Computational Graph Adaptation for Dynamic Machine Learning Input Shapes
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Solution Overview
Problem
Existing systems face inefficiencies in processing input data of varying shapes for dynamically structured machine learning models, as they often require rewriting the computational graph for each input, leading to complex and resource-intensive operations.
Innovation Solution
A method to construct a single computational graph that can accommodate inputs of different shapes by using wiring vectors and gather operations, allowing data flow adaptation and layer copying to handle hierarchical label data, thereby eliminating the need for graph rewriting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the computational graph is rewritten for each input to accommodate different input shapes, then the model can process diverse input data, but the computational resources and time required increase significantly
Solution Approach 1:
The patent creates a universal computational graph structure that can handle multiple input shapes without rewriting. The graph is designed with flexible node connections and parameter mappings that adapt to different input dimensions, allowing a single graph to serve multiple input configurations efficiently.
Solution Approach 2:
The computational graph incorporates dynamic parameter mapping mechanisms where wiring vectors and gather operations can be configured at runtime based on input characteristics. This allows the graph structure to remain static while its behavior dynamically adapts to different input shapes through parameter adjustment rather than structural rewriting.
2Adaptability or versatility
If the computational graph is rewritten for each input shape, then the model accommodates diverse inputs, but the complexity of graph construction and processing increases
Solution Approach 1:
The patent segments the computational graph into modular components with standardized interfaces. Each node and connection can be independently configured through parameter mappings, allowing the system to handle diverse input shapes by adjusting parameters within existing modular structures rather than creating entirely new complex graphs.
Solution Approach 2:
The patent uses parameter changes as the primary mechanism for adapting to different input shapes. Wiring vectors, gather operation parameters, and node configurations are adjusted based on input characteristics, allowing the computational graph to maintain structural simplicity while achieving flexibility through parameter variation rather than structural complexity.
3Adaptability or versatility
If computational graph rewriting is performed for each input, then input-specific processing is achieved, but the time required for graph construction increases
Solution Approach 1:
The patent performs preliminary configuration of the computational graph with pre-defined node structures, connection patterns, and parameter mapping templates. At runtime, only parameter values need to be adjusted rather than reconstructing the entire graph, significantly reducing the time required to adapt to different inputs while maintaining input-specific processing capability.
Solution Approach 2:
The patent uses parameter copying and mapping mechanisms where configuration parameters for different input shapes can be copied and adapted from existing configurations. This allows rapid adaptation to new input types by copying proven parameter settings and making minimal adjustments, rather than creating configurations from scratch for each input.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing and processing computational graphs that represent dynamically structured machine learning models are disclosed. An example system receives data identifying a plurality of operations that can be performed on input data for processing by a dynamically structured machine learning model. The system also receives a plurality of labels corresponding to arguments for the plurality of operations. A directed computational graph representing a comprehensive layer of the dynamically structured machine learning model. The system then receives an input for processing and specifies data flow through the directed computational graph. By constructing a single computational graph in accordance with various implementations, a machine learning system can accept and efficiently process input data with different characteristics. In particular, the system can process inputs of various shapes without requiring that the computational graph be rewritten for each input.