volcengine/verl
Dec 2025 – Dec 2025
1 Month active
Languages Used
Python
Technical Skills
GPU ProgrammingMachine LearningPerformance OptimizationTriton
TeFeng Chen
PROFILE
Tefeng Chen
Worked on the volcengine/verl repository to optimize training kernel performance on Hopper GPUs by implementing Tensor Memory Access (TMA) for linear_cross_entropy kernels. Leveraging Python and Triton, introduced a USE_TMA flag and tensor descriptors to enable more efficient memory access patterns, resulting in substantial reductions in both forward and backward pass latency. The approach integrated TMA with Triton kernels using tl.make_tensor_descriptor, and performance improvements were validated through targeted latency tests. These changes improved throughput for machine learning workloads and were structured for broader adoption, with clear traceability and test coverage to ensure maintainability and reliability within the codebase.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
353
Activity Months1
Your Network
415 peopleWork History
December 2025
1 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12 (volcengine/verl). Focused on performance optimization for training kernels on Hopper GPUs. Implemented Tensor Memory Access (TMA) optimization for linear_cross_entropy kernels, introducing USE_TMA and tensor descriptors to enable efficient memory access. This optimization yielded substantial latency improvements in both forward and backward passes, accelerating training workloads on Hopper GPUs and improving overall throughput. The work included code changes in training_utils (PR #4576) and Triton kernels, with validation via targeted latency tests. Latency improvements observed across test cases: forward latency reduced from ~129.6 ms to ~18.8 ms, and backward latency reduced from ~157.9 ms to ~56.1 ms (case 0); similar gains across other cases (case 1: 35.4 -> 6.0 forward; 49.9 -> 22.1 backward; case 2: 12.3 -> 2.1; 16.4 -> 6.6; case 3: 71.4 -> 10.3; 89.5 -> 31.3; case 4: 406.0 -> 56.5; 478.0 -> 173.4).
1 Commits • 1 Features
Dec 1, 2025Monthly summary for 2025-12 (volcengine/verl). Focused on performance optimization for training kernels on Hopper GPUs. Implemented Tensor Memory Access (TMA) optimization for linear_cross_entropy kernels, introducing USE_TMA and tensor descriptors to enable efficient memory access. This optimization yielded substantial latency improvements in both forward and backward passes, accelerating training workloads on Hopper GPUs and improving overall throughput. The work included code changes in training_utils (PR #4576) and Triton kernels, with validation via targeted latency tests. Latency improvements observed across test cases: forward latency reduced from ~129.6 ms to ~18.8 ms, and backward latency reduced from ~157.9 ms to ~56.1 ms (case 0); similar gains across other cases (case 1: 35.4 -> 6.0 forward; 49.9 -> 22.1 backward; case 2: 12.3 -> 2.1; 16.4 -> 6.6; case 3: 71.4 -> 10.3; 89.5 -> 31.3; case 4: 406.0 -> 56.5; 478.0 -> 173.4).
December 2025
Activity
Loading activity data...
Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
GPU ProgrammingMachine LearningPerformance OptimizationTriton
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline