Layered Gradient Accumulation for Pipeline Parallel Model Training
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Solution Overview
Problem
Large enterprises face challenges in integrating and streamlining operations due to the proliferation of siloed custom software applications, which hinder integration, efficiency, and compliance with regulatory requirements.
Innovation Solution
A remotely-hosted Application Platform as a Service (aPaaS) system that automates workflows, supports standardized application components, and provides secure, scalable, and flexible development environments to unify enterprise operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard pipeline parallelism is used to distribute model layers across compute nodes, then model training can be performed on large-scale systems, but compute nodes experience idle time waiting for data transmission between layers
Solution Approach 1:
The patent segments the model training process into distinct phases (forward propagation, backward propagation, parameter update) and organizes compute nodes into groups that can process micro-batches independently. This segmentation allows compute nodes to work in parallel without sequential dependencies, eliminating idle waiting time while maintaining high training throughput.
Solution Approach 2:
The patent introduces a new dimension of parallelism by organizing compute nodes into multiple groups that process different micro-batches simultaneously. Instead of a single sequential pipeline, the system creates parallel processing streams where each group operates independently on its assigned micro-batch, transforming the training architecture from one-dimensional sequential processing to multi-dimensional parallel processing.
2Adaptability or versatility
If micro-batch processing is used to reduce memory requirements, then larger models can be trained, but the training process becomes more complex with multiple synchronization points
Solution Approach 1:
The patent implements self-service mechanisms where each compute node group independently manages its own micro-batch processing, gradient computation, and parameter updates. The system automatically handles load balancing and synchronization without requiring complex external coordination, reducing training process complexity while enabling scalable model training through micro-batch processing.
3Productivity
If data is transmitted between compute nodes during forward and backward propagation, then distributed training is enabled, but network communication overhead increases training time
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing data into micro-batches before distribution to compute nodes. This preliminary organization allows each node to process data independently with minimal communication overhead, as the data is already structured for parallel processing. The system also performs preliminary gradient aggregation planning to minimize communication rounds during backward propagation.
Data Source
AI summary
A method is provided including: (i) assigning sequentially-ordered layers of a machine learning model to a plurality of compute nodes, each of the layers being assigned to exactly one of the nodes; (ii) dividing training data into micro-batches; (iii) forward-propagating the micro-batches through the model, each node operating in parallel to generate respective activation states for the micro-batches with their assigned layers, and with the activation states being communicated between the nodes according to the layers' sequential ordering; and (iv) backward-propagating the micro-batches through the model, each node operating in parallel to generate respective error states for the micro-batches with their assigned layers, with the error states being communicated between the nodes according to the layers' reverse sequential ordering, wherein each of the nodes completes the backward-propagation of all micro-batches through a given layer prior to performing backward-propagation through any layer that precedes the given layer in the sequential ordering.