| --- |
| license: apache-2.0 |
| task_categories: |
| - other |
| tags: |
| - mixture-of-experts |
| - expert-routing |
| - qwen3 |
| - inference-optimization |
| size_categories: |
| - 100M<n<1B |
| configs: |
| - config_name: default |
| data_files: layers_v1/*/labels/*.safetensors |
| --- |
| |
| # CommitMoE — Qwen3.5-35B-A3B-FP8 expert-routing traces, columnar by layer |
|
|
| Per-token MoE routing decisions from `Qwen/Qwen3.5-35B-A3B-FP8`, laid out |
| **one directory per layer** so a predictor for a single layer reads only what it |
| needs instead of scanning interleaved shards. |
|
|
| The model has **40 MoE layers, 256 experts each, top-8 routing**, hidden size |
| 2048. Every row is one `(prompt, decode token, layer)` triple. |
|
|
| ## What this is for |
|
|
| Predicting *which experts a layer will route to* a couple of layers ahead, so |
| their weights can be prefetched over PCIe into a small GPU-resident cache |
| before the layer runs. A prediction that misses costs a stall; the metric that |
| matters is recall at the cache size `C`, and under a `W = C` admission policy |
| the stall count per token per layer is exactly `8 · (1 − recall@C)`. |
|
|
| ## Layout |
|
|
| ``` |
| layers_v1/<chunk>/ |
| prompts.jsonl # id + prompt_idx, one row per prompt |
| labels/part_*.safetensors # top_indices[8], prompt_idx, tok, layer, gen_token_id |
| hidden/layer=NN/part_*.safetensors # h[2048] (pre-MoE hidden), prompt_idx, tok |
| logits/layer=NN/part_*.safetensors # logits[256] (router), prompt_idx, tok |
| ``` |
|
|
| `labels` covers all 40 layers in one table — it is small (~2.6 GB per 10k |
| prompts) and window features read every layer of it. `hidden` is the expensive |
| part at about **10.0 GB per layer per 10k prompts**; `logits` is **1.27 GB**. |
|
|
| ## Chunks and prompt ids |
|
|
| `prompt_idx` is **globally unique across chunks**, so they can be pooled |
| without collision: |
|
|
| | chunk | prompts | `prompt_idx` range | |
| |---|---|---| |
| | `chunk_10k` | 10,000 | 0 – 9,999 | |
| | `chunk00` | 4,000 | 10,000 – 13,999 | |
| | `chunk01` | 6,000 | 14,000 – 19,999 | |
|
|
| `chunk00` lives in the companion repo |
| [`commitmoe-qwen35-fp8-layers`](https://huggingface.co/datasets/RASMUS/commitmoe-qwen35-fp8-layers). |
| Each chunk's `prompts.jsonl` starts at its own base, and readers derive that |
| base from the file's first row rather than assuming zero. |
|
|
| **A note on the ids, because it is easy to get wrong.** The raw shards store |
| `prompt_idx` as a *shard-local slot* (0–31, an index into that shard's own |
| 32-entry prompt list), not a global id. Writing it through unchanged collapses |
| every shard's prompts onto the same 32 ids — silently, since the output still |
| looks well-formed. The files here are built by resolving each shard's prompt-id |
| *strings* through the chunk's index, and every upload is checked by comparing |
| the written `prompt_idx` set against the set resolved from the shards. |
|
|
| ## How the shards were laid out upstream |
|
|
| Each raw shard is a **decode-step slice** of a 32-prompt batch — 32 prompts × |
| ~26 decode steps × 40 layers ≈ 32,768 rows — so roughly **ten consecutive raw |
| shards share the same 32 prompts**. That is invisible in this columnar form but |
| matters if you rebuild from the raw traces. |
|
|
| ## Splits |
|
|
| Not stored. The split is derived from the prompt-id string, so it stays |
| consistent across chunks and rebuilds. |
|
|
| ## Related |
|
|
| - Raw interleaved traces: [`commitmoe-qwen35-fp8-expert-routing-traces`](https://huggingface.co/datasets/RASMUS/commitmoe-qwen35-fp8-expert-routing-traces) |
| - `chunk00` + raw for `chunk00`/`chunk01`: [`commitmoe-qwen35-fp8-layers`](https://huggingface.co/datasets/RASMUS/commitmoe-qwen35-fp8-layers) |
|
|
