tenstorrent/tt-mlir
Jul 2025 – Mar 2026
9 Months active
Languages Used
C++TclLLVM IRTableGenMLIRPythonYAML
Technical Skills
API DesignC++Compiler DevelopmentHardware AccelerationMLIRMachine Learning Operations
Chris Karavasilis
PROFILE
Chris Karavasilis
Christos Karavasilis contributed to the tenstorrent/tt-mlir repository by developing and refining core compiler infrastructure for machine learning workloads. Over nine months, he engineered features such as unified constraint APIs, advanced typecasting, and runtime analysis for tensor operations, focusing on correctness and performance. His work included implementing FPU-based execution paths, L1 scratchpad memory management, and robust data type conversion across F32, BF16, and BFP8. Using C++, MLIR, and Python, Christos addressed low-level optimization, CI/CD integration, and test reliability. His engineering demonstrated depth in backend development, enabling safer model conversions, improved validation, and efficient deployment for heterogeneous hardware targets.
Overall Statistics
Feature vs Bugs
68%Features
Repository Contributions
33Total
Bugs
6
Commits
33
Features
13
Lines of code
11,332
Activity Months9
Your Network
438 peopleWork History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly summary for tenstorrent/tt-mlir: delivered a critical bug fix for numeric typecasting and expanded CI coverage across architectures, reinforcing reliability ahead of releases. Key achievements include a low-level fix for f32→bf16 typecasting by unpacking to the destination and re-enabling tests affected by the new rounding algorithm, plus a CI enhancement introducing a multi-architecture matrix and a minimal Blackhole test for TTSim to improve coverage across wormhole/blackhole architectures. Impact: improved numeric correctness, reduced flaky failures, and stronger confidence in cross-arch behavior; Skills demonstrated: low-level numeric typecasting, UnpackToDest handling, and CI/test instrumentation across architectures.
2 Commits • 1 Features
Mar 1, 2026March 2026 (2026-03) monthly summary for tenstorrent/tt-mlir: delivered a critical bug fix for numeric typecasting and expanded CI coverage across architectures, reinforcing reliability ahead of releases. Key achievements include a low-level fix for f32→bf16 typecasting by unpacking to the destination and re-enabling tests affected by the new rounding algorithm, plus a CI enhancement introducing a multi-architecture matrix and a minimal Blackhole test for TTSim to improve coverage across wormhole/blackhole architectures. Impact: improved numeric correctness, reduced flaky failures, and stronger confidence in cross-arch behavior; Skills demonstrated: low-level numeric typecasting, UnpackToDest handling, and CI/test instrumentation across architectures.
March 2026
February 2026
3 Commits • 2 Features
Feb 1, 2026February 2026: Delivered core platform enhancements for tt-mlir focused on performance readiness, testing robustness, and PR validation velocity. Key outcomes include an L1 scratchpad for fused kernels with new scratch memory ops and management passes to handle scratch inputs in generic operations, enabling cleaner handling of intermediate results for multi-binary FPU fusion. Strengthened testing across data types by fixating bf16 dtype handling in unary operation tests and adding bf16 coverage. Enhanced CI validation by enabling tt-sim workflows and golden tests in PRs for wormhole_b0, with artifact management to streamline PR validation. Collectively, these efforts improve memory efficiency, correctness across data types, and developer velocity for iterative improvements.
February 2026
3 Commits • 2 Features
Feb 1, 2026February 2026: Delivered core platform enhancements for tt-mlir focused on performance readiness, testing robustness, and PR validation velocity. Key outcomes include an L1 scratchpad for fused kernels with new scratch memory ops and management passes to handle scratch inputs in generic operations, enabling cleaner handling of intermediate results for multi-binary FPU fusion. Strengthened testing across data types by fixating bf16 dtype handling in unary operation tests and adding bf16 coverage. Enhanced CI validation by enabling tt-sim workflows and golden tests in PRs for wormhole_b0, with artifact management to streamline PR validation. Collectively, these efforts improve memory efficiency, correctness across data types, and developer velocity for iterative improvements.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary focused on performance improvements and foundational backend enhancements in the MLIR backend for tt-mlir.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary focused on performance improvements and foundational backend enhancements in the MLIR backend for tt-mlir.
January 2026
December 2025
5 Commits • 2 Features
Dec 1, 2025Monthly work summary for 2025-12 focusing on business value and technical achievements.
December 2025
5 Commits • 2 Features
Dec 1, 2025Monthly work summary for 2025-12 focusing on business value and technical achievements.
November 2025
6 Commits • 2 Features
Nov 1, 2025November 2025 delivered focused improvements in data type handling, runtime planning, and test robustness for TT-MLIR, accelerating realistic deployment and efficient inference.
6 Commits • 2 Features
Nov 1, 2025November 2025 delivered focused improvements in data type handling, runtime planning, and test robustness for TT-MLIR, accelerating realistic deployment and efficient inference.
November 2025
October 2025
2 Commits • 1 Features
Oct 1, 2025Month 2025-10: Delivered key correctness and reliability improvements for D2M to TTKernel conversion within tt-mlir, plus SFPU initialization enhancements and related lowering/bitwidth fixes. Implemented dst_reinterpret_cast to reconcile type mismatches when storing/loading destination registers, and extended lowering to TTKernel to accommodate reinterpret casts. Added ttkernel.init_sfpu for typecast configurations to fix CB formats for unpacker/packer, improving typecast accuracy. Fixed bitwidth handling in D2MToTTMetal by switching from getIntOrFloatBitWidth() to getNumberOfBits(), enabling fp32DestAccum and better support for bf8. These changes are complemented by a TypecastRewriter adjustment to respect original TileType in inputs/outputs. Commit highlights: 10cb22ddfecab8deb8c99176fc9cfcfc2465df77; dced9e23c410bb66a8dfb3db23fdde0a4f339da0.
October 2025
2 Commits • 1 Features
Oct 1, 2025Month 2025-10: Delivered key correctness and reliability improvements for D2M to TTKernel conversion within tt-mlir, plus SFPU initialization enhancements and related lowering/bitwidth fixes. Implemented dst_reinterpret_cast to reconcile type mismatches when storing/loading destination registers, and extended lowering to TTKernel to accommodate reinterpret casts. Added ttkernel.init_sfpu for typecast configurations to fix CB formats for unpacker/packer, improving typecast accuracy. Fixed bitwidth handling in D2MToTTMetal by switching from getIntOrFloatBitWidth() to getNumberOfBits(), enabling fp32DestAccum and better support for bf8. These changes are complemented by a TypecastRewriter adjustment to respect original TileType in inputs/outputs. Commit highlights: 10cb22ddfecab8deb8c99176fc9cfcfc2465df77; dced9e23c410bb66a8dfb3db23fdde0a4f339da0.
September 2025
6 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) delivered core architectural improvements in TTNN and TTIR/TTMetal paths, paired with stabilizing test reliability. Key features introduce a unified constraint API across core TTNN dialect ops for analysis, validation, and safe handling of unsupported runtime paths, including placeholder constraints for Scatter and interfaces for ProdOp, ArgMaxOp, Quantize/Dequantize/Requantize, and Atan2/Remainder, with op definitions refactored and tests updated. TTIR->TTMetal gained fidelity controls to balance performance and precision via configurable compute fidelity levels (LoFi, HiFi2, HiFi3, HiFi4). A bug fix removed unnecessary device resets in OpModel tests, reducing hangs in getOpRuntime/getOpConstraints and simplifying test setup. These efforts improve safety, reliability, and enable performance-precision tradeoffs for production workloads.
6 Commits • 2 Features
Sep 1, 2025September 2025 (2025-09) delivered core architectural improvements in TTNN and TTIR/TTMetal paths, paired with stabilizing test reliability. Key features introduce a unified constraint API across core TTNN dialect ops for analysis, validation, and safe handling of unsupported runtime paths, including placeholder constraints for Scatter and interfaces for ProdOp, ArgMaxOp, Quantize/Dequantize/Requantize, and Atan2/Remainder, with op definitions refactored and tests updated. TTIR->TTMetal gained fidelity controls to balance performance and precision via configurable compute fidelity levels (LoFi, HiFi2, HiFi3, HiFi4). A bug fix removed unnecessary device resets in OpModel tests, reducing hangs in getOpRuntime/getOpConstraints and simplifying test setup. These efforts improve safety, reliability, and enable performance-precision tradeoffs for production workloads.
September 2025
August 2025
5 Commits • 1 Features
Aug 1, 2025In August 2025, reinforced TTNN operation modeling in the tt-mlir project, delivering constraints-driven runtime analysis across unary, binary, and tensor ops to enable earlier validation, safer optimization, and more predictable performance of TTNN tensor operations. Implementations span constraints for unary eltwise ops, Cbrt and BitwiseNot, binary composite ops, and tensor manipulation ops, underpinned by a new constraints API and integration with runtime estimations. Added Min/Max reduction constraints with a runtime workaround to address a hanging issue in min's getOpRuntime, improving reliability of the analysis pipeline. These changes establish a solid foundation for cost-aware optimizations and robust validation of TTNN workloads, with direct business value in faster iteration, reduced risk in deployment, and improved tensor operation performance.
August 2025
5 Commits • 1 Features
Aug 1, 2025In August 2025, reinforced TTNN operation modeling in the tt-mlir project, delivering constraints-driven runtime analysis across unary, binary, and tensor ops to enable earlier validation, safer optimization, and more predictable performance of TTNN tensor operations. Implementations span constraints for unary eltwise ops, Cbrt and BitwiseNot, binary composite ops, and tensor manipulation ops, underpinned by a new constraints API and integration with runtime estimations. Added Min/Max reduction constraints with a runtime workaround to address a hanging issue in min's getOpRuntime, improving reliability of the analysis pipeline. These changes establish a solid foundation for cost-aware optimizations and robust validation of TTNN workloads, with direct business value in faster iteration, reduced risk in deployment, and improved tensor operation performance.
July 2025
3 Commits • 1 Features
Jul 1, 2025July 2025 monthly performance summary focusing on business value and technical achievements for tenstorrent/tt-mlir. No major bugs fixed this month.
3 Commits • 1 Features
Jul 1, 2025July 2025 monthly performance summary focusing on business value and technical achievements for tenstorrent/tt-mlir. No major bugs fixed this month.
July 2025
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Quality Metrics
Correctness87.2%
Maintainability85.4%
Architecture87.0%
Performance78.2%
AI Usage26.0%
Skills & Technologies
Programming Languages
C++LLVM IRMLIRPythonTableGenTclYAML
Technical Skills
API DesignAPI DevelopmentC++C++ DevelopmentC++ developmentC++ programmingCI/CDCompiler DevelopmentData TypesData type managementDebuggingDialect ConversionDialect DevelopmentGitHub ActionsHardware Acceleration
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