ROCm/tensorflow-upstream
Dec 2025 – Jan 2026
2 Months active
Languages Used
C++
Technical Skills
C++Machine LearningTensorFlowC++ developmentNumerical ComputingSoftware Refactoring
Anuj Chincholikar
PROFILE
Anuj Chincholikar
Over a two-month period, contributed to the ROCm/tensorflow-upstream repository by enhancing TensorFlow’s MKL convolution operations for dynamic input scenarios. Addressed a crash in MKL Conv1D by implementing logic in C++ to infer batch size from gradient tensors, improving robustness when handling dynamic shapes. Delivered dynamic shape handling enhancements, including support for both int32 and int64 types, and refactored template utilities to return vectors by value for cleaner interfaces. Fixed a type compatibility bug in MklConvCustomBackpropInputOp, aligning with TensorFlow conventions. Demonstrated expertise in C++, numerical computing, and TensorFlow internals while improving runtime flexibility and code maintainability.
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
1
Lines of code
240
Activity Months2
Your Network
432 peopleSame Organization
@iitj.ac.in6
Work History
January 2026
6 Commits • 1 Features
Jan 1, 2026Month: 2026-01 | Repository: ROCm/tensorflow-upstream. Key features delivered include Dynamic Shape Handling Enhancements for TensorFlow MKL Convolution with dynamic batch-size support and int32/int64 compatibility, plus refactoring to return vectors by value and template-based improvements. Major bugs fixed include a type-compatibility bug fix in MklConvCustomBackpropInputOp (replacing int64_t with int64) to resolve compiler scope error and align with TF conventions. Overall impact: improved runtime flexibility for dynamic shapes in MKL convolutions, stronger compile reliability, and reduced maintenance burden through cleaner code and clearer interfaces. Technologies demonstrated: advanced C++ templates, vector semantics, careful type usage, and code quality discipline.
6 Commits • 1 Features
Jan 1, 2026Month: 2026-01 | Repository: ROCm/tensorflow-upstream. Key features delivered include Dynamic Shape Handling Enhancements for TensorFlow MKL Convolution with dynamic batch-size support and int32/int64 compatibility, plus refactoring to return vectors by value and template-based improvements. Major bugs fixed include a type-compatibility bug fix in MklConvCustomBackpropInputOp (replacing int64_t with int64) to resolve compiler scope error and align with TF conventions. Overall impact: improved runtime flexibility for dynamic shapes in MKL convolutions, stronger compile reliability, and reduced maintenance burden through cleaner code and clearer interfaces. Technologies demonstrated: advanced C++ templates, vector semantics, careful type usage, and code quality discipline.
January 2026
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for ROCm/tensorflow-upstream: Implemented a robustness fix for MKL Conv1D when the input batch size is dynamic (-1). The fix infers the batch size from the gradient tensor to support adaptive handling, preventing crashes and improving stability for dynamic input scenarios in TensorFlow on ROCm.
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for ROCm/tensorflow-upstream: Implemented a robustness fix for MKL Conv1D when the input batch size is dynamic (-1). The fix infers the batch size from the gradient tensor to support adaptive handling, preventing crashes and improving stability for dynamic input scenarios in TensorFlow on ROCm.
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Quality Metrics
Correctness100.0%
Maintainability94.2%
Architecture94.2%
Performance88.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
C++
Technical Skills
C++C++ developmentMachine LearningNumerical ComputingSoftware RefactoringTensorFlowTensorFlow internals
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