PaddlePaddle/Paddle
Jul 2025 – Aug 2025
2 Months active
Languages Used
C++Python
Technical Skills
CUDA programmingDeep LearningNumerical StabilityCode RefactoringPerformance OptimizationUser Experience Improvement
HU Shenwei
PROFILE
Hu Shenwei
Shenweihu contributed to the PaddlePaddle/Paddle repository by addressing a critical bug in the fused_bias_dropout_residual_layer_norm API, ensuring correct backward gradient computation when dropout is disabled. Using C++ and CUDA programming, Shenweihu modified the dropout mask logic to apply only when dropout is active, restoring stable training behavior and resolving accuracy discrepancies in regression tests. In a separate effort, Shenweihu refactored profiler instrumentation by removing nvprof_nvtx_pop() calls from compiler.py and gemm.py, reducing profiling overhead and improving user experience. These targeted changes demonstrated a strong focus on numerical stability, performance optimization, and maintainable code within deep learning workflows.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
12
Activity Months2
Your Network
178 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered profiler instrumentation cleanup in Paddle repository by removing nvprof_nvtx_pop() calls from compiler.py and gemm.py, reducing profiling overhead and simplifying user experience. This aligns with performance goals and maintainability initiatives for Paddle projects.
1 Commits • 1 Features
Aug 1, 2025For 2025-08, delivered profiler instrumentation cleanup in Paddle repository by removing nvprof_nvtx_pop() calls from compiler.py and gemm.py, reducing profiling overhead and simplifying user experience. This aligns with performance goals and maintainability initiatives for Paddle projects.
August 2025
July 2025
1 Commits
Jul 1, 2025July 2025 (PaddlePaddle/Paddle): Implemented a critical backward pass fix for fused_bias_dropout_residual_layer_norm when dropout is disabled. The backward gradients now reflect the correct computation by applying the dropout mask only when dropout is active, restoring stable training behavior and reducing gradient-related accuracy diffs observed in tests. The change is integration-tested and recorded under commit 28be65039b839fba7dfdc009776555beaea67e1b, addressing accuracy diff No.90 in related tests.
July 2025
1 Commits
Jul 1, 2025July 2025 (PaddlePaddle/Paddle): Implemented a critical backward pass fix for fused_bias_dropout_residual_layer_norm when dropout is disabled. The backward gradients now reflect the correct computation by applying the dropout mask only when dropout is active, restoring stable training behavior and reducing gradient-related accuracy diffs observed in tests. The change is integration-tested and recorded under commit 28be65039b839fba7dfdc009776555beaea67e1b, addressing accuracy diff No.90 in related tests.
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
CUDA programmingCode RefactoringDeep LearningNumerical StabilityPerformance OptimizationUser Experience Improvement
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