Efficient Memory Management for Large Language Model Serving with PagedAttention
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• 2309.06180 • Published
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Attention
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LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models
Paper • 2308.16137 • Published • 41 -
Scaling Transformer to 1M tokens and beyond with RMT
Paper • 2304.11062 • Published • 3 -
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
Paper • 2309.14509 • Published • 20 -
SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills
Paper • 2308.16369 • Published • 1 -
LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Paper • 2309.12307 • Published • 90 -
PoSE: Efficient Context Window Extension of LLMs via Positional Skip-wise Training
Paper • 2309.10400 • Published • 26 -
Efficient Streaming Language Models with Attention Sinks
Paper • 2309.17453 • Published • 14 -
Replacing softmax with ReLU in Vision Transformers
Paper • 2309.08586 • Published • 19 -
Adapting Language Models to Compress Contexts
Paper • 2305.14788 • Published • 1 -
In-context Autoencoder for Context Compression in a Large Language Model
Paper • 2307.06945 • Published • 29 -
Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture
Paper • 2310.12109 • Published • 1 -
Linformer: Self-Attention with Linear Complexity
Paper • 2006.04768 • Published • 2 -
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Paper • 2305.13245 • Published • 6 -
BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
Paper • 2309.11568 • Published • 11 -
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression
Paper • 2304.13276 • Published • 1 -
S^{3}: Increasing GPU Utilization during Generative Inference for Higher Throughput
Paper • 2306.06000 • Published • 1 -
Self-slimmed Vision Transformer
Paper • 2111.12624 • Published • 1 -
Robustifying Token Attention for Vision Transformers
Paper • 2303.11126 • Published • 1 -
Combiner: Full Attention Transformer with Sparse Computation Cost
Paper • 2107.05768 • Published • 1 -
A Unified View of Long-Sequence Models towards Modeling Million-Scale Dependencies
Paper • 2302.06218 • Published • 1 -
Attention Bottlenecks for Multimodal Fusion
Paper • 2107.00135 • Published • 1 -
Blockwise Self-Attention for Long Document Understanding
Paper • 1911.02972 • Published • 1 -
LSG Attention: Extrapolation of pretrained Transformers to long sequences
Paper • 2210.15497 • Published • 1 -
Cure the headache of Transformers via Collinear Constrained Attention
Paper • 2309.08646 • Published • 14 -
VSA: Learning Varied-Size Window Attention in Vision Transformers
Paper • 2204.08446 • Published • 1 -
Bird-Eye Transformers for Text Generation Models
Paper • 2210.03985 • Published • 1 -
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers
Paper • 2303.13755 • Published • 1 -
TRAMS: Training-free Memory Selection for Long-range Language Modeling
Paper • 2310.15494 • Published • 2 -
Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head Attention
Paper • 2310.07911 • Published • 1 -
Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture
Paper • 2310.03052 • Published • 3 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 15 -
Only 5\% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation
Paper • 2309.14174 • Published • 1 -
Attention Is Not All You Need Anymore
Paper • 2308.07661 • Published • 1 -
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Paper • 2103.03404 • Published • 1 -
Semantics-aware Attention Improves Neural Machine Translation
Paper • 2110.06920 • Published • 1 -
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
Paper • 2211.11315 • Published • 1 -
Attention Is All You Need
Paper • 1706.03762 • Published • 115 -
Ultra-Long Sequence Distributed Transformer
Paper • 2311.02382 • Published • 6 -
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Paper • 2311.02262 • Published • 14 -
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency
Paper • 2311.02772 • Published • 8 -
MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers
Paper • 2012.15828 • Published • 1 -
MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Paper • 2002.10957 • Published • 2 -
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention
Paper • 2304.02147 • Published • 1 -
GateLoop: Fully Data-Controlled Linear Recurrence for Sequence Modeling
Paper • 2311.01927 • Published • 1 -
Improving Transformers with Probabilistic Attention Keys
Paper • 2110.08678 • Published • 1 -
Wide Attention Is The Way Forward For Transformers?
Paper • 2210.00640 • Published • 1 -
A Practical Survey on Faster and Lighter Transformers
Paper • 2103.14636 • Published • 1 -
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing
Paper • 2306.12929 • Published • 13 -
Scaling TransNormer to 175 Billion Parameters
Paper • 2307.14995 • Published • 23 -
ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
Paper • 2306.06446 • Published • 1 -
Are Sixteen Heads Really Better than One?
Paper • 1905.10650 • Published • 2 -
FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores
Paper • 2311.05908 • Published • 14 -
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
Paper • 2311.05884 • Published • 9 -
Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation
Paper • 2211.12292 • Published • 1 -
Latency Adjustable Transformer Encoder for Language Understanding
Paper • 2201.03327 • Published • 1 -
AxFormer: Accuracy-driven Approximation of Transformers for Faster, Smaller and more Accurate NLP Models
Paper • 2010.03688 • Published • 1 -
Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers
Paper • 2305.17328 • Published • 2 -
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
Paper • 2112.05364 • Published • 1 -
Alleviating the Inequality of Attention Heads for Neural Machine Translation
Paper • 2009.09672 • Published • 1 -
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure
Paper • 2210.04633 • Published • 1 -
Are We Falling in a Middle-Intelligence Trap? An Analysis and Mitigation of the Reversal Curse
Paper • 2311.07468 • Published • 1 -
Shifting Attention to Relevance: Towards the Uncertainty Estimation of Large Language Models
Paper • 2307.01379 • Published • 1 -
The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles
Paper • 2306.01705 • Published • 1 -
Relaxed Attention for Transformer Models
Paper • 2209.09735 • Published • 1 -
System 2 Attention (is something you might need too)
Paper • 2311.11829 • Published • 43 -
Rethinking Attention: Exploring Shallow Feed-Forward Neural Networks as an Alternative to Attention Layers in Transformers
Paper • 2311.10642 • Published • 25 -
Superiority of Softmax: Unveiling the Performance Edge Over Linear Attention
Paper • 2310.11685 • Published • 1 -
Attention Sorting Combats Recency Bias In Long Context Language Models
Paper • 2310.01427 • Published • 1 -
Gated recurrent neural networks discover attention
Paper • 2309.01775 • Published • 10 -
Your Transformer May Not be as Powerful as You Expect
Paper • 2205.13401 • Published • 1 -
Adaptive Sparse and Monotonic Attention for Transformer-based Automatic Speech Recognition
Paper • 2209.15176 • Published • 1 -
Low Rank Factorization for Compact Multi-Head Self-Attention
Paper • 1912.00835 • Published • 1 -
Linear Self-Attention Approximation via Trainable Feedforward Kernel
Paper • 2211.04076 • Published • 1 -
Low-Rank Bottleneck in Multi-head Attention Models
Paper • 2002.07028 • Published • 1 -
EfficientFormer: Vision Transformers at MobileNet Speed
Paper • 2206.01191 • Published • 1 -
Transformer in Transformer
Paper • 2103.00112 • Published • 1 -
COMCAT: Towards Efficient Compression and Customization of Attention-Based Vision Models
Paper • 2305.17235 • Published • 2 -
CoLT5: Faster Long-Range Transformers with Conditional Computation
Paper • 2303.09752 • Published • 2 -
Fourier Transformer: Fast Long Range Modeling by Removing Sequence Redundancy with FFT Operator
Paper • 2305.15099 • Published • 1 -
SparQ Attention: Bandwidth-Efficient LLM Inference
Paper • 2312.04985 • Published • 40 -
SwitchHead: Accelerating Transformers with Mixture-of-Experts Attention
Paper • 2312.07987 • Published • 41 -
Efficient Monotonic Multihead Attention
Paper • 2312.04515 • Published • 8 -
SCCA: Shifted Cross Chunk Attention for long contextual semantic expansion
Paper • 2312.07305 • Published • 1 -
Zebra: Extending Context Window with Layerwise Grouped Local-Global Attention
Paper • 2312.08618 • Published • 13 -
Is Model Attention Aligned with Human Attention? An Empirical Study on Large Language Models for Code Generation
Paper • 2306.01220 • Published • 1 -
Mixture of Attention Heads: Selecting Attention Heads Per Token
Paper • 2210.05144 • Published • 2 -
LKCA: Large Kernel Convolutional Attention
Paper • 2401.05738 • Published • 1 -
HyperAttention: Long-context Attention in Near-Linear Time
Paper • 2310.05869 • Published • 2 -
Rethinking Attention with Performers
Paper • 2009.14794 • Published • 1 -
Attention Lens: A Tool for Mechanistically Interpreting the Attention Head Information Retrieval Mechanism
Paper • 2310.16270 • Published • 1 -
Softmax-free Linear Transformers
Paper • 2207.03341 • Published • 1 -
Gated Linear Attention Transformers with Hardware-Efficient Training
Paper • 2312.06635 • Published • 9 -
Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Models
Paper • 2112.00029 • Published • 1 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 32 -
A Quantitative Review on Language Model Efficiency Research
Paper • 2306.01768 • Published • 2 -
Agent Attention: On the Integration of Softmax and Linear Attention
Paper • 2312.08874 • Published • 2 -
FLatten Transformer: Vision Transformer using Focused Linear Attention
Paper • 2308.00442 • Published • 1 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 81 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 56 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 20 -
Linear Transformers are Versatile In-Context Learners
Paper • 2402.14180 • Published • 7 -
Attention Approximates Sparse Distributed Memory
Paper • 2111.05498 • Published -
Multi-Scale Self-Attention for Text Classification
Paper • 1912.00544 • Published -
Scattered Mixture-of-Experts Implementation
Paper • 2403.08245 • Published • 1 -
Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost
Paper • 2312.08614 • Published • 1 -
JetMoE: Reaching Llama2 Performance with 0.1M Dollars
Paper • 2404.07413 • Published • 38 -
SLAB: Efficient Transformers with Simplified Linear Attention and Progressive Re-parameterized Batch Normalization
Paper • 2405.11582 • Published • 17 -
Yuan 2.0-M32: Mixture of Experts with Attention Router
Paper • 2405.17976 • Published • 21 -
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Paper • 2402.10685 • Published • 1 -
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Paper • 2406.07522 • Published • 40 -
SinkLoRA: Enhanced Efficiency and Chat Capabilities for Long-Context Large Language Models
Paper • 2406.05678 • Published • 1 -
Various Lengths, Constant Speed: Efficient Language Modeling with Lightning Attention
Paper • 2405.17381 • Published -
The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry
Paper • 2402.04347 • Published • 15 -
GoldFinch: High Performance RWKV/Transformer Hybrid with Linear Pre-Fill and Extreme KV-Cache Compression
Paper • 2407.12077 • Published • 57 -
SampleAttention: Near-Lossless Acceleration of Long Context LLM Inference with Adaptive Structured Sparse Attention
Paper • 2406.15486 • Published -
RazorAttention: Efficient KV Cache Compression Through Retrieval Heads
Paper • 2407.15891 • Published -
Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters
Paper • 2408.04093 • Published • 4 -
Theory, Analysis, and Best Practices for Sigmoid Self-Attention
Paper • 2409.04431 • Published • 2 -
Weighted Grouped Query Attention in Transformers
Paper • 2407.10855 • Published -
On the Benefits of Rank in Attention Layers
Paper • 2407.16153 • Published -
Beyond KV Caching: Shared Attention for Efficient LLMs
Paper • 2407.12866 • Published • 1 -
Beyond Uniform Query Distribution: Key-Driven Grouped Query Attention
Paper • 2408.08454 • Published -
Efficient LLM Training and Serving with Heterogeneous Context Sharding among Attention Heads
Paper • 2407.17678 • Published -
Post-Training Sparse Attention with Double Sparsity
Paper • 2408.07092 • Published -
Palu: Compressing KV-Cache with Low-Rank Projection
Paper • 2407.21118 • Published • 1 -
Inference-Friendly Models With MixAttention
Paper • 2409.15012 • Published -
PerceiverS: A Multi-Scale Perceiver with Effective Segmentation for Long-Term Expressive Symbolic Music Generation
Paper • 2411.08307 • Published • 7