --recursive https://github.com/dmlc/dgl.git 
+git clone --recursive https://github.com/$REPO/dgl.git 
+git checkout $BRANCH
 cd dgl
 mkdir asv
 cp -r ~/asv_data/* asv/