Embedding Matrix Segmentation for Memory-Constrained ML Models
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Solution Overview
Problem
The size of embedding matrices in machine learning models is a limiting factor due to high memory requirements, especially in devices with constrained memory, as they need to store vectors for large vocabularies, leading to inefficient processing and storage.
Innovation Solution
The technique involves learning the relative importance of items in the vocabulary and restructuring the embedding matrix into two matrices, where frequently used items are represented with more dimensions and less frequently used items are compressed, using methods like the Gumbel Softmax Trick to update and restructure the vectors based on a loss function, reducing memory requirements while maintaining model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the embedding matrix stores vectors for all vocabulary items, then the model can represent all tokens, but the memory requirements become excessively high
Solution Approach 1:
The patent segments the embedding matrix into multiple sub-matrices, where each sub-matrix stores embedding vectors for a specific subset of vocabulary items. This segmentation allows the system to represent all vocabulary items while reducing the size of each individual sub-matrix, thereby lowering overall memory requirements compared to a single large embedding matrix.
Solution Approach 2:
The patent applies local quality by differentiating the treatment of frequently used versus less frequently used tokens. Frequently used tokens are stored with higher precision in dedicated sub-matrices, while less frequently used tokens are compressed or stored with reduced precision. This selective approach maintains model accuracy for important tokens while reducing overall memory consumption.
2Quantity of substance
If the embedding matrix is compressed to reduce memory usage, then memory requirements decrease, but the accuracy of semantic representation may be compromised
Solution Approach 1:
The patent implements local quality by applying different compression levels to different portions of the embedding matrix based on token frequency. Frequently used tokens maintain their full-dimensional vectors to preserve semantic accuracy, while less frequently used tokens are compressed. This selective compression maintains high accuracy for critical semantic representations while reducing overall memory usage.
Solution Approach 2:
The patent performs preliminary action by pre-calculating and storing compressed representations of less frequently used tokens during the training phase. These pre-compressed representations are then used during inference, avoiding the need for real-time compression and ensuring that semantic accuracy is maintained where needed while minimizing memory requirements.
3Quantity of substance
If the embedding matrix is restructured into multiple sub-matrices, then memory requirements are reduced, but the complexity of the system increases
Solution Approach 1:
The patent segments the embedding matrix into multiple sub-matrices, each handling a specific subset of vocabulary items. This segmentation is implemented through a systematic partitioning strategy that organizes tokens by frequency or category, allowing the system to manage memory more efficiently while maintaining a structured and manageable complexity through clear organizational principles.
Solution Approach 2:
The patent applies universality by designing a unified framework that can handle both frequent and infrequent tokens through the same multi-sub-matrix architecture. The system uses a common mechanism for accessing and retrieving embedding vectors across all sub-matrices, simplifying the overall system design despite the increased number of components.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improving the representation of items of a vocabulary in an embedding space for use in machine learning models. An embedding matrix is generated wherein each row in the embedding matrix is a vector of elements and corresponds to an item of a vocabulary. A score is assigned to each vector in the embedding matrix indicating a probability of its corresponding vector being used in the machine learning model. The scores are iteratively updated by sampling a proper subset of vectors and updating the elements of each respective vector in the proper subset of vectors based on the respective scores of vectors. The score of each vector are then updated based on a loss function of the machine learning model. The embedding matrix is then re-structured based on the updated scores of the vectors.