Expert Parallelism
A distributed training parallelism strategy for MoE models in which different expert sub-networks are assigned to different GPU devices. Token routing and aggregation are achieved through All-to-All communication, enabling efficient training of extremely large-scale MoE models.
Core Facts
Mixture of Experts (MoE) is a model architecture composed of multiple expert sub-networks that each specialize in different sub-tasks, together with a gating network that decides which expert receives a given input.
The essence of an expert agent is to build a digital avatar of a domain expert.
Timeline (last 90 days)
Expert parallelism places different experts on different devices, and after routing, tokens are sent to the corresponding devices for computation; with a batch size of 1, cross-device communication latency accounts for a very high proportion.
Optimizing expert scheduling can reduce cross-device data movement by adjusting the layout of expert weights according to access patterns, keeping hot experts resident in cache, and coordinating with expert parallelism strategies, and it usually does not require changing the model structure.
V4.1 Flash's expert scheduling strategy: load is monitored every 1,000 steps, experts are reallocated every 10,000 steps, and ultimately the busiest card is only 9% busier than the average level.
Distributed inference parallelism strategies include tensor parallelism (TP), pipeline parallelism (PP), expert parallelism (EP), and PD disaggregation.
At the GPU level, with wide expert parallelism (wide-EP), the expert load carried on different GPUs can vary enormously, causing some GPUs to become bottlenecks and slowing the processing of the entire batch.
In expert parallelism, after routing decisions are made, an All-to-All communication is required to send tokens to the corresponding devices and then aggregate the results
In MoE models, each token is routed to only a small number of experts, producing two communication primitives: Dispatch and Combine
Mixture of Experts (MoE) is a model architecture composed of multiple expert sub-networks that each specialize in different sub-tasks, together with a gating network that decides which expert receives a given input.
The essence of an expert agent is to build a digital avatar of a domain expert.
All Facts (9)
Mixture of Experts (MoE) is a model architecture composed of multiple expert sub-networks that each specialize in different sub-tasks, together with a gating network that decides which expert receives a given input.
75%VerifiedThe essence of an expert agent is to build a digital avatar of a domain expert.
75%UnverifiedExpert parallelism places different experts on different devices, and after routing, tokens are sent to the corresponding devices for computation; with a batch size of 1, cross-device communication latency accounts for a very high proportion.
50%UnverifiedOptimizing expert scheduling can reduce cross-device data movement by adjusting the layout of expert weights according to access patterns, keeping hot experts resident in cache, and coordinating with expert parallelism strategies, and it usually does not require changing the model structure.
50%UnverifiedV4.1 Flash's expert scheduling strategy: load is monitored every 1,000 steps, experts are reallocated every 10,000 steps, and ultimately the busiest card is only 9% busier than the average level.
50%UnverifiedDistributed inference parallelism strategies include tensor parallelism (TP), pipeline parallelism (PP), expert parallelism (EP), and PD disaggregation.
50%UnverifiedAt the GPU level, with wide expert parallelism (wide-EP), the expert load carried on different GPUs can vary enormously, causing some GPUs to become bottlenecks and slowing the processing of the entire batch.
50%UnverifiedIn expert parallelism, after routing decisions are made, an All-to-All communication is required to send tokens to the corresponding devices and then aggregate the results
50%UnverifiedIn MoE models, each token is routed to only a small number of experts, producing two communication primitives: Dispatch and Combine
50%