conv.py

# 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
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"""This package implements common layers to help building
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

__all__ = ['gcn', 'gat', 'gin', 'gaan', 'gen_conv', 'appnp']


def gcn(gw, feature, hidden_size, activation, name, norm=None):
    """Implementation of graph convolutional neural networks (GCN)

    This is an implementation of the paper SEMI-SUPERVISED CLASSIFICATION
    WITH GRAPH CONVOLUTIONAL NETWORKS (https://arxiv.org/pdf/1609.02907.pdf).

    Args:
        gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)

        feature: A tensor with shape (num_nodes, feature_size).

        hidden_size: The hidden size for gcn.

        activation: The activation for the output.

        name: Gcn layer names.

        norm: If :code:`norm` is not None, then the feature will be normalized. Norm must
              be tensor with shape (num_nodes,) and dtype float32.

    Return:
        A tensor with shape (num_nodes, hidden_size)
    """

    def send_src_copy(src_feat, dst_feat, edge_feat):
        return src_feat["h"]

    size = feature.shape[-1]
    if size > hidden_size:
        feature = L.fc(feature,
                                  size=hidden_size,
                                  bias_attr=False,
                                  param_attr=fluid.ParamAttr(name=name))

    if norm is not None:
        feature = feature * norm

    msg = gw.send(send_src_copy, nfeat_list=[("h", feature)])

    if size > hidden_size:
        output = gw.recv(msg, "sum")
    else:
        output = gw.recv(msg, "sum")
        output = L.fc(output,
                                 size=hidden_size,
                                 bias_attr=False,
                                 param_attr=fluid.ParamAttr(name=name))

    if norm is not None:
        output = output * norm

    bias = L.create_parameter(
        shape=[hidden_size],
        dtype='float32',
        is_bias=True,
        name=name + '_bias')
    output = L.elementwise_add(output, bias, act=activation)
    return output


def gat(gw,
        feature,
        hidden_size,
        activation,
        name,
        num_heads=8,
        feat_drop=0.6,
        attn_drop=0.6,
        is_test=False):
    """Implementation of graph attention networks (GAT)

    This is an implementation of the paper GRAPH ATTENTION NETWORKS
    (https://arxiv.org/abs/1710.10903).

    Args:
        gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)

        feature: A tensor with shape (num_nodes, feature_size).

        hidden_size: The hidden size for gat.

        activation: The activation for the output.

        name: Gat layer names.

        num_heads: The head number in gat.

        feat_drop: Dropout rate for feature.

        attn_drop: Dropout rate for attention.

        is_test: Whether in test phrase.

    Return:
        A tensor with shape (num_nodes, hidden_size * num_heads)
    """

    def send_attention(src_feat, dst_feat, edge_feat):
        output = src_feat["left_a"] + dst_feat["right_a"]
        output = L.leaky_relu(
            output, alpha=0.2)  # (num_edges, num_heads)
        return {"alpha": output, "h": src_feat["h"]}

    def reduce_attention(msg):
        alpha = msg["alpha"]  # lod-tensor (batch_size, seq_len, num_heads)
        h = msg["h"]
        alpha = paddle_helper.sequence_softmax(alpha)
        old_h = h
        h = L.reshape(h, [-1, num_heads, hidden_size])
        alpha = L.reshape(alpha, [-1, num_heads, 1])
        if attn_drop > 1e-15:
            alpha = L.dropout(
                alpha,
                dropout_prob=attn_drop,
                is_test=is_test,
                dropout_implementation="upscale_in_train")
        h = h * alpha
        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 = L.dropout(
            feature,
            dropout_prob=feat_drop,
            is_test=is_test,
            dropout_implementation='upscale_in_train')

    ft = L.fc(feature,
                         hidden_size * num_heads,
                         bias_attr=False,
                         param_attr=fluid.ParamAttr(name=name + '_weight'))
    left_a = L.create_parameter(
        shape=[num_heads, hidden_size],
        dtype='float32',
        name=name + '_gat_l_A')
    right_a = L.create_parameter(
        shape=[num_heads, hidden_size],
        dtype='float32',
        name=name + '_gat_r_A')
    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 = L.create_parameter(
        shape=[hidden_size * num_heads],
        dtype='float32',
        is_bias=True,
        name=name + '_bias')
    bias.stop_gradient = True
    output = L.elementwise_add(output, bias, act=activation)
    return output


def gin(gw,
        feature,
        hidden_size,
        activation,
        name,
        init_eps=0.0,
        train_eps=False):
    """Implementation of Graph Isomorphism Network (GIN) layer.

    This is an implementation of the paper How Powerful are Graph Neural Networks?
    (https://arxiv.org/pdf/1810.00826.pdf).

    In their implementation, all MLPs have 2 layers. Batch normalization is applied
    on every hidden layer.

    Args:
        gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)

        feature: A tensor with shape (num_nodes, feature_size).

        name: GIN layer names.

        hidden_size: The hidden size for gin.

        activation: The activation for the output.

        init_eps: float, optional
            Initial :math:`\epsilon` value, default is 0.

        train_eps: bool, optional
            if True, :math:`\epsilon` will be a learnable parameter.

    Return:
        A tensor with shape (num_nodes, hidden_size).
    """

    def send_src_copy(src_feat, dst_feat, edge_feat):
        return src_feat["h"]

    epsilon = L.create_parameter(
        shape=[1, 1],
        dtype="float32",
        attr=fluid.ParamAttr(name="%s_eps" % name),
        default_initializer=fluid.initializer.ConstantInitializer(
            value=init_eps))

    if not train_eps:
        epsilon.stop_gradient = True

    msg = gw.send(send_src_copy, nfeat_list=[("h", feature)])
    output = gw.recv(msg, "sum") + feature * (epsilon + 1.0)

    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 = L.layer_norm(
        output,
        begin_norm_axis=1,
        param_attr=fluid.ParamAttr(
            name="norm_scale_%s" % (name),
            initializer=fluid.initializer.Constant(1.0)),
        bias_attr=fluid.ParamAttr(
            name="norm_bias_%s" % (name),
            initializer=fluid.initializer.Constant(0.0)), )

    if activation is not None:
        output = getattr(L, activation)(output)

    output = L.fc(output,
                             size=hidden_size,
                             act=activation,
                             param_attr=fluid.ParamAttr(name="%s_w_1" % name),
                             bias_attr=fluid.ParamAttr(name="%s_b_1" % name))

    return output


def gaan(gw, feature, hidden_size_a, hidden_size_v, hidden_size_m, hidden_size_o, heads, name):
    """Implementation of GaAN"""

    def send_func(src_feat, dst_feat, edge_feat):
        # 计算每条边上的注意力分数
        # E * (M * D1), 每个 dst 点都查询它的全部邻边的 src 点
        feat_query, feat_key = dst_feat['feat_query'], src_feat['feat_key']
        # E * M * D1
        old = feat_query
        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 = L.reduce_sum(feat_key * feat_query, dim=-1)

        return {'dst_node_feat': dst_feat['node_feat'],
                'src_node_feat': src_feat['node_feat'],
                'feat_value': src_feat['feat_value'],
                'alpha': alpha,
                'feat_gate': src_feat['feat_gate']}

    def recv_func(message):
        # 每条边的终点的特征
        dst_feat = message['dst_node_feat']
        # 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 = L.sequence_pool(dst_feat, 'average')
        # 每个中心点的邻居的特征的平均值
        z = L.sequence_pool(src_feat, 'average')

        # 计算 gate
        feat_gate = message['feat_gate']
        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']
        alpha = paddle_helper.sequence_softmax(alpha) # E * M

        feat_value = message['feat_value'] # E * (M * D2)
        old = feat_value
        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 = L.sequence_pool(feat_value, 'sum') # N * (M * D2)

        feat_value = L.reshape(feat_value, [-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 = L.concat([x, output], axis=1)

        return output

    # feature N * D

    # 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 = L.fc(feature, hidden_size_a * heads, bias_attr=False,
                     param_attr=fluid.ParamAttr(name=name + '_project_key'))
    # N * (D2 * M)
    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 = L.fc(feature, hidden_size_a * heads, bias_attr=False,
                     param_attr=fluid.ParamAttr(name=name + '_project_query'))
    # N * Dm
    feat_gate = L.fc(feature, hidden_size_m, bias_attr=False, 
                                param_attr=fluid.ParamAttr(name=name + '_project_gate'))

    # send 阶段

    message = gw.send(
        send_func,
        nfeat_list=[('node_feat', feature), ('feat_key', feat_key), ('feat_value', feat_value),
                    ('feat_query', feat_query), ('feat_gate', feat_gate)],
        efeat_list=None,
    )

    # 聚合邻居特征
    output = gw.recv(message, recv_func)
    output = L.fc(output, hidden_size_o, bias_attr=False,
                            param_attr=fluid.ParamAttr(name=name + '_project_output'))
    output = L.leaky_relu(output, alpha=0.1)
    output = L.dropout(output, dropout_prob=0.1)

    return output


def gen_conv(gw,
        feature,
        name,
        beta=None):
    """Implementation of GENeralized Graph Convolution (GENConv), see the paper
    "DeeperGCN: All You Need to Train Deeper GCNs" in
    https://arxiv.org/pdf/2006.07739.pdf

    Args:
        gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)

        feature: A tensor with shape (num_nodes, feature_size).

        beta: [0, +infinity] or "dynamic" or None

        name: deeper gcn layer names.

    Return:
        A tensor with shape (num_nodes, feature_size)
    """
   
    if beta == "dynamic":
        beta = L.create_parameter(
                shape=[1],
                dtype='float32',
                default_initializer=
                    fluid.initializer.ConstantInitializer(value=1.0),
                name=name + '_beta')
    
    # message passing
    msg = gw.send(message_passing.copy_send, nfeat_list=[("h", feature)])
    output = gw.recv(msg, message_passing.softmax_agg(beta))
    
    # msg norm
    output = message_passing.msg_norm(feature, output, name)
    output = feature + output
    
    output = L.fc(output,
                     feature.shape[-1],
                     bias_attr=False,
                     act="relu",
                     param_attr=fluid.ParamAttr(name=name + '_weight1'))
    
    output = L.fc(output,
                     feature.shape[-1],
                     bias_attr=False,
                     param_attr=fluid.ParamAttr(name=name + '_weight2'))

    return output

def get_norm(indegree):
    """Get Laplacian Normalization"""
    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):
    """Implementation of APPNP of "Predict then Propagate: Graph Neural Networks
    meet Personalized PageRank"  (ICLR 2019). 

    Args:
        gw: Graph wrapper object (:code:`StaticGraphWrapper` or :code:`GraphWrapper`)

        feature: A tensor with shape (num_nodes, feature_size).

        edge_dropout: Edge dropout rate.

        k_hop: K Steps for Propagation

    Return:
        A tensor with shape (num_nodes, hidden_size)
    """

    def send_src_copy(src_feat, dst_feat, edge_feat):
       feature = src_feat["h"]
       return feature

    h0 = feature
    ngw = gw 
    norm = get_norm(ngw.indegree())
    
    for i in range(k_hop):
        if edge_dropout > 1e-5:     
            ngw = pgl.sample.edge_drop(gw, edge_dropout) 
            norm = get_norm(ngw.indegree())
            
        feature = feature * norm

        msg = gw.send(send_src_copy, nfeat_list=[("h", feature)])

        feature = gw.recv(msg, "sum")

        feature = feature * norm

        feature = feature * (1 - alpha) + h0 * alpha
    return feature