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, 52 insertions, 0 deletions

diff --git a/configs/s2_gradient_flow.yaml b/configs/s2_gradient_flow.yaml
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+# S2 — Gradient flow check (constant tau=2, ~50M tokens)
+# Purpose: Lower tau gives sharper gates. Does predictor learn useful topology?
+#          Any NLL drop below baseline (2.4569) = gradient flows correctly.
+# Run: python scripts/train.py --config configs/s2_gradient_flow.yaml
+
+# Model
+olmo_model_id: "allenai/OLMo-2-0425-1B"
+qwen_model_id: "Qwen/Qwen3-Embedding-0.6B"
+
+# Predictor
+predictor_hidden_dim: 1024
+predictor_rank: 32
+cascading_gate_k: 5.0
+input_norm: "none"
+
+# Data
+dataset: "allenai/dolma"
+dataset_name: "v1_7"
+seq_len: 1024
+batch_size: 4
+micro_batch_size: 2
+qwen_input_prefix: ""
+
+# Eval
+eval_skip: 10000
+eval_size: 50
+
+# Training — ~50M tokens = 12500 steps @ batch=4, seq=1024
+total_steps: 12500
+lr: 3e-4
+weight_decay: 0.01
+optimizer: "adamw"
+
+# Schedules — constant tau=2 (sharper gates than S1), no sparsity
+tau_init: 2.0
+tau_final: 2.0
+tau_schedule: "cosine"
+lambda_max: 0.0
+lambda_warmup_frac: 0.2
+
+# Logging
+wandb_project: "dagformer"
+wandb_run_name: "s2-gradient-flow"
+log_every: 10
+eval_every: 500
+
+# Checkpointing
+save_every: 2500
+save_dir: "checkpoints/s2/"
+
+# Hardware
+num_gpus: 1