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2 dépôts

Awesome GitHub RepositoriesAdaptive Context Parallelism

Dynamic adjustment of context parallelism sizing to optimize throughput for variable length sequences.

Distinct from Attention Parallelism Optimizers: Specifically addresses adaptive sizing for variable sequences, distinct from general attention-layer data parallelism.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Adaptive Context Parallelism. Refine with filters or upvote what's useful.

Awesome Adaptive Context Parallelism GitHub Repositories

Trouvez les meilleurs dépôts grâce à l'IA.Nous recherchons les dépôts les plus pertinents grâce à l'IA.
  • nvidia/megatron-lmAvatar de NVIDIA

    NVIDIA/Megatron-LM

    16,731Voir sur GitHub↗

    Megatron-LM is a distributed transformer training library and large language model training framework designed to scale models across thousands of GPUs. It functions as a GPU-optimized deep learning toolkit and a scaling engine for mixture-of-experts architectures, enabling the training of models with hundreds of billions of parameters. The project implements multi-dimensional model parallelism, combining tensor, pipeline, data, expert, and context-based workload distribution. It specifically optimizes mixture-of-experts architectures through integrated memory and communication improvements t

    Increases training throughput for variable length sequences by adaptively sizing the context parallelism.

    Python
    Voir sur GitHub↗16,731
  • flashinfer-ai/flashinferAvatar de flashinfer-ai

    flashinfer-ai/flashinfer

    4,996Voir sur GitHub↗

    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

    Computes workspace memory requirements for context-parallel decode attention.

    Pythonattentioncudadistributed-inference
    Voir sur GitHub↗4,996
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Explorer les sous-tags

  • Workspace Size ComputationGPU kernel library for computing workspace memory per rank required for context-parallel decode attention. **Distinct from Adaptive Context Parallelism:** Distinct from Adaptive Context Parallelism: focuses on workspace size calculation, not dynamic parallelism adjustment.