deepspeedai/DeepSpeed
Dec 2024 – Dec 2024
1 Month active
Languages Used
PythonShell
Technical Skills
CI/CDCUDADeep LearningDistributed SystemsPyTorchSequence Parallelism
Jinghan Yao
PROFILE
Jinghan Yao
Yao developed the Flash-Partitioned Distributed Transformer (FPDT) feature for the deepspeedai/DeepSpeed repository, focusing on enabling sequence-parallelism for large language models through CPU-offloaded attention and feedforward networks. By partitioning attention computations across sequence-parallel ranks, Yao improved both memory efficiency and training performance. The work included updating activation checkpointing to further reduce memory usage and enhance throughput during training and inference. Yao also implemented a new continuous integration workflow to validate flash attention, increasing reliability and feedback speed. This project leveraged Python, CUDA, and PyTorch, demonstrating depth in distributed systems and deep learning engineering within a complex codebase.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,434
Activity Months1
Your Network
49 people
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Delivered the Flash-Partitioned Distributed Transformer (FPDT) feature for deepspeedai/DeepSpeed. FPDT introduces CPU-offloaded attention/FFN enabling sequence-parallelism for large language models. The work includes a new CI workflow for flash attention and updates to activation checkpointing to improve memory efficiency and performance by partitioning attention computations across sequence-parallel ranks. Commit: 60a1b57b98c61c322cc76f1936eaec4f18a77b06.
1 Commits • 1 Features
Dec 1, 2024Delivered the Flash-Partitioned Distributed Transformer (FPDT) feature for deepspeedai/DeepSpeed. FPDT introduces CPU-offloaded attention/FFN enabling sequence-parallelism for large language models. The work includes a new CI workflow for flash attention and updates to activation checkpointing to improve memory efficiency and performance by partitioning attention computations across sequence-parallel ranks. Commit: 60a1b57b98c61c322cc76f1936eaec4f18a77b06.
December 2024
Activity
Loading activity data...
Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonShell
Technical Skills
CI/CDCUDADeep LearningDistributed SystemsPyTorchSequence ParallelismTransformer Architecture
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline