parallel

Module/Function Name: Parallel

The Parallel class is a module that applies a list of functions in parallel and sums their outputs. This is particularly useful when you need to concurrently apply multiple operations to the same input and aggregate the results.

Parameters:

The Parallel class can take a variable number of functions as input, which will be applied in parallel. The details for each function is provided when they are passed into the Parallel constructor, which then forms an nn.ModuleList to keep track of them.

Usage Example:

Below is an example of how to use the Parallel class. The example demonstrates creating an instance of Parallel with two nn.Linear modules and running a randomly generated input through both those linear modules in parallel.

import torch
from torch import nn

from zeta.nn import Parallel

# Define two Linear modules
fn1 = nn.Linear(10, 5)
fn2 = nn.Linear(10, 5)

# Create a Parallel instance
parallel = Parallel(fn1, fn2)

# Generate a random input tensor
input = torch.randn(1, 10)

# Pass the input through the parallel functions and aggregate the results
output = parallel(input)

Overview and Introduction:

The Parallel class provides a way to apply a list of functions in parallel and then sum their outputs. It is widely applicable in scenarios where you need to concurrently apply multiple transformations to the same input data.

The purpose of this module is to simplify the process of applying multiple operations to a given input tensor simultaneously and seamlessly aggregating the results. This is achieved by leveraging the nn.ModuleList to organize and execute the passed functions in a parallel manner, and then summing the outputs to provide a single combined result.

By using the Parallel class, users can avoid repetitive code and streamline the process of applying multiple transformations to their input data, leading to cleaner, more organized code with minimal redundancy and better maintainability.