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Yanan Cao (PyTorch)

PROFILE

Yanan Cao (pytorch)

Worked on enhancing model export reliability and quantization workflows in PyTorch and related repositories, focusing on both robustness and test coverage. In ROCm/FBGEMM, introduced strict validation to FP8 quantization export tests using torch.export.export, which improved the reliability of quantization exports and reduced downstream debugging. Within pytorch/benchmark, enabled strict validation for model exports in the Dynamo benchmarking framework, supporting more trustworthy performance evaluations. Additionally, contributed to graphcore/pytorch-fork by adding TorchScript compatibility for torch._check, allowing flexible argument handling and clearer error reporting. Leveraged C++, Python, and PyTorch, with an emphasis on quantization, benchmarking, and unit testing.

Overall Statistics

Feature vs Bugs

100%Features

Repository Contributions

3Total
Bugs
0
Commits
3
Features
3
Lines of code
274
Activity Months2

Work History

August 2025

1 Commits • 1 Features

Aug 1, 2025

Summary for 2025-08: Delivered TorchScript compatibility enhancement for torch._check in graphcore/pytorch-fork, enabling flexible argument handling and improved error reporting in TorchScript; this unlocks more robust script execution and smoother integration with TorchScript workflows. No major bug fixes were recorded this month. Overall impact: increased stability and portability of TorchScript models, reduced runtime script errors, and clearer diagnostics for developers. Technologies/skills demonstrated: TorchScript compatibility work, Python scripting, code review and commit discipline, and collaboration on a PyTorch fork.

December 2024

2 Commits • 2 Features

Dec 1, 2024

December 2024 monthly summary focused on strengthening the reliability of model export and quantization export paths across ROCm/FBGEMM and PyTorch Dynamo benchmarking. Delivered targeted hardening of FP8 export tests and introduced strict validation for model exports, improving robustness, correctness, and test coverage. These efforts reduce downstream debugging, enable more confident performance evaluations, and support broader deployment of FP8 quantization.

Activity

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Quality Metrics

Correctness86.6%
Maintainability86.6%
Architecture73.4%
Performance66.6%
AI Usage33.4%

Skills & Technologies

Programming Languages

C++Python

Technical Skills

C++ DevelopmentMachine LearningPerformance BenchmarkingPyTorchPython DevelopmentQuantizationTestingTorchScriptUnit Testing

Repositories Contributed To

3 repos

Overview of all repositories you've contributed to across your timeline

ROCm/FBGEMM

Dec 2024 Dec 2024
1 Month active

Languages Used

Python

Technical Skills

PyTorchQuantizationTesting

pytorch/benchmark

Dec 2024 Dec 2024
1 Month active

Languages Used

Python

Technical Skills

Machine LearningPerformance Benchmarking

graphcore/pytorch-fork

Aug 2025 Aug 2025
1 Month active

Languages Used

C++Python

Technical Skills

C++ DevelopmentPython DevelopmentTorchScriptUnit Testing