LiteAttention: Efficient Neural Attention

• LiteAttention is a suite of efficient attention mechanisms that reduce computational overhead by exploiting sparsity, coherence, and redundancy in self-attention.
• It integrates techniques such as multi-diagonal windowed attention, temporal skip-masking, and long-short range attention to handle vision, language, and generative modeling tasks.
• These methods demonstrate significant runtime and parameter reductions (e.g., up to 85% FLOPs and 50% fewer parameters) while maintaining minimal performance loss in accuracy and fidelity.

LiteAttention encompasses a family of efficient attention mechanisms developed to reduce computation and memory requirements in neural architectures for vision, language, and generative modeling. These methods achieve significant speedups and compression, with minimal performance degradation, by exploiting redundancy in standard self-attention, leveraging temporal and spatial coherence, and integrating advanced quantization and pruning. LiteAttention refers to distinct techniques and pipelines described in the context of visual autoregressive modeling (Xie et al., 26 Nov 2024), temporal sparse attention for diffusion transformers (Shmilovich et al., 14 Nov 2025), and lightweight transformer variants for mobile NLP (Wu et al., 2020).

1. Efficient Attention Mechanisms: Core Formulations

LiteAttention strategies modify the baseline self-attention protocol to minimize quadratic scaling, targeting visual or sequential domains where resource budgets are tight.

LiteVAR’s Multi-Diagonal Windowed Attention (MDWA)

Given $N$ visual tokens $X\in\mathbb{R}^{N\times d}$, standard attention computes: $Q = XW^Q,\quad K = XW^K,\quad V = XW^V$

$$A = \mathrm{softmax}\!\left(\frac{QK^\top}{\sqrt{d}}\right),\qquad Y = AV$$

MDWA partitions tokens into $K$ scales, and replaces the global attention map with a per-head, block-diagonal masking: $$A_{\rm MDWA} = \mathrm{softmax}\!\left(\frac{QK^\top}{\sqrt{d}}\right)\odot M^{(k,h)}, \qquad Y = A_{\rm MDWA}V$$ where $M^{(k,h)}$ is a learned binary mask restricting nonzero entries to small diagonal windows, tuned so $R_w^{(k,j)}(w_j)\geq R_0$ (default $R_0=0.95$).

Temporal Sparse Attention in Diffusion Transformers

For video diffusion models, LiteAttention partitions query/key/value tensors into tiles, and maintains a monotonic, persistent skip-mask ($\text{SkipMask}$) across denoising steps:

• For each tile-pair $(i,j)$: Skip computation if $(i,j)\in \text{SkipMask}$.
• The skip criterion is: $\max(m_\text{local} - m_{ij}) \leq -\epsilon$, where $m_{ij}$ is the rowmax for tile $(i,j)$.
• $\text{SkipMask}$ grows over time steps, yielding sub-quadratic complexity while maintaining quality (Shmilovich et al., 14 Nov 2025).

Lite Transformer with Long-Short Range Attention (LSRA)

LSRA splits input channels $X\in\mathbb{R}^{N\times d}$:

• $X^l$ receives multi-head self-attention (long-range).
• $X^s$ receives dynamic convolution (short-range).
• Results are concatenated and passed through normalization and FFN (Wu et al., 2020).

2. Pruning, Quantization, and Compression Techniques

LiteAttention integrates advanced compression methods for deployment on memory-constrained devices.

Min-max, Per-tensor PTQ with Mixed Precision

Weights and activations are quantized per-tensor: $$x_{\min},\ x_{\max} \rightarrow s = \frac{x_{\max}-x_{\min}}{2^B-1},\ z = \mathrm{round}\left(-\frac{x_{\min}}{s}\right)$$

$$x_q = \mathrm{clamp}\left(\mathrm{round}(x/s)+z,\,0,\,2^B-1\right),\quad x_{dq}=s(x_q-z)$$

Feed-forward bottleneck layers exhibiting quantization sensitivity (e.g., "ffn.fc2") are maintained at FP16, with others at 4–8 bits. This mixed setup preserves generative quality (FID) (Xie et al., 26 Nov 2024).

Pruning and Distillation

AttentionLite combines knowledge distillation and pruning in a single objective for efficient student model training: $$\mathcal{L} = \mathcal{L}_{CE}(y,y^S) + \lambda\,\mathcal{L}_{KD} + \mu\,\mathcal{R}_{prune}(W,\Pi)$$ Irregular and structured pruning yield hardware-friendly sparsity (Kundu et al., 2020).

3. Temporal and Spatial Coherence Principles

A foundational principle in LiteAttention is the exploitation of redundancy via coherence:

• Spatial Coherence: In visual autoregressive models, attention focuses along diagonals; small windows suffice for capturing context, enabling block-diagonal sparsity (Xie et al., 26 Nov 2024).
• Temporal Coherence: In video diffusion, sparsity patterns are highly consistent across denoising steps. Once marked as non-essential, a tile-pair remains skipped for all following steps, minimizing redundant computation (Shmilovich et al., 14 Nov 2025).

A plausible implication is that these strategies generalize to other modalities exhibiting autocorrelated attention patterns across spatial or temporal axes.

4. Efficiency Benchmarks and Empirical Results

LiteAttention architectures demonstrate substantial reductions in compute and memory with negligible loss in task performance.

Method	FID ↓ / BLEU ↑	FLOPs ↓	Params ↓	Accuracy Drop
MDWA + ASC + PTQ+MP (LiteVAR)	FID +0.06 (vs 13.39)	–85.2%	–50%	≤ 0.06 FID
LiteAttention (Video Diffusion)	None on VBench	–47% runtime	–	None
AttentionLite (CIFAR/Tiny-Img)	≤ 1 pp	up to 2×	up to 30×	≤ 1 pp
LSRA (NLP tasks)	+1.5 BLEU	up to 2.5×	up to 2.5×	≤ 0.3 BLEU

These metrics reflect direct performance comparisons from the cited papers (Xie et al., 26 Nov 2024, Shmilovich et al., 14 Nov 2025, Kundu et al., 2020, Wu et al., 2020).

5. Deployment and Implementation Practices

LiteAttention pipelines prioritize training-free or minimal-retraining solutions.

• Windowed masks and PTQ scales are learned from small calibration datasets; full retraining is not required (Xie et al., 26 Nov 2024).
• Temporal skip-masks propagate in production kernels (e.g., FlashAttention3) with run-length encoding and warpgroup reductions for maximal throughput on GPUs (Shmilovich et al., 14 Nov 2025).
• Compression is supported in inference engines such as ONNX/TensorRT and specialized frameworks (QServe) (Xie et al., 26 Nov 2024).
• The hybrid and homogeneous SA-ResNet variants can be efficiently mapped to hardware for vision tasks (Kundu et al., 2020).

6. Comparative Insights and Domain Extensions

LiteAttention methods outperform baselines and automated architecture search:

• LSRA outperforms Evolved Transformer (AutoML) in BLEU on WMT translation under mobile constraints, without requiring expensive search (Wu et al., 2020).
• Dynamic and static attention sparsity methods suffer from either overhead or accuracy loss; LiteAttention’s exploitation of coherence yields strictly better quality-speedup tradeoffs in video diffusion (Shmilovich et al., 14 Nov 2025).
• Structured pruning and quantization offer hardware-friendly deployment, though at some tradeoff in maximal sparsity.

A plausible implication is that hybrid convolution-attention designs and monotonic mask propagation may inform next-generation attention accelerators for cross-domain applications.

7. Limitations and Prospective Directions

Current evaluations focus on image classification, translation, summarization, and video generation. Extending LiteAttention to segmentation, detection, or dense prediction tasks is unresolved (Kundu et al., 2020). Further benchmarking on custom attention-accelerator hardware would quantify real-world gains. Adaptive head splits, multi-scale windowing, and dynamic mask mechanisms represent promising directions (Wu et al., 2020, Shmilovich et al., 14 Nov 2025).

In summary, LiteAttention constitutes a suite of methodologically distinct but thematically unified techniques that systematically compress attention computation by leveraging inherent redundancy, coherence, and quantization. These approaches yield state-of-the-art tradeoffs in memory, runtime, and generative fidelity across vision, video, and language domains.