tenstorrent/tt-mlir
Mar 2026 – Apr 2026
2 Months active
Languages Used
C++MLIR
Technical Skills
C++ DevelopmentCompiler DesignMLIROptimization TechniquesTensor OperationsTensor Manipulation
Aleksa Malkov
PROFILE
Aleksa Malkov
Worked on the tenstorrent/tt-mlir repository to implement and stabilize constant folding optimizations for tensor operations in C++ and MLIR. Developed a canonical constant folding path for tensor negation and related operators, propagating optimizations to ttir.full, ttir.ones, and ttir.zeros, and expanded support for tensor manipulation ops such as reshape, broadcast, and permute. Addressed stability regressions by coordinating rollbacks and re-enabling features after upstream fixes, ensuring robust test coverage and compatibility. Focused on compiler design and optimization techniques, the work improved inference latency and efficiency in constant-heavy workloads while maintaining reliability across the stack through careful change management.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
1,556
Activity Months2
Your Network
452 peopleWork History
April 2026
1 Commits • 1 Features
Apr 1, 2026April 2026 performance highlights: Implemented and stabilized Tensor Constant Folding for TTIR, enabling compile-time folding of common tensor manipulation operators to reduce runtime work and improve inference latency in constant-heavy workloads. The work encompassed folding for ttir.reshape, ttir.broadcast, ttir.repeat, ttir.repeat_interleave, ttir.permute, and ttir.slice_static, with careful adjustments to maintain compatibility and prevent regressions across the stack. The changes improved overall efficiency of tensor pipelines and set the foundation for further fold-based optimizations.
1 Commits • 1 Features
Apr 1, 2026April 2026 performance highlights: Implemented and stabilized Tensor Constant Folding for TTIR, enabling compile-time folding of common tensor manipulation operators to reduce runtime work and improve inference latency in constant-heavy workloads. The work encompassed folding for ttir.reshape, ttir.broadcast, ttir.repeat, ttir.repeat_interleave, ttir.permute, and ttir.slice_static, with careful adjustments to maintain compatibility and prevent regressions across the stack. The changes improved overall efficiency of tensor pipelines and set the foundation for further fold-based optimizations.
April 2026
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly summary for tenstorrent/tt-mlir focusing on constant-folding optimization work on TT IR and stability. Delivered a canonical constant folding path for ttir.neg with propagation to related ops (ttir.full, ttir.ones, ttir.zeros), broadened support via materializeConstant, and added comprehensive tests to validate correctness and performance benefits. Faced a stability regression when combining the new folding with tiled types, leading to a rollback to restore stability and ensure coverage. Subsequently re-enabled the folding following upstream fixes in cross-repo tt-xla (PR #3768), with tests updated accordingly. Major changes and outcomes were coordinated with the TT-MLIR and TT-XLA teams to maintain compatibility and performance gains across the stack.
March 2026
3 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly summary for tenstorrent/tt-mlir focusing on constant-folding optimization work on TT IR and stability. Delivered a canonical constant folding path for ttir.neg with propagation to related ops (ttir.full, ttir.ones, ttir.zeros), broadened support via materializeConstant, and added comprehensive tests to validate correctness and performance benefits. Faced a stability regression when combining the new folding with tiled types, leading to a rollback to restore stability and ensure coverage. Subsequently re-enabled the folding following upstream fixes in cross-repo tt-xla (PR #3768), with tests updated accordingly. Major changes and outcomes were coordinated with the TT-MLIR and TT-XLA teams to maintain compatibility and performance gains across the stack.
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance85.0%
AI Usage45.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++ DevelopmentCompiler DesignMLIROptimization TechniquesTensor ManipulationTensor Operations
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