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 delivered 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. Using Python and CUDA, Yao partitioned attention computations across sequence-parallel ranks, which improved both memory efficiency and training performance. The work included updating activation checkpointing to further reduce memory usage and increase throughput during model training and inference. Additionally, Yao implemented a new CI workflow to validate flash attention, enhancing reliability and feedback speed. This contribution demonstrated depth in distributed systems, deep learning, and transformer architecture 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
26 peopleSame Organization
@osu.edu9
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
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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
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