.. _guide-distributed-apis:

7.2 Distributed APIs
--------------------

This section covers the distributed APIs used in the training script. DGL provides three distributed
data structures and various APIs for initialization, distributed sampling and workload split.
For distributed training/inference, DGL provides three distributed data structures:
:class:`~dgl.distributed.DistGraph` for distributed graphs, :class:`~dgl.distributed.DistTensor` for
distributed tensors and :class:`~dgl.distributed.DistEmbedding` for distributed learnable embeddings.

Initialization of the DGL distributed module
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

:func:`~dgl.distributed.initialize` initializes the distributed module. When the training script runs
in the trainer mode, this API builds connections with DGL servers and creates sampler processes;
when the script runs in the server mode, this API runs the server code and never returns. This API
has to be called before any of DGL's distributed APIs. When working with Pytorch,
:func:`~dgl.distributed.initialize` has to be invoked before ``torch.distributed.init_process_group``.
Typically, the initialization APIs should be invoked in the following order:

.. code:: python

    dgl.distributed.initialize('ip_config.txt', num_workers=4)
    th.distributed.init_process_group(backend='gloo')

**Note**: If the training script contains user-defined functions (UDFs) that have to be invoked on
the servers (see the section of DistTensor and DistEmbedding for more details), these UDFs have to
be declared before :func:`~dgl.distributed.initialize`.

Distributed graph
~~~~~~~~~~~~~~~~~

:class:`~dgl.distributed.DistGraph` is a Python class to access the graph structure and node/edge features
in a cluster of machines. Each machine is responsible for one and only one partition. It loads
the partition data (the graph structure and the node data and edge data in the partition) and makes
it accessible to all trainers in the cluster. :class:`~dgl.distributed.DistGraph` provides a small subset
of :class:`~dgl.DGLGraph` APIs for data access.

**Note**: :class:`~dgl.distributed.DistGraph` currently only supports graphs of one node type and one edge type.

Distributed mode vs. standalone mode
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

:class:`~dgl.distributed.DistGraph` can run in two modes: distributed mode and standalone mode.
When a user executes a training script in a Python command line or Jupyter Notebook, it runs in
a standalone mode. That is, it runs all computation in a single process and does not communicate
with any other processes. Thus, the standalone mode requires the input graph to have only one partition.
This mode is mainly used for development and testing (e.g., develop and run the code in Jupyter Notebook).
When a user executes a training script with a launch script (see the section of launch script),
:class:`~dgl.distributed.DistGraph` runs in the distributed mode. The launch tool starts servers
(node/edge feature access and graph sampling) behind the scene and loads the partition data in
each machine automatically. :class:`~dgl.distributed.DistGraph` connects with the servers in the cluster
of machines and access them through the network.

DistGraph creation
^^^^^^^^^^^^^^^^^^

In the distributed mode, the creation of :class:`~dgl.distributed.DistGraph` requires the graph name used
during graph partitioning. The graph name identifies the graph loaded in the cluster.

.. code:: python

    import dgl
    g = dgl.distributed.DistGraph('graph_name')

When running in the standalone mode, it loads the graph data in the local machine. Therefore, users need
to provide the partition configuration file, which contains all information about the input graph.

.. code:: python

    import dgl
    g = dgl.distributed.DistGraph('graph_name', part_config='data/graph_name.json')

**Note**: In the current implementation, DGL only allows the creation of a single DistGraph object. The behavior
of destroying a DistGraph and creating a new one is undefined.

Access graph structure
^^^^^^^^^^^^^^^^^^^^^^

:class:`~dgl.distributed.DistGraph` provides a very small number of APIs to access the graph structure.
Currently, most APIs provide graph information, such as the number of nodes and edges. The main use case
of DistGraph is to run sampling APIs to support mini-batch training (see the section of distributed
graph sampling).

.. code:: python

    print(g.number_of_nodes())

Access node/edge data
^^^^^^^^^^^^^^^^^^^^^

Like :class:`~dgl.DGLGraph`, :class:`~dgl.distributed.DistGraph` provides ``ndata`` and ``edata``
to access data in nodes and edges.
The difference is that ``ndata``/``edata`` in :class:`~dgl.distributed.DistGraph` returns
:class:`~dgl.distributed.DistTensor`, instead of the tensor of the underlying framework.
Users can also assign a new :class:`~dgl.distributed.DistTensor` to
:class:`~dgl.distributed.DistGraph` as node data or edge data.

.. code:: python

    g.ndata['train_mask']
    <dgl.distributed.dist_graph.DistTensor at 0x7fec820937b8>
    g.ndata['train_mask'][0]
    tensor([1], dtype=torch.uint8)

Distributed Tensor
~~~~~~~~~~~~~~~~~

As mentioned earlier, DGL shards node/edge features and stores them in a cluster of machines.
DGL provides distributed tensors with a tensor-like interface to access the partitioned
node/edge features in the cluster. In the distributed setting, DGL only supports dense node/edge
features.

:class:`~dgl.distributed.DistTensor` manages the dense tensors partitioned and stored in
multiple machines. Right now, a distributed tensor has to be associated with nodes or edges
of a graph. In other words, the number of rows in a DistTensor has to be the same as the number
of nodes or the number of edges in a graph. The following code creates a distributed tensor.
In addition to the shape and dtype for the tensor, a user can also provide a unique tensor name.
This name is useful if a user wants to reference a persistent distributed tensor (the one exists
in the cluster even if the :class:`~dgl.distributed.DistTensor` object disappears).

.. code:: python

    tensor = dgl.distributed.DistTensor((g.number_of_nodes(), 10), th.float32, name=’test’)

**Note**: :class:`~dgl.distributed.DistTensor` creation is a synchronized operation. All trainers
have to invoke the creation and the creation succeeds only when all trainers call it. 

A user can add a :class:`~dgl.distributed.DistTensor` to a :class:`~dgl.distributed.DistGraph`
object as one of the node data or edge data.

.. code:: python

    g.ndata['feat'] = tensor

**Note**: The node data name and the tensor name do not have to be the same. The former identifies
node data from :class:`~dgl.distributed.DistGraph` (in the trainer process) while the latter
identifies a distributed tensor in DGL servers.

:class:`~dgl.distributed.DistTensor` provides a small set of functions. It has the same APIs as
regular tensors to access its metadata, such as the shape and dtype.
:class:`~dgl.distributed.DistTensor` supports indexed reads and writes but does not support
computation operators, such as sum and mean.

.. code:: python

    data = g.ndata['feat'][[1, 2, 3]]
    print(data)
    g.ndata['feat'][[3, 4, 5]] = data

**Note**: Currently, DGL does not provide protection for concurrent writes from multiple trainers
when a machine runs multiple servers. This may result in data corruption. One way to avoid concurrent
writes to the same row of data is to run one server process on a machine.

Distributed Embedding
~~~~~~~~~~~~~~~~~~~~~

DGL provides :class:`~dgl.distributed.DistEmbedding` to support transductive models that require
node embeddings. Creating distributed embeddings is very similar to creating distributed tensors.

.. code:: python

    def initializer(shape, dtype):
        arr = th.zeros(shape, dtype=dtype)
        arr.uniform_(-1, 1)
        return arr
    emb = dgl.distributed.DistEmbedding(g.number_of_nodes(), 10, init_func=initializer)

Internally, distributed embeddings are built on top of distributed tensors, and, thus, has
very similar behaviors to distributed tensors. For example, when embeddings are created, they
are sharded and stored across all machines in the cluster. It can be uniquely identified by a name.

**Note**: The initializer function is invoked in the server process. Therefore, it has to be
declared before :class:`~dgl.distributed.initialize`.

Because the embeddings are part of the model, a user has to attach them to an optimizer for
mini-batch training. Currently, DGL provides a sparse Adagrad optimizer
:class:`~dgl.distributed.SparseAdagrad` (DGL will add more optimizers for sparse embeddings later).
Users need to collect all distributed embeddings from a model and pass them to the sparse optimizer.
If a model has both node embeddings and regular dense model parameters and users want to perform
sparse updates on the embeddings, they need to create two optimizers, one for node embeddings and
the other for dense model parameters, as shown in the code below:

.. code:: python

    sparse_optimizer = dgl.distributed.SparseAdagrad([emb], lr=lr1)
    optimizer = th.optim.Adam(model.parameters(), lr=lr2)
    feats = emb(nids)
    loss = model(feats)
    loss.backward()
    optimizer.step()
    sparse_optimizer.step()

**Note**: :class:`~dgl.distributed.DistEmbedding` is not an Pytorch nn module, so we cannot
get access to it from parameters of a Pytorch nn module.

Distributed sampling
~~~~~~~~~~~~~~~~~~~~

DGL provides two levels of APIs for sampling nodes and edges to generate mini-batches
(see the section of mini-batch training). The low-level APIs require users to write code
to explicitly define how a layer of nodes are sampled (e.g., using :func:`dgl.sampling.sample_neighbors` ).
The high-level sampling APIs implement a few popular sampling algorithms for node classification
and link prediction tasks (e.g., :class:`~dgl.dataloading.pytorch.NodeDataloader` and
:class:`~dgl.dataloading.pytorch.EdgeDataloader` ).

The distributed sampling module follows the same design and provides two levels of sampling APIs.
For the lower-level sampling API, it provides :func:`~dgl.distributed.sample_neighbors` for
distributed neighborhood sampling on :class:`~dgl.distributed.DistGraph`. In addition, DGL provides
a distributed Dataloader (:class:`~dgl.distributed.DistDataLoader` ) for distributed sampling.
The distributed Dataloader has the same interface as Pytorch DataLoader except that users cannot
specify the number of worker processes when creating a dataloader. The worker processes are created
in :func:`dgl.distributed.initialize`.

**Note**: When running :func:`dgl.distributed.sample_neighbors` on :class:`~dgl.distributed.DistGraph`,
the sampler cannot run in Pytorch Dataloader with multiple worker processes. The main reason is that
Pytorch Dataloader creates new sampling worker processes in every epoch, which leads to creating and
destroying :class:`~dgl.distributed.DistGraph` objects many times.

The same high-level sampling APIs (:class:`~dgl.dataloading.pytorch.NodeDataloader` and
:class:`~dgl.dataloading.pytorch.EdgeDataloader` ) work for both :class:`~dgl.DGLGraph`
and :class:`~dgl.distributed.DistGraph`. When using :class:`~dgl.dataloading.pytorch.NodeDataloader`
and :class:`~dgl.dataloading.pytorch.EdgeDataloader`, the distributed sampling code is exactly
the same as single-process sampling.

When using the low-level API, the sampling code is similar to single-process sampling. The only
difference is that users need to use :func:`dgl.distributed.sample_neighbors` and
:class:`~dgl.distributed.DistDataLoader`.

.. code:: python

    def sample_blocks(seeds):
        seeds = th.LongTensor(np.asarray(seeds))
        blocks = []
        for fanout in [10, 25]:
            frontier = dgl.distributed.sample_neighbors(g, seeds, fanout, replace=True)
            block = dgl.to_block(frontier, seeds)
            seeds = block.srcdata[dgl.NID]
            blocks.insert(0, block)
            return blocks
        dataloader = dgl.distributed.DistDataLoader(dataset=train_nid,
                                                    batch_size=batch_size,
                                                    collate_fn=sample_blocks,
                                                    shuffle=True)
        for batch in dataloader:
            ...

When using the high-level API, the distributed sampling code is identical to the single-machine sampling:

.. code:: python

    sampler = dgl.sampling.MultiLayerNeighborSampler([10, 25])
    dataloader = dgl.sampling.NodeDataLoader(g, train_nid, sampler,
                                             batch_size=batch_size, shuffle=True)
    for batch in dataloader:
        ... 


Split workloads
~~~~~~~~~~~~~~~

Users need to split the training set so that each trainer works on its own subset. Similarly,
we also need to split the validation and test set in the same way.

For distributed training and evaluation, the recommended approach is to use boolean arrays to
indicate the training/validation/test set. For node classification tasks, the length of these
boolean arrays is the number of nodes in a graph and each of their elements indicates the existence
of a node in a training/validation/test set. Similar boolean arrays should be used for
link prediction tasks.

DGL provides :func:`~dgl.distributed.node_split` and :func:`~dgl.distributed.edge_split` to
split the training, validation and test set at runtime for distributed training. The two functions
take the boolean arrays as input, split them and return a portion for the local trainer.
By default, they ensure that all portions have the same number of nodes/edges. This is
important for synchronous SGD, which assumes each trainer has the same number of mini-batches.

The example below splits the training set and returns a subset of nodes for the local process.

.. code:: python

    train_nids = dgl.distributed.node_split(g.ndata['train_mask'])

