Transformer Tricks: Precomputing the First Layer for Faster Inference
Kernekoncepter
Precomputing the first layer of transformers with RoPE can lead to lower latency and cost-per-token, optimizing inference speed.
Resumé
Directory:
Introduction to Transformer Tricks
Describes a trick to speed up inference of transformers with RoPE.
Benefits include lower latency and cost-per-token savings.
Precompute for Parallel Transformers
Illustrates precomputing Q, K, V, FFN for parallel transformers.
Details dimensions and layers involved in precomputation.
Precompute for Serial Transformers
Explains precomputing Q, K, V for serial transformers without parallel attention/FFN scheme.
Examples and Comparisons
Compares configurations and weights of different transformer models like Pythia-6.9B, Mistral-7B, Mixtral-8x7B.
Memory Read Savings and Size Increases
Shows the impact of precompute on memory read savings and size changes for various transformer models.
Key Highlights:
Precomputing first layer can optimize inference speed by reducing computational complexity per token.
Different strategies are employed for parallel and serial transformers in precomputation.
Comparison tables showcase the benefits of precompute in terms of memory read savings and size adjustments.
Tilpas resumé
Genskriv med AI
Generer citater
Oversæt kilde
Til et andet sprog
Generer mindmap
fra kildeindhold
Besøg kilde
arxiv.org
Transformer tricks
Statistik
For example, the maximum savings for a model with only 4 layers (such as Whisper tiny) is limited to 25%, while a 32-layer model is limited to 3% savings.
Reads per batch: B · d + num_weights_Q_K_V_FFN