minibatch-custom-sampler.rst

.. _guide-minibatch-customizing-neighborhood-sampler:

6.4 Customizing Neighborhood Sampler
----------------------------------------------

:ref:`(中文版) <guide_cn-minibatch-customizing-neighborhood-sampler>`

Although DGL provides some neighborhood sampling strategies, sometimes
users would want to write their own sampling strategy. This section
explains how to write your own strategy and plug it into your stochastic
GNN training framework.

Recall that in `How Powerful are Graph Neural
Networks <https://arxiv.org/pdf/1810.00826.pdf>`__, the definition of message
passing is:

.. math::


   \begin{gathered}
     \boldsymbol{a}_v^{(l)} = \rho^{(l)} \left(
       \left\lbrace
         \boldsymbol{h}_u^{(l-1)} : u \in \mathcal{N} \left( v \right)
       \right\rbrace
     \right)
   \\
     \boldsymbol{h}_v^{(l)} = \phi^{(l)} \left(
       \boldsymbol{h}_v^{(l-1)}, \boldsymbol{a}_v^{(l)}
     \right)
   \end{gathered}

where :math:`\rho^{(l)}` and :math:`\phi^{(l)}` are parameterized
functions, and :math:`\mathcal{N}(v)` is defined as the set of
predecessors (or *neighbors* if the graph is undirected) of :math:`v` on graph
:math:`\mathcal{G}`.

For instance, to perform a message passing for updating the red node in
the following graph:

.. figure:: https://data.dgl.ai/asset/image/guide_6_4_0.png
   :alt: Imgur



One needs to aggregate the node features of its neighbors, shown as
green nodes:

.. figure:: https://data.dgl.ai/asset/image/guide_6_4_1.png
   :alt: Imgur



Neighborhood sampling with pencil and paper
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Let's first define a DGL graph according to the above image.

.. code:: python

    import torch
    import dgl

    src = torch.LongTensor(
        [0, 0, 0, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7, 7, 8, 9, 10,
         1, 2, 3, 3, 3, 4, 5, 5, 6, 5, 8, 6, 8, 9, 8, 11, 11, 10, 11])
    dst = torch.LongTensor(
        [1, 2, 3, 3, 3, 4, 5, 5, 6, 5, 8, 6, 8, 9, 8, 11, 11, 10, 11,
         0, 0, 0, 1, 2, 2, 2, 3, 3, 4, 4, 5, 5, 6, 7, 7, 8, 9, 10])
    g = dgl.graph((src, dst))

We then consider how multi-layer message passing works for computing the
output of a single node. In the following text we refer to the nodes
whose GNN outputs are to be computed as *seed nodes*.

Finding the message passing dependency
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Consider computing with a 2-layer GNN the output of the seed node 8,
colored red, in the following graph:

.. figure:: https://data.dgl.ai/asset/image/guide_6_4_2.png
   :alt: Imgur



By the formulation:

.. math::


   \begin{gathered}
     \boldsymbol{a}_8^{(2)} = \rho^{(2)} \left(
       \left\lbrace
         \boldsymbol{h}_u^{(1)} : u \in \mathcal{N} \left( 8 \right)
       \right\rbrace
     \right) = \rho^{(2)} \left(
       \left\lbrace
         \boldsymbol{h}_4^{(1)}, \boldsymbol{h}_5^{(1)},
         \boldsymbol{h}_7^{(1)}, \boldsymbol{h}_{11}^{(1)}
       \right\rbrace
     \right)
   \\
     \boldsymbol{h}_8^{(2)} = \phi^{(2)} \left(
       \boldsymbol{h}_8^{(1)}, \boldsymbol{a}_8^{(2)}
     \right)
   \end{gathered}

We can tell from the formulation that to compute
:math:`\boldsymbol{h}_8^{(2)}` we need messages from node 4, 5, 7 and 11
(colored green) along the edges visualized below.

.. figure:: https://data.dgl.ai/asset/image/guide_6_4_3.png
   :alt: Imgur



This graph contains all the nodes in the original graph but only the
edges necessary for message passing to the given output nodes. We call
that the *frontier* of the second GNN layer for the red node 8.

Several functions can be used for generating frontiers. For instance,
:func:`dgl.in_subgraph()` is a function that induces a
subgraph by including all the nodes in the original graph, but only all
the incoming edges of the given nodes. You can use that as a frontier
for message passing along all the incoming edges.

.. code:: python

    frontier = dgl.in_subgraph(g, [8])
    print(frontier.all_edges())

For a concrete list, please refer to :ref:`api-subgraph-extraction` and
:ref:`api-sampling`.

Technically, any graph that has the same set of nodes as the original
graph can serve as a frontier. This serves as the basis for
:ref:`guide-minibatch-customizing-neighborhood-sampler-impl`.

The Bipartite Structure for Multi-layer Minibatch Message Passing
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

However, to compute :math:`\boldsymbol{h}_8^{(2)}` from
:math:`\boldsymbol{h}_\cdot^{(1)}`, we cannot simply perform message
passing on the frontier directly, because it still contains all the
nodes from the original graph. Namely, we only need nodes 4, 5, 7, 8,
and 11 (green and red nodes) as input, as well as node 8 (red node) as output.
Since the number of nodes
for input and output is different, we need to perform message passing on
a small, bipartite-structured graph instead. We call such a
bipartite-structured graph that only contains the necessary input nodes
(referred as *source* nodes) and output nodes (referred as *destination* nodes)
of a *message flow graph* (MFG).

The following figure shows the MFG of the second GNN layer for node 8.

.. figure:: https://data.dgl.ai/asset/image/guide_6_4_4.png
   :alt: Imgur

.. note::

   See the :doc:`Stochastic Training Tutorial
   <tutorials/large/L0_neighbor_sampling_overview>` for the concept of
   message flow graph.

Note that the destination nodes also appear in the source nodes. The reason is
that representations of destination nodes from the previous layer are needed
for feature combination after message passing (i.e. :math:`\phi^{(2)}`).

DGL provides :func:`dgl.to_block` to convert any frontier
to a MFG where the first argument specifies the frontier and the
second argument specifies the destination nodes. For instance, the frontier
above can be converted to a MFG with destination node 8 with the code as
follows.

.. code:: python

    dst_nodes = torch.LongTensor([8])
    block = dgl.to_block(frontier, dst_nodes)

To find the number of source nodes and destination nodes of a given node type,
one can use :meth:`dgl.DGLHeteroGraph.number_of_src_nodes` and
:meth:`dgl.DGLHeteroGraph.number_of_dst_nodes` methods.

.. code:: python

    num_src_nodes, num_dst_nodes = block.number_of_src_nodes(), block.number_of_dst_nodes()
    print(num_src_nodes, num_dst_nodes)

The MFG’s source node features can be accessed via member
:attr:`dgl.DGLHeteroGraph.srcdata` and :attr:`dgl.DGLHeteroGraph.srcnodes`, and
its destination node features can be accessed via member
:attr:`dgl.DGLHeteroGraph.dstdata` and :attr:`dgl.DGLHeteroGraph.dstnodes`. The
syntax of ``srcdata``/``dstdata`` and ``srcnodes``/``dstnodes`` are
identical to :attr:`dgl.DGLHeteroGraph.ndata` and
:attr:`dgl.DGLHeteroGraph.nodes` in normal graphs.

.. code:: python

    block.srcdata['h'] = torch.randn(num_src_nodes, 5)
    block.dstdata['h'] = torch.randn(num_dst_nodes, 5)

If a MFG is converted from a frontier, which is in turn converted from
a graph, one can directly read the feature of the MFG’s source and
destination nodes via

.. code:: python

    print(block.srcdata['x'])
    print(block.dstdata['y'])

.. note::

   The original node IDs of the source nodes and destination nodes in the MFG
   can be found as the feature ``dgl.NID``, and the mapping from the
   MFG’s edge IDs to the input frontier’s edge IDs can be found as the
   feature ``dgl.EID``.

DGL ensures that the destination nodes of a MFG will always appear in the
source nodes. The destination nodes will always index firstly in the source
nodes.

.. code:: python

    src_nodes = block.srcdata[dgl.NID]
    dst_nodes = block.dstdata[dgl.NID]
    assert torch.equal(src_nodes[:len(dst_nodes)], dst_nodes)

As a result, the destination nodes must cover all nodes that are the
destination of an edge in the frontier.

For example, consider the following frontier

.. figure:: https://data.dgl.ai/asset/image/guide_6_4_5.png
   :alt: Imgur



where the red and green nodes (i.e. node 4, 5, 7, 8, and 11) are all
nodes that is a destination of an edge. Then the following code will
raise an error because the destination nodes did not cover all those nodes.

.. code:: python

    dgl.to_block(frontier2, torch.LongTensor([4, 5]))   # ERROR

However, the destination nodes can have more nodes than above. In this case,
we will have isolated nodes that do not have any edge connecting to it.
The isolated nodes will be included in both source nodes and destination
nodes.

.. code:: python

    # Node 3 is an isolated node that do not have any edge pointing to it.
    block3 = dgl.to_block(frontier2, torch.LongTensor([4, 5, 7, 8, 11, 3]))
    print(block3.srcdata[dgl.NID])
    print(block3.dstdata[dgl.NID])

Heterogeneous Graphs
^^^^^^^^^^^^^^^^^^^^

MFGs also work on heterogeneous graphs. Let’s say that we have the
following frontier:

.. code:: python

    hetero_frontier = dgl.heterograph({
        ('user', 'follow', 'user'): ([1, 3, 7], [3, 6, 8]),
        ('user', 'play', 'game'): ([5, 5, 4], [6, 6, 2]),
        ('game', 'played-by', 'user'): ([2], [6])
    }, num_nodes_dict={'user': 10, 'game': 10})

One can also create a MFG with destination nodes User #3, #6, and #8, as
well as Game #2 and #6.

.. code:: python

    hetero_block = dgl.to_block(hetero_frontier, {'user': [3, 6, 8], 'game': [2, 6]})

One can also get the source nodes and destination nodes by type:

.. code:: python

    # source users and games
    print(hetero_block.srcnodes['user'].data[dgl.NID], hetero_block.srcnodes['game'].data[dgl.NID])
    # destination users and games
    print(hetero_block.dstnodes['user'].data[dgl.NID], hetero_block.dstnodes['game'].data[dgl.NID])


.. _guide-minibatch-customizing-neighborhood-sampler-impl:

Implementing a Custom Neighbor Sampler
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Recall that the following code performs neighbor sampling for node
classification.

.. code:: python

    sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)

To implement your own neighborhood sampling strategy, you basically
replace the ``sampler`` object with your own. To do that, let’s first
see what :class:`~dgl.dataloading.dataloader.BlockSampler`, the parent class of
:class:`~dgl.dataloading.neighbor.MultiLayerFullNeighborSampler`, is.

:class:`~dgl.dataloading.dataloader.BlockSampler` is responsible for
generating the list of MFGs starting from the last layer, with method
:meth:`~dgl.dataloading.dataloader.BlockSampler.sample_blocks`. The default implementation of
``sample_blocks`` is to iterate backwards, generating the frontiers and
converting them to MFGs.

Therefore, for neighborhood sampling, **you only need to implement
the**\ :meth:`~dgl.dataloading.dataloader.BlockSampler.sample_frontier`\ **method**. Given which
layer the sampler is generating frontier for, as well as the original
graph and the nodes to compute representations, this method is
responsible for generating a frontier for them.

Meanwhile, you also need to pass how many GNN layers you have to the
parent class.

For example, the implementation of
:class:`~dgl.dataloading.neighbor.MultiLayerFullNeighborSampler` can
go as follows.

.. code:: python

    class MultiLayerFullNeighborSampler(dgl.dataloading.BlockSampler):
        def __init__(self, n_layers):
            super().__init__(n_layers)
    
        def sample_frontier(self, block_id, g, seed_nodes):
            frontier = dgl.in_subgraph(g, seed_nodes)
            return frontier

:class:`dgl.dataloading.neighbor.MultiLayerNeighborSampler`, a more
complicated neighbor sampler class that allows you to sample a small
number of neighbors to gather message for each node, goes as follows.

.. code:: python

    class MultiLayerNeighborSampler(dgl.dataloading.BlockSampler):
        def __init__(self, fanouts):
            super().__init__(len(fanouts))
    
            self.fanouts = fanouts
    
        def sample_frontier(self, block_id, g, seed_nodes):
            fanout = self.fanouts[block_id]
            if fanout is None:
                frontier = dgl.in_subgraph(g, seed_nodes)
            else:
                frontier = dgl.sampling.sample_neighbors(g, seed_nodes, fanout)
            return frontier

Although the functions above can generate a frontier, any graph that has
the same nodes as the original graph can serve as a frontier.

For example, if one want to randomly drop inbound edges to the seed
nodes with a probability, one can simply define the sampler as follows:

.. code:: python

    class MultiLayerDropoutSampler(dgl.dataloading.BlockSampler):
        def __init__(self, p, num_layers):
            super().__init__(num_layers)
    
            self.p = p
    
        def sample_frontier(self, block_id, g, seed_nodes, *args, **kwargs):
            # Get all inbound edges to `seed_nodes`
            src, dst = dgl.in_subgraph(g, seed_nodes).all_edges()
            # Randomly select edges with a probability of p
            mask = torch.zeros_like(src).bernoulli_(self.p)
            src = src[mask]
            dst = dst[mask]
            # Return a new graph with the same nodes as the original graph as a
            # frontier
            frontier = dgl.graph((src, dst), num_nodes=g.number_of_nodes())
            return frontier
    
        def __len__(self):
            return self.num_layers

After implementing your sampler, you can create a data loader that takes
in your sampler and it will keep generating lists of MFGs while
iterating over the seed nodes as usual.

.. code:: python

    sampler = MultiLayerDropoutSampler(0.5, 2)
    dataloader = dgl.dataloading.NodeDataLoader(
        g, train_nids, sampler,
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=4)
    
    model = StochasticTwoLayerRGCN(in_features, hidden_features, out_features)
    model = model.cuda()
    opt = torch.optim.Adam(model.parameters())
    
    for input_nodes, blocks in dataloader:
        blocks = [b.to(torch.device('cuda')) for b in blocks]
        input_features = blocks[0].srcdata     # returns a dict
        output_labels = blocks[-1].dstdata     # returns a dict
        output_predictions = model(blocks, input_features)
        loss = compute_loss(output_labels, output_predictions)
        opt.zero_grad()
        loss.backward()
        opt.step()

Heterogeneous Graphs
^^^^^^^^^^^^^^^^^^^^

Generating a frontier for a heterogeneous graph is nothing different
than that for a homogeneous graph. Just make the returned graph have the
same nodes as the original graph, and it should work fine. For example,
we can rewrite the ``MultiLayerDropoutSampler`` above to iterate over
all edge types, so that it can work on heterogeneous graphs as well.

.. code:: python

    class MultiLayerDropoutSampler(dgl.dataloading.BlockSampler):
        def __init__(self, p, num_layers):
            super().__init__(num_layers)
    
            self.p = p
    
        def sample_frontier(self, block_id, g, seed_nodes, *args, **kwargs):
            # Get all inbound edges to `seed_nodes`
            sg = dgl.in_subgraph(g, seed_nodes)
    
            new_edges_masks = {}
            # Iterate over all edge types
            for etype in sg.canonical_etypes:
                edge_mask = torch.zeros(sg.number_of_edges(etype))
                edge_mask.bernoulli_(self.p)
                new_edges_masks[etype] = edge_mask.bool()
    
            # Return a new graph with the same nodes as the original graph as a
            # frontier
            frontier = dgl.edge_subgraph(new_edges_masks, relabel_nodes=False)
            return frontier
    
        def __len__(self):
            return self.num_layers