Add auto-resume checkpointing, S1/S2 configs, and experiment results

- Auto-resume: find latest checkpoint in save_dir on startup
- SIGUSR1 handler: save checkpoint before SLURM timeout
- S1 config (constant tau=5, identity init verification)
- S2 config (constant tau=2, gradient flow check)
- Experiment results tracker with S0/S1 data
- Speed estimates and experiment plan

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

1 files changed, 225 insertions, 0 deletions

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+# DAGFormer Experiment Results
+
+## Sanity Checks
+
+### S0 — Dense Baseline (no predictor)
+
+| Item | Value |
+|------|-------|
+| Status | **DONE** (from sanity training eval) |
+| Date | 2025-02-09 |
+| Job ID | 15785016 |
+| Hardware | A40×1 |
+| Eval set | skip=10000, size=50, seq_len=1024 |
+| **NLL_base** | **2.4569** |
+| Notes | All experiments must beat this. Consider re-running with eval_size=1000 for more robust estimate. |
+
+---
+
+### S1 — Predictor identity init (constant tau=5, ~10M tokens)
+
+| Item | Value |
+|------|-------|
+| Status | **DONE** |
+| Date | 2026-02-09 |
+| Job ID | 15788145 |
+| Config | r=32, tau=5→5 (constant), k=5, lambda=0 |
+| Tokens | ~10M (2500 steps @ batch=4, seq=1024) |
+| Hardware | A40×1 (gpub073) |
+| Wall time | ~2 hrs |
+| Target | NLL ≈ NLL_base (within 1%) |
+| Purpose | Verify init reproduces dense topology |
+| **Result** | **PASS** — NLL within 0.3% of baseline |
+
+| Metric | Value (final) |
+|--------|---------------|
+| eval/nll_soft | **2.4500** (baseline: 2.4569, diff: -0.3%) |
+| eval/nll_hard | **2.4506** (diff: -0.3%) |
+| eval/nll_baseline | 2.4569 |
+| topology/mean_A | 0.975 |
+| topology/seq_gate_frac | 0.986 |
+| topology/hyp_gate_frac | 0.988 |
+
+**Per-eval-step data:**
+
+| Step | nll_soft | nll_hard | nll_base | mean_A |
+|------|----------|----------|----------|--------|
+| 100 | 2.4531 | 2.4512 | 2.4569 | 0.970 |
+| 500 | 2.4588 | 2.4609 | 2.4569 | 0.974 |
+| 1000 | 2.4506 | 2.4506 | 2.4569 | 0.978 |
+| 1500 | 2.4562 | 2.4578 | 2.4569 | 0.972 |
+| 2000 | 2.4500 | 2.4506 | 2.4569 | 0.978 |
+| 2500 | 2.4500 | 2.4506 | 2.4569 | 0.975 |
+
+**Observations:**
+- Init NLL matches baseline from step 0 — identity init working correctly
+- Step 700 had transient dip (mean_A=0.916, nll_soft=2.496) but recovered — Gumbel noise exploration at high tau
+- nll_hard ≈ nll_soft throughout — at tau=5, soft gates ≈ 0.95, so hard threshold (>0) gives similar A
+
+---
+
+### S2 — Gradient flow check (constant tau=2, ~50M tokens)
+
+| Item | Value |
+|------|-------|
+| Status | **RUNNING** (attempt 2) |
+| Config | r=32, tau=2→2 (constant), k=5, lambda=0 |
+| Tokens | ~50M (12,500 steps @ batch=4, seq=1024) |
+| Hardware | A40×1 |
+| Est. Time | ~15 hrs (within 48h limit) |
+| Target | NLL < NLL_base (2.4569) |
+| Purpose | Lower tau gives sharper gates — does predictor learn useful topology? |
+
+**Attempt 1** — Job 15789537, crashed at step ~1860 (Dolma HTTP range request error)
+
+| Step | nll_soft | nll_hard | nll_baseline | mean_A |
+|------|----------|----------|--------------|--------|
+| 500 | 2.4581 | 2.4581 | 2.4569 | 0.993 |
+| 1000 | 2.4575 | 2.4569 | 2.4569 | 0.999 |
+| 1500 | 2.4547 | 2.4559 | 2.4569 | 0.993 |
+
+Observations (attempt 1):
+- Eval NLL ≈ baseline throughout — predictor still near init (mean_A ≈ 0.99)
+- Train NLL high variance (0.27–2.96) is normal batch-to-batch variation at batch_size=4
+- No checkpoint saved (save_every=2500, crashed at 1860)
+- Crashed due to Dolma streaming HTTP error, not code bug
+
+**Attempt 2** — Job 15798568 (fresh start, no checkpoint from attempt 1)
+
+| Metric | Value |
+|--------|-------|
+| eval/nll_soft | |
+| eval/nll_hard | |
+| topology/mean_A | |
+
+---
+
+## Phase 1 Core
+
+### P1 — Phase 1 default config (5B tokens)
+
+| Item | Value |
+|------|-------|
+| Status | NOT STARTED |
+| Config | r=32, tau=5→0.2 cosine, k=5, lambda=0→0.01 ramp |
+| Tokens | 5B |
+| Hardware | A40×4 |
+| Est. Time | ~4 days |
+
+| Metric | Value |
+|--------|-------|
+| eval/nll_soft | |
+| eval/nll_hard | |
+| topology/mean_A | |
+| topology/seq_gate_frac | |
+| topology/hyp_gate_frac | |
+
+---
+
+### P2 — Phase 1 extended (10B tokens)
+
+| Item | Value |
+|------|-------|
+| Status | NOT STARTED |
+| Config | Continue P1 if still improving at 5B |
+| Tokens | 10B |
+| Hardware | A40×4 |
+| Est. Time | ~7 days |
+
+---
+
+## Ablations
+
+### A1–A4: Rank r
+
+| ID | Rank | NLL_soft | NLL_hard | Sparsity | Notes |
+|----|------|----------|----------|----------|-------|
+| A1 | 8 | | | | |
+| A2 | 16 | | | | |
+| P1 | 32 | | | | (reference) |
+| A3 | 64 | | | | |
+| A4 | 256 | | | | full rank |
+
+### A5–A7: Temperature schedule
+
+| ID | tau_init | tau_final | NLL_soft | NLL_hard | A entropy | Notes |
+|----|----------|-----------|----------|----------|-----------|-------|
+| A5 | 1 | 1 | | | | constant, perpetually soft |
+| P1 | 5 | 0.2 | | | | (reference) |
+| A6 | 5 | 0.05 | | | | aggressive anneal |
+| A7 | 10 | 1.0 | | | | slow anneal |
+
+### A8–A9: Sparsity lambda
+
+| ID | lambda | NLL_soft | NLL_hard | Density | Notes |
+|----|--------|----------|----------|---------|-------|
+| A8 | 0 | | | | no sparsity |
+| P1 | 0→0.01 | | | | (reference) |
+| A9 | 0→0.05 | | | | high sparsity |
+
+### A10–A11: Cascading gate
+
+| ID | Gate | NLL_soft | NLL_hard | Dead heads | Notes |
+|----|------|----------|----------|------------|-------|
+| A10 | OFF | | | | |
+| P1 | k=5 fixed | | | | (reference) |
+| A11 | k=5 learnable | | | | |
+
+---
+
+## Analysis Experiments
+
+### X1 — Topology variance analysis
+| Item | Value |
+|------|-------|
+| Status | NOT STARTED |
+| Result | |
+
+### X2 — Domain-specific topology
+| Item | Value |
+|------|-------|
+| Status | NOT STARTED |
+| Result | |
+
+### X3 — Topology-NLL sensitivity
+| Item | Value |
+|------|-------|
+| Status | NOT STARTED |
+| Result | |
+
+---
+
+## Speed Estimates (A40×1, batch=4, micro_batch=2, seq=1024)
+
+| Component | Time | Notes |
+|-----------|------|-------|
+| Training step | ~3s | Forward + backward + optimizer |
+| Eval round (50 samples) | ~2 min | 25 batches × 3 modes (soft/hard/baseline) |
+| Model loading | ~10 min | OLMo + Qwen + eval set build |
+| 1K steps (no eval) | ~50 min | |
+| 1K steps (eval every 100) | ~70 min | 10 eval rounds add ~20 min |
+| 10K steps | ~12 hrs | |
+| 100K steps | ~5 days | Exceeds 48h SLURM limit, needs auto-resume |
+
+**Previous 14s/step estimate was wrong** — it included model loading and eval overhead in wall-clock average.
+
+---
+
+## Preliminary Data (from sanity training job 15785016)
+
+Run with cascading gate bug (layer 0 not exempted). 500/1000 steps completed before timeout.
+
+| Step | train/nll | eval/nll_soft | eval/nll_hard | eval/nll_baseline | mean_A | tau |
+|------|-----------|---------------|---------------|-------------------|--------|-----|
+| 0 | 3.539 | — | — | — | 0.417 | 5.00 |
+| 100 | 2.750 | 2.635 | 4.744 | 2.457 | 0.416 | 4.88 |
+| 200 | 3.102 | 2.630 | 4.570 | 2.457 | 0.416 | 4.54 |
+| 300 | 2.844 | 2.621 | 4.680 | 2.457 | 0.418 | 4.01 |
+| 400 | 2.492 | 2.641 | 4.893 | 2.457 | 0.419 | 3.34 |
+| 500 | 1.805 | 2.639 | 4.503 | 2.457 | 0.419 | 2.60 |
+
+**Key observations:**
+- train/nll decreasing (3.54 → 1.80) but eval/nll_soft flat (~2.63) — overfitting or predictor not generalizing
+- eval/nll_hard very high (4.5-4.9) due to cascading gate layer 0 bug (now fixed in `80579d6`)
+- mean_A stable ~0.42 (= ~0.89 over valid entries), no collapse
+- Baseline NLL = 2.4569 confirmed correct after double-shift fix