Accelerated Scan is a focused, high-performance library for solving first-order linear recurrences (associative scans) on GPU. It provides a pure CUDA kernel (`warp`) that uses warp shuffle intrinsics, shared memory, and a hierarchical chunked algorithm to achieve state-of-the-art speed, and a Triton-based implementation (`scalar`) that leverages `tl.associative_scan` for portability. A reference PyTorch implementation (`ref`) implements the classic Blelloch tree scan for validation. The library is designed for integration into state space models and linear RNNs, where the recurrence `x[t] = gate[t] * x[t-1] + token[t]` is central. The codebase is small but exceptionally well-crafted, with clear separation of concerns, comprehensive tests, and a clean API. The CUDA kernel in particular is a standout example of GPU optimization, using warp-level primitives, shared memory, and chunking to handle sequences of arbitrary length (up to 65536) with minimal overhead.
This repository provides a PyTorch implementation of Vision Mamba, a visual representation learning model based on bidirectional state space models (SSM). The core code in vision_mamba/model.py defines two classes: VisionEncoderMambaBlock, which applies a bidirectional SSM with convolutional processing, and Vim, which assembles a full vision classifier using patch embedding, a stack of encoder blocks, and an output head. The implementation relies heavily on the external `zetascale` library for the SSM primitive and `einops` for tensor rearrangements. Several issues indicate a work-in-progress state: the forward pass contains active print statements, a class token is defined but never used in the forward path, the convolutional layers use a kernel size of 1 (which may be incorrect for sequence modeling), and there are no unit tests or validation data. The abundant GitHub Actions workflows suggest an intent for continuous integration, but they reference non-existent test directories, so they would not pass. Overall, the repository is a functional but unpolished research prototype suitable for experimentation, but not production use or reuse as a library.