3 Repos
Generation of API documentation from compiled binary modules and frameworks using symbol graphs.
Distinct from Extension Module Compilers: Existing candidates focus on compilers themselves; this is about analyzing already compiled modules for documentation.
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Jazzy is a source code documentation tool and API generator designed for Swift and Objective-C. It analyzes project roots and compiled modules to produce searchable HTML websites or offline docsets. The system functions as a multi-module API documenter, aggregating documentation from separate source modules into a single site with cross-module linking. It serves as a markdown-based documentation engine that integrates technical guides and LaTeX mathematical equations to complement generated API references. The tool covers a broad capability surface including multi-language API generation for
Generates API documentation from compiled module files or frameworks using symbol graphs for faster processing.
FlashInfer is a library of high-performance GPU kernels purpose-built for accelerating large language model inference. It provides optimized implementations for attention operations (including flash attention, page attention, multi-head latent attention, and cascade attention) using paged key-value caches, fused kernel composition, and just-in-time compilation. The library also includes specialized kernels for mixture-of-experts layers, block-scaled low-precision quantization (FP8, FP4), and distributed collective communication. What distinguishes FlashInfer is its fused all-reduce communicat
Lists available compilation modules and reports their compilation status.
IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Prints the internal data of a compiled module file for debugging purposes.