#!/usr/bin/env bash ############################################################################### # StepProbe — Ablation Study Runner # # Runs all ablation experiments: # A1: Dataset size ablation (50, 100, 200, 500 samples for restoration) # A2: Error-type targeted ablation (restore only one error type at a time) # A3: LoRA rank ablation (r=4, 8, 16, 32) # A4: Quantization method comparison (AWQ vs GPTQ vs NF4 at same bit-width) # A5: Model size scaling (1.5B, 7B, 14B, 32B) # # Usage: # bash scripts/run_ablations.sh # all ablations # bash scripts/run_ablations.sh --ablation A1 # single ablation # bash scripts/run_ablations.sh --quick # small sample size ############################################################################### set -euo pipefail PROJECT_DIR="$(cd "$(dirname "$0")/.." && pwd)" RESULTS_DIR="${PROJECT_DIR}/results" ABLATION_DIR="${RESULTS_DIR}/ablations" LOG_FILE="${PROJECT_DIR}/logs/ablations_$(date +%Y%m%d_%H%M%S).log" # Defaults TARGET_ABLATION="all" BASE_MODEL="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" BASE_TAG="r1-qwen-7b" QUICK=false while [[ $# -gt 0 ]]; do case $1 in --ablation) TARGET_ABLATION=$2; shift 2 ;; --quick) QUICK=true; shift ;; --model) BASE_MODEL=$2; shift 2 ;; *) shift ;; esac done mkdir -p "$ABLATION_DIR" "$(dirname $LOG_FILE)" log() { echo "[$(date '+%H:%M:%S')] $1" | tee -a "$LOG_FILE"; } # ================================================================== # A1: Dataset Size Ablation # How many Silver Bullet samples do you actually need? # ================================================================== ablation_a1() { log "========== A1: Dataset Size Ablation ==========" local sizes=(50 100 200 500) if $QUICK; then sizes=(50 100); fi local diag_dir="${RESULTS_DIR}/diagnosis/bnb_nf4/${BASE_TAG}" local ref_dir="${RESULTS_DIR}/segmented/fp16/${BASE_TAG}" [[ -d "$diag_dir" ]] || { log "SKIP A1: No diagnosis data. Run main pipeline first."; return; } for n in "${sizes[@]}"; do local out_dir="${ABLATION_DIR}/A1_dataset_size/n${n}" if [[ -d "${out_dir}/qlora/adapter" ]]; then log "SKIP: A1 n=$n already done" continue fi log "A1: Restoring with n=$n samples" python -m stepprobe.restore \ --model "$BASE_MODEL" \ --diagnosis "$diag_dir" \ --ref "$ref_dir" \ --output "$out_dir" \ --method qlora \ --max-samples "$n" \ --epochs 3 \ --lr 2e-4 \ --batch-size 4 \ 2>&1 | tee -a "$LOG_FILE" # Re-evaluate log "A1: Evaluating restored model (n=$n)" python "${PROJECT_DIR}/scripts/run_inference_restored.py" \ --model "$BASE_MODEL" \ --adapter "${out_dir}/qlora/adapter" \ --benchmark gsm8k \ --output "${out_dir}/eval" \ --max-samples 200 \ 2>&1 | tee -a "$LOG_FILE" python -c "import torch; torch.cuda.empty_cache() if torch.cuda.is_available() else None" done log "A1 complete." } # ================================================================== # A2: Error-Type Targeted Ablation # Does fixing one error type help with others? # ================================================================== ablation_a2() { log "========== A2: Error-Type Targeted Ablation ==========" local error_types=("conceptual" "methodological" "executional" "logical") local diag_dir="${RESULTS_DIR}/diagnosis/bnb_nf4/${BASE_TAG}" local ref_dir="${RESULTS_DIR}/segmented/fp16/${BASE_TAG}" [[ -d "$diag_dir" ]] || { log "SKIP A2: No diagnosis data."; return; } for etype in "${error_types[@]}"; do local out_dir="${ABLATION_DIR}/A2_error_type/${etype}" if [[ -d "${out_dir}/qlora/adapter" ]]; then log "SKIP: A2 $etype already done" continue fi log "A2: Restoring with only $etype errors" python -m stepprobe.restore \ --model "$BASE_MODEL" \ --diagnosis "$diag_dir" \ --ref "$ref_dir" \ --output "$out_dir" \ --method qlora \ --max-samples 500 \ --target-errors "$etype" \ --epochs 3 \ --lr 2e-4 \ --batch-size 4 \ 2>&1 | tee -a "$LOG_FILE" # Evaluate python "${PROJECT_DIR}/scripts/run_inference_restored.py" \ --model "$BASE_MODEL" \ --adapter "${out_dir}/qlora/adapter" \ --benchmark gsm8k \ --output "${out_dir}/eval" \ --max-samples 200 \ 2>&1 | tee -a "$LOG_FILE" python -c "import torch; torch.cuda.empty_cache() if torch.cuda.is_available() else None" done log "A2 complete." } # ================================================================== # A3: LoRA Rank Ablation # ================================================================== ablation_a3() { log "========== A3: LoRA Rank Ablation ==========" local ranks=(4 8 16 32) if $QUICK; then ranks=(8 16); fi local diag_dir="${RESULTS_DIR}/diagnosis/bnb_nf4/${BASE_TAG}" local ref_dir="${RESULTS_DIR}/segmented/fp16/${BASE_TAG}" [[ -d "$diag_dir" ]] || { log "SKIP A3: No diagnosis data."; return; } for r in "${ranks[@]}"; do local out_dir="${ABLATION_DIR}/A3_lora_rank/r${r}" if [[ -d "${out_dir}/qlora/adapter" ]]; then log "SKIP: A3 r=$r already done" continue fi log "A3: Restoring with LoRA r=$r" python -c " import sys, os sys.path.insert(0, '${PROJECT_DIR}') from stepprobe.restore import build_silver_bullet_dataset, format_for_sft, run_qlora_restoration from stepprobe.utils import load_jsonl import glob diag = [] for f in sorted(glob.glob('${diag_dir}/*.jsonl')): diag.extend(load_jsonl(f)) ref = [] for f in sorted(glob.glob('${ref_dir}/*.jsonl')): ref.extend(load_jsonl(f)) samples, stats = build_silver_bullet_dataset(diag, ref, [], max_samples=500) if samples: train_data = format_for_sft(samples) run_qlora_restoration( model_name='${BASE_MODEL}', train_data=train_data, output_dir='${out_dir}/qlora', r=${r}, lora_alpha=$((r * 2)), num_epochs=3, ) " 2>&1 | tee -a "$LOG_FILE" # Evaluate if [[ -d "${out_dir}/qlora/adapter" ]]; then python "${PROJECT_DIR}/scripts/run_inference_restored.py" \ --model "$BASE_MODEL" \ --adapter "${out_dir}/qlora/adapter" \ --benchmark gsm8k \ --output "${out_dir}/eval" \ --max-samples 200 \ 2>&1 | tee -a "$LOG_FILE" fi python -c "import torch; torch.cuda.empty_cache() if torch.cuda.is_available() else None" done log "A3 complete." } # ================================================================== # A4: Quantization Method Comparison (at same bit-width) # Already handled by main pipeline, this generates the comparison # ================================================================== ablation_a4() { log "========== A4: Quant Method Comparison (metrics only) ==========" local metrics_dir="${RESULTS_DIR}/metrics" [[ -d "$metrics_dir" ]] || { log "SKIP A4: No metrics data."; return; } python -c " import glob, json, os files = sorted(glob.glob('${metrics_dir}/*_metrics.json')) if not files: print('No metrics found') exit() # Group by bit-width by_bits = {} for f in files: with open(f) as fp: m = json.load(fp) quant = m.get('quantization', '') if '_w' not in quant: continue parts = quant.split('_w') method = parts[0] bits = parts[1].split('_')[0] by_bits.setdefault(bits, []).append(m) for bits, metrics_list in sorted(by_bits.items()): print(f'\\n=== {bits}-bit comparison ===') print(f'{\"Method\":<15} {\"Acc\":<8} {\"FFS\":<8} {\"ECR\":<8}') print('-' * 40) for m in sorted(metrics_list, key=lambda x: -x.get('accuracy', 0)): print(f'{m[\"quantization\"]:<15} {m.get(\"accuracy\",0):.1%} {m.get(\"avg_ffs\",0):.1f} {m.get(\"ecr\",0):.1%}') " 2>&1 | tee -a "$LOG_FILE" log "A4 complete." } # ================================================================== # A5: Model Size Scaling # Already handled by main pipeline, this generates the scaling plot # ================================================================== ablation_a5() { log "========== A5: Model Size Scaling (figure only) ==========" local metrics_dir="${RESULTS_DIR}/metrics" python -c " import glob, json, os import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt files = sorted(glob.glob('${metrics_dir}/*_metrics.json')) if not files: print('No metrics found') exit() # Group by model size by_model = {} for f in files: with open(f) as fp: m = json.load(fp) model = m.get('model', '') quant = m.get('quantization', '') if 'bnb_nf4' not in quant: continue by_model[model] = m if len(by_model) < 2: print('Need at least 2 model sizes for scaling plot') exit() # Extract sizes from model names sizes = {'1.5b': 1.5, '7b': 7, '8b': 8, '14b': 14, '32b': 32} data = [] for model, m in by_model.items(): for s, v in sizes.items(): if s.lower() in model.lower(): data.append((v, m.get('accuracy', 0), m.get('avg_ffs', 0), m.get('ecr', 0))) break data.sort() if data: fig, axes = plt.subplots(1, 3, figsize=(14, 4)) x = [d[0] for d in data] axes[0].plot(x, [d[1] for d in data], 'o-', color='#2E86AB', linewidth=2, markersize=8) axes[0].set_xlabel('Model size (B params)'); axes[0].set_ylabel('Accuracy (4-bit NF4)') axes[0].set_title('Accuracy vs model size') axes[1].plot(x, [d[2] for d in data], 's-', color='#A23B72', linewidth=2, markersize=8) axes[1].set_xlabel('Model size (B params)'); axes[1].set_ylabel('Avg FFS') axes[1].set_title('First failure step vs model size') axes[2].plot(x, [d[3] for d in data], 'D-', color='#F18F01', linewidth=2, markersize=8) axes[2].set_xlabel('Model size (B params)'); axes[2].set_ylabel('ECR') axes[2].set_title('Error cascade rate vs model size') for ax in axes: ax.grid(True, alpha=0.3) ax.spines['top'].set_visible(False); ax.spines['right'].set_visible(False) plt.tight_layout() out = '${ABLATION_DIR}/A5_model_scaling.pdf' os.makedirs(os.path.dirname(out), exist_ok=True) plt.savefig(out, dpi=300, bbox_inches='tight') print(f'Scaling plot saved: {out}') " 2>&1 | tee -a "$LOG_FILE" log "A5 complete." } # ================================================================== # Summary: collect all ablation results # ================================================================== collect_ablation_results() { log "========== Collecting Ablation Results ==========" python -c " import glob, json, os abl_dir = '${ABLATION_DIR}' results = {} # A1: dataset size for d in sorted(glob.glob(os.path.join(abl_dir, 'A1_dataset_size/n*/eval/*.jsonl'))): n = d.split('/n')[1].split('/')[0] lines = open(d).readlines() results.setdefault('A1', []).append({'n': int(n), 'n_samples': len(lines)}) # A2: error type for d in sorted(glob.glob(os.path.join(abl_dir, 'A2_error_type/*/eval/*.jsonl'))): etype = d.split('A2_error_type/')[1].split('/')[0] lines = open(d).readlines() results.setdefault('A2', []).append({'error_type': etype, 'n_samples': len(lines)}) # A3: LoRA rank for d in sorted(glob.glob(os.path.join(abl_dir, 'A3_lora_rank/r*/eval/*.jsonl'))): r = d.split('/r')[1].split('/')[0] lines = open(d).readlines() results.setdefault('A3', []).append({'rank': int(r), 'n_samples': len(lines)}) out = os.path.join(abl_dir, 'ablation_summary.json') with open(out, 'w') as f: json.dump(results, f, indent=2) print(f'Ablation summary: {out}') print(json.dumps(results, indent=2)) " 2>&1 | tee -a "$LOG_FILE" } # ================================================================== # MAIN # ================================================================== log "StepProbe Ablation Runner — Started $(date)" case $TARGET_ABLATION in A1|a1) ablation_a1 ;; A2|a2) ablation_a2 ;; A3|a3) ablation_a3 ;; A4|a4) ablation_a4 ;; A5|a5) ablation_a5 ;; all) ablation_a1 ablation_a2 ablation_a3 ablation_a4 ablation_a5 collect_ablation_results ;; *) echo "Unknown ablation: $TARGET_ABLATION"; exit 1 ;; esac log "Ablation runner complete."