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9f3ece2 a20d541 9f3ece2 a20d541 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # 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.
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