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164 lines
8.1 KiB
ReStructuredText
164 lines
8.1 KiB
ReStructuredText
.. _apifunction:
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.. currentmodule:: dgl.function
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dgl.function
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==================================
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In DGL, message passing is mainly expressed by ``update_all(message_func, reduce_func)``.
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This API computes messages on all edges and sends to the destination nodes; the nodes
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that receive messages perform aggregation and update their own node data.
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Internally, DGL fuses the message generation and aggregation into one kernel so no
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explicit messages are generated and stored. To achieve this, we recommend using our **built-in
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message and reduce functions** so that DGL can analyze and map them to fused dedicated kernels. Here
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are some examples (in PyTorch syntax).
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.. code:: python
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import dgl
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import dgl.function as fn
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import torch as th
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g = ... # create a DGLGraph
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g.ndata['h'] = th.randn((g.number_of_nodes(), 10)) # each node has feature size 10
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g.edata['w'] = th.randn((g.number_of_edges(), 1)) # each edge has feature size 1
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# collect features from source nodes and aggregate them in destination nodes
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g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h_sum'))
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# multiply source node features with edge weights and aggregate them in destination nodes
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g.update_all(fn.u_mul_e('h', 'w', 'm'), fn.max('m', 'h_max'))
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# compute edge embedding by multiplying source and destination node embeddings
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g.apply_edges(fn.u_mul_v('h', 'h', 'w_new'))
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``fn.copy_u``, ``fn.u_mul_e``, ``fn.u_mul_v`` are built-in message functions, while ``fn.sum``
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and ``fn.max`` are built-in reduce functions. We use ``u``, ``v`` and ``e`` to represent
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source nodes, destination nodes, and edges among them, respectively. Hence, ``copy_u`` copies the source
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node data as the messages, ``u_mul_e`` multiplies source node features with edge features, for example.
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To define a unary message function (e.g. ``copy_u``) specify one input feature name and one output
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message name. To define a binary message function (e.g. ``u_mul_e``) specify
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two input feature names and one output message name. During the computation,
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the message function will read the data under the given names, perform computation, and return
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the output using the output name. For example, the above ``fn.u_mul_e('h', 'w', 'm')`` is
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the same as the following user-defined function:
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.. code:: python
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def udf_u_mul_e(edges):
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return {'m' : edges.src['h'] * edges.data['w']}
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To define a reduce function, one input message name and one output node feature name
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need to be specified. For example, the above ``fn.max('m', 'h_max')`` is the same as the
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following user-defined function:
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.. code:: python
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def udf_max(nodes):
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return {'h_max' : th.max(nodes.mailbox['m'], 1)[0]}
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Broadcasting is supported for binary message function, which means the tensor arguments
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can be automatically expanded to be of equal sizes. The supported broadcasting semantic
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is standard and matches `NumPy <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_
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and `PyTorch <https://pytorch.org/docs/stable/notes/broadcasting.html>`_. If you are not familiar
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with broadcasting, see the linked topics to learn more. In the
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above example, ``fn.u_mul_e`` will perform broadcasted multiplication automatically because
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the node feature ``'h'`` and the edge feature ``'w'`` are of different shapes, but they can be broadcast.
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All DGL's built-in functions support both CPU and GPU and backward computation so they
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can be used in any `autograd` system. Also, built-in functions can be used not only in ``update_all``
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or ``apply_edges`` as shown in the example, but wherever message and reduce functions are
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required (e.g. ``pull``, ``push``, ``send_and_recv``).
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.. _api-built-in:
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DGL Built-in Function
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-------------------------
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Here is a cheatsheet of all the DGL built-in functions.
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+-------------------------+-----------------------------------------------------------------+-----------------------+
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| Category | Functions | Memo |
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+=========================+=================================================================+=======================+
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| Unary message function | ``copy_u`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``copy_e`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``copy_src`` | alias of ``copy_u`` |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``copy_edge`` | alias of ``copy_e`` |
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+-------------------------+-----------------------------------------------------------------+-----------------------+
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| Binary message function | ``u_add_v``, ``u_sub_v``, ``u_mul_v``, ``u_div_v``, ``u_dot_v`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``u_add_e``, ``u_sub_e``, ``u_mul_e``, ``u_div_e``, ``u_dot_e`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``v_add_u``, ``v_sub_u``, ``v_mul_u``, ``v_div_u``, ``v_dot_u`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``v_add_e``, ``v_sub_e``, ``v_mul_e``, ``v_div_e``, ``v_dot_e`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``e_add_u``, ``e_sub_u``, ``e_mul_u``, ``e_div_u``, ``e_dot_u`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``e_add_v``, ``e_sub_v``, ``e_mul_v``, ``e_div_v``, ``e_dot_v`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``src_mul_edge`` | alias of ``u_mul_e`` |
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+-------------------------+-----------------------------------------------------------------+-----------------------+
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| Reduce function | ``max`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``min`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``sum`` | |
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| +-----------------------------------------------------------------+-----------------------+
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| | ``mean`` | |
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+-------------------------+-----------------------------------------------------------------+-----------------------+
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Message functions
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-----------------
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.. autosummary::
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:toctree: ../../generated/
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copy_src
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copy_edge
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src_mul_edge
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copy_u
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copy_e
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u_add_v
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u_sub_v
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u_mul_v
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u_div_v
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u_add_e
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u_sub_e
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u_mul_e
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u_div_e
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v_add_u
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v_sub_u
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v_mul_u
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v_div_u
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v_add_e
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v_sub_e
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v_mul_e
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v_div_e
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e_add_u
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e_sub_u
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e_mul_u
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e_div_u
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e_add_v
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e_sub_v
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e_mul_v
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e_div_v
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u_dot_v
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u_dot_e
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v_dot_e
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v_dot_u
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e_dot_u
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e_dot_v
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Reduce functions
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----------------
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.. autosummary::
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:toctree: ../../generated/
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sum
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max
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min
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mean
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