tenstorrent/tt-mlir
Jun 2025 – Dec 2025
6 Months active
Languages Used
C++MLIRPython
Technical Skills
Code ConversionCompiler DevelopmentIntermediate Representation (IR) ManipulationTensor OperationsGolden Master TestingOperator Implementation
Mountagha
PROFILE
Mountagha
Muntaha Ghaba contributed to the tenstorrent/tt-mlir repository by developing and enhancing core compiler features for tensor operations and machine learning workloads. Over six months, Muntaha implemented new operator support, such as TOSA-to-TTIR lowering and StableHLO dot_general, and introduced robust CPU fallback paths for operations like DotGeneralOp and ReduceOr. Using C++, MLIR, and Python, Muntaha focused on modular code conversion, low-level optimization, and comprehensive testing with Pytest and golden master validation. The work improved model portability, correctness, and reliability across backends, demonstrating depth in compiler development, intermediate representation manipulation, and end-to-end validation for cross-platform machine learning pipelines.
Overall Statistics
Feature vs Bugs
83%Features
Repository Contributions
6Total
Bugs
1
Commits
6
Features
5
Lines of code
968
Activity Months6
Work History
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for tenstorrent/tt-mlir: Strengthened the ReduceOr op path with CPU fallback and a dedicated decomposition pattern, aligning semantics with the original ReduceOr behavior, validating through tests, and stabilizing type handling. This work enhances portability, correctness, and reliability of tensor reduction on CPU targets while keeping TTIR semantics consistent with higher-level operations.
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for tenstorrent/tt-mlir: Strengthened the ReduceOr op path with CPU fallback and a dedicated decomposition pattern, aligning semantics with the original ReduceOr behavior, validating through tests, and stabilizing type handling. This work enhances portability, correctness, and reliability of tensor reduction on CPU targets while keeping TTIR semantics consistent with higher-level operations.
December 2025
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11 — Delivered StableHLO dot_general operation support in tenstorrent/tt-mlir, with added tests and golden-result validation; implemented in stablehlo_builder; co-authored by Julia Grim; linked to ticket #4865 and PR #5336; results in improved reliability and performance for models requiring advanced dot products.
November 2025
1 Commits • 1 Features
Nov 1, 2025Month: 2025-11 — Delivered StableHLO dot_general operation support in tenstorrent/tt-mlir, with added tests and golden-result validation; implemented in stablehlo_builder; co-authored by Julia Grim; linked to ticket #4865 and PR #5336; results in improved reliability and performance for models requiring advanced dot products.
October 2025
1 Commits • 1 Features
Oct 1, 2025Month 2025-10: Delivered a robust CPU fallback path for DotGeneralOp in tenstorrent/tt-mlir by decomposing the operation into permute, reshape, and matmul. The decomposition is performed before hoisting, and the generated matmul operations are ensured to be DPS compliant, improving correctness and portability on CPU backends. Added automated tests to validate hoisted matmul behavior and coverage for the new path. This work reduces risk when accelerators are unavailable and lays groundwork for consistent performance and determinism across backends.
1 Commits • 1 Features
Oct 1, 2025Month 2025-10: Delivered a robust CPU fallback path for DotGeneralOp in tenstorrent/tt-mlir by decomposing the operation into permute, reshape, and matmul. The decomposition is performed before hoisting, and the generated matmul operations are ensured to be DPS compliant, improving correctness and portability on CPU backends. Added automated tests to validate hoisted matmul behavior and coverage for the new path. This work reduces risk when accelerators are unavailable and lays groundwork for consistent performance and determinism across backends.
October 2025
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Key enhancements to the Gather operation in tt-mlir focusing on robustness, correctness, and business impact. Delivered a set of changes that improve index handling and output shape computation, and fixed a critical bug (#4757).
September 2025
1 Commits • 1 Features
Sep 1, 2025Month: 2025-09 — Key enhancements to the Gather operation in tt-mlir focusing on robustness, correctness, and business impact. Delivered a set of changes that improve index handling and output shape computation, and fixed a critical bug (#4757).
July 2025
1 Commits
Jul 1, 2025July 2025 performance summary focusing on numerical correctness, test coverage, and maintainability for the tt-mlir repository. Delivered a targeted bug fix with expanded validation, improving reliability for model training workloads and downstream PyTorch integrations.
1 Commits
Jul 1, 2025July 2025 performance summary focusing on numerical correctness, test coverage, and maintainability for the tt-mlir repository. Delivered a targeted bug fix with expanded validation, improving reliability for model training workloads and downstream PyTorch integrations.
July 2025
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for tenstorrent/tt-mlir: Expanded TOSA-to-TTIR lowering to cover negate, multiply, and shifted multiply operations. Implemented a dedicated shifted-multiply pattern that disallows non-zero shifts, refactored conversion logic for modularity, and updated tests to validate the new paths. These changes strengthen the correctness and maintainability of the TOSA TTIR lowering pipeline, enabling broader model support and faster iteration for downstream AMIR/TTIR consumers. Business value: broader model portability, reduced manual workaround, and faster deployment cycles through robust IR lowering.
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for tenstorrent/tt-mlir: Expanded TOSA-to-TTIR lowering to cover negate, multiply, and shifted multiply operations. Implemented a dedicated shifted-multiply pattern that disallows non-zero shifts, refactored conversion logic for modularity, and updated tests to validate the new paths. These changes strengthen the correctness and maintainability of the TOSA TTIR lowering pipeline, enabling broader model support and faster iteration for downstream AMIR/TTIR consumers. Business value: broader model portability, reduced manual workaround, and faster deployment cycles through robust IR lowering.
Activity
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Quality Metrics
Correctness88.4%
Maintainability80.0%
Architecture83.4%
Performance76.6%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++MLIRPython
Technical Skills
C++C++ DevelopmentCPU Fallback ImplementationCode ConversionCompiler DevelopmentGolden Master TestingIntermediate Representation (IR) ManipulationLow-Level OptimizationMLIRMachine LearningOperator ImplementationPytestPythonPython TestingTensor Operations
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