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
During two months on the tenstorrent/tt-mlir repository, Amalkov developed and stabilized constant folding optimizations for tensor operations in TTIR using C++ and MLIR. He implemented compile-time folding for operators such as negation, reshape, broadcast, and permute, reducing runtime computation and improving inference latency for constant-heavy workloads. Amalkov addressed stability regressions by carefully managing rollbacks and coordinating with upstream changes, ensuring robust integration across the stack. His work included comprehensive test automation, adjustments for backend compatibility, and enhancements to tensor materialization paths, demonstrating depth in compiler design, optimization techniques, and tensor manipulation while maintaining reliability and performance throughout the pipeline.
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
438 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.
Activity
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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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