DualPipe

DualPipe is an innovative bidirectional pipeline parallelism algorithm introduced in the DeepSeek-V3 Technical Report. It achieves full overlap of forward and backward computation-communication phases, also reducing pipeline bubbles. For detailed information on computation-communication overlap, please refer to the profile data.

Schedules

Example DualPipe scheduling for 8 PP ranks and 20 micro-batches in two directions. The micro-batches in the reverse direction are symmetric to those in the forward direction, so we omit their batch ID for illustration simplicity. Two cells enclosed by a shared black border have mutually overlapped computation and communication

DualPipeV

DualPipeV is a concise V-shape schedule derived from DualPipe using a "cut-in-half" procedure, introduced by Sea AI Lab as "Cut-in-half" in their blog post. Thanks to them for this efficient schedule!

Schedules

Example DualPipeV scheduling for 4 PP ranks (8 PP stages) and 10 micro-batches.

Pipeline Bubbles and Memory Usage Comparison (based on the same number of PP stages)

Method	Bubble	Parameter Per Device	Activation Per Device	#Devices
1F1B	(PP-1)(𝐹+𝐵)	1×	PP	PP
ZB1P	(PP-1)(𝐹+𝐵-2𝑊)	1×	PP	PP
DualPipe	(PP/2-1)(𝐹&𝐵+𝐵-3𝑊)	2×	PP+1	PP
DualPipeV	(PP/2-1)(𝐹&𝐵+𝐵-3𝑊)	2×	PP+1	PP/2

PP denotes the number of pp stages (even). 𝐹 denotes the execution time of a forward chunk, 𝐵 denotes the execution time of a full backward chunk, 𝑊 denotes the execution time of a "backward for weights" chunk, and 𝐹&𝐵 denotes the execution time of two mutually overlapped forward and backward chunks.

Quick Start

The usage is shown in the following example:

python examples/example_dualpipe.py
python examples/example_dualpipev.py

Note: For real-world applications, you will need to implement a custom overlapped_forward_backward method tailored to your specific module.

Requirements

• PyTorch 2.0 and above

Developers

DualPipe was created and developed by Jiashi Li and Chengqi Deng and Wenfeng Liang.

Citation

@misc{deepseekai2025deepseekv3technicalreport,
      title={DeepSeek-V3 Technical Report}, 
      author={DeepSeek-AI},
      year={2025},
      eprint={2412.19437},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.19437}, 
}