104 lines
5.8 KiB
Markdown
104 lines
5.8 KiB
Markdown
# US-044 — Tracker as model-file source; nodes download only their shard
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Status: in progress
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Priority: High (blocks multi-machine big-model serving; pairs with US-042)
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Stage: Designed (grill remaining decisions before build)
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## Context
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Common deployment: the tracker and the first node share a machine that already
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holds the model files (e.g. `/run/media/popov/DATA/llm/safetensor`). When a
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second node joins with no model selected, the tracker assigns it the uncovered
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layer range — and today that node then downloads the **entire snapshot from
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HuggingFace**, even for a 20-layer shard of a 160 GB model.
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What exists already (build on it, don't duplicate):
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- Nodes serve their shard dir as a tar at `GET /v1/shards/download` with
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checksum verification; `download_shard` tries assignment-provided `peers`
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before HF (`downloader.py`). But it only matches **identical layer ranges**,
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and the HF fallback runs `snapshot_download` of the whole repo.
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- The torch path (`--model-id`) bypasses `download_shard` entirely:
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`TorchModelShard` → `from_pretrained` downloads **and loads into RAM** the
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full model, then executes only the assigned layers. Sharding currently saves
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compute, not memory or bandwidth.
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## Design
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1. **Tracker `--models-dir PATH`** (env `MESHNET_MODELS_DIR`). When set, the
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tracker indexes HF-layout snapshots under it and advertises itself as a
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model-file source in `/v1/nodes/assign` responses.
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2. **Layer-aware file selection.** For safetensors models, read
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`model.safetensors.index.json` and map the assigned layer range → the
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subset of weight files containing those layers, plus the always-needed
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files (config, tokenizer, index, embeddings/head files for head/tail
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shards). Serve exactly that subset (tar stream, per-file checksums).
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GGUF (US-042): single file or naive byte-range — phase 2.
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3. **Node download order**: exact-shard peer (existing) → tracker/peer file
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subset (new) → HF `snapshot_download` with `allow_patterns` for the same
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subset (new — stop downloading the whole repo even from HF) → full snapshot
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(last resort).
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- The `allow_patterns` subset must not depend on the tracker having a local
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snapshot: when the tracker has no `--models-dir` match for a repo (or
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hasn't cached it yet — the common case for a fresh public tracker),
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`model_sources` comes back empty and `download_shard` falls straight to
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`_download_huggingface_subset(..., allow_patterns=None)`, i.e. the full
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repo. Reported 2026-07-06: a CPU node assigned layers 0–2 of
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`unsloth/Qwen3.6-35B-A3B` (42 safetensor shards) sat downloading the
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entire model unauthenticated because of this. Fix: fetch
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`model.safetensors.index.json` + `config.json` directly from HF (a few
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KB) and compute the same layer-scoped file subset client-side, so the
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HF-fallback path is filtered even with an empty `model_sources`.
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4. **Partial LOAD (the hard half).** Downloading a subset is wasted unless the
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node stops instantiating the full model: build the model skeleton on the
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`meta` device, materialize only assigned layers (+embeddings/norm/head as
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role requires) from the local files, leave the rest on meta. Without this,
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an 80 GB machine can never hold a shard of a 160 GB model regardless of
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how the bytes arrive. This is the acceptance bar for the issue.
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## Open questions (grill before building)
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- Trust: joining nodes fetch weights from the tracker/peers — checksum against
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what root of trust? (HF etag/sha vs tracker-signed manifest.)
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- Disk layout: partial snapshots must not corrupt the HF cache dir; probably
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a meshnet-owned layout keyed by repo+revision.
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- Serving cost: a 100 GB tar stream per joining node on the tracker box —
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rate-limit/queue? LAN-only heuristic?
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## Acceptance criteria
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- [x] Tracker can be started with `--models-dir PATH` / `MESHNET_MODELS_DIR`
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and advertises a local model-file source in assignment responses when it has
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a matching HF snapshot.
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- [x] Tracker serves a tar stream containing only the safetensors files selected
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for the assigned layer range plus config/tokenizer/index metadata.
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- [x] Node downloader keeps exact-shard peers first, then races tracker model
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sources against a HuggingFace `snapshot_download(..., allow_patterns=...)`
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subset download, using the first successful source.
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- [x] When no tracker model source is available at all, the HuggingFace
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fallback still computes `allow_patterns` from the repo's own
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`model.safetensors.index.json` (fetched directly, not via the tracker) —
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it never silently downloads the full model just because the tracker has
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nothing cached.
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- [x] Real PyTorch model startup can use tracker `full_url` sources to fetch
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the full local snapshot over LAN before `from_pretrained`, so local-network
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testing no longer has to pull from HuggingFace first.
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- [ ] Two-machine test: machine A (tracker + node, holds full snapshot) serves
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layers 0–k; machine B joins with no model and receives **only** the files
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for its assigned range from A — nothing fetched from HF
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- [ ] Machine B's resident memory scales with its shard size, not model size
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- [ ] Checksums verified end-to-end; corrupted transfer falls back cleanly
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- [x] Single-node/full-model flows unchanged
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- [ ] `python -m pytest` passes from repo root
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## Implementation notes
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- 2026-07-06: Added the tracker/node download path. For immediate Qwen3.6-35B
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LAN testing, real PyTorch nodes fetch the full snapshot from the tracker via
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`full_url`; HuggingFace remains fallback-only, and when it is used the node
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computes `allow_patterns` from the repo's remote SafeTensors index so it
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stays layer-filtered even without tracker-cached files. Remaining hard half
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is true partial model materialization: the backend can prefer a downloaded
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local model directory, but Transformers still needs a `meta`-device load
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path that materializes only assigned layers.
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