ROCm/triton
Nov 2024 – Dec 2024
2 Months active
Languages Used
CudaPythonC++Markdown
Technical Skills
Code MaintenanceDeep LearningGPU ComputingPerformance OptimizationQuantizationTesting
Tianxing Wu
PROFILE
Tianxing Wu
Over two months, contributed to the ROCm/triton repository by developing and refining INT8 per-channel quantization for the Flash Attention kernel, enhancing both memory efficiency and inference performance for low-precision workloads. The work involved implementing kernel logic for per-channel scales, de-quantization, and robust quantization-aware testing, using Python, CUDA, and Triton. Improvements included FP32 scaling support, expanded test coverage with isolated and no-causal scenarios, and upstream synchronization to maintain alignment with ongoing development. Additional efforts focused on code maintenance, CI stability, and test infrastructure, addressing bugs and streamlining validation to reduce production risk and accelerate quantized kernel deployment.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
25Total
Bugs
4
Commits
25
Features
8
Lines of code
2,150
Activity Months2
Your Network
17 peopleShared Repositories
17
Work History
December 2024
23 Commits • 7 Features
Dec 1, 2024December 2024 focused on advancing quantization accuracy and reliability in ROCm/triton, strengthening the test framework, and ensuring CI stability and upstream alignment. Delivered int8 FA/KV scaling enhancements with in-test tiling and p_scale handling, added FP32 scaling support, and extended test coverage with no-causal and isolated tests. Performed upstream synchronization with FA-int8 branch and implemented CI/test infrastructure improvements (pre-commit, code cleanup, and enabling full test suite). Major bugs fixed include ref_out order alignment, disabling gradient for testing to save memory, applying code-review fixes, and removing deprecated autotune config. These changes reduce production risk in quantized paths, improve numerical precision, and accelerate development with stronger CI and upstream collaboration.
23 Commits • 7 Features
Dec 1, 2024December 2024 focused on advancing quantization accuracy and reliability in ROCm/triton, strengthening the test framework, and ensuring CI stability and upstream alignment. Delivered int8 FA/KV scaling enhancements with in-test tiling and p_scale handling, added FP32 scaling support, and extended test coverage with no-causal and isolated tests. Performed upstream synchronization with FA-int8 branch and implemented CI/test infrastructure improvements (pre-commit, code cleanup, and enabling full test suite). Major bugs fixed include ref_out order alignment, disabling gradient for testing to save memory, applying code-review fixes, and removing deprecated autotune config. These changes reduce production risk in quantized paths, improve numerical precision, and accelerate development with stronger CI and upstream collaboration.
December 2024
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024: Delivered production-ready INT8 per-channel quantization for the Flash Attention kernel in ROCm/triton, including per-channel scales, a de-quantization path, and dedicated tests. The test suite was streamlined by removing an obsolete INT8 test to improve validation reliability. No major defects reported; focus was on feature delivery with emphasis on performance, memory efficiency, and maintainability. This work strengthens ROCm/triton's low-precision inference capabilities and expands deployment potential for latency-sensitive workloads. Technologies demonstrated include low-level Triton kernel development, per-channel quantization, and robust testing practices.
November 2024
2 Commits • 1 Features
Nov 1, 2024November 2024: Delivered production-ready INT8 per-channel quantization for the Flash Attention kernel in ROCm/triton, including per-channel scales, a de-quantization path, and dedicated tests. The test suite was streamlined by removing an obsolete INT8 test to improve validation reliability. No major defects reported; focus was on feature delivery with emphasis on performance, memory efficiency, and maintainability. This work strengthens ROCm/triton's low-precision inference capabilities and expands deployment potential for latency-sensitive workloads. Technologies demonstrated include low-level Triton kernel development, per-channel quantization, and robust testing practices.
Activity
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Quality Metrics
Correctness81.6%
Maintainability83.2%
Architecture74.4%
Performance72.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++CudaMarkdownPython
Technical Skills
CUDACode FormattingCode MaintenanceDebuggingDeep LearningDeep Learning FrameworksDeep Learning KernelsGPU ComputingKernel DevelopmentKernel TuningMachine LearningPerformance OptimizationPerformance TestingPythonQuantization
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