quic/aimet
Oct 2024 – Dec 2024
2 Months active
Languages Used
C++Python
Technical Skills
DeepSpeedDistributed TrainingParameter SynchronizationPyTorchQuantizationDeep Learning
Huan Zhao
PROFILE
Huan Zhao
Worked on the quic/aimet repository to enhance distributed training workflows by introducing SafeGatheredParameters, a class designed to manage parameter synchronization with DeepSpeed’s zero offload. This addition addressed edge cases where parameters are already gathered or offloaded, improving the reliability of quantization processes in PyTorch-based distributed systems. Further extended DeepSpeed Zero Offload support to SeqMSE, integrating SafeGatheredParameters and updating tests to ensure compatibility with Zero3 Offload. The work established a foundation for scalable model training, optimizing performance and resource utilization. Solutions were implemented primarily in Python and C++, leveraging expertise in Deep Learning, distributed systems, and model optimization.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
498
Activity Months2
Your Network
232 peopleShared Repositories
47
Khobare, AbhijitMember
Mukherjee, AnanyaMember
Kumar, AshvinMember
Sonawane, BhushanMember
Xu, BozheMember
Yuan, ChaoMember
Dinh, Dao Lan VyMember
Gajula, Sai ChaitanyaMember
Mehta, HitarthMember
Work History
December 2024
1 Commits • 1 Features
Dec 1, 2024Month 2024-12 — quic/aimet: Delivered DeepSpeed Zero Offload support for SeqMSE, advancing distributed training compatibility and performance. Implemented SafeGatheredParameters integration for parameter handling and updated tests to exercise SeqMSE with Zero3 Offload. This work lays groundwork for scalable training in large models and improves efficiency in distributed environments, delivering measurable business value through faster training iterations and better resource utilization.
1 Commits • 1 Features
Dec 1, 2024Month 2024-12 — quic/aimet: Delivered DeepSpeed Zero Offload support for SeqMSE, advancing distributed training compatibility and performance. Implemented SafeGatheredParameters integration for parameter handling and updated tests to exercise SeqMSE with Zero3 Offload. This work lays groundwork for scalable training in large models and improves efficiency in distributed environments, delivering measurable business value through faster training iterations and better resource utilization.
December 2024
October 2024
1 Commits • 1 Features
Oct 1, 20242024-10 Monthly Recap for quic/aimet: Delivered SafeGatheredParameters to manage parameter synchronization with DeepSpeed's zero offload. The new class ensures correct synchronization when parameters are already gathered or using zero3 offload, improving robustness of quantization workflows. Change captured in commit e5a89edaf3215c6534592c2ddac4fcc55f7a95c4 (Ensure the synchronization of parameters using zero offload (#3435)).
October 2024
1 Commits • 1 Features
Oct 1, 20242024-10 Monthly Recap for quic/aimet: Delivered SafeGatheredParameters to manage parameter synchronization with DeepSpeed's zero offload. The new class ensures correct synchronization when parameters are already gathered or using zero3 offload, improving robustness of quantization workflows. Change captured in commit e5a89edaf3215c6534592c2ddac4fcc55f7a95c4 (Ensure the synchronization of parameters using zero offload (#3435)).
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
Deep LearningDeepSpeedDistributed SystemsDistributed TrainingModel OptimizationParameter SynchronizationPyTorchQuantization
Repositories Contributed To
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