MoE Training Parallelism to Cut Attention-Expert Communication
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Solution Overview
Problem
Large-scale training of mixture-of-experts (MoE) models faces challenges due to disparities in attention and feed-forward network components, leading to high communication overhead and inefficiencies in distributed training environments, particularly with advancements in GPU capabilities and reduced training precision.
Innovation Solution
A system optimized for efficient large-scale MoE training on high-performance GPU clusters, employing Sequence Parallel Attention (SPA) and Out-of-Order Expert Parallelism, along with overlapping communication strategies to minimize communication overhead and balance computation loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional attention mechanisms are used in MoE models, then model performance is maintained, but communication overhead becomes excessively high in distributed training environments
Solution Approach 1:
The patent segments the attention mechanism by partitioning the sequence dimension across multiple devices. Each device processes a subset of sequence positions independently, eliminating the need for all-to-all communication between devices. This segmentation of the computation task directly reduces communication overhead while maintaining training efficiency.
Solution Approach 2:
The patent transitions from traditional tensor parallelism (partitioning across feature dimensions) to sequence parallelism (partitioning across sequence positions). This dimensional change in parallelization strategy fundamentally alters the communication pattern from all-to-all to only essential communication, reducing communication overhead significantly.
2Adaptability or versatility
If expert parallelism is implemented to scale MoE models, then model capacity increases, but computation load becomes unbalanced across devices
Solution Approach 1:
The patent implements dynamic routing of sequences to experts based on the gating mechanism's predictions. Rather than static expert assignment, the system dynamically determines which experts process which sequences at each layer, allowing flexible load distribution that adapts to the specific data and model state, thereby balancing computation load while maintaining high model capacity.
Solution Approach 2:
The patent applies different parallelization strategies to different components: sequence parallelism for the attention mechanism and expert parallelism for the feed-forward networks. This localized application of quality-optimized parallelization ensures that each component is handled according to its specific computational characteristics, balancing overall load while maximizing model capacity.
3Speed
If training precision is reduced to match GPU capabilities, then training speed increases, but computational accuracy may be compromised
Solution Approach 1:
The patent changes the data type parameters of computational operations to match GPU capabilities (e.g., using FP16 or BF16 instead of FP32). This parameter change enables faster computation and higher throughput while maintaining sufficient accuracy for training, as modern GPUs are optimized for these precision levels and the training process is designed to be robust to the reduced precision.
Data Source
AI summary
The present disclosure describes techniques for accelerating a process of training mixture-of-experts (MOE) models. A sequence in training data is partitioned into a plurality of segments. The plurality of segments are input in parallel into a plurality of devices. Attention computations of a layer are implemented in parallel by the plurality of devices. Tokens from the attention computations of the layer are dispatched to different devices among the plurality of devices and implementing expert computations of the layer by the different devices. A communication volume is reduced by maintaining, after completing the expert computations of the layer, at least a portion of tokens from each of the different devices on the same device for implementing attention computations of a subsequent layer.