Flash Attention - Hugging Face Flash Attention is an attention algorithm used to reduce this problem and scale transformer-based models more efficiently, enabling faster training and inference Standard attention mechanism uses High Bandwidth Memory (HBM) to store, read and write keys, queries and values
Attention Wasnt All We Needed Flash Attention (particularly the latest implementation FlashAttention-3) addresses the significant memory bottleneck inherent in standard self-attention mechanisms within Transformers, particularly for long sequences The conventional approach computes the full attention score matrix \( S = QK^T \), where \(Q, K \in \mathbb{R}^{N \times d
Flash attention(Fast and Memory-Efficient Exact Attention . . . Given transformer models are slow and memory hungry on long sequences (time and memory complexity is quadratic in nature), flash attention provides a 15% end-to-end wall-clock speedup on BERT-large, 3x speed on GPT-2
FlashAttention-3: Fast and Accurate Attention with Asynchrony . . . Attention, as a core layer of the ubiquitous Transformer architecture, is a bottleneck for large language models and long-context applications FlashAttention (and FlashAttention-2) pioneered an approach to speed up attention on GPUs by minimizing memory reads writes, and is now used by most libraries to accelerate Transformer training and
The I O Complexity of Attention, or How Optimal is Flash . . . Self-attention is at the heart of the popular Transformer architecture, yet suffers from quadratic time and memory complexity The breakthrough FlashAttention algorithm revealed I O complexity as the true bottleneck in scaling Transformers
What is Flash Attention? | Modal Blog The Transformers library supports Flash Attention for certain models You can often enable it by setting the attn_implementation="flash_attention_2" parameter when initializing a model However, support may vary depending on the specific model architecture