ROCm/tensorflow-upstream
Apr 2025 – May 2025
2 Months active
Languages Used
C++MLIR
Technical Skills
Build SystemCompiler DevelopmentMLIRQuantizationRefactoringTensorFlow
Laura Pak
PROFILE
Laura Pak
During two months on the ROCm/tensorflow-upstream repository, Lpak focused on unifying and optimizing quantization across TensorFlow and TensorFlow Lite using C++ and MLIR. Lpak developed an MLIR-based pass to standardize 8-bit FakeQuant representations, improving consistency and optimization potential between frameworks. The work included consolidating quantization utilities, migrating dialects for long-term maintainability, and decoupling TensorFlow Lite dependencies to streamline builds. Lpak also introduced a convolution fusion optimization pass and enhanced test tooling with litert-opt for flexible MLIR optimizations. The contributions demonstrated depth in compiler development, build system management, and quantization, addressing cross-project consistency and runtime performance challenges.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
7Total
Bugs
0
Commits
7
Features
4
Lines of code
1,560
Activity Months2
Your Network
4562 people
Work History
May 2025
4 Commits • 3 Features
May 1, 2025May 2025 monthly summary for ROCm/tensorflow-upstream focused on build simplification, runtime-optimization readiness, and expanded test tooling for MLIR/TensorFlow quantization. The work reduces maintenance burden, accelerates build cycles, and strengthens test coverage for optimization passes.
4 Commits • 3 Features
May 1, 2025May 2025 monthly summary for ROCm/tensorflow-upstream focused on build simplification, runtime-optimization readiness, and expanded test tooling for MLIR/TensorFlow quantization. The work reduces maintenance burden, accelerates build cycles, and strengthens test coverage for optimization passes.
May 2025
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly work summary for ROCm/tensorflow-upstream focused on delivering cross-project quantization improvements and ensuring consistent optimization opportunities across TensorFlow and TensorFlow Lite. The month's work centered on a key feature: quantization unification and optimization across TF and TFLite, driven by an MLIR-based pass that unifies 8-bit FakeQuant representations to enable consistent quantization behavior and better optimization potential.
April 2025
3 Commits • 1 Features
Apr 1, 2025April 2025 monthly work summary for ROCm/tensorflow-upstream focused on delivering cross-project quantization improvements and ensuring consistent optimization opportunities across TensorFlow and TensorFlow Lite. The month's work centered on a key feature: quantization unification and optimization across TF and TFLite, driven by an MLIR-based pass that unifies 8-bit FakeQuant representations to enable consistent quantization behavior and better optimization potential.
Activity
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Quality Metrics
Correctness92.8%
Maintainability88.6%
Architecture92.8%
Performance78.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
Build SystemBuild System ManagementBuild SystemsC++Compiler DevelopmentMLIROptimizationQuantizationRefactoringTensorFlowTensorFlow Lite
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