moe/README.md

MoE Architecture

The MoE model is the main scaling direction for code completion. The strongest current structure is sparse FFN experts with shared attention and optional multi-token prediction heads.

Recommended Structure

• Decoder-only Transformer.
• RMSNorm before attention and FFN.
• RoPE positional encoding.
• Grouped-query attention.
• SwiGLU experts.
• Top-2 routing during training.
• Shared expert for common syntax and indentation.
• Router z-loss and auxiliary load-balancing loss.
• QK norm for long-context attention stability.
• Expert-choice telemetry from the first step, not after loss diverges.
• Optional multi-token prediction heads with low loss weight.

Best Current Shape

Use shared attention and sparse FFN. Keep early layers mostly dense/shared so the model learns lexical syntax before specializing.

• Early blocks: dense FFN or shared expert heavy.
• Middle blocks: sparse MoE every other block.
• Late blocks: sparse MoE with stricter router stability checks.
• Expert count: start with 8 experts for smoke, then 16-32 for H100 ablation.
• Routing: top-2 with capacity factor 1.25 during training.
• Inference ablation: top-1 only after top-2 training is stable.

For code completion, the highest-value specialization is not one expert per language. Better targets are syntax/indentation, FIM reconstruction, repository-context patterns, generated-code continuation, and real-code APIs.

Routing Metrics

Log:

• Per-layer expert load.
• Per-expert token fraction.
• Dropped-token count.
• Router entropy.
• Auxiliary loss.
• Domain-to-expert correlation.

Failure Modes

• Expert collapse.
• Router churn.
• Synthetic Python overfitting.
• MTP loss overpowering next-token loss.
• Shared expert absorbing too much load.
• Dropped tokens above 1% for sustained windows.