"""
Write your own GNN module
=========================

Sometimes, your model goes beyond simply stacking existing GNN modules.
For example, you would like to invent a new way of aggregating neighbor
information by considering node importance or edge weights.

By the end of this tutorial you will be able to

-  Understand DGL’s message passing APIs.
-  Implement GraphSAGE convolution module by your own.

This tutorial assumes that you already know :doc:`the basics of training a
GNN for node classification <1_introduction>`.

(Time estimate: 10 minutes)

"""

import dgl
import torch
import torch.nn as nn
import torch.nn.functional as F


######################################################################
# Message passing and GNNs
# ------------------------
# 
# DGL follows the *message passing paradigm* inspired by the Message
# Passing Neural Network proposed by `Gilmer et
# al. <https://arxiv.org/abs/1704.01212>`__ Essentially, they found many
# GNN models can fit into the following framework:
# 
# .. math::
# 
# 
#    m_{u\to v}^{(l)} = M^{(l)}\left(h_v^{(l-1)}, h_u^{(l-1)}, e_{u\to v}^{(l-1)}\right)
# 
# .. math::
# 
# 
#    m_{v}^{(l)} = \sum_{u\in\mathcal{N}(v)}m_{u\to v}^{(l)}
# 
# .. math::
# 
# 
#    h_v^{(l)} = U^{(l)}\left(h_v^{(l-1)}, m_v^{(l)}\right)
# 
# where DGL calls :math:`M^{(l)}` the *message function*, :math:`\sum` the
# *reduce function* and :math:`U^{(l)}` the *update function*. Note that
# :math:`\sum` here can represent any function and is not necessarily a
# summation.
# 


######################################################################
# For example, the `GraphSAGE convolution (Hamilton et al.,
# 2017) <https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf>`__
# takes the following mathematical form:
# 
# .. math::
# 
# 
#    h_{\mathcal{N}(v)}^k\leftarrow \text{Average}\{h_u^{k-1},\forall u\in\mathcal{N}(v)\}
# 
# .. math::
# 
# 
#    h_v^k\leftarrow \text{ReLU}\left(W^k\cdot \text{CONCAT}(h_v^{k-1}, h_{\mathcal{N}(v)}^k) \right)
# 
# You can see that message passing is directional: the message sent from
# one node :math:`u` to other node :math:`v` is not necessarily the same
# as the other message sent from node :math:`v` to node :math:`u` in the
# opposite direction.
# 
# Although DGL has builtin support of GraphSAGE via
# :class:`dgl.nn.SAGEConv <dgl.nn.pytorch.SAGEConv>`,
# here is how you can implement GraphSAGE convolution in DGL by your own.
# 

import dgl.function as fn

class SAGEConv(nn.Module):
    """Graph convolution module used by the GraphSAGE model.
    
    Parameters
    ----------
    in_feat : int
        Input feature size.
    out_feat : int
        Output feature size.
    """
    def __init__(self, in_feat, out_feat):
        super(SAGEConv, self).__init__()
        # A linear submodule for projecting the input and neighbor feature to the output.
        self.linear = nn.Linear(in_feat * 2, out_feat)
    
    def forward(self, g, h):
        """Forward computation
        
        Parameters
        ----------
        g : Graph
            The input graph.
        h : Tensor
            The input node feature.
        """
        with g.local_scope():
            g.ndata['h'] = h
            # update_all is a message passing API.
            g.update_all(message_func=fn.copy_u('h', 'm'), reduce_func=fn.mean('m', 'h_N'))
            h_N = g.ndata['h_N']
            h_total = torch.cat([h, h_N], dim=1)
            return self.linear(h_total)


######################################################################
# The central piece in this code is the
# :func:`g.update_all <dgl.DGLGraph.update_all>`
# function, which gathers and averages the neighbor features. There are
# three concepts here:
#
# * Message function ``fn.copy_u('h', 'm')`` that
#   copies the node feature under name ``'h'`` as *messages* sent to
#   neighbors.
#
# * Reduce function ``fn.mean('m', 'h_N')`` that averages
#   all the received messages under name ``'m'`` and saves the result as a
#   new node feature ``'h_N'``.
#
# * ``update_all`` tells DGL to trigger the
#   message and reduce functions for all the nodes and edges.
# 


######################################################################
# Afterwards, you can stack your own GraphSAGE convolution layers to form
# a multi-layer GraphSAGE network.
#

class Model(nn.Module):
    def __init__(self, in_feats, h_feats, num_classes):
        super(Model, self).__init__()
        self.conv1 = SAGEConv(in_feats, h_feats)
        self.conv2 = SAGEConv(h_feats, num_classes)
    
    def forward(self, g, in_feat):
        h = self.conv1(g, in_feat)
        h = F.relu(h)
        h = self.conv2(g, h)
        return h


######################################################################
# Training loop
# ~~~~~~~~~~~~~
# The following code for data loading and training loop is directly copied
# from the introduction tutorial.
# 

import dgl.data

dataset = dgl.data.CoraGraphDataset()
g = dataset[0]

def train(g, model):
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
    all_logits = []
    best_val_acc = 0
    best_test_acc = 0

    features = g.ndata['feat']
    labels = g.ndata['label']
    train_mask = g.ndata['train_mask']
    val_mask = g.ndata['val_mask']
    test_mask = g.ndata['test_mask']
    for e in range(200):
        # Forward
        logits = model(g, features)

        # Compute prediction
        pred = logits.argmax(1)

        # Compute loss
        # Note that we should only compute the losses of the nodes in the training set,
        # i.e. with train_mask 1.
        loss = F.cross_entropy(logits[train_mask], labels[train_mask])

        # Compute accuracy on training/validation/test
        train_acc = (pred[train_mask] == labels[train_mask]).float().mean()
        val_acc = (pred[val_mask] == labels[val_mask]).float().mean()
        test_acc = (pred[test_mask] == labels[test_mask]).float().mean()

        # Save the best validation accuracy and the corresponding test accuracy.
        if best_val_acc < val_acc:
            best_val_acc = val_acc
            best_test_acc = test_acc

        # Backward
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        all_logits.append(logits.detach())

        if e % 5 == 0:
            print('In epoch {}, loss: {:.3f}, val acc: {:.3f} (best {:.3f}), test acc: {:.3f} (best {:.3f})'.format(
                e, loss, val_acc, best_val_acc, test_acc, best_test_acc))

model = Model(g.ndata['feat'].shape[1], 16, dataset.num_classes)
train(g, model)


######################################################################
# More customization
# ------------------
# 
# In DGL, we provide many built-in message and reduce functions under the
# ``dgl.function`` package. You can find more details in :ref:`the API
# doc <apifunction>`.
# 


######################################################################
# These APIs allow one to quickly implement new graph convolution modules.
# For example, the following implements a new ``SAGEConv`` that aggregates
# neighbor representations using a weighted average. Note that ``edata``
# member can hold edge features which can also take part in message
# passing.
# 

class WeightedSAGEConv(nn.Module):
    """Graph convolution module used by the GraphSAGE model with edge weights.
    
    Parameters
    ----------
    in_feat : int
        Input feature size.
    out_feat : int
        Output feature size.
    """
    def __init__(self, in_feat, out_feat):
        super(WeightedSAGEConv, self).__init__()
        # A linear submodule for projecting the input and neighbor feature to the output.
        self.linear = nn.Linear(in_feat * 2, out_feat)
    
    def forward(self, g, h, w):
        """Forward computation
        
        Parameters
        ----------
        g : Graph
            The input graph.
        h : Tensor
            The input node feature.
        w : Tensor
            The edge weight.
        """
        with g.local_scope():
            g.ndata['h'] = h
            g.edata['w'] = w
            g.update_all(message_func=fn.u_mul_e('h', 'w', 'm'), reduce_func=fn.mean('m', 'h_N'))
            h_N = g.ndata['h_N']
            h_total = torch.cat([h, h_N], dim=1)
            return self.linear(h_total)


######################################################################
# Because the graph in this dataset does not have edge weights, we
# manually assign all edge weights to one in the ``forward()`` function of
# the model. You can replace it with your own edge weights.
# 

class Model(nn.Module):
    def __init__(self, in_feats, h_feats, num_classes):
        super(Model, self).__init__()
        self.conv1 = WeightedSAGEConv(in_feats, h_feats)
        self.conv2 = WeightedSAGEConv(h_feats, num_classes)
    
    def forward(self, g, in_feat):
        h = self.conv1(g, in_feat, torch.ones(g.num_edges()).to(g.device))
        h = F.relu(h)
        h = self.conv2(g, h, torch.ones(g.num_edges()).to(g.device))
        return h
    
model = Model(g.ndata['feat'].shape[1], 16, dataset.num_classes)
train(g, model)


######################################################################
# Even more customization by user-defined function
# ------------------------------------------------
# 
# DGL allows user-defined message and reduce function for the maximal
# expressiveness. Here is a user-defined message function that is
# equivalent to ``fn.u_mul_e('h', 'w', 'm')``.
# 

def u_mul_e_udf(edges):
    return {'m' : edges.src['h'] * edges.data['w']}


######################################################################
# ``edges`` has three members: ``src``, ``data`` and ``dst``, representing
# the source node feature, edge feature, and destination node feature for
# all edges.
# 


######################################################################
# You can also write your own reduce function. For example, the following
# is equivalent to the builtin ``fn.sum('m', 'h')`` function that sums up
# the incoming messages:
# 

def sum_udf(nodes):
    return {'h': nodes.mailbox['m'].sum(1)}


######################################################################
# In short, DGL will group the nodes by their in-degrees, and for each
# group DGL stacks the incoming messages along the second dimension. You 
# can then perform a reduction along the second dimension to aggregate
# messages.
# 
# For more details on customizing message and reduce function with
# user-defined function, please refer to the :ref:`API
# reference <apiudf>`.
# 


######################################################################
# Best practice of writing custom GNN modules
# -------------------------------------------
# 
# DGL recommends the following practice ranked by preference:
# 
# -  Use ``dgl.nn`` modules.
# -  Use ``dgl.nn.functional`` functions which contain lower-level complex
#    operations such as computing a softmax for each node over incoming
#    edges.
# -  Use ``update_all`` with builtin message and reduce functions.
# -  Use user-defined message or reduce functions.
# 


######################################################################
# What’s next?
# ------------
# 
# -  :ref:`Writing Efficient Message Passing
#    Code <guide-message-passing-efficient>`.
# 


# Thumbnail Courtesy: Representation Learning on Networks, Jure Leskovec, WWW 2018
# sphinx_gallery_thumbnail_path = '_static/blitz_3_message_passing.png'
