# 2.1 Built-in Functions and Message Passing APIs¶

(中文版)

In DGL, message function takes a single argument edges, which is an EdgeBatch instance. During message passing, DGL generates it internally to represent a batch of edges. It has three members src, dst and data to access features of source nodes, destination nodes, and edges, respectively.

reduce function takes a single argument nodes, which is a NodeBatch instance. During message passing, DGL generates it internally to represent a batch of nodes. It has member mailbox to access the messages received for the nodes in the batch. Some of the most common reduce operations include sum, max, min, etc.

update function takes a single argument nodes as described above. This function operates on the aggregation result from reduce function, typically combining it with a node’s original feature at the the last step and saving the result as a node feature.

DGL has implemented commonly used message functions and reduce functions as built-in in the namespace dgl.function. In general, DGL suggests using built-in functions whenever possible since they are heavily optimized and automatically handle dimension broadcasting.

If your message passing functions cannot be implemented with built-ins, you can implement user-defined message/reduce function (aka. UDF).

Built-in message functions can be unary or binary. DGL supports copy for unary. For binary funcs, DGL supports add, sub, mul, div, dot. The naming convention for message built-in funcs is that u represents src nodes, v represents dst nodes, and e represents edges. The parameters for those functions are strings indicating the input and output field names for the corresponding nodes and edges. The list of supported built-in functions can be found in DGL Built-in Function. For example, to add the hu feature from src nodes and hv feature from dst nodes then save the result on the edge at he field, one can use built-in function dgl.function.u_add_v('hu', 'hv', 'he'). This is equivalent to the Message UDF:

def message_func(edges):
return {'he': edges.src['hu'] + edges.dst['hv']}


Built-in reduce functions support operations sum, max, min, and mean. Reduce functions usually have two parameters, one for field name in mailbox, one for field name in node features, both are strings. For example, dgl.function.sum('m', 'h') is equivalent to the Reduce UDF that sums up the message m:

import torch
def reduce_func(nodes):
return {'h': torch.sum(nodes.mailbox['m'], dim=1)}


For advanced usage of UDF, see User-defined Functions.

It is also possible to invoke only edge-wise computation by apply_edges() without invoking message passing. apply_edges() takes a message function for parameter and by default updates the features of all edges. For example:

import dgl.function as fn


For message passing, update_all() is a high-level API that merges message generation, message aggregation and node update in a single call, which leaves room for optimization as a whole.

The parameters for update_all() are a message function, a reduce function and an update function. One can call update function outside of update_all and not specify it in invoking update_all(). DGL recommends this approach since the update function can usually be written as pure tensor operations to make the code concise. For example：

def updata_all_example(graph):
# store the result in graph.ndata['ft']
graph.update_all(fn.u_mul_e('ft', 'a', 'm'),
fn.sum('m', 'ft'))
# Call update function outside of update_all
final_ft = graph.ndata['ft'] * 2
return final_ft


This call will generate the messages m by multiply src node features ft and edge features a, sum up the messages m to update node features ft, and finally multiply ft by 2 to get the result final_ft. After the call, DGL will clean the intermediate messages m. The math formula for the above function is:

${final\_ft}_i = 2 * \sum_{j\in\mathcal{N}(i)} ({ft}_j * a_{ij})$

DGL’s built-in functions support floating point data types, i.e. the feature must be half (float16) /float/double tensors. float16 data type support is disabled by default as it has a minimum GPU compute capacity requirement of sm_53 (Pascal, Volta, Turing and Ampere architectures).

User can enable float16 for mixed precision training by compiling DGL from source (see Mixed Precision Training tutorial for details).