Hardware-Aware FP4 FlashAttention-4

arXiv:2609.04105 · cs.LG · Submitted 2026-09-03 · Read on arXiv

cs.LG

Submitted: 2026-09-03

Updated: 2026-09-03

Code: https://github.com/hao-ai-lab/flash-attention-fp4

Terminology

• Accuracy — 17× in this paper · explained in 1 episode
• Quantization — 16× in this paper · explained in 12 episodes
• Core — 15× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Calibration — 5× in this paper · explained in 7 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Provenance — 4× in this paper · explained in 3 episodes
• Tensor Memory Accelerator — 3× in this paper
• Decode — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Quantization-Aware Training — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes
• Vision Transformer (ViT) — 1× in this paper · explained in 2 episodes

Sources

• FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
• FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
• Masked Autoencoders Are Scalable Vision Learners
• UE5M3 FP4 Block Scaling for Stable Language Model Pretraining
• Microsoft COCO: Common Objects in Context
• FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
• Wan: Open and Advanced Large-Scale Video Generative Models
• FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling
• SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
• Attn-QAT: 4-Bit Attention With Quantization-Aware Training

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks