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
Overall Statistics
Feature vs Bugs
33%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
1
Lines of code
240
Activity Months2
Your Network
412 people
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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