Jagged Flash Attention
A Flash Attention variant optimized for variable-length sequences. It processes sequences of unequal length directly without padding, thereby eliminating wasted compute cycles. It serves as the core attention kernel in Meta's generative advertising model.
Timeline (last 90 days)
Jagged Flash Attention (JFA) directly processes variable-length (jagged) sequences, avoiding padding with invalid tokens for alignment.
JFA is the core attention kernel underlying Meta's Generative Ads Model (GEM).
The work described in the original article ports and optimizes JFA to the Blackwell platform, with the goal of approaching SOTA-level FA4 performance.
This work is a preview summary, and specific performance data, implementation details, and comparison benchmarks are still pending disclosure in a complete technical blog post.
All Facts (3)
Jagged Flash Attention (JFA) directly processes variable-length (jagged) sequences, avoiding padding with invalid tokens for alignment.
50%UnverifiedJFA is the core attention kernel underlying Meta's Generative Ads Model (GEM).
50%UnverifiedThe work described in the original article ports and optimizes JFA to the Blackwell platform, with the goal of approaching SOTA-level FA4 performance.
50%