graph LR
hflayers_activation_HopfieldCore["hflayers.activation.HopfieldCore"]
hflayers_functional_hopfield_core_forward["hflayers.functional.hopfield_core_forward"]
hflayers_activation_HopfieldCore_forward["hflayers.activation.HopfieldCore:forward"]
hflayers_activation_HopfieldCore_reset_parameters["hflayers.activation.HopfieldCore:reset_parameters"]
hflayers_activation_HopfieldCore__check_execution_mode["hflayers.activation.HopfieldCore:_check_execution_mode"]
hflayers_activation_HopfieldCore -- "calls" --> hflayers_activation_HopfieldCore__check_execution_mode
hflayers_activation_HopfieldCore -- "calls" --> hflayers_activation_HopfieldCore_reset_parameters
hflayers_activation_HopfieldCore_forward -- "calls" --> hflayers_functional_hopfield_core_forward
hflayers_activation_HopfieldCore -- "provides context to" --> hflayers_functional_hopfield_core_forward
hflayers_functional_hopfield_core_forward -- "receives context from" --> hflayers_activation_HopfieldCore
The hflayers.activation.HopfieldCore subsystem is central to the Hopfield network implementation, acting as a PyTorch module that encapsulates the core associative memory logic. During its initialization, HopfieldCore configures its operational parameters by calling _check_execution_mode to determine its static or dynamic behavior and reset_parameters to initialize its weights and biases. The primary interaction pathway for performing a Hopfield operation is through the forward method of HopfieldCore. This forward method directly invokes the stateless hflayers.functional.hopfield_core_forward function, passing all necessary contextual parameters (such as projection weights, biases, and configuration flags) from the HopfieldCore instance. This design clearly separates the stateful module management from the stateless computational core, allowing for flexible and efficient Hopfield network operations.
Initializes and manages the foundational state and parameters of the Hopfield core, inheriting from torch.nn.Module. It sets up the necessary projections and biases for the Hopfield operations.
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Encapsulates the low-level, stateless mathematical and computational operations of the Hopfield association mechanism. This includes projections (query, key, value), attention calculations, and iterative updates, making it the core computational engine.
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Orchestrates the overall forward pass for a Hopfield layer. It acts as the public interface for performing a Hopfield operation, directly invoking the core association logic by calling hflayers.functional.hopfield_core_forward with all necessary parameters.
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Internal method called during initialization to set the initial values of the layer's weights and biases, ensuring proper starting conditions for the Hopfield operations.
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Internal method called during initialization to determine and configure the execution mode of the Hopfield core, potentially influencing how computations are performed based on static or dynamic configurations.
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