.. _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.graphbolt.NeighborSampler`
or the equivalent function-like interface :func:`~dgl.graphbolt.sample_neighbor`
which makes the node gather messages from its neighbors.
To use a sampler provided by DGL, one also need to combine it with
:class:`~dgl.graphbolt.DataLoader`, which iterates
over a set of indices (nodes in this case) in minibatches.
For example, the following code creates a DataLoader that
iterates over the training node ID set of ``ogbn-arxiv`` in batches,
putting the list of generated MFGs onto GPU.
.. code:: python
import dgl
import dgl.graphbolt as gb
import dgl.nn as dglnn
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = gb.BuiltinDataset("ogbn-arxiv").load()
g = dataset.graph
feature = dataset.feature
train_set = dataset.tasks[0].train_set
datapipe = gb.ItemSampler(train_set, batch_size=1024, shuffle=True)
datapipe = datapipe.sample_neighbor(g, [10, 10]) # 2 layers.
# Or equivalently:
# datapipe = gb.NeighborSampler(datapipe, g, [10, 10])
datapipe = datapipe.fetch_feature(feature, node_feature_keys=["feat"])
datapipe = datapipe.copy_to(device)
dataloader = gb.DataLoader(datapipe)
Iterating over the DataLoader will yield :class:`~dgl.graphbolt.MiniBatch`
which contains a list of specially created graphs representing the computation
dependencies on each layer. In order to train with DGL, you can access the
*message flow graphs* (MFGs) by calling `mini_batch.blocks`.
.. code:: python
mini_batch = next(iter(dataloader))
print(mini_batch.blocks)
.. note::
See the `Stochastic Training Tutorial
<../notebooks/stochastic_training/neighbor_sampling_overview.nblink>`__
for the concept of message flow graph.
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
:class:`~dgl.graphbolt.MiniBatch`, we:
1. Access the node features corresponding to the input nodes via
``data.node_features["feat"]``. These features are already moved to the
target device (CPU or GPU) by the data loader.
2. Access the node labels corresponding to the output nodes via
``data.labels``. These labels are already moved to the target device
(CPU or GPU) by the data loader.
3. Feed the list of MFGs and the input node features to the multilayer
GNN and get the outputs.
4. Compute the loss and backpropagate.
.. code:: python
model = StochasticTwoLayerGCN(in_features, hidden_features, out_features)
model = model.to(device)
opt = torch.optim.Adam(model.parameters())
for data in dataloader:
input_features = data.node_features["feat"]
output_labels = data.labels
output_predictions = model(data.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/sampling/graphbolt/node_classification.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
The samplers provided by DGL also support heterogeneous graphs.
For example, one can still use the provided
:class:`~dgl.graphbolt.NeighborSampler` class and
:class:`~dgl.graphbolt.DataLoader` class for
stochastic training. The only difference is that the itemset is now an
instance of :class:`~dgl.graphbolt.ItemSetDict` which is a dictionary
of node types to node IDs.
.. code:: python
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = gb.BuiltinDataset("ogbn-mag").load()
g = dataset.graph
feature = dataset.feature
train_set = dataset.tasks[0].train_set
datapipe = gb.ItemSampler(train_set, batch_size=1024, shuffle=True)
datapipe = datapipe.sample_neighbor(g, [10, 10]) # 2 layers.
# Or equivalently:
# datapipe = gb.NeighborSampler(datapipe, g, [10, 10])
# For heterogeneous graphs, we need to specify the node feature keys
# for each node type.
datapipe = datapipe.fetch_feature(
feature, node_feature_keys={"author": ["feat"], "paper": ["feat"]}
)
datapipe = datapipe.copy_to(device)
dataloader = gb.DataLoader(datapipe)
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.to(device)
opt = torch.optim.Adam(model.parameters())
for data in dataloader:
# For heterogeneous graphs, we need to specify the node types and
# feature name when accessing the node features. So does the labels.
input_features = {
"author": data.node_features[("author", "feat")],
"paper": data.node_features[("paper", "feat")]
}
output_labels = data.labels["paper"]
output_predictions = model(data.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/sampling/graphbolt/rgcn/hetero_rgcn.py>`__.