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Awesome GitHub RepositoriesGrouped GEMM

Executing multiple matrix multiplications in a single kernel call by grouping axes to optimize expert layers.

Distinct from Batch Matrix Multiplication Utilities: Focuses on axis grouping for expert layers rather than simple batching of matrices.

Explore 2 awesome GitHub repositories matching data & databases · Grouped GEMM. Refine with filters or upvote what's useful.

Awesome Grouped GEMM GitHub Repositories

AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
  • deepseek-ai/deepgemmdeepseek-ai का अवतार

    deepseek-ai/DeepGEMM

    7,385GitHub पर देखें↗

    DeepGEMM is a suite of specialized GPU kernels and a just-in-time compiler designed for low-precision matrix operations, Mixture-of-Experts models, and attention processing. It provides a library of high-performance matrix multiplication kernels using FP8 precision to increase compute throughput and reduce memory usage. The project features a JIT CUDA kernel compiler that generates and loads optimized compute kernels at runtime to eliminate the need for manual compilation during installation. It includes specialized implementations for grouped matrix multiplication that process multiple group

    Provides optimized grouped matrix multiplication that processes multiple grouped axes in a single kernel call.

    Cuda
    GitHub पर देखें↗7,385
  • flashinfer-ai/flashinferflashinfer-ai का अवतार

    flashinfer-ai/flashinfer

    4,996GitHub पर देखें↗

    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

    Delivers grouped matrix multiplication kernels optimized for MoE expert partitions in LLMs.

    Pythonattentioncudadistributed-inference
    GitHub पर देखें↗4,996
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सब-टैग एक्सप्लोर करें

  • MXFP8 Grouped VariantsGrouped matrix multiplication using MXFP8 block-scaled format with zero-padding optimization. **Distinct from Grouped GEMM:** Distinct from general Grouped GEMM: uses block-scaled MXFP8 data type with zero-padding optimization.
  • Mixed-Precision VariantsGrouped matrix multiplication supporting FP8, FP4, and BF16 mixed-precision within single kernel calls. **Distinct from Grouped GEMM:** Distinct from general Grouped GEMM: supports multiple low-precision formats (FP8, FP4, BF16) within grouped kernel launches.
  • MoE MXFP8 Grouped VariantsGrouped matrix multiplication for mixture-of-experts layers using MXFP8 block-scaled quantization. **Distinct from Grouped GEMM:** Distinct from general Grouped GEMM: specializes in MoE expert layers with MXFP8 block-scaling.
  • MoE SpecializationsGrouped matrix multiplication kernels specifically designed for mixture-of-experts layers with expert partition grouping. **Distinct from Grouped GEMM:** Distinct from general Grouped GEMM: specifically designed for mixture-of-experts layers with expert partition grouping.