DeepSeek Open-Source Week Day 2: DeepEP 254

Yesterday was the first day of DeepSeek ai's open-source week. The open-sourced project was FlashMLA. Within less than half an hour, it already had over 300 Stars on Github, and in just 1 hour, it surpassed 100K reads, making it extremely popular. Today is the second day of DeepSeek's open-source week, and DeepSeek open-sourced its second project: Deepep.

What is Deepep?

Deepep is an open-source EP communication library for MOE model training and inference. It is based on an advanced mixture-of-experts architecture, fully leveraging large-scale datasets to improve the effectiveness of pre-training and fine-tuning. DeepEP's design orientation is to reduce computational burden and memory usage, enabling users to efficiently run large-scale models on conventional hardware.

Features of Deepep

MoE Architecture Optimization:

• DeepEP is specifically designed for Mixture-of-Experts (MoE) and Expert Parallelism (EP), providing efficient all-to-all GPU kernels suitable for training and inference tasks.

• Supports low-precision operations, including FP8 and BF16, improving computational efficiency and saving video memory.

High-Performance Communication:

• Provides high-throughput and low-latency communication kernels, supporting NVLink and RDMA networks. Tested on H800 GPUs, the maximum bandwidth can reach 153 GB/s (NVLink) and 46 GB/s (RDMA).

• Optimized for asymmetric bandwidth forwarding scenarios from NVLink to RDMA, ensuring high throughput performance.

Low-Latency Communication:

• Provides pure RDMA low-latency kernels, especially suitable for the inference decoding stage, with latency as low as 163 microseconds.

• Introduces a hook-based communication-computation overlap method that does not occupy GPU SM resources, maximizing computational efficiency.

Hardware Optimization:

• Leverages undisclosed PTX instructions to boost the performance of the Hopper architecture.

• Supports InfiniBand networks and is theoretically compatible with RoCE, providing flexible network configuration options.

Use Cases for Deepep

Large-Scale Model Training:

• Suitable for training Mixture-of-Experts (MoE) models, providing efficient parallel communication support and significantly improving training efficiency.

Inference Tasks:

• Particularly suitable for latency-sensitive inference decoding scenarios, significantly improving efficiency, reducing latency, and increasing inference throughput.

High-Performance Computing Needs:

• Perfectly fits modern high-performance computing needs, supports multiple hardware platforms including the Hopper GPU architecture, and may expand to more devices in the future.

Usage and Integration of Deepep

Ease of Use:

• Supports the Hopper GPU architecture and requires Python 3.8+, CUDA 12.3+, and PyTorch 2.1+ environments.

• Installation requires the team's modified version of NVSHMEM, with detailed installation instructions.

Integration Methods:

• Open-source and easy to integrate, only requiring a few lines of commands to build and run tests.

• After installation, simply import the deep_ep module to use it directly, with example code and test scripts provided.

DeepEP has been open-sourced on GitHub at: https://github.com/deepseek-ai/DeepEP

1. DeepSeek Open-Source Week Day 1 Project: DeepSeek FlashMLA