Distribution Tensors for Efficient Neural Network Probability Processing
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Solution Overview
Problem
Existing probabilistic programming languages (PPLs) require significant memory and processing resources due to the passing of complete distribution class objects between layers in neural networks, necessitating powerful parsers and increased dataflow.
Innovation Solution
Implementing a system where probability distributions are structured as simple parametric representations (distribution tensors) with associated APIs and dummy distribution class objects, allowing for efficient dataflow by assembling and disassembling functions to convert between tensors and class objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete distribution class objects are passed between layers in neural networks, then the neural network can maintain full probabilistic functionality and methods, but memory and processing resources are significantly increased
Solution Approach 1:
The distribution class object is segmented into two separate components: (1) distribution tensors containing only the essential parameters (mean, standard deviation, etc.) that are passed between layers, and (2) distribution methods that remain attached to the original distribution object. This segmentation allows the neural network to transmit only the minimal necessary data (tensors) while preserving full probabilistic functionality through the attached methods, thereby reducing memory and processing overhead without sacrificing reliability.
Solution Approach 2:
The essential parameters of the distribution class object are extracted and stored as separate tensors. These extracted parameters contain only the critical information needed for probabilistic computations, while the heavier computational methods and full object structure are left behind. This extraction process reduces the data footprint passed between layers while maintaining the ability to perform complete probabilistic operations when needed.
2Adaptability or versatility
If distribution class objects with variables and methods are passed in dataflow, then full probabilistic operations can be performed, but extra data requirements and parser complexity increase
Solution Approach 1:
The distribution class object is segmented into distribution tensors (containing parameters) and distribution methods (containing functionality). This segmentation allows the system to pass only lightweight tensors through the dataflow graph, which simplifies parser requirements and reduces dataflow complexity, while still enabling full probabilistic operations by attaching methods to the original distribution objects that can be accessed when needed.
Solution Approach 2:
Instead of copying entire distribution class objects with all their methods and variables between layers, the system creates lightweight copies in the form of distribution tensors that contain only the essential parameters. These tensor copies are much smaller and simpler to handle in the dataflow, reducing parser complexity and dataflow requirements while preserving the ability to reconstruct full distribution objects when probabilistic operations are needed.
3Device complexity
If distribution tensors with minimal parameters are used instead of class objects, then memory and processing requirements are reduced, but the ability to perform probabilistic operations may be limited
Solution Approach 1:
The system merges distribution tensors (lightweight parameter containers) with distribution methods (functional capabilities) to create a hybrid structure. The tensors serve as efficient carriers of probabilistic parameters through the neural network layers, while the methods are attached to the original distribution objects and can be invoked when probabilistic operations are required. This merging approach maintains full adaptability for probabilistic operations while benefiting from the reduced memory and processing requirements of tensor-based representation during dataflow.
Data Source
AI summary
A computer implemented method, a system and a non-transitory storage medium are provided for efficiently processing probability distributions in a neural network using a probabilistic programming language. Probability distribution data structures are provided, as well as corresponding distribution-APIs. Each probability distribution data structure comprises an identification of a distribution type and a list of parameters representative of said distribution type. The distribution data structures correspond to parametric representations of the probability distributions. The distribution data structures are inputted as distribution tensors into the layers, processed within the layer and outputted as modified tensors to downstream layers of the neural network. If needed, distribution class objects can be assembled and dissembled within the layers, but distribution tensors are still inputted or outputted by the layers, thereby limiting dataflow in between the layers of the neural network.