quic/aimet
Dec 2024 – Mar 2025
2 Months active
Languages Used
C++Python
Technical Skills
DebuggingDeep Learning FrameworksKerasModel OptimizationModel PreparationMulti-output Tensors
Sayanta Mukherjee
PROFILE
Sayanta Mukherjee
Sayanta worked on the quic/aimet repository, focusing on improving the reliability and deployment readiness of deep learning model optimization workflows. Over two months, Sayanta enhanced BatchNorm folding to support complex architectures with submodules like RNNs and GRUs, addressing edge cases involving KerasTensors and multi-output layers. Using Python, TensorFlow, and Keras, Sayanta removed batch-size dependencies and redundant type casts in model preparation, increasing interoperability and reducing failure modes. Additionally, Sayanta stabilized quantization workflows by refining quantizer grouping logic and adding targeted test coverage for ConvTranspose models, demonstrating a strong attention to detail and depth in debugging and unit testing practices.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
6Total
Bugs
2
Commits
6
Features
1
Lines of code
549
Activity Months2
Your Network
200 people
Work History
March 2025
1 Commits
Mar 1, 2025March 2025: Focused on stabilizing the quantization workflow in quic/aimet. Implemented a fix for quantizer grouping by ignoring the Transpose operation, refined parent-child grouping logic, and enhanced activation/parameter quantizer handling to ensure accurate quantization simulation. Added ConvTranspose model test coverage to validate changes and guard against regressions. These updates improve deployment reliability and reduce quantization drift for ConvTranspose paths in production models.
1 Commits
Mar 1, 2025March 2025: Focused on stabilizing the quantization workflow in quic/aimet. Implemented a fix for quantizer grouping by ignoring the Transpose operation, refined parent-child grouping logic, and enhanced activation/parameter quantizer handling to ensure accurate quantization simulation. Added ConvTranspose model test coverage to validate changes and guard against regressions. These updates improve deployment reliability and reduce quantization drift for ConvTranspose paths in production models.
March 2025
December 2024
5 Commits • 1 Features
Dec 1, 2024Monthly summary for December 2024 (quic/aimet): Focused on increasing reliability and deployment readiness of the Aimet pipeline. Addressed critical edge cases in BatchNorm folding for models with submodules (e.g., RNN/GRU) and KerasTensors in kwargs; added robust tests to prevent regressions. Improved model preparation: removed batch-size dependency in per-layer output handling, eliminated unnecessary casts in Keras model preparation, and extended support for multiple output tensors per layer. These changes reduce failure modes in production and improve interoperability with complex architectures.
December 2024
5 Commits • 1 Features
Dec 1, 2024Monthly summary for December 2024 (quic/aimet): Focused on increasing reliability and deployment readiness of the Aimet pipeline. Addressed critical edge cases in BatchNorm folding for models with submodules (e.g., RNN/GRU) and KerasTensors in kwargs; added robust tests to prevent regressions. Improved model preparation: removed batch-size dependency in per-layer output handling, eliminated unnecessary casts in Keras model preparation, and extended support for multiple output tensors per layer. These changes reduce failure modes in production and improve interoperability with complex architectures.
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Quality Metrics
Correctness91.6%
Maintainability80.0%
Architecture80.0%
Performance76.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++Python
Technical Skills
DebuggingDeep Learning FrameworksKerasModel OptimizationModel PreparationMulti-output TensorsTensor ManipulationTensorFlowUnit Testingdeep learningmodel optimizationonnxquantization
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