mirror of
https://github.com/deepseek-ai/DualPipe
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57 lines
2.2 KiB
Markdown
57 lines
2.2 KiB
Markdown
# DualPipe
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DualPipe is an innovative bidirectional pipeline parallelism algorithm introduced in the [DeepSeek-V3 Technical Report](https://arxiv.org/pdf/2412.19437). 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](https://github.com/deepseek-ai/profile-data).
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### Schedules
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Example DualPipe scheduling for 8 PP ranks and 20 micro-batches in two directions.
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The micro-batches in the reverse direction are symmetric to those in the forward direction, so
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we omit their batch ID for illustration simplicity. Two cells enclosed by a shared black border
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have mutually overlapped computation and communication
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### Pipeline Bubbles and Memory Usage Comparison
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| Method | Bubble | Parameter | Activation |
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|-------------|---------------------------------|-----------|------------|
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| 1F1B | (PP-1)(𝐹+𝐵) | 1× | PP |
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| ZB1P | (PP-1)(𝐹+𝐵-2𝑊) | 1× | PP |
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| DualPipe | (PP/2-1)(𝐹&𝐵+𝐵-3𝑊) | 2× | PP+1 |
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𝐹 denotes the execution time of a forward chunk, 𝐵 denotes the execution time of a
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full backward chunk, 𝑊 denotes the execution time of a "backward for weights" chunk, and 𝐹&𝐵
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denotes the execution time of two mutually overlapped forward and backward chunks.
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## Quick Start
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The usage is shown in the following example:
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```bash
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python example.py
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```
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Note: For real-world applications, you will need to implement a custom `overlapped_forward_backward` method tailored to your specific module.
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## Requirements
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- PyTorch 2.0 and above
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## Developers
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DualPipe was created and developed by Jiashi Li and Chengqi Deng and Wenfeng Liang.
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## Citation
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```bibtex
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@misc{deepseekai2024deepseekv3technicalreport,
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title={DeepSeek-V3 Technical Report},
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author={DeepSeek-AI},
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year={2024},
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eprint={2412.19437},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2412.19437},
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}
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```
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