ROCm/rocMLIR
Nov 2024 – Mar 2025
3 Months active
Languages Used
C++TOMLCMakeCLLVM IRMLIRN/APython
Technical Skills
Compiler DevelopmentLow-Level OptimizationMLIRTestingEmbedded SystemsMachine Learning Frameworks
Daniel Hernandez-Juarez
PROFILE
Daniel Hernandez-juarez
During three months on the ROCm/rocMLIR repository, Daniel Hernandez enhanced machine learning compiler infrastructure by developing and refining features such as grouped Conv2D support, FP8 data type handling, and causal attention with KV-Cache validation. He improved test robustness and maintainability by restructuring ResNet18 and TOSA test suites, deprecating outdated partitioning passes, and introducing safer type checks. Daniel addressed quantized attention verification bugs by adjusting MLIR test configurations and RMS threshold handling, reducing misverification risk. His work, primarily in C++ and Python, demonstrated depth in compiler development, low-level optimization, and build system management, resulting in broader model support and increased reliability.
Overall Statistics
Feature vs Bugs
79%Features
Repository Contributions
21Total
Bugs
3
Commits
21
Features
11
Lines of code
7,280
Activity Months3
Your Network
22 people
Work History
March 2025
18 Commits • 9 Features
Mar 1, 2025March 2025 monthly performance summary for ROCm/rocMLIR focused on delivering core compatibility improvements, test infrastructure hardening, and feature-rich enhancements across MLIR/ROCM integration. The work emphasized business value through increased stability, broader hardware support, and improved validation and upgradability.
18 Commits • 9 Features
Mar 1, 2025March 2025 monthly performance summary for ROCm/rocMLIR focused on delivering core compatibility improvements, test infrastructure hardening, and feature-rich enhancements across MLIR/ROCM integration. The work emphasized business value through increased stability, broader hardware support, and improved validation and upgradability.
March 2025
February 2025
2 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for ROCm/rocMLIR focused on delivering core MLIR enhancements in TOSA/AMDGPU and the MHAL pipeline to broaden model coverage, strengthen type-safety, and improve maintainability. Key work includes enabling grouped Conv2D support and safer FP checks in TOSA/AMDGPU, along with bufferization API improvements and a pipeline naming update to reflect an updated control-flow role. These efforts contribute to broader model support, reduced risk in type handling, and a clearer, more maintainable MLIR pipeline for future performance work.
February 2025
2 Commits • 2 Features
Feb 1, 2025February 2025 monthly summary for ROCm/rocMLIR focused on delivering core MLIR enhancements in TOSA/AMDGPU and the MHAL pipeline to broaden model coverage, strengthen type-safety, and improve maintainability. Key work includes enabling grouped Conv2D support and safer FP checks in TOSA/AMDGPU, along with bufferization API improvements and a pipeline naming update to reflect an updated control-flow role. These efforts contribute to broader model support, reduced risk in type handling, and a clearer, more maintainable MLIR pipeline for future performance work.
November 2024
1 Commits
Nov 1, 2024November 2024: ROCm/rocMLIR focused on stabilizing quantized attention verification for i8. A verification bug in ROCm MLIR gen was fixed by adjusting test configuration and MLIR RMS_threshold handling, improving verification accuracy for i8 attention operations and overall robustness of the i8 path. This fix reduces risk of misverification in production and supports stable progress toward optimized quantized attention workloads.
1 Commits
Nov 1, 2024November 2024: ROCm/rocMLIR focused on stabilizing quantized attention verification for i8. A verification bug in ROCm MLIR gen was fixed by adjusting test configuration and MLIR RMS_threshold handling, improving verification accuracy for i8 attention operations and overall robustness of the i8 path. This fix reduces risk of misverification in production and supports stable progress toward optimized quantized attention workloads.
November 2024
Activity
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Quality Metrics
Correctness84.2%
Maintainability83.8%
Architecture78.6%
Performance70.4%
AI Usage21.8%
Skills & Technologies
Programming Languages
CC++CMakeLLVM IRMLIRN/APythonTOMLText
Technical Skills
Attention MechanismsBuild System ConfigurationBuild System ManagementBuild SystemsC++CI/CDCode CleanupCode FormattingCode RefactoringCompiler DevelopmentDebuggingDialect DevelopmentEmbedded SystemsFP8 SupportGPU Testing
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