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Unverified Commit 4361f8a6 authored by Rhett Ying's avatar Rhett Ying Committed by GitHub
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[Test] Fix broken regression benchmarks (#4229) 

* dummy

* upgrade torch from 1.8.1 to 1.9.0 for asv

* update torch download link

* remove version for torchvision and torch text

* revert unnecessary change

* fix several test failure

* fix

* remove am dataset

* fix test failure

* fix test failure

* fix rgcn failures
parent c56e27a8
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benchmarks/benchmarks/api/bench_edge_subgraph.py
View file @ 4361f8a6
......@@ -11,7 +11,7 @@ from .. import utils
@utils.skip_if_gpu()
@utils.benchmark('time')
@utils.parametrize('graph_name', ['livejournal', 'reddit'])
@utils.parametrize('format', ['coo', 'csc'])
@utils.parametrize('format', ['coo'])
@utils.parametrize('seed_egdes_num', [500, 5000, 50000])
def track_time(graph_name, format, seed_egdes_num):
device = utils.get_bench_device()
......
benchmarks/benchmarks/api/bench_random_walk.py
View file @ 4361f8a6
......@@ -10,8 +10,9 @@ def _random_walk(g, seeds, length):
def _node2vec(g, seeds, length):
return dgl.sampling.node2vec_random_walk(g, seeds, 1, 1, length)
@utils.skip_if_gpu()
@utils.benchmark('time')
@utils.parametrize_cpu('graph_name', ['cora', 'livejournal', 'friendster'])
@utils.parametrize('graph_name', ['cora', 'livejournal', 'friendster'])
@utils.parametrize('num_seeds', [10, 100, 1000])
@utils.parametrize('length', [2, 5, 10, 20])
@utils.parametrize('algorithm', ['_random_walk', '_node2vec'])
......
benchmarks/benchmarks/api/bench_to_block.py
View file @ 4361f8a6
......@@ -8,7 +8,7 @@ from .. import utils
@utils.skip_if_gpu()
@utils.benchmark('time', timeout=1200)
@utils.parametrize('graph_name', ['reddit', "ogbn-product"])
@utils.parametrize('graph_name', ['reddit', "ogbn-products"])
@utils.parametrize('num_seed_nodes', [32, 256, 1024, 2048])
@utils.parametrize('fanout', [5, 10, 20])
def track_time(graph_name, num_seed_nodes, fanout):
......
benchmarks/benchmarks/model_acc/bench_rgcn.py deleted 100644 → 0
View file @ c56e27a8
import numpy as np
import dgl
from dgl.nn.pytorch import RelGraphConv
import torch
import torch.nn as nn
import torch.nn.functional as F
from .. import utils
class RGCN(nn.Module):
def __init__(self,
num_nodes,
n_hidden,
num_classes,
num_rels,
num_bases,
num_hidden_layers,
dropout,
low_mem):
super(RGCN, self).__init__()
self.layers = nn.ModuleList()
# i2h
self.layers.append(RelGraphConv(num_nodes, n_hidden, num_rels, "basis",
num_bases, activation=F.relu, dropout=dropout,
low_mem=low_mem))
# h2h
for i in range(num_hidden_layers):
self.layers.append(RelGraphConv(n_hidden, n_hidden, num_rels, "basis",
num_bases, activation=F.relu, dropout=dropout,
low_mem=low_mem))
# o2h
self.layers.append(RelGraphConv(n_hidden, num_classes, num_rels, "basis",
num_bases, activation=None, low_mem=low_mem))
def forward(self, g, h, r, norm):
for layer in self.layers:
h = layer(g, h, r, norm)
return h
def evaluate(model, g, feats, edge_type, edge_norm, labels, idx):
model.eval()
with torch.no_grad():
logits = model(g, feats, edge_type, edge_norm)
logits = logits[idx]
_, indices = torch.max(logits, dim=1)
correct = torch.sum(indices == labels)
return correct.item() * 1.0 / len(labels) * 100
@utils.benchmark('acc')
@utils.parametrize('data', ['aifb', 'mutag'])
@utils.parametrize('lowmem', [True, False])
@utils.parametrize('use_type_count', [True, False])
def track_acc(data, lowmem, use_type_count):
# args
if data == 'aifb':
num_bases = -1
l2norm = 0.
elif data == 'mutag':
num_bases = 30
l2norm = 5e-4
elif data == 'am':
num_bases = 40
l2norm = 5e-4
else:
raise ValueError()
data = utils.process_data(data)
device = utils.get_bench_device()
g = data[0]
num_rels = len(g.canonical_etypes)
category = data.predict_category
num_classes = data.num_classes
train_mask = g.nodes[category].data.pop('train_mask').bool().to(device)
test_mask = g.nodes[category].data.pop('test_mask').bool().to(device)
labels = g.nodes[category].data.pop('labels').to(device)
# calculate norm for each edge type and store in edge
for canonical_etype in g.canonical_etypes:
u, v, eid = g.all_edges(form='all', etype=canonical_etype)
_, inverse_index, count = torch.unique(v, return_inverse=True, return_counts=True)
degrees = count[inverse_index]
norm = 1. / degrees.float()
norm = norm.unsqueeze(1)
g.edges[canonical_etype].data['norm'] = norm
# get target category id
category_id = len(g.ntypes)
for i, ntype in enumerate(g.ntypes):
if ntype == category:
category_id = i
if use_type_count:
g, _, edge_type = dgl.to_homogeneous(g, edata=['norm'], return_count=True)
g = g.to(device)
else:
g = dgl.to_homogeneous(g, edata=['norm']).to(device)
edge_type = g.edata.pop(dgl.ETYPE).long()
num_nodes = g.number_of_nodes()
edge_norm = g.edata['norm']
# find out the target node ids in g
target_idx = torch.where(g.ndata[dgl.NTYPE] == category_id)[0]
train_idx = target_idx[train_mask]
test_idx = target_idx[test_mask]
train_labels = labels[train_mask]
test_labels = labels[test_mask]
# since the nodes are featureless, the input feature is then the node id.
feats = torch.arange(num_nodes, device=device)
# create model
model = RGCN(num_nodes,
16,
num_classes,
num_rels,
num_bases,
0,
0,
lowmem).to(device)
optimizer = torch.optim.Adam(model.parameters(),
lr=1e-2,
weight_decay=l2norm)
model.train()
for epoch in range(30):
logits = model(g, feats, edge_type, edge_norm)
loss = F.cross_entropy(logits[train_idx], train_labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
acc = evaluate(model, g, feats, edge_type, edge_norm, test_labels, test_idx)
return acc
benchmarks/benchmarks/model_acc/bench_rgcn_base.py 0 → 100644
View file @ 4361f8a6
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmetrics.functional import accuracy
from .. import utils
from .. import rgcn
@utils.benchmark('acc', timeout=1200)
@utils.parametrize('dataset', ['aifb', 'mutag'])
@utils.parametrize('ns_mode', [False])
def track_acc(dataset, ns_mode):
g, num_rels, num_classes, labels, train_idx, test_idx, target_idx = rgcn.load_data(
dataset, get_norm=True)
num_hidden = 16
if dataset == 'aifb':
num_bases = -1
l2norm = 0.
elif dataset == 'mutag':
num_bases = 30
l2norm = 5e-4
elif dataset == 'am':
num_bases = 40
l2norm = 5e-4
else:
raise ValueError()
model = rgcn.RGCN(g.num_nodes(),
num_hidden,
num_classes,
num_rels,
num_bases=num_bases,
ns_mode=ns_mode)
device = utils.get_bench_device()
labels = labels.to(device)
model = model.to(device)
g = g.int().to(device)
optimizer = torch.optim.Adam(
model.parameters(), lr=1e-2, weight_decay=l2norm)
model.train()
for epoch in range(30):
logits = model(g)
logits = logits[target_idx]
loss = F.cross_entropy(logits[train_idx], labels[train_idx])
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
logits = model(g)
logits = logits[target_idx]
test_acc = accuracy(logits[test_idx].argmax(
dim=1), labels[test_idx]).item()
return test_acc
benchmarks/benchmarks/model_acc/bench_rgcn_ns.py
View file @ 4361f8a6
......@@ -34,9 +34,6 @@ class EntityClassify(nn.Module):
Dropout
use_self_loop : bool
Use self loop if True, default False.
low_mem : bool
True to use low memory implementation of relation message passing function
trade speed with memory consumption
"""
def __init__(self,
device,
......@@ -48,7 +45,6 @@ class EntityClassify(nn.Module):
num_hidden_layers=1,
dropout=0,
use_self_loop=False,
low_mem=False,
layer_norm=False):
super(EntityClassify, self).__init__()
self.device = device
......@@ -60,7 +56,6 @@ class EntityClassify(nn.Module):
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.low_mem = low_mem
self.layer_norm = layer_norm
self.layers = nn.ModuleList()
......@@ -68,19 +63,19 @@ class EntityClassify(nn.Module):
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm = layer_norm))
dropout=self.dropout, layer_norm = layer_norm))
# h2h
for idx in range(self.num_hidden_layers):
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm = layer_norm))
dropout=self.dropout, layer_norm = layer_norm))
# h2o
self.layers.append(RelGraphConv(
self.h_dim, self.out_dim, self.num_rels, "basis",
self.num_bases, activation=None,
self_loop=self.use_self_loop,
low_mem=self.low_mem, layer_norm = layer_norm))
layer_norm = layer_norm))
def forward(self, blocks, feats, norm=None):
if blocks is None:
......@@ -195,8 +190,7 @@ def evaluate(model, embed_layer, eval_loader, node_feats):
return eval_logits, eval_seeds
@utils.benchmark('time', 3600) # ogbn-mag takes ~1 hour to train
@utils.benchmark('acc', timeout=3600) # ogbn-mag takes ~1 hour to train
@utils.parametrize('data', ['am', 'ogbn-mag'])
def track_acc(data):
dataset = utils.process_data(data)
......@@ -220,7 +214,6 @@ def track_acc(data):
dropout = 0.5
use_self_loop = True
lr = 0.01
low_mem = True
num_workers = 4
hg = dataset[0]
......@@ -306,7 +299,6 @@ def track_acc(data):
num_hidden_layers=n_layers - 2,
dropout=dropout,
use_self_loop=use_self_loop,
low_mem=low_mem,
layer_norm=False)
embed_layer = embed_layer.to(device)
......
benchmarks/benchmarks/model_speed/bench_rgcn.py deleted 100644 → 0
View file @ c56e27a8
import time
import numpy as np
import dgl
from dgl.nn.pytorch import RelGraphConv
import torch
import torch.nn as nn
import torch.nn.functional as F
from .. import utils
class RGCN(nn.Module):
def __init__(self,
num_nodes,
n_hidden,
num_classes,
num_rels,
num_bases,
num_hidden_layers,
dropout):
super(RGCN, self).__init__()
self.layers = nn.ModuleList()
# i2h
self.layers.append(RelGraphConv(num_nodes, n_hidden, num_rels, "basis",
num_bases, activation=F.relu, dropout=dropout))
# h2h
for i in range(num_hidden_layers):
self.layers.append(RelGraphConv(n_hidden, n_hidden, num_rels, "basis",
num_bases, activation=F.relu, dropout=dropout))
# o2h
self.layers.append(RelGraphConv(n_hidden, num_classes, num_rels, "basis",
num_bases, activation=None))
def forward(self, g, h, r, norm):
for layer in self.layers:
h = layer(g, h, r, norm)
return h
@utils.benchmark('time', 300)
@utils.parametrize('data', ['aifb'])
@utils.parametrize('use_type_count', [True, False])
def track_time(data, use_type_count):
# args
if data == 'aifb':
if dgl.__version__.startswith("0.8"):
num_bases = None
else:
num_bases = -1
l2norm = 0.
elif data == 'am':
num_bases = 40
l2norm = 5e-4
else:
raise ValueError()
data = utils.process_data(data)
device = utils.get_bench_device()
num_epochs = 30
g = data[0]
num_rels = len(g.canonical_etypes)
category = data.predict_category
num_classes = data.num_classes
train_mask = g.nodes[category].data.pop('train_mask').bool().to(device)
test_mask = g.nodes[category].data.pop('test_mask').bool().to(device)
labels = g.nodes[category].data.pop('labels').to(device)
# calculate norm for each edge type and store in edge
for canonical_etype in g.canonical_etypes:
u, v, eid = g.all_edges(form='all', etype=canonical_etype)
_, inverse_index, count = torch.unique(v, return_inverse=True, return_counts=True)
degrees = count[inverse_index]
norm = 1. / degrees.float()
norm = norm.unsqueeze(1)
g.edges[canonical_etype].data['norm'] = norm
# get target category id
category_id = len(g.ntypes)
for i, ntype in enumerate(g.ntypes):
if ntype == category:
category_id = i
if use_type_count:
g, _, edge_type = dgl.to_homogeneous(g, edata=['norm'], return_count=True)
g = g.to(device)
else:
g = dgl.to_homogeneous(g, edata=['norm']).to(device)
edge_type = g.edata.pop(dgl.ETYPE).long()
num_nodes = g.number_of_nodes()
edge_norm = g.edata['norm']
# find out the target node ids in g
target_idx = torch.where(g.ndata[dgl.NTYPE] == category_id)[0]
train_idx = target_idx[train_mask]
test_idx = target_idx[test_mask]
train_labels = labels[train_mask]
test_labels = labels[test_mask]
# since the nodes are featureless, the input feature is then the node id.
feats = torch.arange(num_nodes, device=device)
# create model
model = RGCN(num_nodes,
16,
num_classes,
num_rels,
num_bases,
0,
0).to(device)
optimizer = torch.optim.Adam(model.parameters(),
lr=1e-2,
weight_decay=l2norm)
model.train()
t0 = time.time()
for epoch in range(num_epochs):
logits = model(g, feats, edge_type, edge_norm)
loss = F.cross_entropy(logits[train_idx], train_labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
t1 = time.time()
return (t1 - t0) / num_epochs
benchmarks/benchmarks/model_speed/bench_rgcn_base.py 0 → 100644
View file @ 4361f8a6
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchmetrics.functional import accuracy
from .. import utils
from .. import rgcn
@utils.benchmark('time', 1200)
@utils.parametrize('data', ['aifb', 'am'])
def track_time(data):
# args
if data == 'aifb':
num_bases = -1
l2norm = 0.
elif data == 'am':
num_bases = 40
l2norm = 5e-4
else:
raise ValueError()
g, num_rels, num_classes, labels, train_idx, test_idx, target_idx = rgcn.load_data(
data, get_norm=True)
num_hidden = 16
model = rgcn.RGCN(g.num_nodes(),
num_hidden,
num_classes,
num_rels,
num_bases=num_bases)
device = utils.get_bench_device()
labels = labels.to(device)
model = model.to(device)
g = g.int().to(device)
optimizer = torch.optim.Adam(
model.parameters(), lr=1e-2, weight_decay=l2norm)
model.train()
num_epochs = 30
t0 = time.time()
for epoch in range(num_epochs):
logits = model(g)
logits = logits[target_idx]
loss = F.cross_entropy(logits[train_idx], labels[train_idx])
optimizer.zero_grad()
loss.backward()
optimizer.step()
t1 = time.time()
return (t1 - t0) / num_epochs
benchmarks/benchmarks/model_speed/bench_rgcn_hetero_ns.py
View file @ 4361f8a6
......@@ -228,15 +228,12 @@ class EntityClassify(nn.Module):
return h
@utils.benchmark('time', 600)
@utils.parametrize('data', ['am', 'ogbn-mag'])
@utils.parametrize('data', ['ogbn-mag'])
def track_time(data):
dataset = utils.process_data(data)
device = utils.get_bench_device()
if data == 'am':
n_bases = 40
l2norm = 5e-4
elif data == 'ogbn-mag':
if data == 'ogbn-mag':
n_bases = 2
l2norm = 0
else:
......
benchmarks/benchmarks/model_speed/bench_rgcn_homogeneous_ns.py
View file @ 4361f8a6
......@@ -34,9 +34,6 @@ class EntityClassify(nn.Module):
Dropout
use_self_loop : bool
Use self loop if True, default False.
low_mem : bool
True to use low memory implementation of relation message passing function
trade speed with memory consumption
"""
def __init__(self,
device,
......@@ -48,7 +45,6 @@ class EntityClassify(nn.Module):
num_hidden_layers=1,
dropout=0,
use_self_loop=False,
low_mem=False,
layer_norm=False):
super(EntityClassify, self).__init__()
self.device = device
......@@ -60,7 +56,6 @@ class EntityClassify(nn.Module):
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.use_self_loop = use_self_loop
self.low_mem = low_mem
self.layer_norm = layer_norm
self.layers = nn.ModuleList()
......@@ -68,19 +63,19 @@ class EntityClassify(nn.Module):
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm = layer_norm))
dropout=self.dropout, layer_norm = layer_norm))
# h2h
for idx in range(self.num_hidden_layers):
self.layers.append(RelGraphConv(
self.h_dim, self.h_dim, self.num_rels, "basis",
self.num_bases, activation=F.relu, self_loop=self.use_self_loop,
low_mem=self.low_mem, dropout=self.dropout, layer_norm = layer_norm))
dropout=self.dropout, layer_norm = layer_norm))
# h2o
self.layers.append(RelGraphConv(
self.h_dim, self.out_dim, self.num_rels, "basis",
self.num_bases, activation=None,
self_loop=self.use_self_loop,
low_mem=self.low_mem, layer_norm = layer_norm))
layer_norm = layer_norm))
def forward(self, blocks, feats, norm=None):
if blocks is None:
......@@ -196,7 +191,6 @@ def track_time(data):
dropout = 0.5
use_self_loop = True
lr = 0.01
low_mem = True
num_workers = 4
iter_start = 3
iter_count = 10
......@@ -276,7 +270,6 @@ def track_time(data):
num_hidden_layers=n_layers - 2,
dropout=dropout,
use_self_loop=use_self_loop,
low_mem=low_mem,
layer_norm=False)
embed_layer = embed_layer.to(device)
......
benchmarks/benchmarks/model_speed/bench_sage_unsupervised_ns.py
View file @ 4361f8a6
......@@ -4,12 +4,9 @@ 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.nn.pytorch as dglnn
import dgl.function as fn
import time
import traceback
from .. import utils
......@@ -123,17 +120,19 @@ def track_time(data, num_negs, batch_size):
# Create PyTorch DataLoader for constructing blocks
sampler = dgl.dataloading.MultiLayerNeighborSampler(
[int(fanout) for fanout in fan_out.split(',')])
dataloader = dgl.dataloading.EdgeDataLoader(
g, train_seeds, sampler, exclude='reverse_id',
sampler = dgl.dataloading.as_edge_prediction_sampler(
sampler, exclude='reverse_id',
# For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2.
reverse_eids=th.cat([
th.arange(n_edges // 2, n_edges),
th.arange(0, n_edges // 2)]),
negative_sampler=NegativeSampler(g, num_negs),
th.arange(
n_edges // 2, n_edges),
th.arange(0, n_edges // 2)]),
negative_sampler=NegativeSampler(g, num_negs))
dataloader = dgl.dataloading.DataLoader(
g, train_seeds, sampler,
batch_size=batch_size,
shuffle=True,
drop_last=False,
pin_memory=True,
num_workers=num_workers)
# Define model and optimizer
......
benchmarks/benchmarks/rgcn.py 0 → 100644
View file @ 4361f8a6
import dgl
from dgl.nn.pytorch import RelGraphConv
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import utils
class RGCN(nn.Module):
def __init__(self, num_nodes, h_dim, out_dim, num_rels,
regularizer="basis", num_bases=-1, dropout=0.,
self_loop=False,
ns_mode=False):
super(RGCN, self).__init__()
if num_bases == -1:
num_bases = num_rels
self.emb = nn.Embedding(num_nodes, h_dim)
self.conv1 = RelGraphConv(h_dim, h_dim, num_rels, regularizer,
num_bases, self_loop=self_loop)
self.conv2 = RelGraphConv(
h_dim, out_dim, num_rels, regularizer, num_bases, self_loop=self_loop)
self.dropout = nn.Dropout(dropout)
self.ns_mode = ns_mode
def forward(self, g, nids=None):
if self.ns_mode:
# forward for neighbor sampling
x = self.emb(g[0].srcdata[dgl.NID])
h = self.conv1(g[0], x, g[0].edata[dgl.ETYPE], g[0].edata['norm'])
h = self.dropout(F.relu(h))
h = self.conv2(g[1], h, g[1].edata[dgl.ETYPE], g[1].edata['norm'])
return h
else:
x = self.emb.weight if nids is None else self.emb(nids)
h = self.conv1(g, x, g.edata[dgl.ETYPE], g.edata['norm'])
h = self.dropout(F.relu(h))
h = self.conv2(g, h, g.edata[dgl.ETYPE], g.edata['norm'])
return h
def load_data(data_name, get_norm=False, inv_target=False):
dataset = utils.process_data(data_name)
# Load hetero-graph
hg = dataset[0]
num_rels = len(hg.canonical_etypes)
category = dataset.predict_category
num_classes = dataset.num_classes
labels = hg.nodes[category].data.pop('labels')
train_mask = hg.nodes[category].data.pop('train_mask')
test_mask = hg.nodes[category].data.pop('test_mask')
train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze()
test_idx = torch.nonzero(test_mask, as_tuple=False).squeeze()
if get_norm:
# Calculate normalization weight for each edge,
# 1. / d, d is the degree of the destination node
for cetype in hg.canonical_etypes:
hg.edges[cetype].data['norm'] = dgl.norm_by_dst(
hg, cetype).unsqueeze(1)
edata = ['norm']
else:
edata = None
# get target category id
category_id = hg.ntypes.index(category)
g = dgl.to_homogeneous(hg, edata=edata)
# Rename the fields as they can be changed by for example DataLoader
g.ndata['ntype'] = g.ndata.pop(dgl.NTYPE)
g.ndata['type_id'] = g.ndata.pop(dgl.NID)
node_ids = torch.arange(g.num_nodes())
# find out the target node ids in g
loc = (g.ndata['ntype'] == category_id)
target_idx = node_ids[loc]
if inv_target:
# Map global node IDs to type-specific node IDs. This is required for
# looking up type-specific labels in a minibatch
inv_target = torch.empty((g.num_nodes(),), dtype=torch.int64)
inv_target[target_idx] = torch.arange(0, target_idx.shape[0],
dtype=inv_target.dtype)
return g, num_rels, num_classes, labels, train_idx, test_idx, target_idx, inv_target
else:
return g, num_rels, num_classes, labels, train_idx, test_idx, target_idx
benchmarks/scripts/torch_gpu_pip.txt
View file @ 4361f8a6
--find-links https://download.pytorch.org/whl/lts/1.8/torch_lts.html
torch==1.8.1+cu111
torchvision==0.9.1+cu111
--find-links https://download.pytorch.org/whl/torch
torch==1.9.0+cu111
torchvision
pytest
nose
numpy
......@@ -12,7 +12,8 @@ nltk
requests[security]
tqdm
awscli
torchtext==0.9.1
torchtext
pandas
rdflib
ogb==1.3.1
\ No newline at end of file
ogb==1.3.1
torchmetrics
\ No newline at end of file
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