Accelerating Mixture-of-Experts Model Training by Overlapping Computation and Communication Across the Entire Training Graph

Keskeiset käsitteet

Lancet, a compiler-based system, accelerates Mixture-of-Experts (MoE) model training by overlapping the entire training graph's computation and communication, going beyond just overlapping all-to-all communication and expert computation.

Tiivistelmä

The paper presents Lancet, a compiler-based system designed to accelerate the training of Mixture-of-Experts (MoE) models. MoE models replace the feed-forward module in Transformer layers with multiple independent sub-networks (experts) that are assigned to different devices for efficient parallelization. However, the all-to-all communication required to distribute inputs to the scattered experts significantly hampers the training speed of MoE models.
Lancet extends the focus region beyond just overlapping all-to-all communication and expert computation, as done in previous work. It identifies two additional opportunities for overlapping:

Weight gradient computation in the backward pass, which is independent of all-to-all communication and can be scheduled to overlap with it.
Non-MoE model computation in the forward pass, which can be partitioned and pipelined with the all-to-all communication.

Lancet addresses the challenges associated with these extensions, such as maintaining mathematical equivalency when partitioning non-MoE computations and determining the optimal partition range. It employs a compiler-based approach, providing flexibility in controlling operator partitioning and scheduling.
Lancet's weight gradient computation scheduling pass reorders the instructions to overlap weight gradient computation with all-to-alls during the backward pass. The operator partition pass then partitions non-MoE computations in the forward pass and schedules them to overlap with all-to-alls. A dynamic programming-based algorithm is used to determine the optimal partition range.
Comprehensive evaluations show that Lancet can decrease non-overlapping communication time by up to 77% and deliver an up to 1.3x end-to-end speedup compared to state-of-the-art solutions, including DeepSpeed and Tutel.

Tilastot

The paper reports the following key metrics:

Lancet can decrease non-overlapping communication time by up to 77% compared to the baseline.
Lancet achieves an up to 1.3x end-to-end speedup compared to state-of-the-art solutions like DeepSpeed and Tutel.

Lainaukset

"For the first time, we expand the focus region to encompass the entire training graph, mitigating the prolonged all-to-all communication's impact on MoE model training."
"We adopt a greedy algorithm to schedule each weight gradient computation operator to overlap with the appropriate all-to-all."
"We devise a partitioning scheme for MoE layers that allows for the extension of partitioning to non-MoE computations while maintaining mathematical equivalency."
"We apply a dynamic programming based algorithm to identify the optimal range of non-MoE computation for partitioning and overlapping."

Tärkeimmät oivallukset

Lancet: Accelerating Mixture-of-Experts Training via Whole Graph Computation-Communication Overlapping

by Chenyu Jiang... klo arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19429.pdf

Syvällisempiä Kysymyksiä

How can Lancet's techniques be extended to other types of large-scale distributed models beyond MoE, such as sparse Transformer models or mixture-of-experts models with different routing strategies

Lancet's techniques can be extended to other types of large-scale distributed models beyond MoE by adapting its optimization strategies to suit the specific characteristics of these models. For sparse Transformer models, where only a subset of the model parameters are active at any given time, Lancet's partitioning and scheduling algorithms can be modified to focus on the active parameters and optimize communication and computation overlap accordingly. By considering the sparsity patterns and communication requirements of sparse models, Lancet can tailor its approach to maximize efficiency in training.
For mixture-of-experts models with different routing strategies, Lancet can be enhanced to accommodate the specific requirements of each routing method. Different routing strategies may result in varying data dependencies and computation patterns, which would need to be considered in the partitioning and scheduling processes. By analyzing the routing algorithms and their impact on model training, Lancet can adjust its optimization techniques to ensure effective overlap of communication and computation for models with diverse routing strategies.

What are the potential limitations or trade-offs of Lancet's approach, and how could it be further improved to handle a wider range of model architectures and training scenarios

One potential limitation of Lancet's approach is the reliance on static-shaped communication patterns, which may not fully capture the dynamic nature of some models during training. To address this limitation, Lancet could be further improved by incorporating dynamic communication strategies that adapt to the varying data dependencies and communication requirements of different model architectures. By introducing more flexibility in handling irregular communication patterns, Lancet can enhance its ability to optimize training for a wider range of model architectures and scenarios.
Additionally, Lancet's current optimization techniques may have overhead costs associated with partitioning and scheduling, especially in scenarios with a large number of partitions or complex model architectures. To mitigate these trade-offs, Lancet could explore more efficient partitioning algorithms and scheduling strategies that minimize overhead while maximizing overlap. By optimizing the resource allocation and task distribution in the training process, Lancet can improve its scalability and performance across diverse training scenarios.

Given the importance of efficient training of large-scale models, how might techniques like those used in Lancet inspire the development of new hardware or system-level optimizations to better support distributed model training in the future

The techniques used in Lancet for optimizing mixture-of-experts model training could inspire the development of new hardware or system-level optimizations to better support distributed model training in the future. By demonstrating the benefits of overlapping computation and communication in large-scale models, Lancet highlights the importance of efficient resource utilization and task parallelization in distributed training environments.
These insights could drive innovations in hardware design, such as specialized accelerators or interconnect technologies that prioritize low-latency communication and high-throughput computation for distributed model training. By integrating Lancet's optimization principles into the architecture of future hardware systems, researchers and engineers can create more efficient and scalable platforms for training complex models at scale.
Furthermore, Lancet's focus on compiler-based optimization could lead to advancements in system-level optimizations that streamline the training process across heterogeneous computing environments. By leveraging compiler techniques to automate and optimize model training workflows, future systems could achieve higher performance and resource efficiency in distributed training scenarios. This could pave the way for the development of more intelligent and adaptive training frameworks that dynamically adjust to the requirements of different models and hardware configurations.