FlashAttention

A fast and memory-efficient implementation of the accurate attention mechanism

CommonProductProgrammingDeep LearningTransformer

FlashAttention is an open-source attention mechanism library designed specifically for Transformer models in deep learning to enhance computational efficiency and memory usage. It optimizes attention calculation using IO-aware methods, reducing memory consumption while maintaining precise computational results. FlashAttention-2 further improves parallelism and workload distribution, while FlashAttention-3 is optimized for Hopper GPUs, supporting FP16 and BF16 data types.

Supports multiple GPU architectures
including Ampere
Ada
and Hopper.
Provides support for data types fp16 and bf16
optimized for specific GPU architectures.
Implements scalable head dimensions
supporting up to 256.
Supports both causal and non-causal attention
adapting to different model requirements.

The target audience is primarily deep learning researchers and developers
especially those who need to optimize computing resources and memory usage when working with large language models. FlashAttention achieves this by reducing memory footprint and improving computational efficiency
allowing the training and deployment of large models even on limited hardware resources.

Accelerate BERT model training in natural language processing tasks using FlashAttention.
Reduce the memory footprint of GPT models in large-scale text generation tasks using FlashAttention.
Improve model runtime efficiency in machine translation or speech recognition projects through FlashAttention.

1. Ensure your system has CUDA 11.6 or higher and PyTorch 1.12 or higher installed.
2. Clone the FlashAttention code repository to your local environment.
3. Enter the hopper directory and install FlashAttention using python setup.py install.
4. Set the PYTHONPATH environment variable to point to the installation path.
5. Run tests by executing pytest -q -s test_flash_attn.py to verify successful installation.

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