run_baselines.sh

#!/usr/bin/env bash
###############################################################################
# StepProbe — Baseline restoration comparison.
#
# Tests the value of the silver-bullet selection by training two baseline
# adapters and evaluating all three on the same benchmark cell:
#
#   silver_bullet  — failed problems, error-type-proportional  (the paper's method)
#   failed_only    — failed problems, uniform random           (strip the balancing)
#   random         — ALL problems (incl. correct), uniform     (strip the diagnosis)
#
# If silver_bullet ≈ random, the paper's restoration contribution collapses.
# If silver_bullet > random, the paper has a defensible novelty claim.
#
# Output: results/baselines/<model>_<quant>/<strategy>/...
#         figures/paper/fig_paper_7_baselines.pdf
#
# Usage:
#   bash run_baselines.sh
#   MODEL_TAG=r1-qwen-1.5b bash run_baselines.sh
#
# Expected runtime: 3 × (~20 min training + ~10 min inference) ≈ 90 min on a 3090 Ti.
# (silver_bullet at N=500 is reused from phase 7 if present — skip included.)
###############################################################################
set -euo pipefail

PROJECT_DIR="$(cd "$(dirname "$0")" && pwd)"
cd "$PROJECT_DIR"

export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-1}"
export HF_HUB_DOWNLOAD_TIMEOUT=300

MODEL_HF="${MODEL_HF:-Qwen/Qwen2.5-7B-Instruct}"
MODEL_TAG="${MODEL_TAG:-qwen25-7b}"
QUANT_TAG="${QUANT_TAG:-gptq_w4}"
BENCHMARK="${BENCHMARK:-math500}"
N_SAMPLES="${N_SAMPLES:-500}"
PY="${PY:-python}"
GPU_MEM="${GPU_MEM:-0.55}"
# Drop MAX_SEQ_LEN=1024 or LORA_RANK=8 if training OOMs while another GPU
# process is resident (e.g. ollama holding ~13 GB).
MAX_SEQ_LEN="${MAX_SEQ_LEN:-2048}"
LORA_RANK="${LORA_RANK:-16}"

BASELINE_ROOT="${PROJECT_DIR}/results/baselines/${MODEL_TAG}_${QUANT_TAG}"
DIAG_DIR="${PROJECT_DIR}/results/diagnosis/${QUANT_TAG}/${MODEL_TAG}"
REF_DIR="${PROJECT_DIR}/results/segmented/fp16/${MODEL_TAG}"
LOG_DIR="${PROJECT_DIR}/logs"
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
LOG_FILE="${LOG_DIR}/baselines_${TIMESTAMP}.log"

mkdir -p "$BASELINE_ROOT" "$LOG_DIR"

log() { echo "[$(date '+%H:%M:%S')] $1" | tee -a "$LOG_FILE"; }
run_or_skip() {
    local desc=$1 skip=$2 cmd=$3
    if eval "$skip"; then log "SKIP: $desc"; else log "RUN: $desc"; eval "$cmd" 2>&1 | tee -a "$LOG_FILE"; fi
}

log "=============================================="
log "Baseline restoration sweep"
log "  Model:      $MODEL_HF ($MODEL_TAG)"
log "  Quant:      $QUANT_TAG"
log "  Benchmark:  $BENCHMARK"
log "  N samples:  $N_SAMPLES"
log "  Strategies: silver_bullet, failed_only, random"
log "=============================================="

[[ -d "$DIAG_DIR" ]] || { log "ERROR: missing $DIAG_DIR (run phase 5)"; exit 1; }
[[ -d "$REF_DIR"  ]] || { log "ERROR: missing $REF_DIR (run phase 4)"; exit 1; }

for STRATEGY in silver_bullet failed_only random; do
    log ""
    log "=============================================="
    log "Strategy: $STRATEGY"
    log "=============================================="

    CFG_DIR="${BASELINE_ROOT}/${STRATEGY}"
    ADAPTER="${CFG_DIR}/qlora/adapter"
    MERGED="${CFG_DIR}/qlora/merged_fp16"
    INF_DIR="${CFG_DIR}/inference"
    OUT_FILE="${INF_DIR}/${BENCHMARK}_run0.jsonl"

    # 1. Train restoration with this sampling strategy.
    run_or_skip "QLoRA ($STRATEGY, N=$N_SAMPLES, seq=$MAX_SEQ_LEN, r=$LORA_RANK)" \
        "[[ -f '${ADAPTER}/adapter_model.safetensors' ]]" \
        "$PY -m stepprobe.restore \
            --model '$MODEL_HF' \
            --diagnosis '$DIAG_DIR' \
            --ref '$REF_DIR' \
            --output '$CFG_DIR' \
            --method qlora \
            --max-samples $N_SAMPLES \
            --sampling-strategy $STRATEGY \
            --epochs 3 \
            --lr 2e-4 \
            --batch-size 4 \
            --max-seq-length $MAX_SEQ_LEN \
            --lora-rank $LORA_RANK"

    # 2. Merge + inference (skip entirely if inference output exists).
    if [[ -f "$OUT_FILE" ]]; then
        log "SKIP: inference ($STRATEGY) — $OUT_FILE exists"
    else
        run_or_skip "Merge adapter → FP16 ($STRATEGY)" \
            "[[ -f '${MERGED}/config.json' ]]" \
            "$PY ${PROJECT_DIR}/scripts/merge_adapter.py \
                --model '$MODEL_HF' \
                --adapter '$ADAPTER' \
                --output '$MERGED'"

        $PY -c "import torch; torch.cuda.empty_cache() if torch.cuda.is_available() else None" 2>&1 | tee -a "$LOG_FILE"

        run_or_skip "vLLM inference ($STRATEGY)" \
            "[[ -f '$OUT_FILE' ]]" \
            "$PY ${PROJECT_DIR}/scripts/run_inference.py \
                --model '$MERGED' \
                --quant bnb_nf4 --bits 4 \
                --benchmark $BENCHMARK \
                --output '$INF_DIR' \
                --max-tokens 4096 --num-runs 1 \
                --gpu-memory-utilization $GPU_MEM"
    fi

    # 3. Segment + diagnose so accuracy can be read from a diagnosed jsonl.
    SEG_DIR="${CFG_DIR}/segmented"
    DIAG_OUT="${CFG_DIR}/diagnosis"
    run_or_skip "Segment ($STRATEGY)" \
        "[[ -f '${SEG_DIR}/${BENCHMARK}_run0.jsonl' ]]" \
        "$PY -m stepprobe.segment --input '$INF_DIR' --output '$SEG_DIR' --quant '${QUANT_TAG}_${STRATEGY}'"
    run_or_skip "Diagnose ($STRATEGY)" \
        "[[ -f '${DIAG_OUT}/${BENCHMARK}_run0.jsonl' ]]" \
        "$PY -m stepprobe.diagnose --ref '$REF_DIR' --hyp '$SEG_DIR' --output '$DIAG_OUT' --alignment dtw"

    # 4. Disk hygiene.
    if [[ -d "$MERGED" ]]; then
        log "Cleaning up merged FP16 dir: $MERGED"
        rm -rf "$MERGED"
    fi
    $PY -c "import torch; torch.cuda.empty_cache() if torch.cuda.is_available() else None" 2>&1 | tee -a "$LOG_FILE"
done

log ""
log "=============================================="
log "Rendering fig_paper_7_baselines.pdf"
log "=============================================="
$PY ${PROJECT_DIR}/scripts/make_baselines_figure.py \
    --baseline-root "$BASELINE_ROOT" \
    --model "$MODEL_TAG" \
    --quant "$QUANT_TAG" \
    --benchmark "$BENCHMARK" \
    --metrics "${PROJECT_DIR}/results/metrics" \
    --segmented "${PROJECT_DIR}/results/segmented" \
    --output "${PROJECT_DIR}/figures/paper/fig_paper_7_baselines.pdf" 2>&1 | tee -a "$LOG_FILE"

log ""
log "=============================================="
log "DONE — compare silver_bullet vs failed_only vs random"
log "  Figure: figures/paper/fig_paper_7_baselines.pdf"
log "  Log:    $LOG_FILE"
log "=============================================="