There are a few ways to incorporate non-sequential context into a transformer model:
Attention Mechanisms: One way to incorporate non-sequential context is to use attention mechanisms that allow the model to "pay attention" to relevant parts of the input as it processes it. For example, the transformer model uses self-attention mechanisms that allow it to consider the entire input sequence as it processes each element in the sequence.
Context Vectors: Another way to incorporate non-sequential context is to use context vectors, which are fixed-length vectors that represent the context for a given input. These vectors can be concatenated to the input embeddings or used to compute attention weights.
Multi-Head Attention: The transformer model also uses multi-head attention, which allows it to attend to multiple different sources of context simultaneously.
Conditional Transformer: The conditional transformer is a variant of the transformer model that is specifically designed to incorporate non-sequential context by using an additional input modality (e.g. an image or a set of control parameters) to condition the transformer's self-attention mechanisms.
Hierarchical Transformer: The hierarchical transformer is another variant of the transformer model that incorporates non-sequential context by using a hierarchy of transformer blocks, where the lower-level blocks process the input at a finer granularity and the higher-level blocks process the lower-level representations to capture more global context.
Graph Transformer: The graph transformer is a variant of the transformer model that is specifically designed to process graph-structured data, which allows it to incorporate non-sequential context by considering the relationships between nodes in the graph.