ROCm/xla
Dec 2024 – Feb 2025
2 Months active
Languages Used
BashMarkdownPython
Technical Skills
Machine LearningModel DeploymentPerformance BenchmarkingPython DevelopmentShell ScriptingBash
Zongye Yang
PROFILE
Zongye Yang
Developed end-to-end benchmarking suites for the ROCm/xla repository, focusing on Gemma2 model evaluation on CPU backends using both Flax and PyTorch. Built reproducible benchmarking pipelines with Bash setup scripts and Python benchmarking tools to measure key metrics such as time-to-first-token, end-to-end latency, and time per output token. Incorporated requirements files to standardize environments and ensure consistent performance evaluation. Leveraged skills in Python development, shell scripting, and machine learning model deployment to enable data-driven optimization of Gemma2 on XLA’s CPU backend. The work established a foundation for quantitative performance analysis and automated benchmarking within the ROCm/xla project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
482
Activity Months2
Your Network
5237 peopleSame Organization
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Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
February 2025
1 Commits • 1 Features
Feb 1, 2025In February 2025, delivered an end-to-end benchmarking toolkit for Gemma2 PyTorch 2b-it on CPU within the ROCm/xla project. Implemented setup and run scripts for end-to-end benchmarks, a Python benchmark script to measure generation time and time per output token, and a requirements file to evaluate Gemma2 performance in the XLA CPU environment. These changes establish reproducible CPU performance evaluation and form a foundation for data-driven optimization across CPU XLA pipelines. The work is captured in commit 609a47d823333aa0072619609bd86828e0663461, which adds the run/setup scripts for e2e Gemma2 PyTorch.
1 Commits • 1 Features
Feb 1, 2025In February 2025, delivered an end-to-end benchmarking toolkit for Gemma2 PyTorch 2b-it on CPU within the ROCm/xla project. Implemented setup and run scripts for end-to-end benchmarks, a Python benchmark script to measure generation time and time per output token, and a requirements file to evaluate Gemma2 performance in the XLA CPU environment. These changes establish reproducible CPU performance evaluation and form a foundation for data-driven optimization across CPU XLA pipelines. The work is captured in commit 609a47d823333aa0072619609bd86828e0663461, which adds the run/setup scripts for e2e Gemma2 PyTorch.
February 2025
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered the Gemma2 Flax CPU End-to-End Benchmark Suite within ROCm/xla, establishing a reproducible benchmarking pipeline to evaluate Gemma2 on the CPU backend. The suite includes setup scripts, a Python benchmarking script to execute benchmarks and compute TTFT, End-to-End Latency, and TPOT metrics, and a requirements file to lock dependencies. This work provides quantitative performance visibility and a foundation for data-driven optimization of Gemma2 on XLA's CPU backend.
December 2024
1 Commits • 1 Features
Dec 1, 2024December 2024: Delivered the Gemma2 Flax CPU End-to-End Benchmark Suite within ROCm/xla, establishing a reproducible benchmarking pipeline to evaluate Gemma2 on the CPU backend. The suite includes setup scripts, a Python benchmarking script to execute benchmarks and compute TTFT, End-to-End Latency, and TPOT metrics, and a requirements file to lock dependencies. This work provides quantitative performance visibility and a foundation for data-driven optimization of Gemma2 on XLA's CPU backend.
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashMarkdownPython
Technical Skills
BashMachine LearningMachine Learning Model DeploymentModel DeploymentPerformance BenchmarkingPythonPython DevelopmentScriptingShell Scripting
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