In a competitive climate where technological advancement is paramount, the recent unveiling of a groundbreaking technology by DeepSeek, a leading AI company, has not gone unnoticedThis was particularly spotlighted during the release event of Elon Musk's Grok 3, which frequently compared its capabilities against other technologiesWhile Grok 3's launch generated significant buzz, DeepSeek has quietly made its mark by introducing innovations that could transform how long text sequences are processed in AI modelsThe release culminated with a revealing paper authored by a team led by DeepSeek's co-founder, Liang Wenfeng, which was published on arXiv, a platform renowned for hosting research papers before they are peer-reviewed.

The paper is titled “Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention," introducing a novel architecture known as NSA (Native Sparse Attention). Users familiar with DeepSeek’s previous model, DeepSeek-R1, are aware of its superior performance across multiple domains but often express concerns regarding its ability to manage extensive input contexts effectivelyThe NSA architecture aims to address precisely this limitation, focusing on resolving the key bottlenecks encountered in the processing of long texts by large models.

Traditional attention mechanisms experience dramatic computational expenses as the length of input sequences expandsIn cases where the sequence length pushes to 64K, calculations for attention can account for up to 80% of the total delay, negatively impacting overall model performanceThe engineers behind NSA have innovated not just with theoretical frameworks but with practical adjustments that promise to reshape how these models function in real-world applications.

The core of NSA's innovation is twofoldFirst, it employs a distinct hierarchical sparse attention framework

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Within this design, the incoming sequence is divided into contiguous blocks over time, which are processed through three concurrent branches of attention: Compressed Attention, Selected Attention, and Sliding AttentionCompressed Attention utilizes a learnable multi-layer perceptron (MLP) to condense each block into a single representation, effectively capturing broader global information while Selected Attention retains the most significant fine-grained token informationThe Sliding Attention branch allows the model to focus on the most recent local contextThis hierarchical structure enables NSA to maintain its expressive capabilities while drastically reducing computation complexity.

The second notable innovation is the hardware-friendly optimizations inherent in NSA's implementationThe research team developed specialized kernels based on Triton, employing a "Group-Centric Data Loading" strategy to facilitate the simultaneous loading of all query heads within a particular Grouped-Query Attention (GQA) batch into SRAMThis strategic design not only maximizes the efficiency of Tensor Cores but also eliminates unnecessary key-value data transfers through optimized loop schedulingParticularly when processing block-wise sparse attention, NSA’s continuous memory access pattern aligns with modern GPU architectures, resulting in heightened performance gains.

An exceptional aspect of NSA is its pioneering approach to enable end-to-end trainable sparse attentionUnlike contemporary methodologies where sparsification is typically introduced at the inference stage, NSA integrates sparse attention mechanisms right from the pre-training phaseThis "native" design allows the attention module to be optimized alongside other model components, paving the way for more effective sparse patterns to emergeTo ensure stable training, the researchers designed unique key and value parameters for each of the three attention branches

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Although this results in a slight increase in parameter overhead, it effectively mitigates interference among the learning processes of the various branches.

In practical application, NSA demonstrates its power by requiring significantly fewer loads to manage 64K length sequencesEach decoding step operates using an efficient combination of compressed tokens, selectively chosen tokens, and nearby tokens, ensuring minimal memory access grows at a manageable rate with increasing sequence lengthsThis is crucial in achieving significant accelerations approaching theoretical limits.

The NSA architecture has undergone rigorous performance validationThe experiments utilized a backbone comprised of 27 billion parameters—integrating GQA and Mixture-of-Experts (MoE) approaches—featuring a 30-layer network with a hidden dimension of 2560. To ensure comparability, the training processes adhered to those of a model employing full attention, with pre-training performed on text spanning 270 billion tokens at an 8K length, followed by continued training and supervised finetuning utilizing YaRN at a 32K text length.

Results from these assessments revealed that NSA surpassed expectations across an array of general capability benchmarks, including knowledge, reasoning, and programming tasksIt achieved notable performances across nine rigorous tests, excelling in seven when compared to baseline models employing full attentionParticularly remarkable was its performance on reasoning tasks, where NSA increased their scores by 4.2% on DROP and 3.4% on GSM8K, delivering evidence that the incorporation of sparse attention during pre-training significantly enhances reasoning capabilities rather than hindering them.

When specifically evaluating long-text processing abilities, NSA shone even brighterIn a rigorous “needle-in-the-haystack” test involving 64K length sequences, NSA achieved perfect retrieval accuracy across all positions

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Its average score on the LongBench evaluation set reached 0.469, a significant edge over all baseline methods, including comprehensive attention modelsNoteworthy gains were quantified in multi-hop question-and-answer tasks like HPQ and 2Wiki, where reevaluated performances surged by 8.7 and 5.1 percentage points, respectively.

Further testing in advanced mathematical problem-solving revealed NSA's enhanced reasoning capabilities, achieved through the distillation of mathematical reasoning from the DeepSeek-R1 modelSupervised retraining using trajectories extending up to 10 billion with 32K lengths led to notable advancements in benchmark performance for mathematical competitions in the USAUnder an 8K context limit, NSA exceeded baseline models by 7.5 percentage points, retaining a 5.4 percentage point advantage when scaling to a 16K context length, reinforcing NSA's capacity to maintain long-distance logical dependencies.

Computational efficiency was another highlight, with NSA recording impressive speed-ups in scenarios involving 64K sequencesThe acceleration was evidenced during decoding, forward propagation, and backpropagation, achieving remarkable ratios of 11.6x, 9.0x, and 6.0x, respectivelyImportantly, these advantages grow even more pronounced as sequence lengths increase, suggesting feasible solutions for managing considerably longer contexts in future applications.

Despite NSA’s considerable accomplishments, several areas warrant continued explorationThe learning process for sparse attention patterns could benefit from further optimization, as effective end-to-end training methods do raise questions about molding superior sparse patterns, particularly within larger-scale modelsFurthermore, while NSA's Triton implementation provides a robust reference for the industry, practical deployment will necessitate considerations for hardware compatibility and the reliability of inference services.

However, NSA demonstrates resoundingly that meticulous algorithmic design coupled with hardware-coordinated optimizations can yield significant enhancements in computational efficiency without compromising model performance

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