This image explains the Multi-Token Prediction (MTP) architecture that improves inference speed.
Overall Structure
Left: Main Model
- Starts with an Embedding Layer that converts input tokens to vectors
- Deep neural network composed of L Transformer Blocks
- RMSNorm stabilizes the range of Transformer input/output values
- Finally, the Output Head (BF16 precision) calculates the probability distribution for the next token
Right: MTP Module 1 (Speculative Decoding Module) + More MTP Modules
- Maximizes efficiency by reusing the Main Model’s outputs
- Two RMSNorms normalize the intermediate outputs from the Main Model
- Performs lightweight operations using a single Transformer Block with FP8 Mixed Precision
- Generates specialized vectors for future token prediction through Linear Projection and concatenation
- Produces candidate tokens with BF16 precision
Key Features
- Two-stage processing: The Main Model accurately predicts the next token, while the MTP Module generates additional candidate tokens in advance
- Efficiency:
- Shares the Embedding Layer with the Main Model to avoid recalculation
- Reduces computational load with FP8 Mixed Precision
- Uses only a single Transformer Block
- Stability: RMSNorm ensures stable processing of outputs that haven’t passed through the Main Model’s deep layers
Summary
MTP architecture accelerates inference by using a lightweight module alongside the main model to speculatively generate multiple future tokens in parallel. It achieves efficiency through shared embeddings, mixed precision operations, and a single transformer block while maintaining stability through normalization layers. This approach significantly reduces latency in large language model generation.
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With Claude
Lechuck Park 