Distributed Word Vector Training via Asynchronous Gradient Updates
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Solution Overview
Problem
Existing natural language processing solutions face inefficiencies in large-scale word vector training, as current algorithms typically run on single computers and lack effective distributed processing capabilities.
Innovation Solution
A cluster-based word vector processing method utilizing multiple worker computers and a server, where each worker computer asynchronously calculates and updates gradients, allowing for distributed training without waiting for other computers, and the server updates word vectors accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If synchronous gradient updating is used in distributed word vector training, then consistency of updates is improved, but training efficiency deteriorates due to waiting for all worker computers
Solution Approach 1:
The patent transitions from static synchronous updating to dynamic asynchronous updating, where worker computers send gradients immediately without waiting for others. This dynamic approach allows the system to adapt to varying computation speeds of different workers, improving overall training efficiency while maintaining acceptable consistency through the server's accumulation and periodic aggregation of gradients.
Solution Approach 2:
The server performs preliminary actions by maintaining a local copy of word vectors and preparing to accumulate gradients from multiple workers. This preliminary preparation enables the server to process gradients as they arrive asynchronously, rather than waiting for all workers to complete their computations first, thus eliminating the waiting bottleneck.
2Productivity
If distributed processing is implemented across multiple worker computers, then training speed is improved, but system complexity increases
Solution Approach 1:
The patent segments the word vector training task across multiple worker computers, each handling portions of the corpus. This segmentation enables parallel processing and improves training speed. The server acts as a coordinator that manages the distributed workers, maintaining the overall training process without requiring complex inter-worker communication.
Solution Approach 2:
The server serves as an intermediary between multiple worker computers, receiving gradients from workers and updating the central word vector model. This intermediary role simplifies the system architecture by centralizing coordination functions, avoiding the need for complex peer-to-peer communication protocols between workers, and managing the distributed processing overhead.
3Loss of time
If asynchronous gradient sending is used, then waiting time is reduced, but gradient synchronization accuracy deteriorates
Solution Approach 1:
The server implements a feedback mechanism by accumulating gradients from multiple workers and periodically performing aggregated updates to the word vectors. This feedback loop ensures that despite asynchronous submission, the system maintains synchronization accuracy through regular consolidation of all gradient contributions, balancing speed and precision.
Data Source
AI summary
A cluster includes a server cluster and a worker computer cluster. Each worker computer included in the worker computer cluster separately obtains a word and at least one context word of the word that are extracted from a corpus. The worker computer obtains word vectors for the word and the at least one context word. The worker computer calculates a gradient according to the word, the at least one context word, and the word vectors. The worker computer asynchronously updates the gradient to a server included in the server cluster. The server updates the word vectors for the word and the at least one context word of the word according to the gradient.