Dao-AILab/flash-attention
flash-attention
Fast and memory-efficient exact attention
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Dao-AILab
Dao-AILab • individual
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May 19, 2022
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README Summary
FlashAttention This repository provides the official implementation of FlashAttention and FlashAttention-2 from the following papers.
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Algorithm OptimizationAttention Mechanism DesignAttention Mechanism OptimizationAttention MechanismsComputational Complexity AnalysisCUDA/GPU AccelerationCUDA ProgrammingGPU Kernel DevelopmentGPU Kernel ProgrammingGPU OptimizationHigh-Performance ComputingLarge Language Model TrainingLow-level Performance TuningMemory Efficiency in Deep LearningMemory-Efficient Deep LearningMixed Precision TrainingNumerical StabilityNumerical Stability in Neural NetworksTransformer ArchitectureTransformer Architecture Optimization
Tags
Algorithm OptimizationAttention Mechanism DesignAttention Mechanism OptimizationAttention MechanismsComputational Complexity AnalysisCUDA/GPU AccelerationCUDA ProgrammingGPU Kernel DevelopmentGPU Kernel ProgrammingGPU OptimizationHigh-Performance ComputingLarge Language Model TrainingLow-level Performance TuningMemory Efficiency in Deep LearningMemory-Efficient Deep LearningMixed Precision TrainingNumerical StabilityNumerical Stability in Neural NetworksTransformer ArchitectureTransformer Architecture OptimizationBenchmarkingContext EngineeringDockerEmbeddingsEvalsForkedGPU / CUDAGemmaHuggingFaceKV CacheMistralOpenAIPyTorchPythonResearch / PapersSecurityvLLM
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AI Trends
category
Deployment Context
Skill Areas
Transformer Architecture OptimizationAttention Mechanism DesignGPU Kernel DevelopmentCUDA ProgrammingMemory-Efficient Deep LearningComputational Complexity AnalysisHigh-Performance ComputingAlgorithm OptimizationLarge Language Model TrainingNumerical Stability in Neural NetworksTransformer ArchitectureAttention Mechanism OptimizationGPU Kernel ProgrammingCUDA/GPU AccelerationMixed Precision TrainingAttention MechanismsGPU OptimizationMemory Efficiency in Deep LearningNumerical StabilityLow-level Performance Tuning
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Use Cases
Efficient training of large language modelsLong-context sequence processingReducing GPU memory consumptionAccelerating transformer inferenceTraining with larger batch sizes on constrained hardwareEnabling longer sequence lengths in vision and language tasksTraining Large Language ModelsInference Optimization for TransformersLong Context ProcessingMemory-Constrained Model TrainingEfficient Sequence ProcessingAccelerating Large Language Model InferenceReducing Training Time for TransformersEfficient Fine-tuning of Foundation ModelsLong-context ProcessingMemory-constrained Deployment
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Updated 2 months ago
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production- Quality
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RAG & RetrievalPrimaryEvals & BenchmarkingInference & ServingMLOps & InfrastructureDev Tools & AutomationLearning ResourcesSecurity & SafetyFoundation ModelsAI AgentsModel TrainingSearch & KnowledgeOther AI / ML
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Cost & EfficiencyScale & ReliabilityData & EvaluationProduct DiscoveryAI-Native Architecture
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Python100.0%
Timeline
- Project created
- May 19, 2022
- Forked
- Mar 28, 2026
- Your last push
- 2 months ago
- Upstream last push
- 17 days ago
- Tracked since
- Mar 26, 2026
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