tenstorrent/tt-mlir
Mar 2025 – Apr 2026
11 Months active
Languages Used
C++MLIRPythonYAMLC
Technical Skills
Backend DevelopmentCompiler DevelopmentDistributed SystemsGraph OptimizationIR OptimizationLow-Level Optimization
Hong Kwon
PROFILE
Hong Kwon
Hwan Kwon contributed to the tenstorrent/tt-mlir repository by engineering distributed tensor operations and scalable compiler infrastructure for multi-device machine learning workloads. He developed and integrated features such as collective communication primitives, sharded tensor support, and spatial operation lowering, focusing on correctness, performance, and test coverage. Using C++, Python, and MLIR, Hwan refactored backend pipelines, enhanced runtime stability, and aligned dialects with evolving backend APIs. His work addressed memory safety, buffer lifetime management, and topology flexibility, resulting in robust model compilation and reliable distributed execution. The depth of his contributions reflects strong expertise in compiler design and distributed systems engineering.
Overall Statistics
Feature vs Bugs
59%Features
Repository Contributions
48Total
Bugs
13
Commits
48
Features
19
Lines of code
20,986
Activity Months11
Your Network
438 peopleWork History
April 2026
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-mlir: Focused on correctness and stability improvements in the d2m lowering path. Delivered two core capabilities to protect memory safety and tensor integrity during layout transformations, and strengthened test coverage to prevent regressions. These changes reduce risk of scratch buffer lifetime/address collisions and preserve Virtual Grid Mapping (VGM) across spatial transforms, delivering tangible business value in reliable model compilation and optimization.
2 Commits • 1 Features
Apr 1, 2026April 2026 monthly summary for tenstorrent/tt-mlir: Focused on correctness and stability improvements in the d2m lowering path. Delivered two core capabilities to protect memory safety and tensor integrity during layout transformations, and strengthened test coverage to prevent regressions. These changes reduce risk of scratch buffer lifetime/address collisions and preserve Virtual Grid Mapping (VGM) across spatial transforms, delivering tangible business value in reliable model compilation and optimization.
April 2026
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary focusing on key accomplishments for the tenstorrent/tt-mlir repo. Delivered a production-ready D2M Spatial Operation that significantly enhances spatial workloads by aggregating results across non-overlapping grid ranges and enabling efficient execution on device cores. The feature integrates with the existing grid selection and lowering passes to ttnn.generic, enabling improved performance for spatial workloads and better scalability across devices.
March 2026
1 Commits • 1 Features
Mar 1, 2026March 2026 monthly summary focusing on key accomplishments for the tenstorrent/tt-mlir repo. Delivered a production-ready D2M Spatial Operation that significantly enhances spatial workloads by aggregating results across non-overlapping grid ranges and enabling efficient execution on device cores. The feature integrates with the existing grid selection and lowering passes to ttnn.generic, enabling improved performance for spatial workloads and better scalability across devices.
December 2025
3 Commits • 1 Features
Dec 1, 2025December 2025 TT-MLIR/monthly focus on stabilizing TTNN and aligning the TTNN dialect with backend APIs to enable scalable distributed tensor workflows on TT hardware. Delivered core TTNN distribution/aggregation ops, hardened stability for collective operations, and removed legacy mesh_shard glue, resulting in more reliable builds, fewer runtime failures, and faster iteration for ML workloads.
3 Commits • 1 Features
Dec 1, 2025December 2025 TT-MLIR/monthly focus on stabilizing TTNN and aligning the TTNN dialect with backend APIs to enable scalable distributed tensor workflows on TT hardware. Delivered core TTNN distribution/aggregation ops, hardened stability for collective operations, and removed legacy mesh_shard glue, resulting in more reliable builds, fewer runtime failures, and faster iteration for ML workloads.
December 2025
November 2025
5 Commits • 3 Features
Nov 1, 2025November 2025: Delivered core TT-MLIR enhancements and TTNN API alignments to enable robust tensor copy and in-place duplication, improved distributed runtime accuracy, and scalable topology support. Key outcomes include a new Assign operation, direct TTNN CCL-based P2P runtime, and topology API relocation that collectively reduce overhead and improve performance for same-device and multi-device workloads.
November 2025
5 Commits • 3 Features
Nov 1, 2025November 2025: Delivered core TT-MLIR enhancements and TTNN API alignments to enable robust tensor copy and in-place duplication, improved distributed runtime accuracy, and scalable topology support. Key outcomes include a new Assign operation, direct TTNN CCL-based P2P runtime, and topology API relocation that collectively reduce overhead and improve performance for same-device and multi-device workloads.
October 2025
3 Commits • 1 Features
Oct 1, 2025Month: 2025-10. This period-focused update highlights the delivery of improved test infrastructure and stability enhancements in the tenstorrent/tt-mlir repository, with positive implications for validation efficiency and release readiness.
3 Commits • 1 Features
Oct 1, 2025Month: 2025-10. This period-focused update highlights the delivery of improved test infrastructure and stability enhancements in the tenstorrent/tt-mlir repository, with positive implications for validation efficiency and release readiness.
October 2025
September 2025
11 Commits • 3 Features
Sep 1, 2025September 2025: Delivered substantial business-value improvements across TT-MLIR by hardening distributed ML operations, expanding data type support, and strengthening developer tooling and test infrastructure. These efforts enable more scalable, reliable model training on tensor-parallel hardware, faster iteration cycles, and broader testing coverage for critical workloads like Llama attention.
September 2025
11 Commits • 3 Features
Sep 1, 2025September 2025: Delivered substantial business-value improvements across TT-MLIR by hardening distributed ML operations, expanding data type support, and strengthening developer tooling and test infrastructure. These efforts enable more scalable, reliable model training on tensor-parallel hardware, faster iteration cycles, and broader testing coverage for critical workloads like Llama attention.
August 2025
4 Commits • 2 Features
Aug 1, 2025August 2025 — Delivered multi-device TTIR capabilities and strengthened distributed ops reliability in tenstorrent/tt-mlir. Key business value includes enabling scalable replica-group execution, reducing manual maintenance, and accelerating experimentation for multi-device models. Features include CollectiveBroadcast integration with tests and conversions, ShardedTensor support in the TTIR builder with updated golden-tensor generation, and consolidation of ReduceScatter/AllReduce handling to eliminate duplication and improve consistency across dimension < 4 tensors.
4 Commits • 2 Features
Aug 1, 2025August 2025 — Delivered multi-device TTIR capabilities and strengthened distributed ops reliability in tenstorrent/tt-mlir. Key business value includes enabling scalable replica-group execution, reducing manual maintenance, and accelerating experimentation for multi-device models. Features include CollectiveBroadcast integration with tests and conversions, ShardedTensor support in the TTIR builder with updated golden-tensor generation, and consolidation of ReduceScatter/AllReduce handling to eliminate duplication and improve consistency across dimension < 4 tensors.
August 2025
July 2025
3 Commits • 2 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on delivering scalable ML IR features in tenstorrent/tt-mlir. Highlights include host-side fallback for TTNN PointToPoint to enable multidevice tensor reorganization testing when native TTNN support is incomplete, and AllToAll CCL operation support in TTIR/TTNN dialects with PointToPoint workarounds to mitigate TTNN limitations. Introduced TT Fabric configurability (--fabric-config) to ttrt to enable/disable TT-fabric initialization, enabling runtime selection between legacy and new CCL ops based on configuration. These changes provide business value by improving testing coverage, reliability across multi-device setups, and smoother migration paths for CCL operation implementations. Key commits: 1d9c2a11856f7d3b2c8e6487ac4fbefe1559c3d4; 077720405776cd100e8203574ad1796de20aba69; e9a1194e79b8518a25ad32d92457bc2d68360fe6.
July 2025
3 Commits • 2 Features
Jul 1, 2025Monthly summary for 2025-07 focusing on delivering scalable ML IR features in tenstorrent/tt-mlir. Highlights include host-side fallback for TTNN PointToPoint to enable multidevice tensor reorganization testing when native TTNN support is incomplete, and AllToAll CCL operation support in TTIR/TTNN dialects with PointToPoint workarounds to mitigate TTNN limitations. Introduced TT Fabric configurability (--fabric-config) to ttrt to enable/disable TT-fabric initialization, enabling runtime selection between legacy and new CCL ops based on configuration. These changes provide business value by improving testing coverage, reliability across multi-device setups, and smoother migration paths for CCL operation implementations. Key commits: 1d9c2a11856f7d3b2c8e6487ac4fbefe1559c3d4; 077720405776cd100e8203574ad1796de20aba69; e9a1194e79b8518a25ad32d92457bc2d68360fe6.
May 2025
5 Commits • 1 Features
May 1, 2025May 2025: Stabilized the tt-mlir stack and strengthened test reliability for multi-device and Llama N300 pipelines. Key features delivered: restored and expanded test coverage for multi-device and tt-mlir/Llama N300, including tests for N300, llmbox, mixed-device graphs, and manual sharding; CI/test workflows cleaned up. Major bugs fixed: fixed a NameError by explicitly passing a logger instance into get_atol_rtol_pcc, preventing runtime failures during numerical checks. Overall impact: higher stability, faster feedback, and safer deployments across complex configurations. Technologies demonstrated: Python logging usage, test automation, CI workflow optimization, multi-device testing, Llama N300 edge-case scenarios, and manual sharding.
5 Commits • 1 Features
May 1, 2025May 2025: Stabilized the tt-mlir stack and strengthened test reliability for multi-device and Llama N300 pipelines. Key features delivered: restored and expanded test coverage for multi-device and tt-mlir/Llama N300, including tests for N300, llmbox, mixed-device graphs, and manual sharding; CI/test workflows cleaned up. Major bugs fixed: fixed a NameError by explicitly passing a logger instance into get_atol_rtol_pcc, preventing runtime failures during numerical checks. Overall impact: higher stability, faster feedback, and safer deployments across complex configurations. Technologies demonstrated: Python logging usage, test automation, CI workflow optimization, multi-device testing, Llama N300 edge-case scenarios, and manual sharding.
May 2025
April 2025
6 Commits • 3 Features
Apr 1, 2025In April 2025, tt-mlir focused on expanding testing coverage, stabilizing multi-device execution, and hardening builds for CCL-enabled workloads. Notable work includes graph-level golden tests for TTIRBuilder with CCL support, refactoring for logical device identification in collective permute, and reliability improvements in TTRT evaluation and CI. Key achievements delivered this month: - Graph-level golden tests for TTIRBuilder with CCL Support: Added input/output tensor-based golden comparisons and CCL operation support in TTIRBuilder. (Store graph level golden tensor on flatbuffer; TTIRBuilder: Add CCL Op support in TTIRBuilder) [commits: 7e0c6cbf798529578001df2a98073597d1632293; 9d640fcbc2442aefc33bb41a0f106f0f3f7f159a]. - Logical device IDs for collective permute: Refactored to use logical device IDs with vectors for improved mapping and efficiency. [commit: 1cd3fd63c281111469ed5e6294eec42f0dc02ef9]. - CI: Golden output verification and multidevice golden tests: Added golden test type and broader multidevice coverage to catch regressions early. - Isolate mesh device per evaluation in TTRT: Modified evaluation to open/close the mesh device for each binary evaluation to prevent state leakage. [commit: c34d42043a41fb7a3acfadaa7fe61197852b16ea]. - AllReduce workaround to prevent compilation failures: Introduced an alternate AllGather + local Reduce path when scatter dimension divisibility or memory constraints could cause failures. [commit: e55f2f392e90fa47d034428c6a6ddc1e7e50e2cf]. - Fix program-level golden comparison: Load all golden outputs and compare them individually against runtime outputs, removing references to removed variables. [commit: 136136163533140bc22ddae35779a995398d0911]. Overall impact: These changes improve validation coverage and reliability for multi-device and CCL-enabled workloads, reduce build-time and runtime fragility, and accelerate regression detection via CI. Business value includes fewer false negatives in golden testing, safer deployment of multi-device configurations, and clearer tracing of changes through commit-level provenance. Technologies/skills demonstrated: C++, TTIR/TTensor, flatbuffers integration, device context management, multi-device testing, CI/test automation, and performance-oriented refactoring.
April 2025
6 Commits • 3 Features
Apr 1, 2025In April 2025, tt-mlir focused on expanding testing coverage, stabilizing multi-device execution, and hardening builds for CCL-enabled workloads. Notable work includes graph-level golden tests for TTIRBuilder with CCL support, refactoring for logical device identification in collective permute, and reliability improvements in TTRT evaluation and CI. Key achievements delivered this month: - Graph-level golden tests for TTIRBuilder with CCL Support: Added input/output tensor-based golden comparisons and CCL operation support in TTIRBuilder. (Store graph level golden tensor on flatbuffer; TTIRBuilder: Add CCL Op support in TTIRBuilder) [commits: 7e0c6cbf798529578001df2a98073597d1632293; 9d640fcbc2442aefc33bb41a0f106f0f3f7f159a]. - Logical device IDs for collective permute: Refactored to use logical device IDs with vectors for improved mapping and efficiency. [commit: 1cd3fd63c281111469ed5e6294eec42f0dc02ef9]. - CI: Golden output verification and multidevice golden tests: Added golden test type and broader multidevice coverage to catch regressions early. - Isolate mesh device per evaluation in TTRT: Modified evaluation to open/close the mesh device for each binary evaluation to prevent state leakage. [commit: c34d42043a41fb7a3acfadaa7fe61197852b16ea]. - AllReduce workaround to prevent compilation failures: Introduced an alternate AllGather + local Reduce path when scatter dimension divisibility or memory constraints could cause failures. [commit: e55f2f392e90fa47d034428c6a6ddc1e7e50e2cf]. - Fix program-level golden comparison: Load all golden outputs and compare them individually against runtime outputs, removing references to removed variables. [commit: 136136163533140bc22ddae35779a995398d0911]. Overall impact: These changes improve validation coverage and reliability for multi-device and CCL-enabled workloads, reduce build-time and runtime fragility, and accelerate regression detection via CI. Business value includes fewer false negatives in golden testing, safer deployment of multi-device configurations, and clearer tracing of changes through commit-level provenance. Technologies/skills demonstrated: C++, TTIR/TTensor, flatbuffers integration, device context management, multi-device testing, CI/test automation, and performance-oriented refactoring.
March 2025
5 Commits • 1 Features
Mar 1, 2025February 2025-03 monthly performance summary for tenstorrent/tt-mlir: Delivered multi-device CCL support in TTNN with mesh_shape-aware compilation, garbage-free backend pipeline integration, and folding optimizations for Collectives; implemented extensive all_gather tests across configurations and data types. Removed meaningless CCL operations from TTNN graph on single-mesh devices and added warnings to ensure optimized graphs are emitted. Overall improvements enhance scalability for multi-device deployments, stability of single-mesh optimizations, and test coverage across data types and configurations.
5 Commits • 1 Features
Mar 1, 2025February 2025-03 monthly performance summary for tenstorrent/tt-mlir: Delivered multi-device CCL support in TTNN with mesh_shape-aware compilation, garbage-free backend pipeline integration, and folding optimizations for Collectives; implemented extensive all_gather tests across configurations and data types. Removed meaningless CCL operations from TTNN graph on single-mesh devices and added warnings to ensure optimized graphs are emitted. Overall improvements enhance scalability for multi-device deployments, stability of single-mesh optimizations, and test coverage across data types and configurations.
March 2025
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Quality Metrics
Correctness91.4%
Maintainability83.8%
Architecture87.4%
Performance77.8%
AI Usage23.8%
Skills & Technologies
Programming Languages
CC++MLIRPythonYAML
Technical Skills
Attribute ManagementBackend DevelopmentBuild SystemsBuilderBuilder PatternC++C++ DevelopmentC++ developmentC++ programmingCCL OperationsCI/CDCode MaintenanceCode RefactoringCode ReversionCompiler Design
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