rrm workspace: TRM/HRM/SRM code, Maze dataset, dynamical-analysis pipelineHEADmain

Curated export for clone-and-run Maze training (2x A6000) + diagnostics.
trm/hrm pretrain.py carry trajectory-augmentation code (backward-compatible).
Heavy artifacts (checkpoints/wandb/npz) gitignored; see PROVENANCE.md.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

1 files changed, 92 insertions, 0 deletions

diff --git a/research/flossing/analysis_2x2/run_phase1_queue.sh b/research/flossing/analysis_2x2/run_phase1_queue.sh
new file mode 100755
index 0000000..989c7ee
--- /dev/null
+++ b/research/flossing/analysis_2x2/run_phase1_queue.sh
@@ -0,0 +1,92 @@
+#!/usr/bin/env bash
+# Phase-1 queue (experiment_framework.md): E5 horizon sweeps, E2 run-level replication,
+# E6 matched-objective step9 pairs. Waits for a free GPU (12h fallback), runs sequentially.
+set -o pipefail
+
+cd /home/yurenh2/rrm/research/flossing
+source /home/yurenh2/miniconda3/etc/profile.d/conda.sh
+conda activate rrm
+
+OUTDIR=analysis_2x2/phase1
+mkdir -p "$OUTDIR"
+STATUS="$OUTDIR/queue_status.log"
+TRM_OFF="/home/yurenh2/rrm/trm/checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku_official_gbs768_repro"
+TRM_SGL="/home/yurenh2/rrm/trm/checkpoints/Sudoku-extreme-1k-aug-1000-ACT-torch/pretrain_mlp_t_sudoku_singleGPU"
+HRM_ROOT="/home/yurenh2/rrm/hrm/checkpoints/Sudoku-extreme-1k-aug-1000 ACT-torch/HierarchicalReasoningModel_ACTV1 righteous-python"
+S9=/home/yurenh2/rrm/research/flossing
+
+log() { echo "[$(date '+%Y-%m-%d %H:%M:%S')] $*" >> "$STATUS"; }
+free_gpu() {
+  nvidia-smi --query-gpu=index,utilization.gpu,memory.used --format=csv,noheader,nounits \
+    | awk -F', ' '$2<30 && $3<8000 {print $1; exit}'
+}
+
+log "phase-1 queue started (E5 horizon sweeps, E2 step9_E replication, E6 step9 pairs)"
+DEADLINE=$(( $(date +%s) + 12*3600 ))
+GPU=""
+while true; do
+  g1="$(free_gpu)"
+  if [[ -n "$g1" ]]; then
+    sleep 60; g2="$(free_gpu)"
+    if [[ "$g2" == "$g1" ]]; then GPU="$g1"; break; fi
+  fi
+  if (( $(date +%s) > DEADLINE )); then
+    GPU="$(nvidia-smi --query-gpu=index,memory.used --format=csv,noheader,nounits | sort -t, -k2 -n | head -1 | cut -d, -f1)"
+    log "12h fallback: taking GPU $GPU"
+    break
+  fi
+  sleep 300
+done
+log "claimed GPU $GPU"
+export CUDA_VISIBLE_DEVICES="$GPU"
+
+run_job() { # name horizon script args...
+  local name="$1" hor="$2"; shift 2
+  if [[ -f "$OUTDIR/${name}.npz" ]]; then log "skip $name"; return 0; fi
+  log "start $name"
+  if DIAG_HORIZON="$hor" python "$@" --out "$OUTDIR/${name}.npz" > "$OUTDIR/${name}.log" 2>&1; then
+    log "done $name"
+  else
+    log "FAILED $name"
+  fi
+}
+
+# --- E5: TRM horizon sweep (h=4 already exists in retest/) ---
+for H in 2 6 8 10 12; do
+  run_job "trm_official58590_h${H}_n2048" "$H" diagnose_trm_joint_horizon.py \
+    --ckpt-root "$TRM_OFF" --ckpt-name step_58590 --n-samples 2048 --batch-size 16 \
+    --k-lyap 8 --t-ons 1 --seed 0
+done
+
+# --- E5: HRM horizon sweep ---
+for H in 2 6 8 10 12; do
+  run_job "hrm26040_h${H}_n2048" "$H" diagnose_hrm_joint_horizon.py \
+    --ckpt-root "$HRM_ROOT" --ckpt-name step_26040 --n-samples 2048 --batch-size 32 \
+    --k-lyap 8 --t-ons 1 --seed 0
+done
+
+# --- E2: HRM second training run (step9_E fixed-unroll baseline), full window ---
+run_job "step9E_hrm_best_full_n2048" 16 diagnose_hrm_joint.py \
+  --ckpt-root "$HRM_ROOT" --ckpt-name "$S9/step9_E_hrm_baseline_parallel_fixed_26040_50k_ckpts/best.pt" \
+  --n-samples 2048 --batch-size 32 --k-lyap 8 --t-ons 1 --seed 0
+run_job "step9E_hrm_final_full_n2048" 16 diagnose_hrm_joint.py \
+  --ckpt-root "$HRM_ROOT" --ckpt-name "$S9/step9_E_hrm_baseline_parallel_fixed_26040_50k_ckpts/final.pt" \
+  --n-samples 2048 --batch-size 32 --k-lyap 8 --t-ons 1 --seed 0
+
+# --- E6: matched-objective pairs (n=512): HRM E vs F, TRM G vs H ---
+for CK in step_12500 step_25000 best final; do
+  run_job "step9E_hrm_${CK}_n512" 16 diagnose_hrm_joint.py \
+    --ckpt-root "$HRM_ROOT" --ckpt-name "$S9/step9_E_hrm_baseline_parallel_fixed_26040_50k_ckpts/${CK}.pt" \
+    --n-samples 512 --batch-size 32 --k-lyap 8 --t-ons 1 --seed 0
+  run_job "step9F_hrm_${CK}_n512" 16 diagnose_hrm_joint.py \
+    --ckpt-root "$HRM_ROOT" --ckpt-name "$S9/step9_F_hrm_multi4_loguniform_ramp_26040_50k_ckpts/${CK}.pt" \
+    --n-samples 512 --batch-size 32 --k-lyap 8 --t-ons 1 --seed 0
+  run_job "step9G_trm_${CK}_n512" 16 diagnose_trm_joint.py \
+    --ckpt-root "$TRM_SGL" --ckpt-name "$S9/step9_G_trm_baseline_parallel_fixed_26041_batch4_50k_ckpts/${CK}.pt" \
+    --n-samples 512 --batch-size 16 --k-lyap 8 --t-ons 1 --seed 0
+  run_job "step9H_trm_${CK}_n512" 16 diagnose_trm_joint.py \
+    --ckpt-root "$TRM_SGL" --ckpt-name "$S9/step9_H_trm_multi4_loguniform_ramp_26041_batch4_50k_ckpts/${CK}.pt" \
+    --n-samples 512 --batch-size 16 --k-lyap 8 --t-ons 1 --seed 0
+done
+
+log "phase-1 queue finished"