/docs/source/guide/minibatch-node.rst

.. _guide-minibatch-node-classification-sampler:

6.1 Training GNN for Node Classification with Neighborhood Sampling
-----------------------------------------------------------------------

:ref:`(中文版) <guide_cn-minibatch-node-classification-sampler>`

To make your model been trained stochastically, you need to do the
followings:

-  Define a neighborhood sampler.
-  Adapt your model for minibatch training.
-  Modify your training loop.

The following sub-subsections address these steps one by one.

Define a neighborhood sampler and data loader
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

DGL provides several neighborhood sampler classes that generates the
computation dependencies needed for each layer given the nodes we wish
to compute on.

The simplest neighborhood sampler is
:class:`~dgl.dataloading.neighbor.MultiLayerFullNeighborSampler`
which makes the node gather messages from all of its neighbors.

To use a sampler provided by DGL, one also need to combine it with
:class:`~dgl.dataloading.pytorch.NodeDataLoader`, which iterates
over a set of nodes in minibatches.

For example, the following code creates a PyTorch DataLoader that
iterates over the training node ID array ``train_nids`` in batches,
putting the list of generated MFGs onto GPU.

.. code:: python

    import dgl
    import dgl.nn as dglnn
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    
    sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
    dataloader = dgl.dataloading.NodeDataLoader(
        g, train_nids, sampler,
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=4)

Iterating over the DataLoader will yield a list of specially created
graphs representing the computation dependencies on each layer. They are
called *message flow graphs* (MFGs) in DGL.

.. code:: python

    input_nodes, output_nodes, blocks = next(iter(dataloader))
    print(blocks)

The iterator generates three items at a time. ``input_nodes`` describe
the nodes needed to compute the representation of ``output_nodes``.
``blocks`` describe for each GNN layer which node representations are to
be computed as output, which node representations are needed as input,
and how does representation from the input nodes propagate to the output
nodes.

.. note::

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

   For a complete list of supported builtin samplers, please refer to the
   :ref:`neighborhood sampler API reference <api-dataloading-neighbor-sampling>`.

   If you wish to develop your own neighborhood sampler or you want a more
   detailed explanation of the concept of MFGs, please refer to
   :ref:`guide-minibatch-customizing-neighborhood-sampler`.


.. _guide-minibatch-node-classification-model:

Adapt your model for minibatch training
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

If your message passing modules are all provided by DGL, the changes
required to adapt your model to minibatch training is minimal. Take a
multi-layer GCN as an example. If your model on full graph is
implemented as follows:

.. code:: python

    class TwoLayerGCN(nn.Module):
        def __init__(self, in_features, hidden_features, out_features):
            super().__init__()
            self.conv1 = dglnn.GraphConv(in_features, hidden_features)
            self.conv2 = dglnn.GraphConv(hidden_features, out_features)
    
        def forward(self, g, x):
            x = F.relu(self.conv1(g, x))
            x = F.relu(self.conv2(g, x))
            return x

Then all you need is to replace ``g`` with ``blocks`` generated above.

.. code:: python

    class StochasticTwoLayerGCN(nn.Module):
        def __init__(self, in_features, hidden_features, out_features):
            super().__init__()
            self.conv1 = dgl.nn.GraphConv(in_features, hidden_features)
            self.conv2 = dgl.nn.GraphConv(hidden_features, out_features)
    
        def forward(self, blocks, x):
            x = F.relu(self.conv1(blocks[0], x))
            x = F.relu(self.conv2(blocks[1], x))
            return x

The DGL ``GraphConv`` modules above accepts an element in ``blocks``
generated by the data loader as an argument.

:ref:`The API reference of each NN module <apinn>` will tell you
whether it supports accepting a MFG as an argument.

If you wish to use your own message passing module, please refer to
:ref:`guide-minibatch-custom-gnn-module`.

Training Loop
~~~~~~~~~~~~~

The training loop simply consists of iterating over the dataset with the
customized batching iterator. During each iteration that yields a list
of MFGs, we:

1. Load the node features corresponding to the input nodes onto GPU. The
   node features can be stored in either memory or external storage.
   Note that we only need to load the input nodes’ features, as opposed
   to load the features of all nodes as in full graph training.
   
   If the features are stored in ``g.ndata``, then the features can be loaded
   by accessing the features in ``blocks[0].srcdata``, the features of
   source nodes of the first MFG, which is identical to all the
   necessary nodes needed for computing the final representations.

2. Feed the list of MFGs and the input node features to the multilayer
   GNN and get the outputs.

3. Load the node labels corresponding to the output nodes onto GPU.
   Similarly, the node labels can be stored in either memory or external
   storage. Again, note that we only need to load the output nodes’
   labels, as opposed to load the labels of all nodes as in full graph
   training.
   
   If the features are stored in ``g.ndata``, then the labels
   can be loaded by accessing the features in ``blocks[-1].srcdata``,
   the features of destination nodes of the last MFG, which is identical to
   the nodes we wish to compute the final representation.

4. Compute the loss and backpropagate.

.. code:: python

    model = StochasticTwoLayerGCN(in_features, hidden_features, out_features)
    model = model.cuda()
    opt = torch.optim.Adam(model.parameters())
    
    for input_nodes, output_nodes, blocks in dataloader:
        blocks = [b.to(torch.device('cuda')) for b in blocks]
        input_features = blocks[0].srcdata['features']
        output_labels = blocks[-1].dstdata['label']
        output_predictions = model(blocks, input_features)
        loss = compute_loss(output_labels, output_predictions)
        opt.zero_grad()
        loss.backward()
        opt.step()

DGL provides an end-to-end stochastic training example `GraphSAGE
implementation <https://github.com/dmlc/dgl/blob/master/examples/pytorch/graphsage/train_sampling.py>`__.

For heterogeneous graphs
~~~~~~~~~~~~~~~~~~~~~~~~

Training a graph neural network for node classification on heterogeneous
graph is similar.

For instance, we have previously seen
:ref:`how to train a 2-layer RGCN on full graph <guide-training-rgcn-node-classification>`.
The code for RGCN implementation on minibatch training looks very
similar to that (with self-loops, non-linearity and basis decomposition
removed for simplicity):

.. code:: python

    class StochasticTwoLayerRGCN(nn.Module):
        def __init__(self, in_feat, hidden_feat, out_feat, rel_names):
            super().__init__()
            self.conv1 = dglnn.HeteroGraphConv({
                    rel : dglnn.GraphConv(in_feat, hidden_feat, norm='right')
                    for rel in rel_names
                })
            self.conv2 = dglnn.HeteroGraphConv({
                    rel : dglnn.GraphConv(hidden_feat, out_feat, norm='right')
                    for rel in rel_names
                })
    
        def forward(self, blocks, x):
            x = self.conv1(blocks[0], x)
            x = self.conv2(blocks[1], x)
            return x

Some of the samplers provided by DGL also support heterogeneous graphs.
For example, one can still use the provided
:class:`~dgl.dataloading.neighbor.MultiLayerFullNeighborSampler` class and
:class:`~dgl.dataloading.pytorch.NodeDataLoader` class for
stochastic training. For full-neighbor sampling, the only difference
would be that you would specify a dictionary of node
types and node IDs for the training set.

.. code:: python

    sampler = dgl.dataloading.MultiLayerFullNeighborSampler(2)
    dataloader = dgl.dataloading.NodeDataLoader(
        g, train_nid_dict, sampler,
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=4)

The training loop is almost the same as that of homogeneous graphs,
except for the implementation of ``compute_loss`` that will take in two
dictionaries of node types and predictions here.

.. code:: python

    model = StochasticTwoLayerRGCN(in_features, hidden_features, out_features, etypes)
    model = model.cuda()
    opt = torch.optim.Adam(model.parameters())
    
    for input_nodes, output_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()

DGL provides an end-to-end stochastic training example `RGCN
implementation <https://github.com/dmlc/dgl/blob/master/examples/pytorch/rgcn-hetero/entity_classify_mb.py>`__.