This file defines a GatingModel class, which is a PyTorch module designed for the Bittensor network. At a high level, the class is responsible for encoding input messages, generating scores for each unique identifier (uid) in the network, and updating the model based on mean squared error loss between the normalized scores and normalized rewards.
The GatingModel class leverages a pre-trained transformer-based language model as its encoding layer. It then applies a linear layer to generate scores for each uid. To optimize the model, it uses the stochastic gradient descent (SGD) algorithm with configurable learning rate and momentum.
To use the class, a user needs to create an instance of GatingModel, providing necessary configuration details, such as the pre-trained model name and number of uids. Once the instance is created, the user can call the forward method to obtain scores for a given input message and use the backward method to update the model based on rewards obtained from the network.
Overall, this file provides a flexible and configurable solution for creating and updating a gating model that can be easily integrated into the Bittensor network.
This chapter explains the GatingModel class, which is a PyTorch module for creating a gating model in the Bittensor network. The main functions of this module are:
Encoding input messages and generating scores for each unique identifier (uid) in the network. Running a backward pass through the model using mean squared error between normalized scores and normalized rewards as the loss function.
• add_args: Adds command line arguments to configure the gating model. • config: Returns a configuration object containing the command line arguments for the gating model. • check_config: Validates the configuration object for the gating model. • init: Initializes the gating model. • backward: Runs a backward pass through the model. • forward: Runs a forward pass through the model, encoding the input message and generating scores for each uid in the network.
def add_args(cls, parser: argparse.ArgumentParser)
The module uses a pre-trained transformer-based language model as the encoding layer and a linear layer to generate scores for each uid. The model is optimized using the stochastic gradient descent (SGD) algorithm.
To use the gating model, a user would first create an instance of the GatingModel class with the desired configuration and then run the forward and backward methods for their specific use case.