turnkey resume runbook: restore layout, OOM fix options, full 3.25 chain, operational traps

RESUME-RUNBOOK.md

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+# GLM-5.3-BF16 ShapleyMCG — 3.25 bpw resume runbook
+
+State at archive (2026-08-30): all 75 main layers hold sealed K3+K4 candidate
+pairs; MTP78 has a sealed uniform-K4 inventory (plus a decode-verified K5 side
+store); the uniform-K3 draft is published with reconciled KLD evidence. The only
+unfinished chain is: five-node Aumann-Shapley attribution -> exact global
+3.25-bpw allocation -> mixed materialization -> KLD replay -> publication.
+
+## 0. Inputs (all public/HF)
+
+- This dataset: `tars/candidates-layer-*.tar` (75), `tars/capture-*.tar` (4 roles),
+  `tars/mtp78.tar`, `tars/panel.tar`, `tars/evidence.tar`, `tars/attribution-final.tar`,
+  `candidates/main-inventory.json`, `code/glm53wt.bundle`, `code/campaign-scripts.tar`
+- Teacher logits: `brandonmusic/GLM-5.3-BF16-full-logits` -> `bf16-nonfinal/`
+  (fit 32 / conditional_fit 16 / selection 16 / confirmation 16, fp32 full vocab)
+- Weights: `zai-org/GLM-5.3-BF16` at revision `304b8051cfb2b260b61ce0cbe330e02a98e73639`
+- Transformers checkout at `805a9e939fa8c1bff8d8ffdf041c051b71a914aa` (PYTHONPATH-bound)
+- exllamav3 upstream @c5d9c65, `setup.py build_ext --inplace` ONLY (pip -e tries flash_attn)
+
+## 1. Restore layout (any 8-GPU node with >=268 GiB/GPU; B300-class for TP8 BF16)
+
+```
+/root/glm53wt            <- git clone from code/glm53wt.bundle (ALL node-local
+                            commits: no-intra-LDLQ env gate, cold-expert
+                            fallbacks, attribution data_ptr/__func__ fixes)
+/root/campaign           <- untar code/campaign-scripts.tar (runners incl. all
+                            13-attempt fixes already applied)
+/root/glm53-shapleymcg   <- untar: panel, evidence, attribution-final,
+                            candidates (75 layer tars + main-inventory.json),
+                            capture/mtp78 not needed; mtp78.tar -> mtp78/
+/root/logits/bf16-nonfinal <- hf download the 80 windows
+/root/models/GLM-5.3-BF16  <- hf download at the pinned revision
+```
+Then reseal nothing — the shipped evidence/ closures match the bundle tree.
+Env: `GLM53_NO_INTRA_LDLQ=1`, venv torch 2.12.1+cu132.
+
+## 2. Attribution (the one unfinished computation)
+
+Known-good up to the OOM: `bash /root/campaign/run_attribution.sh` (fires
+`glm53_attribution_runner_draft.py --mode attribute`, TP8, path-nodes 5,
+fisher-rank 32, probes 2/window; endpoint receipt already in
+attribution-final.tar — or regenerate with `--mode seal-endpoint`, ~25 min CPU).
+
+Blocker to fix first (documented in ATTRIBUTION-STATUS.md): OOM at
+264/267.7 GiB in the path-node phase. Fix in
+`campaign/glm53_attribution_runner_draft.py` near the node loop (search
+`retain_graph=True`):
+  a. preferred: drop `retain_graph=True`; recompute the blended forward for the
+     Fisher-probe backward passes (second forward per node; ~2x node compute,
+     ~halves peak graph memory), or
+  b. wrap the blended forward in `torch.utils.checkpoint.checkpoint`
+     (use_reentrant=False) per decoder layer, or
+  c. stream the K4 residency per layer instead of fully resident
+     (`GLM53TP8PackedK4Residency`).
+All alpha-0/alpha-1 exactness gates stay armed; the f32 softmax path is already
+in the shipped runner (alpha-0 KL is exactly zero by construction).
+
+## 3. Allocation -> materialization -> KLD -> publish (all staged, tested paths)
+
+```
+PYTHONPATH=src python scripts/allocate_glm53_mcg_exact_3p25.py \
+  --scope main --candidate-inventory .../candidates/main-inventory.json \
+  --attribution <attribution output> \
+  --output .../allocation/main-3p25.json --execute
+
+# materialize: /root/campaign/post_attribution.sh already does everything below
+#  - 3-layer-wide pool, per-rank materialize_glm53_selected_packed --allocation
+#  - MTP78: --uniform-bits 4 with mtp78/candidate-inventory.json
+#  - KLD replay fires in parallel from the CANDIDATE store (no materialization
+#    dependency): kld_student_capture.py --allocation ... then kld_compare.py
+#    + kld_axes.py on the 16 confirmation windows (teacher = bf16-nonfinal)
+# pack for HF with pack_layers.py (HF hard limit: 20,000 files/repo; one
+# consolidated safetensors per layer; xet dedups re-uploads)
+```
+
+Expected: uniform-K3 measured 0.0979 (new panel) / 0.0375 (published windows);
+the allocated 3.25 should land near ~0.03 on the new panel given the
+davidsyoung 3.42-mixed reference at 0.0240 on the mild published windows.
+
+## 4. Operational traps (all hit once already)
+
+- Per-process GPU isolation for 8-wide encode/decode workers:
+  `CUDA_VISIBLE_DEVICES=$R ... --device cuda:0` (8 procs with `--device cuda:$R`
+  = illegal-access collisions on GPU0).
+- Kill stale rank processes by exact PID from nvidia-smi query-compute-apps
+  before any TP8 relaunch (orphans hold ~190 GiB/GPU).
+- Write-once artifacts: rm stale `attribution-run-plan.json` (keep
+  `uniform-k4-byte-verification.json`) before every attribution relaunch;
+  the endpoint receipt binds the code-closure sha — reseal endpoint after any
+  sealed-repo edit.
+- Every sealed-repo edit => reseal 3 code closures + capture-verification
+  (fix chain exists: `campaign/fix_reseal_attr.sh`).
+- lm_head is UNTIED (`tie_word_embeddings: false`); MTP hnorm consumes the RAW
+  pre-`model.norm` hidden (vLLM `Glm4MoeMultiTokenPredictorLayer` semantics).