Xilinx/onnx-mlir
Feb 2025 – Apr 2025
2 Months active
Languages Used
C++MLIR
Technical Skills
Compiler DevelopmentGraph TransformationMLIRONNX RuntimeC++ DevelopmentONNX
Abhishek Bisht
PROFILE
Abhishek Bisht
Abhishek Bisht contributed to the Xilinx/onnx-mlir repository by developing two core features over a two-month period, focusing on compiler development and graph transformation using C++ and MLIR. He implemented RMS Layer Normalization recomposition by detecting Pow operations with exponent -0.5, enabling more flexible and optimized graph transformations within ONNX models. Additionally, he delivered shape inference support for the ONNX STFT operation, allowing accurate propagation of output tensor dimensions based on input signals, frame lengths, and window sizes. His work improved static analysis and optimization opportunities in the MLIR-based toolchain, demonstrating depth in shape inference and dialect integration.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
422
Activity Months2
Your Network
52 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focusing on key accomplishments and business value. The major feature delivered this month is the ONNX STFT shape inference support integrated into the Xilinx/onnx-mlir project. This work enables accurate propagation of output tensor dimensions for the STFT operation based on input signals, frame lengths, and window sizes, reducing runtime errors and improving optimization opportunities across the MLIR-based toolchain. The STFT feature was added to the ONNX dialect's build and the shape inference logic was implemented in C++, aligning with existing dialect infrastructure and build processes.
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary focusing on key accomplishments and business value. The major feature delivered this month is the ONNX STFT shape inference support integrated into the Xilinx/onnx-mlir project. This work enables accurate propagation of output tensor dimensions for the STFT operation based on input signals, frame lengths, and window sizes, reducing runtime errors and improving optimization opportunities across the MLIR-based toolchain. The STFT feature was added to the ONNX dialect's build and the shape inference logic was implemented in C++, aligning with existing dialect infrastructure and build processes.
April 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for Xilinx/onnx-mlir: Delivered a feature to recomposition RMS Layer Normalization by recognizing a pattern containing Pow with exponent -0.5 to express reciprocal square root, enabling flexible and potentially optimized graph transformations. This work lays groundwork for streamlined RMSNormalisation handling and future performance optimizations across models using layer normalization.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for Xilinx/onnx-mlir: Delivered a feature to recomposition RMS Layer Normalization by recognizing a pattern containing Pow with exponent -0.5 to express reciprocal square root, enabling flexible and potentially optimized graph transformations. This work lays groundwork for streamlined RMSNormalisation handling and future performance optimizations across models using layer normalization.
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture95.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
C++MLIR
Technical Skills
C++ DevelopmentCompiler DevelopmentGraph TransformationMLIRONNXONNX RuntimeShape Inference
Repositories Contributed To
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