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 recognizing Pow operations with exponent -0.5, enabling more flexible and optimized graph transformations within the ONNX-MLIR backend. Additionally, Abhishek delivered shape inference support for the ONNX STFT operation, integrating it into the ONNX dialect and ensuring accurate propagation of output tensor dimensions. His work addressed static shape propagation and error detection, demonstrating depth in both ONNX Runtime integration and shape inference logic.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
422
Activity Months2
Your Network
61 peopleShared Repositories
61
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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