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提交 b809f488 编写于 作者: Y Yelrose
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reproduce paper results

上级 3eb6d2a6
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Showing with 127 addition and 172 deletion
examples/citation_benchmark/build_model.py
浏览文件 @ b809f488
......@@ -4,7 +4,6 @@ from pgl import data_loader
import paddle.fluid as fluid
import numpy as np
import time
from optimization import AdamW
def build_model(dataset, config, phase, main_prog):
gw = pgl.graph_wrapper.GraphWrapper(
......@@ -15,6 +14,7 @@ def build_model(dataset, config, phase, main_prog):
m = GraphModel(config=config, num_class=dataset.num_classes)
logits = m.forward(gw, gw.node_feat["words"], phase)
# Take the last
node_index = fluid.layers.data(
"node_index",
shape=[None, 1],
......@@ -33,10 +33,11 @@ def build_model(dataset, config, phase, main_prog):
loss = fluid.layers.mean(loss)
if phase == "train":
AdamW(loss=loss,
adam = fluid.optimizer.Adam(
learning_rate=config.learning_rate,
weight_decay=config.weight_decay,
train_program=main_prog)
regularization=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=config.weight_decay))
adam.minimize(loss)
return gw, loss, acc
examples/citation_benchmark/config/appnp.yaml
浏览文件 @ b809f488
......@@ -6,4 +6,4 @@ learning_rate: 0.01
dropout: 0.5
hidden_size: 64
weight_decay: 0.0005
edge_dropout: 0.00
edge_dropout: 0.0
examples/citation_benchmark/config/sgc.yaml
浏览文件 @ b809f488
......@@ -2,3 +2,4 @@ model_name: SGC
num_layers: 2
learning_rate: 0.2
weight_decay: 0.000005
feature_pre_normalize: False
examples/citation_benchmark/model.py
浏览文件 @ b809f488
......@@ -3,8 +3,9 @@ import paddle.fluid.layers as L
import pgl.layers.conv as conv
def get_norm(indegree):
norm = L.pow(L.cast(indegree, dtype="float32") + 1e-6, factor=-0.5)
norm = norm * L.cast(indegree > 0, dtype="float32")
float_degree = L.cast(indegree, dtype="float32")
float_degree = L.clamp(float_degree, min=1.0)
norm = L.pow(float_degree, factor=-0.5)
return norm
......@@ -29,10 +30,6 @@ class GCN(object):
ngw = graph_wrapper
norm = graph_wrapper.node_feat["norm"]
feature = L.dropout(
feature,
self.dropout,
dropout_implementation='upscale_in_train')
feature = pgl.layers.gcn(ngw,
feature,
......@@ -150,10 +147,10 @@ class SGC(object):
def forward(self, graph_wrapper, feature, phase):
feature = conv.appnp(graph_wrapper,
feature=feature,
norm=graph_wrapper.node_feat["norm"],
edge_dropout=0,
alpha=0,
k_hop=self.num_layers)
feature.stop_gradient=True
feature = L.fc(feature, self.num_class, act=None, name="output")
feature = L.fc(feature, self.num_class, act=None, bias_attr=False, name="output")
return feature
examples/citation_benchmark/optimization.py 已删除 100644 → 0
浏览文件 @ 3eb6d2a6
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Optimization and learning rate scheduling."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle.fluid as fluid
def AdamW(loss,
learning_rate,
train_program,
weight_decay):
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
def exclude_from_weight_decay(name):
if name.find("layer_norm") > -1:
return True
bias_suffix = ["_bias", "_b", ".b_0"]
for suffix in bias_suffix:
if name.endswith(suffix):
return True
return False
param_list = dict()
for param in train_program.global_block().all_parameters():
param_list[param.name] = param * 1.0
param_list[param.name].stop_gradient = True
_, param_grads = optimizer.minimize(loss)
if weight_decay > 0:
for param, grad in param_grads:
if exclude_from_weight_decay(param.name):
continue
with param.block.program._optimized_guard(
[param, grad]), fluid.framework.name_scope("weight_decay"):
updated_param = param - param_list[
param.name] * weight_decay * learning_rate
fluid.layers.assign(output=param, input=updated_param)
examples/citation_benchmark/train.py
浏览文件 @ b809f488
......@@ -22,33 +22,35 @@ import argparse
from build_model import build_model
import yaml
from easydict import EasyDict as edict
import tqdm
def normalize(feat):
return feat / np.maximum(np.sum(feat, -1, keepdims=True), 1)
def load(name):
def load(name, normalized_feature=True):
if name == 'cora':
dataset = data_loader.CoraDataset()
elif name == "pubmed":
dataset = data_loader.CitationDataset("pubmed", symmetry_edges=False)
dataset = data_loader.CitationDataset("pubmed", symmetry_edges=True)
elif name == "citeseer":
dataset = data_loader.CitationDataset("citeseer", symmetry_edges=False)
dataset = data_loader.CitationDataset("citeseer", symmetry_edges=True)
else:
raise ValueError(name + " dataset doesn't exists")
return dataset
def main(args, config):
dataset = load(args.dataset)
indegree = dataset.graph.indegree()
norm = np.zeros_like(indegree, dtype="float32")
norm[indegree > 0] = np.power(indegree[indegree > 0], -0.5)
norm = np.maximum(indegree.astype("float32"), 1)
norm = np.power(norm, -0.5)
dataset.graph.node_feat["norm"] = np.expand_dims(norm, -1)
dataset.graph.node_feat["words"] = normalize(dataset.graph.node_feat["words"])
return dataset
def main(args, config):
dataset = load(args.dataset, args.feature_pre_normalize)
place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
train_program = fluid.default_main_program()
startup_program = fluid.default_startup_program()
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
gw, loss, acc = build_model(dataset,
......@@ -67,13 +69,11 @@ def main(args, config):
test_program = test_program.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_program)
train_index = dataset.train_index
train_label = np.expand_dims(dataset.y[train_index], -1)
train_index = np.expand_dims(train_index, -1)
log.info("Number of Train %s" % len(train_index))
val_index = dataset.val_index
val_label = np.expand_dims(dataset.y[val_index], -1)
......@@ -84,13 +84,20 @@ def main(args, config):
test_index = np.expand_dims(test_index, -1)
dur = []
cal_val_acc = []
cal_test_acc = []
for epoch in range(args.epoch):
if epoch >= 3:
t0 = time.time()
# Feed data
feed_dict = gw.to_feed(dataset.graph)
best_test = []
for run in range(args.runs):
exe.run(startup_program)
cal_val_acc = []
cal_test_acc = []
cal_val_loss = []
cal_test_loss = []
for epoch in tqdm.tqdm(range(args.epoch)):
feed_dict["node_index"] = np.array(train_index, dtype="int64")
feed_dict["node_label"] = np.array(train_label, dtype="int64")
train_loss, train_acc = exe.run(train_program,
......@@ -98,11 +105,7 @@ def main(args, config):
fetch_list=[loss, acc],
return_numpy=True)
if epoch >= 3:
time_per_epoch = 1.0 * (time.time() - t0)
dur.append(time_per_epoch)
feed_dict = gw.to_feed(dataset.graph)
feed_dict["node_index"] = np.array(val_index, dtype="int64")
feed_dict["node_label"] = np.array(val_label, dtype="int64")
val_loss, val_acc = exe.run(test_program,
......@@ -110,9 +113,8 @@ def main(args, config):
fetch_list=[v_loss, v_acc],
return_numpy=True)
val_loss = val_loss[0]
val_acc = val_acc[0]
cal_val_acc.append(val_acc)
cal_val_acc.append(val_acc[0])
cal_val_loss.append(val_loss[0])
feed_dict["node_index"] = np.array(test_index, dtype="int64")
feed_dict["node_label"] = np.array(test_label, dtype="int64")
......@@ -121,17 +123,16 @@ def main(args, config):
fetch_list=[v_loss, v_acc],
return_numpy=True)
test_loss = test_loss[0]
test_acc = test_acc[0]
cal_test_acc.append(test_acc)
cal_test_acc.append(test_acc[0])
cal_test_loss.append(test_loss[0])
log.info("Runs %s: Model: %s Best Test Accuracy: %f" % (run, config.model_name,
cal_test_acc[np.argmin(cal_val_loss)]))
log.info("Epoch %d " % epoch +
"Train Loss: %f " % train_loss + "Train Acc: %f " % train_acc
+ "Val Loss: %f " % val_loss + "Val Acc: %f " % val_acc)
best_test.append(cal_test_acc[np.argmin(cal_val_loss)])
log.info("Best Test Accuracy: %f ( stddev: %f )" % (np.mean(best_test), np.std(best_test)))
cal_val_acc = np.array(cal_val_acc)
log.info("Model: %s Best Test Accuracy: %f" % (config.model_name,
cal_test_acc[np.argmax(cal_val_acc)]))
if __name__ == '__main__':
......@@ -141,6 +142,8 @@ if __name__ == '__main__':
parser.add_argument("--use_cuda", action='store_true', help="use_cuda")
parser.add_argument("--conf", type=str, help="config file for models")
parser.add_argument("--epoch", type=int, default=200, help="Epoch")
parser.add_argument("--runs", type=int, default=5, help="runs")
parser.add_argument("--feature_pre_normalize", type=bool, default=True, help="pre_normalize feature")
args = parser.parse_args()
config = edict(yaml.load(open(args.conf), Loader=yaml.FullLoader))
log.info(args)
......
pgl/graph_wrapper.py
浏览文件 @ b809f488
......@@ -735,7 +735,7 @@ def get_degree(edge, num_nodes):
class DropEdgeWrapper(BaseGraphWrapper):
"""Implement of Edge Drop """
def __init__(self, graph_wrapper, dropout):
def __init__(self, graph_wrapper, dropout, keep_self_loop=True):
super(DropEdgeWrapper, self).__init__()
# Copy Node's information
......@@ -751,11 +751,16 @@ class DropEdgeWrapper(BaseGraphWrapper):
src, dst = graph_wrapper.edges
u = L.uniform_random(shape=L.cast(L.shape(src), 'int64'), min=0., max=1.)
# Avoid Empty Edges
keeped = L.cast(u > dropout, dtype="float32")
self._num_edges = L.reduce_sum(L.cast(keeped, "int32"))
keeped = keeped + L.cast(self._num_edges == 0, dtype="float32")
if keep_self_loop:
self_loop = L.cast(src == dst, dtype="float32")
keeped = keeped + self_loop
keeped = (keeped > 0.5)
src = paddle_helper.masked_select(src, keeped)
dst = paddle_helper.masked_select(dst, keeped)
......
pgl/layers/conv.py
浏览文件 @ b809f488
......@@ -16,6 +16,7 @@ graph neural networks.
"""
import pgl
import paddle.fluid as fluid
import paddle.fluid.layers as L
from pgl.utils import paddle_helper
from pgl import message_passing
......@@ -51,7 +52,7 @@ def gcn(gw, feature, hidden_size, activation, name, norm=None):
size = feature.shape[-1]
if size > hidden_size:
feature = fluid.layers.fc(feature,
feature = L.fc(feature,
size=hidden_size,
bias_attr=False,
param_attr=fluid.ParamAttr(name=name))
......@@ -65,7 +66,7 @@ def gcn(gw, feature, hidden_size, activation, name, norm=None):
output = gw.recv(msg, "sum")
else:
output = gw.recv(msg, "sum")
output = fluid.layers.fc(output,
output = L.fc(output,
size=hidden_size,
bias_attr=False,
param_attr=fluid.ParamAttr(name=name))
......@@ -73,12 +74,12 @@ def gcn(gw, feature, hidden_size, activation, name, norm=None):
if norm is not None:
output = output * norm
bias = fluid.layers.create_parameter(
bias = L.create_parameter(
shape=[hidden_size],
dtype='float32',
is_bias=True,
name=name + '_bias')
output = fluid.layers.elementwise_add(output, bias, act=activation)
output = L.elementwise_add(output, bias, act=activation)
return output
......@@ -121,7 +122,7 @@ def gat(gw,
def send_attention(src_feat, dst_feat, edge_feat):
output = src_feat["left_a"] + dst_feat["right_a"]
output = fluid.layers.leaky_relu(
output = L.leaky_relu(
output, alpha=0.2) # (num_edges, num_heads)
return {"alpha": output, "h": src_feat["h"]}
......@@ -130,54 +131,54 @@ def gat(gw,
h = msg["h"]
alpha = paddle_helper.sequence_softmax(alpha)
old_h = h
h = fluid.layers.reshape(h, [-1, num_heads, hidden_size])
alpha = fluid.layers.reshape(alpha, [-1, num_heads, 1])
h = L.reshape(h, [-1, num_heads, hidden_size])
alpha = L.reshape(alpha, [-1, num_heads, 1])
if attn_drop > 1e-15:
alpha = fluid.layers.dropout(
alpha = L.dropout(
alpha,
dropout_prob=attn_drop,
is_test=is_test,
dropout_implementation="upscale_in_train")
h = h * alpha
h = fluid.layers.reshape(h, [-1, num_heads * hidden_size])
h = fluid.layers.lod_reset(h, old_h)
return fluid.layers.sequence_pool(h, "sum")
h = L.reshape(h, [-1, num_heads * hidden_size])
h = L.lod_reset(h, old_h)
return L.sequence_pool(h, "sum")
if feat_drop > 1e-15:
feature = fluid.layers.dropout(
feature = L.dropout(
feature,
dropout_prob=feat_drop,
is_test=is_test,
dropout_implementation='upscale_in_train')
ft = fluid.layers.fc(feature,
ft = L.fc(feature,
hidden_size * num_heads,
bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_weight'))
left_a = fluid.layers.create_parameter(
left_a = L.create_parameter(
shape=[num_heads, hidden_size],
dtype='float32',
name=name + '_gat_l_A')
right_a = fluid.layers.create_parameter(
right_a = L.create_parameter(
shape=[num_heads, hidden_size],
dtype='float32',
name=name + '_gat_r_A')
reshape_ft = fluid.layers.reshape(ft, [-1, num_heads, hidden_size])
left_a_value = fluid.layers.reduce_sum(reshape_ft * left_a, -1)
right_a_value = fluid.layers.reduce_sum(reshape_ft * right_a, -1)
reshape_ft = L.reshape(ft, [-1, num_heads, hidden_size])
left_a_value = L.reduce_sum(reshape_ft * left_a, -1)
right_a_value = L.reduce_sum(reshape_ft * right_a, -1)
msg = gw.send(
send_attention,
nfeat_list=[("h", ft), ("left_a", left_a_value),
("right_a", right_a_value)])
output = gw.recv(msg, reduce_attention)
bias = fluid.layers.create_parameter(
bias = L.create_parameter(
shape=[hidden_size * num_heads],
dtype='float32',
is_bias=True,
name=name + '_bias')
bias.stop_gradient = True
output = fluid.layers.elementwise_add(output, bias, act=activation)
output = L.elementwise_add(output, bias, act=activation)
return output
......@@ -220,7 +221,7 @@ def gin(gw,
def send_src_copy(src_feat, dst_feat, edge_feat):
return src_feat["h"]
epsilon = fluid.layers.create_parameter(
epsilon = L.create_parameter(
shape=[1, 1],
dtype="float32",
attr=fluid.ParamAttr(name="%s_eps" % name),
......@@ -233,13 +234,13 @@ def gin(gw,
msg = gw.send(send_src_copy, nfeat_list=[("h", feature)])
output = gw.recv(msg, "sum") + feature * (epsilon + 1.0)
output = fluid.layers.fc(output,
output = L.fc(output,
size=hidden_size,
act=None,
param_attr=fluid.ParamAttr(name="%s_w_0" % name),
bias_attr=fluid.ParamAttr(name="%s_b_0" % name))
output = fluid.layers.layer_norm(
output = L.layer_norm(
output,
begin_norm_axis=1,
param_attr=fluid.ParamAttr(
......@@ -250,9 +251,9 @@ def gin(gw,
initializer=fluid.initializer.Constant(0.0)), )
if activation is not None:
output = getattr(fluid.layers, activation)(output)
output = getattr(L, activation)(output)
output = fluid.layers.fc(output,
output = L.fc(output,
size=hidden_size,
act=activation,
param_attr=fluid.ParamAttr(name="%s_w_1" % name),
......@@ -270,10 +271,10 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
feat_query, feat_key = dst_feat['feat_query'], src_feat['feat_key']
# E * M * D1
old = feat_query
feat_query = fluid.layers.reshape(feat_query, [-1, heads, hidden_size_a])
feat_key = fluid.layers.reshape(feat_key, [-1, heads, hidden_size_a])
feat_query = L.reshape(feat_query, [-1, heads, hidden_size_a])
feat_key = L.reshape(feat_key, [-1, heads, hidden_size_a])
# E * M
alpha = fluid.layers.reduce_sum(feat_key * feat_query, dim=-1)
alpha = L.reduce_sum(feat_key * feat_query, dim=-1)
return {'dst_node_feat': dst_feat['node_feat'],
'src_node_feat': src_feat['node_feat'],
......@@ -287,15 +288,15 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
# 每条边的出发点的特征
src_feat = message['src_node_feat']
# 每个中心点自己的特征
x = fluid.layers.sequence_pool(dst_feat, 'average')
x = L.sequence_pool(dst_feat, 'average')
# 每个中心点的邻居的特征的平均值
z = fluid.layers.sequence_pool(src_feat, 'average')
z = L.sequence_pool(src_feat, 'average')
# 计算 gate
feat_gate = message['feat_gate']
g_max = fluid.layers.sequence_pool(feat_gate, 'max')
g = fluid.layers.concat([x, g_max, z], axis=1)
g = fluid.layers.fc(g, heads, bias_attr=False, act="sigmoid")
g_max = L.sequence_pool(feat_gate, 'max')
g = L.concat([x, g_max, z], axis=1)
g = L.fc(g, heads, bias_attr=False, act="sigmoid")
# softmax
alpha = message['alpha']
......@@ -303,19 +304,19 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
feat_value = message['feat_value'] # E * (M * D2)
old = feat_value
feat_value = fluid.layers.reshape(feat_value, [-1, heads, hidden_size_v]) # E * M * D2
feat_value = fluid.layers.elementwise_mul(feat_value, alpha, axis=0)
feat_value = fluid.layers.reshape(feat_value, [-1, heads*hidden_size_v]) # E * (M * D2)
feat_value = fluid.layers.lod_reset(feat_value, old)
feat_value = L.reshape(feat_value, [-1, heads, hidden_size_v]) # E * M * D2
feat_value = L.elementwise_mul(feat_value, alpha, axis=0)
feat_value = L.reshape(feat_value, [-1, heads*hidden_size_v]) # E * (M * D2)
feat_value = L.lod_reset(feat_value, old)
feat_value = fluid.layers.sequence_pool(feat_value, 'sum') # N * (M * D2)
feat_value = L.sequence_pool(feat_value, 'sum') # N * (M * D2)
feat_value = fluid.layers.reshape(feat_value, [-1, heads, hidden_size_v]) # N * M * D2
feat_value = L.reshape(feat_value, [-1, heads, hidden_size_v]) # N * M * D2
output = fluid.layers.elementwise_mul(feat_value, g, axis=0)
output = fluid.layers.reshape(output, [-1, heads * hidden_size_v]) # N * (M * D2)
output = L.elementwise_mul(feat_value, g, axis=0)
output = L.reshape(output, [-1, heads * hidden_size_v]) # N * (M * D2)
output = fluid.layers.concat([x, output], axis=1)
output = L.concat([x, output], axis=1)
return output
......@@ -324,16 +325,16 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
# 计算每个点自己需要发送出去的内容
# 投影后的特征向量
# N * (D1 * M)
feat_key = fluid.layers.fc(feature, hidden_size_a * heads, bias_attr=False,
feat_key = L.fc(feature, hidden_size_a * heads, bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_project_key'))
# N * (D2 * M)
feat_value = fluid.layers.fc(feature, hidden_size_v * heads, bias_attr=False,
feat_value = L.fc(feature, hidden_size_v * heads, bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_project_value'))
# N * (D1 * M)
feat_query = fluid.layers.fc(feature, hidden_size_a * heads, bias_attr=False,
feat_query = L.fc(feature, hidden_size_a * heads, bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_project_query'))
# N * Dm
feat_gate = fluid.layers.fc(feature, hidden_size_m, bias_attr=False,
feat_gate = L.fc(feature, hidden_size_m, bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_project_gate'))
# send 阶段
......@@ -347,10 +348,10 @@ def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o
# 聚合邻居特征
output = gw.recv(message, recv_func)
output = fluid.layers.fc(output, hidden_size_o, bias_attr=False,
output = L.fc(output, hidden_size_o, bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_project_output'))
output = fluid.layers.leaky_relu(output, alpha=0.1)
output = fluid.layers.dropout(output, dropout_prob=0.1)
output = L.leaky_relu(output, alpha=0.1)
output = L.dropout(output, dropout_prob=0.1)
return output
......@@ -377,7 +378,7 @@ def gen_conv(gw,
"""
if beta == "dynamic":
beta = fluid.layers.create_parameter(
beta = L.create_parameter(
shape=[1],
dtype='float32',
default_initializer=
......@@ -392,13 +393,13 @@ def gen_conv(gw,
output = message_passing.msg_norm(feature, output, name)
output = feature + output
output = fluid.layers.fc(output,
output = L.fc(output,
feature.shape[-1],
bias_attr=False,
act="relu",
param_attr=fluid.ParamAttr(name=name + '_weight1'))
output = fluid.layers.fc(output,
output = L.fc(output,
feature.shape[-1],
bias_attr=False,
param_attr=fluid.ParamAttr(name=name + '_weight2'))
......@@ -407,9 +408,9 @@ def gen_conv(gw,
def get_norm(indegree):
"""Get Laplacian Normalization"""
norm = fluid.layers.pow(fluid.layers.cast(indegree, dtype="float32") + 1e-6,
factor=-0.5)
norm = norm * fluid.layers.cast(indegree > 0, dtype="float32")
float_degree = L.cast(indegree, dtype="float32")
float_degree = L.clamp(float_degree, min=1.0)
norm = L.pow(float_degree, factor=-0.5)
return norm
def appnp(gw, feature, edge_dropout=0, alpha=0.2, k_hop=10):
......
pgl/sample.py
浏览文件 @ b809f488
......@@ -518,8 +518,10 @@ def graph_saint_random_walk_sample(graph,
return subgraph
def edge_drop(graph_wrapper, dropout_rate):
def edge_drop(graph_wrapper, dropout_rate, keep_self_loop=True):
if dropout_rate < 1e-5:
return graph_wrapper
else:
return pgl.graph_wrapper.DropEdgeWrapper(graph_wrapper, dropout_rate)
return pgl.graph_wrapper.DropEdgeWrapper(graph_wrapper,
dropout_rate,
keep_self_loop)
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