docs: update TEST_REPORT.md and model/NOTES.md with audit log

docs/TEST_REPORT.md

@@ -223,6 +223,27 @@ Har test ke baad yaha entry hogi: date, env, command, result, verdict.
 - Screenshot verified (user-provided)
 - Screenshot verified (user-provided)
 
+## 2026-09-18 — Full Codebase Audit & P0/P1 Fixes (Local smoke verify)
+- Env: Windows, CPU, torch 2.13 (Python 3.13)
+- Cmds:
+  - `python model/scripts/viu1_moe.py --config model/configs/viu1_moe_config.yaml`
+  - `python model/scripts/train.py --smoke`
+  - `python model/scripts/push_to_hf.py --repo ViuAI/ViuMini-MoE-242M --message "fix: audit fixes for multi-token alignment, weight decay groups, and MoE monitoring"`
+- Audit Fixes Covered:
+  1. Multi-token prediction alignment: fixed off-by-one (`mt_tgt = targets[:, 1:]` + `h[:, :-1, :]`) so token t+2 (next-next) is predicted instead of t+3.
+  2. AdamW weight decay parameter grouping: decoupled 1D weights (RMSNorm scale, biases) from weight decay (0.0), applying 0.1 only to 2D matrices.
+  3. MoE routing monitoring: exported expert usages in `aux_info["usages"]` and added real-time `max_share` tracking with >60% router imbalance alert.
+  4. Attention mask caching: cached `_mask_cache` in `Attention` to eliminate 224 allocations per optimizer step.
+  5. RoPE dtype alignment: cast `cos`/`sin` to `x.dtype` in `apply_rope` to avoid half-precision upcasting.
+  6. Switch-gate modulation: routed gate to multiply attention output rather than cancelling out in softmax.
+  7. Evaluation AMP & Scaler checkpoints: wrapped `evaluate()` in AMP autocast and preserved `scaler.state_dict()` in checkpoints.
+  8. Sampling generation: added temperature (0.7) and top-k (40) sampling to eliminate repetition loops.
+- Result:
+  - Param check: total 241,668,224 (241.7M) / active 166,158,464 (166.2M), MoE=9 dense=19 sliding=19 full=9, forward loss ~12.73, usages exported OK.
+  - Train smoke: 3 steps loss 6.32 -> 6.34, real-time `max_share` logged (0.383 -> 0.453 -> 0.461), ckpt/eval OK.
+  - HF push: pushed to `https://huggingface.co/ViuAI/ViuMini-MoE-242M` successfully.
+- Verdict: PASS
+
 ## Template (next test ke liye)
 ```
 ## YYYY-MM-DD — <test naam> (<env>)

model/NOTES.md

@@ -15,3 +15,4 @@ model/
 - 2026-09-15: `scripts/viu1_moe.py` + `configs/viu1_moe_config.yaml` (VIU-1 Frontier) — test: total 217.8M / active 147.3M (target 200/135, ~9% upar, quality ke liye OK), smoke OK (loss 12.05, moe_aux 0.21), MoE=8 dense=16 sliding=16 full=8, n_kv bug fix
 - 2026-09-15: 28 layers LOCK — test: total 241.7M / active 166.2M, smoke OK (loss 12.18), MoE=9 dense=19 sliding=19 full=9
 - 2026-09-15: `scripts/train.py` + `configs/train_config.yaml` — smoke OK CPU 3 steps (loss 6.18->6.01 down), ckpt/resume/eval/sample ready, warnings fix
+- 2026-09-18: Codebase audit & P0/P1 fixes (`viu1_moe.py`, `train.py`) — multi-token target alignment fix (predict t+2, not t+3), RoPE half-precision dtype alignment, causal/sliding mask caching in Attention, export expert usages in aux_info, switch_gate output gating, AdamW weight decay groups (0.1 on 2D weights, 0.0 on RMSNorm/biases), AMP autocast in evaluate, scaler save/restore in checkpoints, temperature+top-k sampling in sample_text, real-time MoE max_share monitoring. Tests pass (241.7M/166.2M verified, smoke OK), HF pushed.