Document layout, chunks, prompt-id convention
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README.md
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`manifest.jsonl` is stale at 938 shards and must not be used as the source list)
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Columns carry their own `prompt_idx` / `tok`, so joins key on values, never on
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row position.
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---
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license: apache-2.0
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task_categories:
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- other
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tags:
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- mixture-of-experts
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- expert-routing
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- qwen3
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- inference-optimization
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size_categories:
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- 100M<n<1B
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configs:
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- config_name: default
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data_files: layers_v1/*/labels/*.safetensors
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---
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# CommitMoE — Qwen3.5-35B-A3B-FP8 expert-routing traces, columnar by layer
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Per-token MoE routing decisions from `Qwen/Qwen3.5-35B-A3B-FP8`, laid out
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**one directory per layer** so a predictor for a single layer reads only what it
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needs instead of scanning interleaved shards.
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The model has **40 MoE layers, 256 experts each, top-8 routing**, hidden size
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2048. Every row is one `(prompt, decode token, layer)` triple.
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## What this is for
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Predicting *which experts a layer will route to* a couple of layers ahead, so
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their weights can be prefetched over PCIe into a small GPU-resident cache
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before the layer runs. A prediction that misses costs a stall; the metric that
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matters is recall at the cache size `C`, and under a `W = C` admission policy
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the stall count per token per layer is exactly `8 · (1 − recall@C)`.
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## Layout
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```
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layers_v1/<chunk>/
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prompts.jsonl # id + prompt_idx, one row per prompt
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labels/part_*.safetensors # top_indices[8], prompt_idx, tok, layer, gen_token_id
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hidden/layer=NN/part_*.safetensors # h[2048] (pre-MoE hidden), prompt_idx, tok
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logits/layer=NN/part_*.safetensors # logits[256] (router), prompt_idx, tok
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```
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`labels` covers all 40 layers in one table — it is small (~2.6 GB per 10k
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prompts) and window features read every layer of it. `hidden` is the expensive
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part at about **10.0 GB per layer per 10k prompts**; `logits` is **1.27 GB**.
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## Chunks and prompt ids
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`prompt_idx` is **globally unique across chunks**, so they can be pooled
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without collision:
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| chunk | prompts | `prompt_idx` range |
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|---|---|---|
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| `chunk_10k` | 10,000 | 0 – 9,999 |
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| `chunk00` | 4,000 | 10,000 – 13,999 |
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| `chunk01` | 6,000 | 14,000 – 19,999 |
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`chunk00` lives in the companion repo
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[`commitmoe-qwen35-fp8-layers`](https://huggingface.co/datasets/RASMUS/commitmoe-qwen35-fp8-layers).
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Each chunk's `prompts.jsonl` starts at its own base, and readers derive that
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base from the file's first row rather than assuming zero.
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**A note on the ids, because it is easy to get wrong.** The raw shards store
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`prompt_idx` as a *shard-local slot* (0–31, an index into that shard's own
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32-entry prompt list), not a global id. Writing it through unchanged collapses
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every shard's prompts onto the same 32 ids — silently, since the output still
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looks well-formed. The files here are built by resolving each shard's prompt-id
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*strings* through the chunk's index, and every upload is checked by comparing
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the written `prompt_idx` set against the set resolved from the shards.
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## How the shards were laid out upstream
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Each raw shard is a **decode-step slice** of a 32-prompt batch — 32 prompts ×
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~26 decode steps × 40 layers ≈ 32,768 rows — so roughly **ten consecutive raw
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shards share the same 32 prompts**. That is invisible in this columnar form but
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matters if you rebuild from the raw traces.
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## Splits
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Not stored. The split is derived from the prompt-id string, so it stays
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consistent across chunks and rebuilds.
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## Related
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- Raw interleaved traces: [`commitmoe-qwen35-fp8-expert-routing-traces`](https://huggingface.co/datasets/RASMUS/commitmoe-qwen35-fp8-expert-routing-traces)
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- `chunk00` + raw for `chunk00`/`chunk01`: [`commitmoe-qwen35-fp8-layers`](https://huggingface.co/datasets/RASMUS/commitmoe-qwen35-fp8-layers)
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