tenstorrent/tt-mlir
Dec 2025 – Dec 2025
1 Month active
Languages Used
C++MLIR
Technical Skills
C++ ProgrammingMLIR DevelopmentTensor Operations
Andjela Bogdanovic
PROFILE
Andjela Bogdanovic
Developed scatter operation reduction support in the tenstorrent/tt-mlir repository, enabling reduction semantics such as SUM, PROD, MIN, and MAX within scatter operations. This work introduced the scatter_reduce_type attribute to relevant MLIR dialects, allowing for flexible reduction behaviors and paving the way for future TT-Metal integration. Addressed compatibility for bf16 and int32 input tensors to ensure consistent behavior across data types, and implemented comprehensive tests to maintain regression safety. Leveraged C++ programming, MLIR development, and tensor operations expertise to enhance model throughput, numerical correctness, and cross-repository integration readiness in mixed-precision machine learning workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,501
Activity Months1
Your Network
452 peopleWork History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Scatter operation reduction support in the tt-mlir MLIR path, enabling future TT-Metal reduction integration. Introduced a new attribute scatter_reduce_type on ttir.scatter and ttir.scatter_in_dim to support reductions (SUM, PROD, MIN, MAX). Implemented compatibility workarounds for bf16 and int32 input tensors to preserve behavior across data types. This work directly addresses TT-MLIR issue #5091 and aligns with the TT-Metal reduction effort (#31909). Added tests to cover the changes and ensure regression safety. Impact: unlocks reduction semantics in scatter operations, improves model throughput and numerical correctness in mixed-precision scenarios, and strengthens cross-repo integration readiness. Technologies/skills demonstrated: MLIR dialect extension, data-type compatibility handling (bf16/int32), feature flagging via attributes, collaborative cross-repo work, and test-driven development.
1 Commits • 1 Features
Dec 1, 2025December 2025: Delivered Scatter operation reduction support in the tt-mlir MLIR path, enabling future TT-Metal reduction integration. Introduced a new attribute scatter_reduce_type on ttir.scatter and ttir.scatter_in_dim to support reductions (SUM, PROD, MIN, MAX). Implemented compatibility workarounds for bf16 and int32 input tensors to preserve behavior across data types. This work directly addresses TT-MLIR issue #5091 and aligns with the TT-Metal reduction effort (#31909). Added tests to cover the changes and ensure regression safety. Impact: unlocks reduction semantics in scatter operations, improves model throughput and numerical correctness in mixed-precision scenarios, and strengthens cross-repo integration readiness. Technologies/skills demonstrated: MLIR dialect extension, data-type compatibility handling (bf16/int32), feature flagging via attributes, collaborative cross-repo work, and test-driven development.
December 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++ ProgrammingMLIR DevelopmentTensor Operations
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