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cover of episode MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

MegaBlocks: Efficient Sparse Training with Mixture-of-Experts

2023/12/14
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Trevor Gale, D. Narayanan, C. Young, M. Zaharia：MegaBlocks 系统通过块稀疏运算重新定义了混合专家模型 (MoE) 的计算方式，解决了现有框架中动态路由的限制，避免了标记丢弃或填充带来的计算和内存浪费。该系统在现代硬件上高效映射，实现了高达 40% 的端到端训练速度提升，相比于使用最先进的 Tuttle 库训练的 MoE 模型提升了 40%，相比于使用高度优化的 Megatron-LM 框架训练的 DNN 模型提升了 2.4 倍。MegaBlocks 使用块稀疏矩阵乘法来处理 MoE 层中的动态和负载不平衡计算，并开发了高性能的 GPU 内核，支持所有转置和非转置输入的组合。该系统还支持 MoE 的分布式训练，包括数据并行和专家模型并行。 Trevor Gale, D. Narayanan, C. Young, M. Zaharia：在避免标记丢弃方面，MegaBlocks 的方法显著优于 Tuttle 的动态容量因子技术。实验结果表明，MegaBlocks 在 MoE 模型训练中实现了显著的端到端训练速度提升，与使用 Tuttle 的方法相比，MoXS、MoE-small 和 MoE-medium 模型的训练速度分别提升了 1.38 倍、2.0 倍和 4.35 倍。与 Megatron-LM 训练的 Transformer 模型相比，MegaBlocks 训练的 DMOE 模型的训练速度也提升了 1.8 倍到 2.4 倍。MegaBlocks 的块稀疏矩阵乘法内核性能与 Kubla 的批处理矩阵乘法相当，实现了 98.6% 的吞吐量。

Deep Dive

Key Insights

What are the limitations of current frameworks for Mixture-of-Experts (MoE) training?

Current frameworks restrict dynamic routing in MoE layers, forcing a tradeoff between model quality and hardware efficiency. Users must choose between dropping tokens or wasting computation and memory on padding.

How does MegaBlocks address the limitations of existing MoE frameworks?

MegaBlocks reformulates MoE computation in terms of block-sparse operations and develops new GPU kernels to handle dynamic routing efficiently. It avoids dropping tokens and maps well to modern hardware.

What are the performance improvements of MegaBlocks compared to other frameworks?

MegaBlocks achieves up to 40% end-to-end training speedups over state-of-the-art Tutel library and 2.4x speedups over highly-optimized Megatron-LM framework for dense neural networks (DNNs).

Why is fine-grained sparse computation less efficient on hardware accelerators like GPUs and TPUs?

Hardware accelerators like GPUs and TPUs are optimized for dense computation, making fine-grained sparse computation less efficient due to the irregularity of sparse operations.

What is the role of block-sparse operations in MegaBlocks?

Block-sparse operations allow MegaBlocks to handle the dynamic and load-imbalanced computation in MoE layers efficiently, enabling the system to process all tokens without dropping any.

What is the significance of the block size in MegaBlocks' implementation?

The block size is chosen to ensure high arithmetic intensity and efficient use of GPU resources. A 128x128 block size was selected based on performance benchmarks, enabling high throughput on modern GPUs.

How does MegaBlocks handle dynamic routing in MoE layers?

MegaBlocks uses block-sparse matrix multiplication kernels that can handle variable numbers of tokens assigned to experts, ensuring no tokens are dropped and enabling efficient computation.

What is the impact of token dropping on model quality?

Token dropping significantly reduces model quality. For example, an MoE model avoiding token dropping achieved a 0.26 reduction in validation loss, compared to 0.15 for a model with token dropping.

What are the challenges in implementing MoE layers on TPUs?

TPUs require static tensor shapes and struggle with fine-grained operations like scatters and gathers, making it difficult to implement dynamic routing in MoE layers directly on TPUs.

How does MegaBlocks compare to Tutel in terms of memory usage?

MegaBlocks reduces memory usage compared to Tutel, which increases memory requirements due to padding. This allows MegaBlocks to use larger micro-batch sizes, improving hardware efficiency.

Chapters
Current MoE training frameworks limit dynamic routing in MoE layers due to software and hardware constraints. This forces a trade-off between model quality and hardware efficiency, requiring users to choose between dropping tokens or wasting computation on padding. The paper introduces Megablocks to address these limitations.
  • Current MoE frameworks restrict dynamic routing due to software and hardware limitations.
  • This leads to a trade-off between model quality and hardware efficiency.
  • Users must choose between dropping tokens or wasting computation on padding.

Shownotes Transcript

We present MegaBlocks, a system for efficient Mixture-of-Experts (MoE) training on GPUs. Our system is motivated by the limitations of current frameworks, which restrict the dynamic routing in MoE layers to satisfy the constraints of existing software and hardware. These formulations force a tradeoff between model quality and hardware efficiency, as users must choose between dropping tokens from the computation or wasting computation and memory on padding. To address these limitations, we reformulate MoE computation in terms of block-sparse operations and develop new block-sparse GPU kernels that efficiently handle the dynamism present in MoEs. Our approach never drops tokens and maps efficiently to modern hardware, enabling end-to-end training speedups of up to 40% over MoEs trained with the state-of-the-art Tutel library and 2.4x over DNNs trained with the highly-optimized Megatron-LM framework.

2022: Trevor Gale, D. Narayanan, C. Young, M. Zaharia

https://arxiv.org/pdf/2211.15841.pdf